ParcelPilot

Given a seeded historical order dataset with known counts across channel, client, SKU/kit, date range, promo window, destination country/zone, service level, weight/size bands, order tags, fulfillment node, and custom attributes When a user selects values in multiple dimensions with include lists and exclude lists and executes the filter Then results contain only orders that match any included values within each selected dimension (OR within a dimension) and match all selected dimensions concurrently (AND across dimensions) and do not match any excluded values (NOT takes precedence) And the total returned count equals the expected count for the seeded dataset And multi-select is supported for all listed dimensions, including custom attributes And filters can be cleared individually and as a whole, restoring the unfiltered baseline count

Given an indexed order history containing 500,000+ orders in the tenant And the query engine is warmed (no index rebuild in the prior 5 minutes) When a user applies a filter slice containing at least 3 dimensions and 5+ selected values total Then the server-side p95 latency to return total count and the first 100 preview rows is ≤ 3.0 seconds and p99 ≤ 5.0 seconds And subsequent pagination requests for additional preview pages have p95 latency ≤ 2.0 seconds And the system returns identical results across three repeated runs of the same filter within a 60-second window And no request times out under the documented timeout threshold

Given a user with permissions limited to clients A and B, channels Shopify and Etsy, and fulfillment nodes X and Y When the user opens filter pickers Then only permitted clients, channels, and nodes are listed and selectable And attempts to paste or query values outside permissions are blocked with a 403 error and a non-permissible value pill is flagged And executing a saved preset that references unauthorized values removes those values and informs the user, or blocks execution with a clear message per policy And results contain no orders from unauthorized clients/channels/nodes, validated by spot-checking 100 random results

Given a user composes a multi-dimensional filter with include/exclude lists When the user saves it as a preset with a unique name Then the preset persists to the user’s tenant and is available after sign-out/sign-in When the user applies the preset on a new session Then the UI restores all selected dimensions, includes/excludes, and values in the same order and executes the filter automatically (if auto-run is enabled) or awaits user run (if disabled) When the user renames or deletes the preset Then the changes are reflected immediately, and deleted presets no longer appear in any list And saving a preset with a duplicate name prompts to overwrite or choose a new name and behaves accordingly

Given any executed filter slice When results are returned Then the UI displays the sample size equal to the total matched order count And shows data freshness as the index timestamp in the tenant’s time zone with an explicit “as of” label And if sample size < 100, a low-sample warning banner appears And if ≥ 0.5% or ≥ 100 of matched orders (whichever is greater) are missing required attributes (e.g., dimensions, weight), a data quality warning appears listing the attribute(s) and count of affected orders And warnings can be clicked to view a focused subset of impacted orders

Given a tenant time zone setting and historical orders spanning multiple months When the user selects an absolute date range (calendar start and end) or a relative range (e.g., last 30 days) Then the filter includes orders with created_at timestamps within the inclusive start and end in the tenant time zone, correctly handling DST transitions When the user filters by a promo window defined via order tags or custom attributes (start/end) Then only orders whose timestamps fall within the promo window are included And combining date range and promo window applies intersection logic (AND) unless the user explicitly selects multiple promo windows, which are ORed together

Given normalized order history where units and taxonomies are standardized (e.g., weight in grams, dimensions in centimeters, carrier service levels mapped to a common taxonomy, zones resolved by destination and ship-from) When a user filters by SKU and kit identifiers Then orders are matched by exact SKU and by kit parent identifiers as selected, with clear distinction between parent kits and component SKUs When a user filters by weight and size bands Then band boundaries are inclusive of lower bound and exclusive of upper bound, computed on normalized units When a user filters by destination country/zone and service level Then results reflect normalized zone and service mappings regardless of carrier-specific labels And filtering by order tags supports multi-select and include/exclude with OR-within, AND-across semantics

Given I have Merchant Admin access to the Rule Set Composer When I create a new rule set and define rules for: carrier selection, SLA <= 2 days, delivery days Mon-Fri, destination zones 2-5, packaging override "Box S", insurance value $100, adult signature required, rate strategy "Cheapest within $12", margin ceiling 15%, cost ceiling $10, cutoff 15:00 PST, and fallback carrier "Carrier B" Then the no-code builder allows me to configure each parameter without writing code and saves successfully And switching to Expression Mode shows an equivalent validated expression And switching back to No-Code preserves all entered values with no loss or mutation And both modes produce identical evaluation results for a 25-order sample

Given a rule set with two rules that can match the same order and have different outcomes When I assign explicit numerical priorities 1 and 2 and validate Then the engine always selects the lowest numerical priority matching rule And a conflict warning is displayed when overlapping rules have different outcomes And unreachable rules are flagged with linting warnings And rules with validation errors cannot be saved or promoted

Given I select a historical slice of 2,000 orders by channel, date range, and SKU When I run Test on Sample Then the system returns preview label choices, packaging selections, costs, and exceptions within 10 seconds And no production configuration or live label generation is modified And the output shows per-rule match counts and total coverage percentage And I can filter preview results by exception type and rule ID

Given an existing rule set version labeled Approved When I clone it and make edits Then a new version is created with label Draft, a unique version ID, and a timestamp And I can compare the new version to the source to see added, removed, and changed rules with field-level diffs And I can relabel the new version to Candidate and then Approved after all validations pass And only one version per rule set can be labeled Approved at a time

Given a rule set version labeled Approved When I apply it to production Then the change is applied atomically with no impact to in-flight label generation And I can roll back to the previous Approved version in one action And an audit trail records actor, timestamp, version IDs, and change reason And no configuration changes affect production until Apply is executed

Given a test order and an Approved rule set When I evaluate the order in preview and in production Then the system uses existing carrier connectors, packaging predictor, and rate shopping logic to produce outcomes And if any connector is unavailable, the evaluation fails closed and surfaces an exception with error code And outcomes include selected carrier/service, package, insurance/signature, and estimated cost consistent across preview and production And the composer does not alter connector or predictor configurations

Given two simulations use the same order slice, rule set, and snapshot ID, When the engine executes both runs independently, Then per-order outputs (label/service, packaging, cost breakdown, exception flags) and aggregate metrics are byte-identical, And the run metadata records the snapshot ID and a content hash. Given carrier rates change after a snapshot is taken, When a run is executed with the prior snapshot ID, Then results match the pre-change baseline with 100% field equality. Given two runs use different snapshot IDs, When results are compared, Then differences are attributed to snapshot version in the run metadata diff report.

Given a historical slice of 100,000 orders with typical data completeness, When the simulation executes from a cold cache, Then end-to-end P95 completion time is ≤ 45 seconds and P50 ≤ 25 seconds, And average throughput is ≥ 2,500 orders/second, And no run exceeds 60 seconds. Given a historical slice of 10,000 orders, When the simulation executes, Then P95 completion time is ≤ 5 seconds and P50 ≤ 3 seconds. Given the run completes, When metrics are emitted, Then wall-clock time, CPU time, throughput, and queue wait time are reported for the run and each chunk.

Given an input slice larger than 100,000 orders and up to 1,000,000 orders, When the simulation executes, Then the engine automatically partitions work into chunks of ≤ 50,000 orders, preserving original order IDs, And memory usage remains below configured limits without OOM events. Given chunked execution, When progress is published, Then progress updates occur at least every 2 seconds or 5% completion (whichever is sooner), include percent complete and ETA, and partial results stream per chunk. Given a transient failure during chunk N, When retry policy applies, Then the run resumes from the last successful chunk without reprocessing completed chunks, And final status reflects success with zero duplication of results.

Given a baseline run for slice S with snapshot ID X completes (cold cache), When a second run applies a different rule set B to the same slice S with snapshot X, Then the second run completes ≥ 3x faster than the baseline (end-to-end), And cache hit rate for reusable computations (dimensional weight, candidate service sets, packaging predictions, normalized addresses, static rate tables) is ≥ 80%. Given the snapshot ID changes, When a run executes, Then caches tied to snapshot X are not reused, and the cache hit rate for snapshot-sensitive entries is ≤ 5%. Given two runs share cached intermediates, When outputs are compared for shared computations, Then values are identical bit-for-bit and are marked as cache-sourced in metadata.

Given any simulation run, When per-order results are produced, Then each order record includes required fields: order_id, selected_carrier, selected_service, package_type, dimensions, dimensional_weight, billed_weight, base_rate, itemized_surcharges[{code,amount}], total_cost, currency, exception_flags[], and rule_set_id. Given the results payload, When validated against the published JSON Schema, Then 100% of records pass schema validation, units are consistent (weight in lb or kg as configured; currency in ISO 4217), and unknown fields are rejected. Given an order cannot be labeled, When results are produced, Then the order still appears with total_cost null, exception_flags populated, and a reason code (not a missing record).

Given an order is unroutable, When the engine evaluates it, Then a standardized reason code is returned from the controlled vocabulary {NO_RATE, NO_PACKAGE_FIT, ADDRESS_INVALID, DATA_MISSING, RULE_CONFLICT, SERVICE_BLOCKED, CARRIER_DOWN}, along with a human-readable message and remediation hint. Given a batch contains unroutable orders, When the run completes, Then an aggregate report includes counts by reason code and affected order IDs, and 99%+ of unroutable orders have a non-generic reason code (OTHER ≤ 1%). Given systemic failures (e.g., datastore outage), When the job fails, Then a terminal run status is set with an error category and no partial records are lost; otherwise, per-order errors do not fail the entire job (HTTP 200 with per-order statuses).

Given any simulation run, When metrics are emitted, Then they include: start/end timestamps, wall time, CPU time, max concurrency, throughput (orders/s), cache hit rates by category, chunk timings, memory high-water mark, and error counts, all tagged by run_id, scenario_id, and snapshot_id. Given a running job, When subscribers query progress via API or UI, Then percent complete, processed/total orders, current chunk, ETA, and recent exceptions are available with P90 update interval ≤ 2 seconds. Given metrics collection, When exported to monitoring, Then time-series are available for dashboards and alerts with 1-second resolution for throughput and latencies, and logs include per-chunk summaries with reason-code breakdowns.

Given a user selects one or more scenarios and a baseline (e.g., production rules) for a specified historical order slice, When the user runs Impact & Cost Diff Reporting, Then the system produces a per-scenario summary and A/B diff versus the baseline for the selected slice. Given report generation is initiated, When computing deltas, Then each metric includes absolute_change and percent_change with correct sign and consistent rounding (monetary: 2 decimals, percentages: 2 decimals). Given a baseline metric value of zero, When percent_change cannot be computed, Then the percent_change displays as N/A and no Infinity or NaN values appear in the output. Given the report completes, When results are presented, Then run metadata includes scenario_id(s), baseline_id, snapshot_id, snapshot_timestamp (UTC), order_count_in_scope, and applied filters. Given an order slice up to 100,000 orders and no external service outages, When generating the report, Then end-to-end latency is p90 ≤ 10s and p99 ≤ 30s measured from Run to data ready.

Given a completed report, When inspecting the metrics set, Then it contains at minimum: total_spend, average_cost_per_order, service_mix (% by carrier/service), SLA_attainment_proxy, average_delivery_distance and zone_distribution, packaging_consumption_changes, dimensional_weight_deltas, surcharges_by_type, and exception_counts. Given the same input data and scenario rules, When totals are recomputed independently from underlying transactions, Then each reported monetary aggregate is within ±0.1% or ±$0.01 (whichever is greater) of the recomputation, and counts match exactly. Given service_mix percentages, When validating composition, Then the parts sum to 100% ± 0.1% due to rounding, and each component’s denominator matches the order_count_in_scope for that breakdown. Given dimensional_weight_deltas and packaging_consumption_changes, When verifying formulae, Then reported deltas equal scenario_value − baseline_value for each metric and SKU/package, within rounding rules. Given SLA_attainment_proxy, When computing against transit day estimates and service commitments, Then the proxy rate is reproducible from the provided data dictionary and equals the displayed value within ±0.1%.

Given a user selects breakdowns by channel, client, SKU, destination region, and time bucket, When the report is generated, Then each requested breakdown is present with both baseline and scenario sections and associated deltas. Given filters for channel(s), client(s), SKU(s), date range, and promo window, When applied before generation, Then only orders matching all filters are included and the order_count_in_scope reflects the filtered set. Given multiple breakdowns are requested, When viewing totals, Then the overall totals equal the sum of the disjoint groups for that breakdown and remain consistent across breakdown types. Given time buckets of day, week, or month, When switching bucket granularity, Then counts and metric aggregations reflow correctly and the sum across buckets equals the overall value for the same filtered slice. Given a breakdown by SKU or client with no data in the slice, When generating, Then the report omits empty groups and displays a clear "no data" indicator for that dimension.

Given a generated report, When viewing visualizations, Then charts display baseline vs scenario values and deltas for required metrics with labeled axes, units, and tooltips showing absolute and percent change. Given proportions (e.g., service_mix) and means (e.g., average_cost_per_order, average_delivery_distance), When significance is computed, Then a two-proportion z-test (proportions) and two-sample t-test (means) are applied with default α = 0.05 using the report’s sample sizes. Given a metric’s delta is not statistically significant at α = 0.05, When displayed, Then the visualization and tabular row are visually de-emphasized (e.g., grey) and include a tooltip showing p_value and test_type. Given any group has sample_size < 30 per arm (baseline or scenario), When rendering significance, Then significance is not computed and the UI shows "insufficient sample" with no p_value. Given users export or refresh the view, When the same snapshot_id is used, Then the visualized numbers match the tabular values exactly for that snapshot.

Given a completed report, When the user exports CSV, Then the file downloads in UTF-8 with a header row and includes at minimum: dimension_keys, metric_name, baseline_value, scenario_value, absolute_delta, percent_delta, sample_size_baseline, sample_size_scenario, p_value, significance_flag, scenario_id, baseline_id, snapshot_id, snapshot_timestamp. Given applied filters and breakdown selections, When exporting CSV or PDF, Then the exports reflect exactly the on-screen slice and breakdowns for the same snapshot_id. Given a completed report with visualizations, When exporting PDF, Then all charts for the selected sections render without truncation and include legends and units, and the PDF is ≤ 50 MB. Given normal network conditions, When initiating an export for a report ≤ 100k orders, Then the CSV is ready within 10s and the PDF within 30s (p95). Given tenant access controls, When a user without permission attempts export, Then the system denies the action with a clear error and no artifact is produced.

Given an authenticated client, When POSTing to /api/v1/reports/impact-diff with scenario_id(s), baseline_id, and slice filters, Then the API responds 202 Accepted with a report_id and status URL. Given a valid report_id, When GET /api/v1/reports/{report_id} is called, Then the API returns 200 with status (queued|running|complete|failed), snapshot_id, metadata, metrics payload (JSON), and signed URLs for CSV and PDF when complete. Given a completed report, When retrieving the JSON payload, Then it conforms to the published schema with types and units, and includes deltas, p_values (or null), and significance_flags per group. Given invalid input (e.g., unknown scenario_id), When requesting report generation, Then the API returns 400 with a structured error code and message; unauthorized requests return 401 and rate-limited requests return 429 with Retry-After. Given ETag and Last-Modified headers, When clients use conditional GET, Then unchanged artifacts return 304 and downloads are cacheable for 24 hours.

Given a report is generated, When persisting artifacts, Then CSV, PDF, and JSON are stored with an immutable snapshot_id and content_hash and retained for at least 90 days (or tenant policy if longer). Given a stored artifact, When accessed via its link, Then the content matches the original content_hash and numbers, regardless of later data or rule changes. Given a stored artifact, When viewing metadata, Then it includes scenario_id(s), scenario_version(s), baseline_id, baseline_version, data_extract_range, filters, generator_user_id, and created_at (UTC), with a link back to the Scenario Sandbox configuration used. Given tenant isolation, When a user from another tenant attempts to access an artifact, Then access is denied and no metadata is leaked. Given retention expiry, When the artifact is purged, Then subsequent access returns 404 and an audit log records the deletion event.

Given a saved Scenario Sandbox rule set and a historical order slice of up to 10,000 orders When the user runs the scenario Then processing completes within 30 seconds and an Exceptions view is displayed And each exceptioned order is assigned one or more standardized reason codes, with a single primary code selected by priority rules And category-level counts and percentages are shown and totals reconcile to the number of exceptioned orders And each exception lists the triggering constraint(s) and referenced data fields

Given an exceptioned order in a scenario run When the user opens the Drilldown Then the rule evaluation trace is shown in execution order with pass/fail and evaluated variable values for each condition And raw carrier rate responses (service, cost, delivery estimate, constraint flags) are displayed alongside the selected service and top 3 alternatives with reasons And packaging choice is explained with dimensional fit check, weight calculations, and data source for SKU dimensions And sensitive credentials are redacted and all displayed payloads are copyable

Given an exception with one or more reason codes When the Drilldown is open Then tailored remediation guidance is shown per reason code (e.g., add missing data fields, adjust thresholds, add fallback services) And quick links open the Rule Set editor pre-filtered to impacted rules and highlight relevant constraints And links to edit the order, SKU, or client settings are available when missing data is detected And the user can queue a re-run of the scenario on the same slice via a single action

Given a completed scenario run and subsequent rule or data changes When the user clicks Rerun Scenario on the same slice Then processing completes within 30 seconds for up to 10,000 orders And a before/after comparison shows exception counts by reason code, net change, resolved vs new exceptions, and impacted orders And the diff view and underlying lists are exportable to CSV

Given a filtered Exceptions view When the user multi-selects exceptioned orders Then the user can apply one or more tags with audit logging of tag, actor, and timestamp And the user can export the visible exception list to CSV and XLSX with columns: order ID, channel, client, SKUs, scenario ID, primary reason code, secondary codes, suggested remediation, tags And exports of up to 20,000 rows complete within 10 seconds

Given a maintained reason code catalog When exceptions are generated Then each exception maps deterministically to code(s) using documented priority rules And each code includes ID, label, severity, category, and remediation template and the catalog can be exported And catalog edits require Admin role, are versioned, and each scenario run records the catalog version used

Given role-based access control is configured When a non-admin user opens Drilldown Then sensitive rate payloads are redacted while decision summaries remain visible And only Ops or Tech roles can trigger reruns and only Tech can edit rules; unauthorized attempts are blocked and logged And all bulk tagging, exports, reruns, and rule edits initiated from exception views are captured in an immutable audit log with actor, timestamp, scenario ID, and change summary

Given I am an authenticated user with permission to create scenarios When I save a new scenario providing name, owner, description, tags, data slice (channel, client, SKU, date range, promo window), and rule set version, and I execute a run to capture the data snapshot Then the system persists the scenario with a data_snapshot_timestamp equal to the run completion time And the scenario is assigned a unique scenario_id and version number 1 And required fields (name, owner, rule_set_version, data slice) are validated and descriptive errors are returned if missing or invalid And the scenario is private by default (no shares set)

Given I am the scenario owner or have Edit permission on the scenario When I set sharing so that Ops=View, Tech=Edit, and Finance=Comment and save the changes Then a user in the Ops role can open and view the scenario but cannot modify metadata, rules, or sharing (attempts return 403) And a user in the Tech role can modify editable fields and save changes successfully And a user in the Finance role can post comments but cannot modify scenario metadata, rules, or sharing And users without an assigned permission cannot access the scenario (404 or 403) And the current sharing matrix is visible in scenario metadata

Given a scenario exists and I have Comment or Edit permission When I post a comment and another user replies to it with a threaded response and an @mention Then both entries are displayed in a threaded view with author, timestamp, and persistent IDs And users with View permission can read all comments; users with Comment or Edit permission can add comments and replies And comments remain available when the scenario is locked; only posting is restricted by permission, not lock state

Given a scenario exists When any of the following fields change and are saved: description, tags, data slice, rule set version, sharing permissions, lock state Then a history entry is recorded with actor, timestamp, field(s) changed, and before/after values And the history is immutable and viewable in chronological order And users can filter the history by field and date and export it to CSV

Given a scenario is currently unlocked and I am the owner or a workspace admin When I apply a lock with an optional reason Then the scenario enters a locked state where metadata, rules, and sharing cannot be changed; viewing and commenting remain allowed And a lock banner shows the locker, timestamp, and reason And only the locker or a workspace admin can unlock the scenario And any attempted edit while locked is blocked with a clear message indicating the lock

Given the latest scenario run has zero critical errors, the sample size meets or exceeds the configured minimum threshold, and a baseline scenario has been selected When a user with Approve permission initiates approval Then validations are executed and, if all pass, the scenario status is set to Approved for Go-Live with approver, timestamp, baseline reference, and validation summary recorded And if any validation fails, approval is blocked and specific failure reasons are shown (which check failed and current values) And while Approved, the scenario is read-only except for comments; edits require revocation of approval by a user with Approve permission

Given a service or user holds a valid API token with scenario scope When it calls the Scenario API endpoints for Create (POST), Read (GET by id), Update (PATCH), Delete (DELETE), and List (GET with filters for owner, tag, status, date range) Then responses enforce permissions consistent with UI sharing (401 for unauthenticated, 403 for unauthorized) And the GET response includes all scenario metadata fields including owner, description, tags, data slice, rule set version, data_snapshot_timestamp, sharing, lock state, and approval status And List responses support pagination (page, per_page) and sorting by created_at and updated_at

Given a scenario marked Approved and a user with Deploy permission When the user clicks Apply to Staging Then the scenario rules are deployed to the staging environment within 30 seconds And the staging configuration version equals the scenario version identifier And a success notification and audit entry (who, what, when, scenario id, checksum) are created And attempting to apply a Non-Approved scenario is blocked with a clear error message

Given shadow mode is enabled for Scenario X and target segment Y When live orders from segment Y are processed during the shadow window Then the engine computes packaging and label choices using Scenario X without purchasing or printing labels and without modifying live fulfillment state And for each order, the system logs divergence vs live choices at decision, cost, and exception fields And carrier API calls for purchase/print are never invoked (zero requests logged) And disabling shadow mode stops divergence logging within 10 seconds

Given a rollout scope selecting Channel=Shopify and Client=Acme When the rollout is active Then only orders matching Channel=Shopify and Client=Acme are affected by the new configuration And orders not matching the scope are unaffected and continue using the current live configuration And the active scope is displayed in the rollout panel and recorded in the audit entry

Given a rollout is scheduled with Start=2025-09-01 08:00 and End=2025-09-07 20:00 in warehouse time zone When the current time enters the window Then the rollout activates automatically within 60 seconds And when the current time passes the end Then the rollout deactivates automatically within 60 seconds And a manual Pause immediately suspends the rollout and is captured in the audit trail

Given an active rollout has modified configuration from Baseline V12 to Scenario V13 When the user clicks Rollback Then the system restores Baseline V12 as the active configuration within 15 seconds And no new orders after rollback use Scenario V13 And an audit entry records rollback initiator, timestamp, from/to versions, scope, and optional reason And the previous rollout remains available for re-apply without reconfiguration

Given a shadow or scoped rollout is active When viewing the Rollout Health dashboard Then Exception Rate, Cost Delta (per order and aggregate), and SLA Proxy metrics are displayed with baseline comparisons And metrics update at least every 60 seconds with data latency under 2 minutes And configurable thresholds per metric compute a Ready/Not Ready indicator And crossing a threshold triggers an in-app alert and is logged

Given shadow mode has processed at least 500 orders When the user opens the Divergence Report Then per-order and aggregate divergences in packaging choice, service level, carrier, label cost, and exception flags are shown And calculations match recomputed results within 0.1% tolerance And the report can be exported as CSV and JSON within 30 seconds, preserving order ids, timestamps, and scenario version

Given a user with edit permission, When they create a new scenario with name, owner, and notes, Then the system assigns a unique ID and persists name, owner, notes, created_at, and updated_at Given a scenario exists, When the user runs a simulation, Then no records in production rules, labels, or shipments are created, updated, or deleted Given the scenario is saved, When viewed in the scenario list, Then its status is Draft and its environment is Sandbox

Given a ruleset containing a syntax error, When validation runs, Then the response returns an error code, message, and line:column for each error and the ruleset is rejected Given a valid ruleset up to 500 rules, When validation runs, Then validation succeeds and returns a compiled hash and warnings list in under 2 seconds Given rules reference an unknown carrier or service, When validation runs, Then unknown references are flagged with specific codes and suggested matches

Given date range, channels, clients, SKUs, and warehouses are selected, When preview count is requested, Then only shipments matching all non-empty filters are counted Given inclusive date boundaries, When the range is 2025-07-01 to 2025-07-31, Then shipments on 2025-07-01 and 2025-07-31 are included Given no filters are set, When preview count is requested, Then all shipments in the tenant history are in scope

Given a new scenario is created, When baseline snapshot is taken, Then versions and hashes of live rules, carrier rate tables, surcharges, and fuel indices with effective dates are stored under a snapshot_id Given a stored snapshot, When the same scenario is re-run with identical inputs, Then per-shipment simulated outcomes are identical to the prior run Given the user opts to refresh rates, When a new snapshot is taken, Then snapshot_id changes and prior results remain accessible and immutable

Given historical shipments in scope and a snapshot, When simulation runs, Then each result includes carrier, service, packaging, billable weight, base rate, surcharges, fuel, negotiated discounts, taxes, and total cost rounded to 2 decimals Given a control shipment unaffected by proposed rules, When simulated, Then the simulated decision equals the live decision and cost delta equals 0 Given shipments subject to dimensional weight, When simulated, Then billable weight equals max(actual_weight, DIM_weight) using snapshot dimensions and divisors Given 10,000 shipments in scope, When simulation runs, Then it completes within 5 minutes and at least 99.9% of shipments return results with failures logged

Given simulated results and live outcomes, When diff is generated, Then each shipment shows cost_delta, cost_pct_delta, decision change indicators, and rule hit trace Given aggregations by carrier, service, zone, SKU, and client are requested, When group totals are computed, Then each group total equals the sum of its shipments with rounding drift ≤ 0.01 per group Given proposed rules reproduce live behavior, When diff is generated, Then all per-shipment and aggregated deltas equal 0 and no changes are flagged

Given an existing scenario, When a user saves rule changes, Then a new immutable version is created with incremented version number and required changelog reason Given a scenario with multiple versions, When a simulation is run, Then the selected version and its snapshot are used and the run is recorded with user, timestamp, version, snapshot_id, and checksum Given audit logs are requested for a scenario, When filtered by scenario ID, Then all create, update, run, and delete events are returned with actor, timestamp, and before/after hashes

Given a dataset of shipments within a selected time window and available baselines (Live Rules, Locked Snapshot S, Custom Rule Set C) When the analyst selects Live Rules as the baseline and runs cost delta computation against one or more scenarios Then each shipment’s baseline cost equals the production rating output for the same inputs and ship date within 0.01 of the base currency and the baseline type is recorded in the run metadata Given the same dataset When the analyst selects Locked Snapshot S with frozen rate tables, zone maps, and rule logic Then baseline costs are computed using S’s immutable artifacts regardless of subsequent configuration changes and the snapshot id is recorded in the run metadata Given the same dataset When the analyst selects Custom Rule Set C (versioned) Then the system validates rule coverage (carriers/services/zones configured ≥ 99.9% of shipments by count) before execution and refuses to run with a validation report if coverage is insufficient And all baseline costs use C’s versioned artifacts and the rule set version id is recorded in the run metadata

Given per-shipment baseline and scenario costs in minor currency units (e.g., cents) When computing aggregate totals and deltas across any grouping or the full set Then the sum of shipment-level deltas equals the reported aggregate delta exactly (no rounding drift) And all aggregates are computed in minor units with rounding applied only at presentation Given the same inputs and parameters When the computation is rerun Then the outputs (shipment-level, rollups, KPIs) are bit-for-bit identical and presented in the same deterministic order

Given shipments rated in multiple currencies and a selected base currency with an FX rate source When computing deltas and KPIs over the time window Then all non-base currency amounts are converted using the FX rate effective at each shipment’s ship date (fallback to the most recent prior rate if missing) and the FX source and rate used per day are recorded And totals by native currency and the normalized base currency are both available for audit And if an FX rate is unavailable for any day and no prior rate exists, the affected shipments are flagged and excluded from KPIs with a count and explicit error code

Given carriers with multiple rate cards and zone map versions each with effective start and end timestamps When computing baseline and scenario costs across a window that spans changes Then for each shipment the applicable rate card and zone map version are selected where effectiveStart <= shipDate < effectiveEnd and costs match expected test fixtures at boundaries And if a shipment cannot be zoned due to missing map coverage, it is flagged with a non-rateable code and excluded from KPIs and rollups with counts reported

Given computed per-shipment costs for baseline and one or more scenarios When generating KPIs Then for each scenario the system outputs total spend delta, average cost per shipment, savings rate percentage, variance distribution percentiles (P5, P50, P95), and standard deviation And KPI values match an independently computed reference within 0.01 base currency units and 0.01 percentage points And KPIs update within 2 seconds after applying any supported filter (date range, carrier, service, zone, SKU, client, channel, warehouse, weight bracket) on a 100k-shipment dataset

Given shipment-level deltas are computed When the user requests rollups by carrier, service, zone, SKU, client, channel, warehouse, and weight bracket Then each rollup returns counts, baseline total, scenario total(s), and delta(s) and the sum across buckets equals the overall total for that dimension And shipments map to exactly one bucket per dimension; weight brackets are applied from configuration and cover ≥ 99.9% of shipments by count And outliers are flagged per scenario using Tukey IQR (mild: 1.5x, extreme: 3x) on per-shipment delta and at least the top and bottom 1% by delta are flagged; outlier flags are filterable and countable

Given a reference environment documented by the team When processing 100k shipments against up to 3 scenarios with all rollups enabled Then end-to-end computation completes in under 5 minutes, with CPU and memory utilization not exceeding agreed SLO thresholds for the environment, and a run summary with timing is recorded Given a dataset of 1M shipments and the same configuration When processing begins Then end-to-end computation completes in under 45 minutes with successful completion status and timing recorded Given a repeat run with identical inputs and parameters within a 24-hour cache TTL When the computation is executed again Then cached artifacts are reused and results are returned in under 30 seconds for 100k shipments and under 5 minutes for 1M shipments

Given a connected Delta Explorer dataset, When the user selects any combination of dimensions (carrier, service, zone, SKU, client, channel, warehouse, package type, date) to pivot rows/columns, Then the pivot renders aggregates (shipment count, delta $, delta %, throughput) matching the active filters. Given multi-select include/exclude filters and an absolute or relative date range, When applied, Then only matching records are reflected and an Applied Filters summary is visible. Given a dimension with 1,000+ distinct values, When the user searches and selects values, Then server-side search returns results and the pivot updates with P95 interaction latency ≤ 2.5s and P50 ≤ 800ms. Given missing values in a dimension, When displayed, Then an Unassigned/Unknown bucket is shown and included in totals. Given Reset All is clicked, When executed, Then all filters and pivots clear and grand totals are shown.

Given a pivot cell is selected, When the user drills down, Then a paginated shipment table opens scoped to that cell’s filters and groupings. Given shipment rows are displayed, When rendered, Then each row shows shipment ID, carrier, service, zone, SKU/line summary, client, channel, warehouse, package type, weight, label cost, predicted best-rate cost, delta $, delta %, and applied-rule rationale (rule name/ID and decision reason). Given a drill-down is opened, When the user navigates back, Then the previous pivot state and scroll position are preserved. Given the drill-down is triggered, When data loads, Then initial render P95 ≤ 2.0s and P50 ≤ 700ms.

Given a metric column header, When the user sorts by delta $ descending, Then rows reorder with a deterministic secondary sort by key ASC and a sort indicator is shown; sorting is stable across pages. Given group-by selections (e.g., client > carrier > service), When applied, Then nested groups render with expandable subtotals for each level. Given a Top-N control on a grouped dimension, When N=20 is set for a selected metric, Then only the top 20 groups are shown plus an Others bucket, and Grand Total = Top 20 + Others within currency rounding tolerance. Given Top-N is active, When filters or sort change, Then the Top-N set recomputes automatically. Given the Top-N input, When N is outside 5–100, Then validation prevents submission and shows an inline error.

Given a configured pivot (row/col dimensions, metrics, filters, date range, sorts, groupings, Top-N, leakage/profitability toggles, currency and weight units), When saved with a unique name, Then it appears in Saved Views and can be set as Default. Given a saved view, When loaded, Then the UI reproduces the exact state (including unit preferences) and aggregates match the state used at save time for the same underlying data. Given a saved view is renamed or deleted, When confirmed, Then the list updates immediately; if the default view is deleted, the Default resets to System Default. Given a duplicate name is entered on save, When submitted, Then the user is prompted to overwrite or choose a different name and the view is not duplicated without confirmation. Given the user signs out and back in, When opening Delta Explorer, Then saved views are available per user and workspace and the Default view auto-loads.

Given any grouping and pivot configuration, When Grand Total is displayed, Then Grand Total equals the sum of visible subgroup totals within ±$0.01 currency and ±0.001 weight tolerance. Given the same filters are applied, When row/column assignments are rearranged, Then Grand Total remains identical. Given a Top-N with an Others bucket is active, When totals render, Then Top-N subtotal + Others subtotal equals the filtered total within tolerance. Given currency display rounding, When values are shown, Then UI rounds half up for display but internal aggregation uses full precision to preserve reconciliation within tolerance. Given missing/unknown category buckets, When present, Then they are included in totals unless explicitly filtered out, and totals change accordingly.

Given a dataset of ≥10M shipments across 12 months, When applying a filter, changing a pivot dimension, sorting, toggling Top-N, or drilling down, Then server-side aggregation is used and P50 interaction latency ≤ 800ms and P95 ≤ 2.5s; shipment list first page P95 ≤ 2.0s. Given a request is in-flight, When a new interaction occurs, Then the prior request is canceled and only the latest response is rendered. Given pagination sizes of 50/100/250 rows, When paging next/previous, Then totals remain constant, sorting remains stable, and there are no duplicates or gaps across pages. Given an operation exceeds 8 seconds, When a timeout occurs, Then the UI shows a retry action and a diagnostic message without freezing; subsequent retries respect backoff. Given transient network loss, When connectivity returns, Then the last attempted state can be retried without losing the prior configuration.

Given Leakage Hotspots toggle is off, When toggled on, Then a filter is applied to include shipments where actual label cost exceeds predicted best-rate cost by ≥ $0.25 or ≥ 2% (whichever is greater), and the pivot updates accordingly. Given Profitability Tiers are available, When the user selects a tiering mode (e.g., Low <0%, Medium 0–15%, High 15–30%, Very High >30% margin), Then results are filtered or grouped by the chosen tier and a legend shows tier definitions. Given both toggles are on, When combined with existing filters, Then they apply with AND logic by default and an option is available to switch to OR; the Applied Filters summary reflects the logic. Given toggles are turned off, When disabled, Then the toggle-introduced filters are removed and metrics revert to the pre-toggle state. Given a view is saved with toggle states, When reloaded, Then the same toggle states and tier definitions persist.

Given a selected baseline period, a proposed rule-change scenario, and access to historical scan events and pick/pack timestamps When the throughput model is executed for the selected filters (carrier, service, zone, SKU, client, warehouse) Then it returns baseline and scenario values for orders per labor hour, average cycle time, on-time SLA %, and station utilization % (per station and overall) And then it returns deltas (scenario minus baseline) for each metric And then calculations are deterministic and reproducible for identical inputs And then metric definitions and time windows used are displayed alongside results

Given global defaults, warehouse-level overrides, and scenario-level overrides for time coefficients (e.g., cartonization time, signature-required handling, service-specific handoffs) When a scenario is executed Then the effective coefficient applied follows precedence: scenario override > warehouse override > global default And then coefficients accept decimal values in seconds or minutes within allowable bounds (e.g., 0–600 seconds per step) And then changes to coefficients are applied without application redeploy and are auditable with timestamp and author And then results reflect the updated coefficients in all computed metrics

Given a warehouse calendar with holidays and closures, defined shifts with start/end and breaks, carrier pickup cutoffs, and station capacity definitions When modeling throughput for a date range Then non-working times are excluded from capacity and cycle-time computations And then station utilization is computed per station using its capacity within active shifts And then on-time SLA % accounts for pickup cutoffs and calendar exceptions And then multi-warehouse scenarios aggregate correctly while preserving per-warehouse metrics

Given two recent cohorts (A and B) selected by date range and filters with observed cycle-time outcomes When the model is calibrated using A as training and validated on B Then the tool reports error metrics (e.g., MAPE and RMSE) comparing predicted vs observed cycle times And then it displays the calibrated coefficient set and the baseline defaults used And then users can accept the calibrated set for subsequent scenarios or revert And then calibration selections (filters, dates, coefficients) are saved with scenario version metadata

Given scenario inputs for batch picking size, wave configuration, and label printing sequence (e.g., by carrier, zone, SKU, FIFO) When the scenario is executed Then picking, packing, and handoff time components are adjusted per the provided batch and sequencing rules And then queueing and handoff wait times are recomputed to reflect batching impacts And then metric deltas vs baseline isolate the effects of these assumptions in the results view

Given selected time coefficients with defined ranges/steps and volume mix sliders by carrier/service/zone When sensitivity analysis is run Then the system produces outputs showing how orders per labor hour, average cycle time, on-time SLA %, and station utilization % vary across the parameter space And then it ranks the top drivers by absolute impact on orders per labor hour and on-time SLA % And then users can export the full sensitivity dataset and view a tornado or comparable summary of drivers

Given a baseline and a proposed scenario with selected filters When the results are exported Then the export includes per carrier, service, zone, SKU, client, warehouse, and station: baseline metrics, scenario metrics, and deltas for orders per labor hour, average cycle time, on-time SLA %, and station utilization % And then the export provides CSV and XLSX formats with a metadata sheet (scenario name, timestamp, coefficient versions, filters) And then totals and row counts reconcile with the on-screen results within ±0.1%

Given 2–6 saved scenarios with distinct rule sets and the same evaluation dataset window selected When the user enables Normalized Assumptions Then all KPI calculations for each scenario use a shared baseline for carrier rates, destination mix, and SKU cube/weight model, and a "Normalized" badge appears on the comparison header. Given Normalized Assumptions is enabled When the user toggles it off Then KPIs recalculate using each scenario’s native assumptions within 5 seconds and the badge disappears. Given scenarios include different base dates or datasets When the user attempts to enable Normalized Assumptions Then the system prompts to align the dataset and prevents enabling until the dataset matches. Given aligned scenarios When the comparison table renders Then each scenario appears in a dedicated column with consistent KPI rows in the same order.

Given a comparison view When KPIs render Then the following tiles are present and populated: Spend Delta ($), Cost per Shipment ($), Savings Rate (%), Throughput Change (%). Given test fixture data with known outcomes When tiles render Then each KPI value matches the known outcome within ±0.1% for percentages and ±$0.01 for currency. Given a user opens the KPI info tooltip When displayed Then each KPI definition and formula corresponds to the implementation used for calculations. Given rounding is applied When exporting or drilling down Then underlying unrounded values are preserved and totals reconcile within ±0.01 to the UI.

Given a comparison with at least two scenarios When the user selects Variance Charts Then an absolute delta chart and a percent delta chart are available. Given the user switches the dimension to Carrier, Service, Zone, SKU, Client, or Week When applied Then the chart, legend, and tooltips update within 2 seconds and reflect the selected dimension. Given a segment is clicked in the chart When selected Then the corresponding rows in the winner/loser list are filtered to that segment and a filter chip is added. Given an export is requested from the chart When downloaded Then the CSV includes columns for dimension, baseline value, scenario values, absolute delta, and percent delta.

Given a comparison is loaded When viewing the Winner/Loser panel Then the list shows top 50 winners and top 50 losers by Spend Delta by default with controls to change metric and count. Given a row is clicked When drilldown opens Then the shipment-level table lists all affected shipments for that segment and scenario with columns: order_id, SKU, weight, zone, service, package, pre/post rate, delta, and rule applied. Given filters are applied in any panel When moving between panels Then filters persist and the record counts remain consistent across tiles, charts, and lists. Given pagination is present When navigating pages Then total counts and sums remain stable and match the KPI totals within ±0.1%.

Given a segment and scenario are selected When Explain Changes is opened Then the system attributes at least 80% of the spend delta to specific rule effects (e.g., service switch at weight threshold, zone re-route, package size change, carrier selection), with each effect’s percentage and dollar impact listed. Given a listed rule effect When View Rule Diff is clicked Then a diff modal shows the exact rule condition and outcome that changed between scenarios. Given unattributed residual remains When displayed Then it is labeled Unattributed with an explanation of potential causes (data noise, mixed effects), and its share is less than or equal to 20%. Given a sampled explanation When the user requests Evidence Then a sample of at least 30 shipments supporting each effect is shown with IDs and computed deltas.

Given a user sets Leakage Tolerance thresholds (e.g., Savings Rate >= 5%, Cost per Shipment <= $4.00, Max negative delta per client <= $200) When a comparison is recomputed Then any segment or scenario breaching a threshold is flagged with a red indicator and a tooltip explaining the breach. Given alert channels are configured (email and Slack) When a breach occurs Then an alert is sent within 60 seconds including: comparison name, breached metric, segment, value, threshold, normalization state, and deep link. Given an approval is attempted on a breached scenario When Approve is clicked Then approval is blocked until an override reason of at least 20 characters is entered and the user has Override permission. Given thresholds are edited When saved Then changes are versioned with timestamp, user, and previous values in the audit log.

Given a comparison is configured When Save as Bundle is clicked Then the user can name the bundle (unique per workspace), add tags (up to 20), and set visibility (Private, Team, Client) before saving. Given a bundle is saved When Share Link is generated Then recipients with access can open a read-only view showing the same normalization state, filters, KPIs, charts, and segments as the owner. Given Export is clicked When choosing CSV or XLSX Then a side-by-side table for all selected scenarios is exported with KPI tiles, dimension breakdowns, and metadata (timestamp, dataset window, normalization state, filters) and matches UI values within rounding rules. Given notes are added When saved Then notes support @mentions with notifications, are immutable after 15 minutes, and appear in the bundle timeline with author and timestamp. Given approvals are requested When an approver approves or rejects Then the decision captures approver, decision, comment (required for reject), and locks the bundle from edits unless the decision is revoked by an admin.

Given a user filters Delta Explorer by date range, clients, and carriers with a selected proposed rule set When the user requests an export and selects CSV, XLSX, or PDF Then the system generates the export for up to 50,000 shipments within 60 seconds And each format contains both Pivot Summary and Shipment Detail with Baseline, Proposed, and Delta columns side‑by‑side And the XLSX contains two worksheets named "Summary" and "Detail" And the CSV export provides two files (summary.csv and detail.csv) packaged in a single ZIP And the PDF contains sequential sections labeled "Summary" and "Detail" And the export filename includes org/client scope, date range, and timestamp

Given an export is generated from a Delta Explorer comparison When the file is opened Then every shipment row includes applied rule rationale (RuleID, RuleName, Rationale), GL mapping code(s), and carrier invoice fields (carrier, service, zone, billed weight, dimensions, invoice number, invoice date, accessorial codes/amounts, fuel surcharge, net charge) And the Pivot Summary includes totals by carrier, service, zone, SKU, and client for Baseline, Proposed, and Delta And the export metadata includes the selected date range and schema version And all monetary fields are currency-formatted with 2 decimal places and ISO currency code

Given a user views totals and column labels in the in-app Delta Explorer for a specific filter set When the same data is exported in any format Then all column headers in the export exactly match the in-app labels (case and spacing) And group subtotals and grand totals in the Pivot Summary equal the in-app values within a tolerance of ±0.01 And the count of shipments in Shipment Detail equals the in-app shipment count for the filter set

Given an organization or client has uploaded a logo and enabled watermarking When a user exports an XLSX or PDF Then the exported PDF includes the client-branded header (logo + client name) and a diagonal watermark containing the client name and export date And the XLSX includes the branded header in the workbook header/footer for both Summary and Detail worksheets And CSV exports contain no branding or watermark and include only data rows and headers And branding reflects the selected scope (org or client)

Given a completed export exists When a user creates a share link with a specified scope (org or client) and TTL between 1 hour and 30 days Then the system issues a unique, unguessable URL token scoped to the selected org/client and the specific export And the link expires at the configured TTL and returns HTTP 410 after expiry And the owner can revoke the link immediately, after which access is denied within 60 seconds And downloading via the link always serves the exact export artifact and filename without exposing data outside the selected scope

Given audit logging is enabled by default When an export is created, a share link is created/revoked, or an export is viewed/downloaded via UI, share link, or API Then an immutable audit record is written with timestamp, actor (user ID or link token), IP, action, export ID, scope, and outcome (success/failure) And administrators can filter logs by date range, action, actor, scope, and export ID And audit logs are retained for at least 365 days and are exportable to CSV

Given a system integrator has valid API credentials scoped to an org or client When they call the export API with filters (date range required; optional carrier, service, zone, SKU, client) and an Accept header of application/json or text/csv Then the API returns Summary and Detail with the same columns, labels, totals, and field formats as the in-app exports And large result sets support server-side pagination for JSON and streamed responses for CSV And the response includes the selected date range and schema version metadata And unauthorized or out-of-scope access returns 401/403 with no data leakage

Given a request with origin_zip3, destination_zip3, carrier_code, service_code, handoff_timestamp (ISO 8601), and sla_days When the Historical Transit Model service is called Then it returns HTTP 200 with a JSON body containing: distribution.pmf (array of {days:int, probability:float} summing to 1.0 ± 0.001, covering days 0–15 with a tail bucket), distribution.cdf (array aligned to pmf), stats.p50, stats.p90, stats.p95 (integer days), on_time_probability (0–1), model_version (string), and training_window (ISO date range) And the response validates against the published JSON schema And invalid or missing parameters produce HTTP 400 with machine-readable error details And p95 service latency ≤ 300 ms for warm cache and ≤ 700 ms for cold requests at 50 rps sustained load

Given a handoff_timestamp after the carrier’s origin pickup cutoff on a business day When computing the effective handoff Then the model rolls the handoff to the next available pickup day per the carrier/service calendar in the origin ZIP3 time zone And weekends and carrier-observed holidays are skipped using the maintained service calendar And a unit test suite covering all cutoff, weekend, and holiday cases for the next 18 months passes 100% And on a validation set of shipments straddling cutoffs, the KS distance between adjusted predictions and empirical delivery distributions is ≤ 0.10

Given historical shipments labeled by month and weekday When training the model Then seasonality and weekday features are incorporated into the prediction And over a 12-month rolling backtest the seasonal model achieves ≥ 5% relative Brier score improvement versus a non-seasonal baseline (p < 0.05) And weekday-specific median absolute error for p50 is ≤ 0.5 days across the top 50 lanes by volume

Given a time-based backtest over the most recent 26 weeks When evaluating on-time probability calibration Then for each decile bin of predicted probability, the observed on-time rate is within ±5 percentage points of the bin center and overall Brier score ≤ 0.18 And quantile accuracy: ≥ 90% of shipments deliver on or before the predicted p90 day (±1 day tolerance), and median absolute error of p50 ≤ 0.5 days And across lanes with ≥ 200 samples, the maximum absolute calibration error per lane is ≤ 8 percentage points

Given raw event feeds from supported carriers When normalizing to the unified schema Then each shipment record includes origin_zip3, destination_zip3, carrier_code, service_code, handoff_datetime, delivery_datetime, and is_success computed with consistent rules And ≥ 98% of shipments in the last 6 months map successfully to a normalized record And duplicate/corrupt records are deduplicated/filtered such that the false duplicate rate is ≤ 0.5% And records missing any required field are excluded with explicit reason codes, with total data loss ≤ 2% per carrier

Given a lane-service with < 50 historical shipments in the training window When a prediction is requested Then the model backs off hierarchically to broader cohorts (e.g., ZIP3→state, carrier+service nationwide) and returns a distribution with widened credible intervals And in low-sample backtests, calibration error per decile is within ±8 percentage points and p90 coverage is ≥ 85% And for completely unseen lanes, the service returns HTTP 200 with a fallback distribution and reason=fallback in the payload; it never returns an empty distribution

Given any prediction response Then it includes model_version (semver), training_window_start, training_window_end, and cohort_level used And runtime metrics expose p50/p95 latency, error rate, and weekly calibration drift by lane; alerts trigger within 5 minutes when p95 latency > 700 ms or weekly Brier score degrades by > 10% versus the trailing 4-week average And the service achieves ≥ 99.9% monthly availability with no single outage > 15 minutes, measured via synthetic checks on the /predict endpoint

Given carrier calendars (UPS, USPS, FedEx, DHL eCom) and regional holiday feeds are available by 03:00 UTC Sunday When the weekly sync job runs Then 99.9% of parsable records are ingested and deduplicated, processing completes within 15 minutes per 100k source rows, and a new unified calendar version ID is created only if changes are detected Given conflicting inputs across sources for the same (carrier, service, origin, date) When reconciliation is applied Then precedence is AccountOverride > CarrierServiceSpecific > RegionalHoliday > CarrierDefault and the resulting availability matches the precedence matrix in tests Given ≥0.1% source records fail validation When ingestion runs Then the run is marked Partial, invalid records are quarantined with error codes, valid records are applied, and an alert is emitted within 10 minutes Given any single provider feed times out When the sync runs Then the job retries up to 3 times with exponential backoff, proceeds with other providers, marks the missing provider as stale, and completes without blocking others

Given origin America/Chicago and destination Europe/Berlin, ship date 2025-03-30 16:30 local origin, origin pickup cutoff 17:00 local When computing nextPickupAt and earliestDeliveryDate Then nextPickupAt=2025-03-30T22:00:00Z (17:00 CDT), destination DST change is respected, and all timestamps returned are ISO 8601 with UTC (Z) plus a timezone field per location Given origin America/Phoenix (no DST) on 2025-11-03 When computing availability and cutoffs Then no DST shift is applied to origin times and calculations remain correct across the DST boundary Given any API response containing times When validated Then fields include timezone identifiers (IANA), offset at event time, and pass schema validation

Given Account A has an override: Carrier=FedEx, Service=Ground, Origin=ORD1, No Saturday pickups When querying availability for Account A on a Saturday at ORD1 Then serviceAvailable=false and nextPickupAt rolls to Monday 09:00 local (or next defined pickup window) Given the same query without account context When querying availability Then serviceAvailable reflects the carrier default (Saturday available if carrier default allows) Given overlapping overrides at account and service level When reconciliation runs Then the more specific scope wins (origin+service > service-only > account-only) and results are deterministic Given an override conflicts with carrier blackout When applied Then blackout remains authoritative and override cannot make a blacked-out day available

Given a valid request GET /v1/calendar/availability?accountId=A&origin=ORD1&service=UPS_GROUND&date=2025-09-15 When the API is called Then respond 200 within p95<=300ms (p99<=700ms under 200 RPS), with body containing only {serviceAvailable:boolean,nextPickupAt:string,nextDeliveryDate:string,sourceVersion:string} and all timestamps ISO 8601 Given invalid parameters (missing origin or malformed date) When the API is called Then respond 400 with error code and message; 404 for unknown origin/service; 429 includes Retry-After header; responses include requestId for traceability Given cacheable results When repeated identical requests are made within 5 minutes Then responses include Cache-Control: max-age=300 and ETag; conditional requests with If-None-Match return 304 when unchanged Given normal operations over a calendar month When monitoring availability Then API achieves 99.9% uptime with no 5xx rate >0.1% of requests

Given the scheduled sync window at 03:00 UTC Sundays When no upstream changes are detected Then the unified calendar version remains unchanged and a heartbeat metric is emitted Given upstream changes are detected When the sync completes Then the unified calendar version increments (semver minor), a changelog summary is stored, and updated data is served within 10 minutes Given a provider or job failure When the sync fails Then the previous calendar version continues to be served, an alert is issued within 15 minutes, and the run status is marked Failed with detailed errors Given the unified calendar has not updated in >7 days When health checks run Then a StaleCalendar alert is raised and surfaced in system status and metrics

Given a user with role Operations Admin When creating a manual override Then the system requires scope (account/service/origin), reason, start/end timestamps, and priority; payload passes validation and is applied within 5 minutes to API responses and simulator reads Given any override is created, edited, or deleted When auditing Then an immutable audit record is stored with {actor, action, timestamp, before, after, reason, ticketRef} and is queryable by time range and scope Given an erroneous override When rollback is requested from the audit UI or API Then the previous effective state is restored within 2 minutes and propagated, with a new audit entry linking to the rollback Given overlapping overrides produce a conflict When saving Then the system rejects the change with 409 Conflict and a resolution hint

Given SLA Forecaster runs rule set R with calendar version V When simulating lanes with service blackouts or regional holidays Then predicted delivery dates exclude non-service days and pickup blackouts, and at-risk order counts reflect the exclusions Given the same simulation under calendar version V-1 When results are compared Then differences in projected SLA hits correspond exactly to the calendar deltas recorded in the changelog Given high-volume simulation (>=100k orders) When running Then the transit model or simulator reuses calendar results (per unique account+origin+service+date) to limit calendar API calls to <=1 per unique key, keeping sim runtime within 1.2x baseline

Given a candidate rule set with sampling_window_start, sampling_window_end, confidence_level, and precision_target_pp configured When the simulator runs Then it filters historical orders to created_at in [sampling_window_start, sampling_window_end] inclusive and reports sample_size And it computes sla_hit_rate with a two-sided confidence interval at confidence_level and returns ci_lower, ci_upper And if (ci_upper - ci_lower)/2 > precision_target_pp, it sets insufficient_confidence = true and tags all impacted aggregates; otherwise insufficient_confidence = false And all reported aggregates are derived solely from the filtered sample

Given historical orders with origin-destination lanes and sales channels, each with a promised delivery date When the simulator applies the rule set and transit model Then it returns for each lane and channel: sla_hit_rate_pct, at_risk_count, met_count, total_count And at_risk_count equals the number of orders where predicted_delivery_date > promised_date And the sum of met_count and at_risk_count equals total_count for every lane/channel And global totals equal the sum of per-lane/channel totals; no negative counts; lanes/channels with zero orders are omitted

Given complete carrier rate cards and a baseline derived from historical actual services When the simulator prices each order for both baseline and simulated selections and computes transit via the transit model Then it returns scenario-level avg_transit_days, avg_cost_per_order, cost_delta_total, and cost_delta_per_order And per-order outputs include cost_baseline, cost_simulated, cost_delta, selected_service, baseline_service, predicted_transit_days And missing_rate_count equals 0; if any rate is missing and allow_rate_estimation != true, the run fails with error code RATE_MISSING and no aggregates are persisted

Given a rule set defining carrier/service exclusions, transit buffer_days, and origin-specific daily cutoff times with timezones When the simulator selects services and computes ship_date and predicted_delivery_date Then no excluded carrier/service is selected for any order And orders created after the origin cutoff time are assigned the next valid ship_date per origin timezone And buffer_days are added to predicted transit before SLA evaluation And service calendars (non-pickup/delivery days and holidays) are respected when computing ship_date and predicted_delivery_date

Given a POST /simulate request with up to 50,000 orders and a valid rule set When the simulation is executed Then the API returns a run_id immediately and final metrics are available for retrieval, with p95 end-to-end time ≤ 120 seconds And a CSV export is generated containing columns: order_id, origin_zip, destination_zip, channel, baseline_service, selected_service, ship_date, promised_date, predicted_delivery_date, sla_hit, at_risk, predicted_transit_days, cost_baseline, cost_simulated, cost_delta And CSV aggregates match API aggregates: counts exactly equal; averages within ±0.01; totals within ±0.5% And the CSV is downloadable via a signed URL valid for at least 7 days

Given identical inputs (orders, rule set, transit model, rate cards) and a fixed random_seed When the simulator is run multiple times Then per-order outputs and aggregate metrics are identical across runs And outputs include metadata: run_id, created_at_utc, random_seed, ruleset_version_hash, transit_model_version, rate_card_version And completed runs and their inputs are retained and queryable for at least 30 days for audit

Given a selected warehouse, date range, and rule set When the user opens the Lane Risk Dashboard Heatmap tab Then a matrix of origin–destination lanes is rendered within 2 seconds for up to 5,000 lanes And each cell’s color encodes projected on-time rate with a visible legend (green ≥95%, yellow 90–94.99%, red <90%) And hovering a cell shows: lane ID, projected on-time %, projected SLA hits (count and %), total forecasted orders, 95% CI lower/upper bounds, and data sufficiency state And adjusting legend thresholds updates cell colors within 500 ms And switching rule sets recalculates projections using service calendars and updates the heatmap within 2 seconds

Given the Filters panel is open When the user selects one or more warehouses, a preset or custom date range, and one or more SKU classes Then all visualizations and KPIs recompute within 2 seconds And applied filters appear as removable chips; Clear All resets to defaults (last 28 days, all warehouses, all SKU classes) And date ranges honor carrier service calendars (non-service days excluded from SLA windows) And filter selections persist across tabs and browser sessions for 7 days per user And totals and counts reflect filters within 0.5% tolerance of backend results And invalid combinations (no data) show a zero-state with guidance to adjust filters

Given the user clicks a red or yellow heatmap cell When the At-Risk panel opens Then it lists projected at-risk orders for the selected lane with columns: order identifier, channel, promised date, projected delivery date, days-late risk, carrier/service, SKU class, lane, and probability of lateness And the list is sortable by any column and filterable by channel and service And a “See historical examples” action returns up to 20 most similar past shipments (same lane + service, matching SKU class and seasonality ±14 days) with actual delivery outcomes And each historical record links to the shipment detail view in ParcelPilot in a new tab And if fewer than 20 examples exist, the count shown matches available data and a Low Data indicator is displayed

Given past shipment history over the last 180 days When computing projected on-time for each lane/service Then a 95% confidence interval (Wilson score) is displayed per cell And sufficiency states are derived: Sufficient (N ≥ 100 or ≥ 8 service weeks), Low (20 ≤ N < 100), Insufficient (N < 20 or < 2 service weeks) And Insufficient cells render hatched and are excluded from KPI totals by default; an “Include low-data” toggle includes them and shows a reliability warning And tooltips show N, lookback window, and last data refresh timestamp; if data is >24h old, a Stale Data badge appears

Given a lane/service with projected on-time below target (default 95%) When suggestions are computed Then at least one suggestion is shown if an alternative improves on-time to ≥ target with cost delta ≤ 15% over baseline And each suggestion displays expected on-time %, cost impact %, and affected volume, sorted by lowest cost delta And clicking a suggestion opens the Rule Editor prefilled with lane, warehouse, channel, and proposed action; a confirmation modal allows Save or Cancel And after saving, the user returns to the dashboard with filters preserved and sees updated projections for the selected (draft or active) rule set within 2 seconds And a “View rationale” link reveals top drivers (e.g., historical on-time by service, cutoff conflicts, blackout days)

Given a lane is selected When viewing the breakdown panel Then the dashboard shows projected on-time %, projected volume, SLA hits count, and 95% CI by carrier/service and by sales channel And totals reconcile to the lane totals within 0.1% And the user can toggle between stacked-by-channel and faceted-by-service views; switching renders within 500 ms And segments with N < 20 display a Low Data icon and are excluded from lane KPIs unless the Include low-data toggle is on And selecting a segment filters the At-Risk list and highlights corresponding cells in the heatmap

Given a draft rule set contains a specific origin–destination lane with a target SLA threshold T% and acceptable cost variance V% and ≥90 days of historical shipments are available When the user requests service swap suggestions for that lane Then the system returns 0–10 suggested carrier/service options where each suggestion has projected on-time probability ≥ T% and postage cost delta ≤ V% versus the current rule, or returns “No eligible suggestions” if none meet both constraints And each suggestion includes carrier, service, projected on-time probability (%), 95% confidence interval, projected average postage, cost delta (%), estimated pickup cutoff time, and historical sample size used And suggestions are ranked by Pareto dominance (maximize on-time probability, minimize cost delta), with ties broken by higher sample size then lower variance And the response time is ≤ 2 seconds at p95 for a single lane with ≥ 500 historical shipments And results are deterministic for identical inputs, data snapshot, and model version, and the payload includes model version and data window metadata

Given service swap suggestions exist for a lane When the user opens the Trade-offs view Then the UI displays a chart and table where each suggestion is plotted by cost delta (%) vs on-time probability (%) and the current rule is clearly labeled for comparison And users can sort by on-time probability, cost delta, or Pareto rank and filter by carrier, service type, and minimum sample size And hovering or selecting a point reveals a tooltip with carrier, service, on-time probability with 95% CI, cost delta, sample size, and cutoff time And the view supports CSV export of the currently filtered table and preserves numeric precision to two decimals And all elements meet AA contrast and keyboard navigation requirements (tab order, focus states)

Given a suggestion is selected for a lane in a draft rule set When the user clicks Apply and provides a required justification (minimum 10 characters) Then the system replaces the lane’s service in the draft rule set with the selected suggestion and increments the draft version number by 1 And a confirmation modal shows a diff summarizing: previous vs new carrier/service, projected on-time probability change, projected cost delta, and affected lanes/orders count And an audit record is created capturing user, timestamp, previous value, new value, justification text, suggestion metadata, and model version And changes are applied in ≤ 1 second p95 and are atomic; on validation or write failure, no partial updates occur and a clear error is shown

Given one or more applied suggestions exist in the draft rule set version history When the user initiates a rollback to a prior draft version Then the system restores the exact prior ruleset state, including lane-level services and parameters, and records a rollback audit entry with user, timestamp, source and target versions, and reason And the UI refreshes to show the restored version’s forecasts and trade-offs within ≤ 2 seconds p95 And rollback is blocked for published/locked rule sets with a descriptive error and a link to view-only history

Given a lane and a set of suggestions are available When the user opens the At-Risk panel and selects a suggestion Then the system displays projected counts and percentages of orders expected to miss SLA by lane for the next 14 days under (a) current rule and (b) selected suggestion, based on connected platform forecasts and service calendars And at-risk is defined as on-time probability < target threshold; the definition is shown inline And totals update within ≤ 2 seconds p95 after selection and include 95% confidence bands And the panel shows the last model refresh timestamp and data window used

Given the user has configured a target SLA threshold T%, acceptable cost variance V%, and optional constraints (carrier allowlist/denylist, service blackout dates, cutoff windows) When the system generates service swap suggestions Then no suggestion violates the provided constraints or service calendars And if no options satisfy both T% and V%, the system returns “No eligible suggestions” and also lists up to 3 nearest-feasible alternatives with explicit reasons (e.g., “exceeds cost variance by 1.2%” or “SLA shortfall 0.8%”) ordered by minimum constraint violation And users can persist default T% and V% at the merchant level, and the saved defaults are reapplied on subsequent sessions

Given warehouse W with defined carrier pickup calendars, processing SLAs, labor shifts/capacity, and historical handling-time distributions When the optimizer runs for a configurable horizon (>=14 days) with W's timezone set Then it outputs per-channel daily cutoff times and batch release windows with ISO-8601 timestamps in W's timezone And no cutoff is scheduled later than [earliest pickup time − required processing buffer] for that carrier/day And the plan satisfies labor capacity in each 15-minute interval (no interval utilization > 100%) And the projected SLA hit rate for W is >= baseline by at least 3 percentage points, or equal with fewer overtime hours (<= baseline overtime hours) And the output includes rationale fields: constraint drivers, assumed buffers, and data freshness

Given 6 months of historical origin–destination pairs and carrier service calendars When simulating the baseline and at least 2 alternative cutoff schedules Then the system calculates projected SLA hit rate per lane, channel, and day-of-week with 95% confidence intervals And produces at-risk order count and percentage per lane where forecast < target SLA And exposes a downloadable CSV and an API endpoint returning the projections within 5 seconds for datasets up to 100k orders And each projection includes versioned model/run IDs for auditability

Given channel-level ship-by targets and delivery promises per lane When no feasible cutoff schedule meets the target SLA without exceeding labor capacity or carrier limits Then the system suggests promise adjustments per channel (e.g., advance cutoff by 30 minutes or relax delivery promise by 1 day on specified lanes) And each suggestion includes estimated impact on SLA hit rate, affected order volume, and any incremental postage/cost impact if available, with 95% CI And suggestions are only surfaced if projected SLA improvement >= 2 percentage points or overtime reduction >= 10% And suggestions are exportable via API/CSV with effective dates and channels/lane scopes

Given carrier holidays/blackout days, ad-hoc pickup changes, and partial-day labor shifts When generating schedules for those dates Then the optimizer avoids proposing cutoffs on blackout days and shifts to the next available pickup And it respects user-locked overrides for specific channels, cutoffs, or batches And it returns validation errors for infeasible inputs (e.g., no pickup windows, zero labor capacity) with actionable messages and codes And all timestamps include timezone offsets; daylight saving transitions do not create overlapping or missing windows

Given a baseline and up to 5 candidate rule sets When the user compares scenarios Then the system returns deltas for SLA hit rate, at-risk orders, labor utilization, and number of batches/day And highlights Pareto-efficient candidates across SLA and labor axes And allows exporting the selected schedule to a staging environment with a unique version ID and full audit trail (who/when/what) And supports rollback to baseline within one click/API call, restoring prior cutoffs and batches

Given an optimized schedule is applied to production When 7 consecutive days of post-launch fulfillment and delivery data are available Then the observed SLA hit rate per lane deviates from forecast by no more than ±2 percentage points for at least 90% of lanes And if deviation exceeds threshold on any lane, the system flags it and recommends re-optimization with updated inputs And a daily monitoring report is generated and accessible via UI and API with timestamped comparisons to the forecast run

Given a user with edit permission and an unsaved simulation When they click "Save As" and provide a unique scenario name Then the system persists the rule set, assigns version v1, and displays name and version in the list within 2 seconds. Given a scenario name already exists When the user saves changes as a new version Then the system creates the next sequential version (v2, v3, …), prevents manual version collisions, and records author and timestamp. Given required fields are missing (e.g., rule set title, selection criteria) When the user attempts to save Then the save is blocked and field-level validation messages identify missing/invalid inputs. Given a saved scenario When the user opens its details Then authorship, created/updated timestamps (UTC and local), and a change summary are visible. Given a transient backend failure during save When the user retries Then the operation is idempotent and no partial or duplicate versions are created.

Given two or more saved scenarios are selected When the user opens Compare Then a table shows SLA hit rate (%), at-risk order count, and cost delta per scenario and overall totals. Given a lane filter (origin-destination) is applied When the filter is active Then all displayed metrics recompute for the filtered lanes and the active filter is clearly shown. Given a historical date range is set When metrics are computed Then calculations use past origin-destination pairs and carrier service calendars within that range. Given the user sorts by any metric column When sorting is applied Then rows sort correctly and stably; ties preserve alphabetical scenario name order. Given a scenario lacks sufficient historical volume When comparison runs Then the scenario is flagged "insufficient volume" and excluded from overall totals with a tooltip explanation.

Given the comparison table is visible When the user clicks a specific lane Then a drilldown view shows per-lane SLA hit %, at-risk count, average cost, and suggested service swaps or cutoff rules. Given the user applies a suggested service swap in drilldown When the change is previewed Then metrics update in real time and the change is marked as a temporary what-if until saved as a new version. Given outlier thresholds are configured (e.g., hit rate delta > 5%, cost delta > 8%) When viewing lanes Then lanes breaching thresholds are highlighted and can be filtered. Given the user exports from drilldown When export is initiated Then only lanes currently in scope are included in the export file with the active filters and thresholds noted.

Given a saved scenario version When a user posts a comment Then the comment records author, timestamp, and version context and appears in chronological order within 1 second. Given a comment authored by the current user within 15 minutes When the user edits the comment Then the edit is saved, an "edited" badge appears, and prior revisions are retained in history accessible to admins. Given a comment authored by the current user When the user deletes the comment Then it is soft-deleted, visible as a tombstone to admins, and excluded from standard views. Given a user mentions a teammate using @email When the comment is posted Then the mentioned user receives a notification with a deep link to the scenario version.

Given a comparison view with selected scenarios When the user requests export Then CSV and XLSX generate within 10 seconds and PDF within 20 seconds, containing scenario names, versions, authors, timestamps, applied filters, date range, and metrics (including per-lane data). Given locale differences When numbers and dates are exported Then machine-readable formats are used (dot decimal, ISO-8601 dates) with a secondary locale-formatted sheet in XLSX. Given the result set exceeds 100k rows When export runs Then the export streams without freezing the UI and an email with a secure download link is sent upon completion. Given potential PII in underlying data When exporting Then PII fields are excluded by default and require an explicit opt-in with a warning and audit entry.

Given a user with Promote permission views a scenario version When they click Promote and confirm Then that version becomes the active production rule set within 60 seconds, the previously active version is snapshotted, and an audit log entry records actor, timestamp, and diff summary. Given pre-promotion validation runs When conflicting or invalid rules are detected Then the promotion aborts, no partial changes are applied, and the user sees actionable error details. Given a successful promotion When notifications are configured Then subscribers receive a summary message with links to the audit record and the active rule set. Given the audit log lists a previous production snapshot When a user with appropriate permission initiates Rollback and confirms Then production reverts to that snapshot within 60 seconds and a rollback audit entry with reason is recorded.

Given a valid rule payload with name, conditions, actions, and scope (warehouse/channel/carrier-service/SKU set), When POST /api/rules is called, Then the API returns 201 with body containing rule_id, version="v1", and status="Draft". Given an invalid payload (missing required fields or type mismatch), When POST /api/rules is called, Then the API returns 400 with a validation_errors array including path, code, and message for each error. Given a duplicate rule name within the same scope, When POST /api/rules is called, Then the API returns 409 with conflict details and no new rule is created. Then the created rule is persisted with a change_history entry capturing actor, timestamp, and payload checksum.

Given an authenticated admin with "Rules:Write", When a Draft rule is created and "Preview Impact (last 14 days)" is requested, Then the system evaluates the rule against the last 14 days of orders and displays counts of affected orders grouped by warehouse and channel, completing within 10 seconds for up to 10,000 orders. When the admin clicks Publish, Then the rule status changes to Active, version increments (vN+1), and a change_history entry "Published" is recorded with actor and timestamp. When the admin discards the draft, Then no new version is created and the draft is deleted.

Given an Active rule v1, When the rule is edited and published, Then a new immutable version v2 is created and becomes Active while v1 is retained as read-only. Given an Active rule v2, When Rollback to v1 is initiated, Then evaluation switches to v1 within 2 minutes and change_history records "Rollback from v2 to v1" with actor and reason. Then GET /api/rules/{id}/versions returns an ordered list of versions with diffs for fields changed between adjacent versions.

Given an order with warehouse=W1, channel=Shopify, carrier_service=UPS Ground, and SKUs in set S1, When evaluating applicable rules, Then only rules whose scope includes W1 AND Shopify AND UPS Ground AND any SKU∈S1 are applied. Given an order with SKUs not in a referenced SKU set, When evaluating, Then SKU-scoped rules are not applied. Then the evaluation trace for the order includes matched_rule_ids and the scope attributes that matched for each rule.

Given a new rule configured with a 10% canary rollout, When published, Then 10%±1% of eligible orders are deterministically assigned to treatment based on a stable hash of order_id, and exposure is logged per order. Given an A/B rollout at 50/50, When evaluating, Then orders are deterministically bucketed into control (no rule) and treatment (rule applied) with a maximum imbalance of 1% over 10,000 orders. When canary is disabled or rollback is performed, Then no new orders are assigned to treatment within 1 minute, and previous assignments cease to apply to subsequent orders.

Given a rule with a preview computed on the last 14 days, When the rule is published and a backtest is run on the same 14-day snapshot, Then the difference between preview_affected_orders and backtest_affected_orders is ≤1% relative error. When the difference exceeds 1%, Then the system flags the discrepancy in the UI and API and records an alert in change_history.

Given an order evaluated by both the legacy automation engine and the new risk rule engine, When requesting GET /api/evaluation?order_id={id}, Then the response includes a combined list of applied constraints from both engines with source metadata (legacy|risk) and is used by the Risk Heatmap. On a curated regression set of 500 recent orders, Then the combined evaluation replicates legacy constraint outcomes exactly for legacy rules (0 mismatches) while also including applicable new risk rules.

Given complete inputs for order, SKU, channel, carrier/service, and packaging When the model computes risk Then it returns a riskScore integer in the range [0,100] and a confidence in the range [0.00,1.00] (two decimal places) for each dimension Given identical inputs and modelVersion When scoring is repeated Then outputs are deterministic and exactly identical Given some dimensions are not applicable to an entity (e.g., no packaging yet) When scoring occurs Then present dimensions are scored and absent dimensions are omitted without error, and an order-level score is still produced

Given any computed risk score When explanations are requested Then the response includes the top 5 contributing factors with signed contribution weights that sum to 1.00 ±0.01 and human-readable labels plus machine codes Given a score ≥ 70 on any dimension When explanations are returned Then at least one actionable guardrail recommendation (e.g., weight buffer, minimum box constraint, service lock) is included with a referenced rule template Given any rule change affecting the entity in the last 24 hours When explanations are returned Then a rule-delta factor appears among the contributors with its timestamp and change description

Given a new event (order created/updated, weight or dimension update, carrier rule change, shipment outcome logged) When the event is ingested Then all affected risk scores are recomputed and available via API within p95 ≤ 5s and p99 ≤ 15s end-to-end Given duplicate events with the same idempotency key When processed Then exactly one scoring run is executed and no duplicate score records are persisted Given sustained load of 5,000 events/min/node When running on up to 4 nodes Then throughput scales linearly to ≥ 20,000 events/min total with p95 latency ≤ 7s and error rate ≤ 0.1% Given a node failure during processing When the cluster rebalance occurs Then no data loss occurs and any backlog drains within 10 minutes of node recovery

Given a rolling 7-day window When cohorts are formed by SKU, channel, carrier/service, packaging, and warehouse Then the system computes cohort-level mean risk, incident rate, and sample size for each cohort Given cohort sample size ≥ 30 When mean risk increases by ≥ 25 points versus the prior 7-day window OR the 7-day incident rate exceeds the 95th percentile of the last 90 days Then the cohort is flagged as a hotspot with a severity label and change delta Given a hotspot is flagged When the cohort output is returned Then it includes the top 3 shared root-cause factors and at least one recommended guardrail per factor Given normal operations When aggregation runs Then cohort metrics refresh at least every 5 minutes

Given a 90-day holdout set of labeled mislabel/mis-cartonization incidents When the model is evaluated Then the Brier score ≤ 0.18 and the calibration slope is within [0.9, 1.1] Given a decision threshold of riskScore ≥ 70 When computing metrics Then the false positive rate ≤ 10% and recall ≥ 75% for incident detection across dimensions Given a candidate model update When compared to the current production model Then it must meet or exceed these calibration and error-rate targets before promotion, with metrics versioned and stored

Given an incoming record with missing/invalid required fields (e.g., negative weight, non-numeric dimensions, unknown carrier code) When validated Then the record is quarantined with a specific validation code and is not scored nor exposed via API Given an incoming record with missing optional fields (e.g., dimensions) When scoring Then values are imputed from SKU history medians (last 60 days), the confidence is reduced by at least 0.20, and dqFlags enumerate the imputation applied Given upstream data latency > 10 minutes or checksum failure for a feed When scoring outputs are served Then scores are marked stale=true and excluded from safeguards until freshness is restored Given a 15-minute window When > 1% of events fail validation Then a tenant-scoped DQ alert is emitted with trend and top validation codes

Given the GET /v1/risk-scores endpoint with filters (orderId, sku, channel, service, packaging) and batch query up to 500 entities When requested Then the response contains, per dimension, fields: riskScore [0..100], confidence [0..1], topFactors[], guardrails[], cohortIds[], modelVersion, computedAt, stale, dqFlags Given single-entity queries When executed Then p95 latency ≤ 300ms; for batch (≤ 500 entities), p95 latency ≤ 1.5s Given high usage When the rate exceeds 600 requests/min per org Then the API returns HTTP 429 with Retry-After and no degradation for other tenants Given schema evolution When backward-compatible changes are deployed under versioned paths (e.g., /v1) Then OpenAPI contract tests pass and no breaking changes occur

Given a dataset aggregated into ≤ 2,000 cells across supported dimension pairs, when the user opens the Risk Heatmap, then the first interactive render completes in ≤ 1.5 seconds and maintains ≥ 55 FPS during pan/scroll. Given risk scores (0–100) and cell volumes, when the heatmap is rendered, then risk is encoded by a colorblind-safe sequential palette and volume by a visible size/overlay indicator, with a legend explaining mappings present. Given viewports at widths 320, 768, and 1280 pixels, when the heatmap is viewed, then axes, legends, and cells adapt without horizontal overflow, and interactive targets are ≥ 44×44 px on touch devices. Given any filter change, when the heatmap re-renders, then color scale normalization remains consistent within the current view and legends update accordingly.

Given filters for date range (absolute and last N days), warehouse, channel, carrier/service, SKU tags, and hazmat/fragile flags, when the user applies any combination, then the heatmap updates within ≤ 300 ms for ≤ 2,000 cells and results reflect the filter. Given selected filters, when the page URL is copied and reopened or shared, then the exact filter state and view are restored. Given a drilldown and return navigation, when the user navigates back, then prior filter state, scroll position, and selection are preserved. Given Clear All is invoked, when confirmed, then all filters reset to defaults (last 14 days; all warehouses/channels/carriers; all tags; hazmat/fragile = all).

Given a heatmap cell is clicked or focused and activated via keyboard, when drilldown opens, then the panel loads within ≤ 300 ms and anchors context (dimension values and active filters) at the top. Given contributing rules, when displayed, then each rule shows contribution % to risk, trigger count, recent trend, and a recommended guardrail with prefilled parameters and an Apply action gated by permissions. Given sample orders for the selected cell, when displayed, then at least 50 orders are listed with pagination, sortable columns, and an Export Orders CSV action limited to the current selection. Given the user closes the drilldown, when returning to the heatmap, then the previously selected cell remains highlighted.

Given a user hovers a cell or focuses it via keyboard, when the tooltip is requested, then it appears within ≤ 100 ms and remains within viewport without clipping. Given tooltip content, when shown, then it includes risk score (0–100) with qualitative label, impacted orders count, mislabel rate, mis-cartonization rate, 14-day trend vs prior 14 days (%), top 2 triggered rules, and last updated timestamp, with locale-aware number formatting. Given screen readers, when a cell receives focus, then an accessible name is announced that includes cell coordinates, risk score, and order volume in a concise sentence.

Given the heatmap is visible with current filters, when CSV export is requested, then a file is generated in ≤ 2 seconds containing one row per visible cell with columns: dimensions, risk_score, order_volume, mislabel_rate, miscarton_rate, trend_pct, top_rules, filters_applied (JSON), generated_at_utc (ISO 8601). Given the heatmap is visible, when PNG export is requested, then a 2× resolution image (≤ 10 MB) is downloaded showing the current viewport, title, legend, filters summary, and timestamp. Given drilldown sample orders, when Export Orders CSV is requested, then only the filtered drilldown orders are exported with columns: order_id, channel, service, box_type, weight, triggered_rules, risk_tags, generated_at_utc.

Given keyboard-only navigation, when interacting with filters, cells, tooltips, drilldown, and export controls, then all are reachable in logical tab order with visible focus indicators and operable actions. Given color usage, when the heatmap is viewed, then color contrast ratios are ≥ 4.5:1, a colorblind-safe palette is used for risk, and non-color cues (patterns or value labels on focus) indicate intensity. Given screen readers, when navigating, then ARIA roles/labels expose cell coordinates, risk score, and volume; drilldown headings and regions are landmarked; tooltip content is programmatically associated. Given high-contrast mode, when enabled at OS/browser level, then the heatmap remains usable with no loss of information.

Given an embeddable heatmap instance inside a ParcelPilot dashboard, when initialized with signed parameters (filters, scope), then only data within the scope is visible and all actions honor the embedding user’s permissions. Given embed mode, when rendered, then navigation chrome is suppressed, resizing events are handled without visual artifacts, and first interactivity occurs in ≤ 1.5 seconds. Given cross-origin constraints, when embedded, then no third-party trackers are loaded and no mixed-content or CORS errors appear in the console during standard interactions.

Given a user opens a Risk Heatmap hotspot details panel When the root-cause explanation is displayed Then it lists at least one primary driver and up to three secondary drivers, each with a standardized driver_code and human-readable label And each driver includes working deep links to at least one relevant rule and one SKU (if applicable) and any implicated packaging config And activating a deep link opens the correct destination with the hotspot context pre-filtered (rule/SKU/packaging) in a new tab

Given an explanation is rendered for a hotspot within the selected time window When the user views the Evidence section Then it shows affected_order_share as a percentage with numerator and denominator And it shows at least one variance metric with unit (e.g., weight_diff_lb, dim_vs_billed_in3) and value rounded to two decimals And it shows before_rule_version and after_rule_version identifiers with ISO 8601 timestamps when a rule change is implicated And if no rule change is detected in the last 30 days, before/after rule versions display "N/A" in UI and null in API

Given the system generates root-cause explanations When the explanation text is displayed Then each explanation contains three parts: driver description, quantified impact (percentage or count), and a recommended guardrail category And the explanation contains no unresolved template tokens (e.g., {{ }}) and no more than 2 sentences And the explanation length is between 80 and 280 characters and uses the user’s locale and number formatting

Given the UI and the API provide the same hotspot explanation When comparing the UI payload and GET /api/v1/risk/heatmap/{hotspotId}/explanations response Then the fields type_code, severity, driver_code, guardrail_code, and evidence.metric keys are present and match exactly in value and casing And the ordering of drivers is by severity desc, then impact desc consistently across UI and API And unknown codes are rejected with a 400 in API and flagged with an error toast in UI

Given a hotspot is attributed to missing packaging mapping When the user clicks the packaging mapping link in the explanation Then the Packaging Mapping view opens with the implicated SKU(s) pre-selected and facility/channel filters preserved from the heatmap context And an inline banner references the originating hotspot id and timestamp for traceability

Given a client requests GET /api/v1/risk/heatmap/{hotspotId}/explanations When the hotspot has up to 5 drivers Then the response includes an array of explanations with fields: id, hotspot_id, driver_code, label, severity, evidence (affected_order_share, variance_metrics[], rule_versions), links (rules[], skus[], packaging[]), recommendations[] And the response conforms to JSON schema v1.0.0 without additionalProperties And p95 latency <= 500 ms and p99 <= 900 ms over the last 24h in production

Given a mislabel hotspot where SKUs tagged "Glassware" have ≥15 incidents in the last 30 days with measured weight exceeding declared weight by >4 oz and root-cause confidence ≥80% When the user opens the hotspot's Guardrail Recommendations Then the system generates a recommendation "Add 6 oz weight buffer" scoped to SKU tag "Glassware" And the recommendation includes editable buffer amount (1–16 oz) and editable scope (SKU tag/warehouse/channel) And the recommendation displays rationale with incident count, lookback window, and confidence value And the recommendation displays predicted risk reduction (%) and estimated postage delta ($/order)

Given a hotspot indicating hazmat mis-service on channel "C" for warehouse "WH1" When the user selects the recommendation "Lock service to Ground Hazmat" for channel "C" Then a side-by-side preview renders within 3 seconds (p95) And the preview shows rule summary, scope (channel "C", warehouse "WH1"), affected orders count (last 30 days), and top SKUs count And the preview shows expected risk reduction (%) and expected cost impact ($/order and total/month) And the preview displays before/after service examples for the top 3 affected SKUs And the Apply button reflects any required approval before activation

Given a user with permission "Guardrail:Apply" and no approval policy required for the selected scope When the user clicks Apply on a recommendation Then a new rule version is created with status "Active" and a unique version_id And the version stores rule parameters, scope, linked hotspot_id, author, and timestamp (ISO 8601) And an audit log entry records author, version_id, before/after diff, and justification (if provided) And the API responds 201 with the version_id and status And the UI displays confirmation with version_id

Given the organization requires at least one approver for guardrail changes and an approver group is configured When a user with "Guardrail:Apply" clicks Apply on a recommendation Then a new rule version is created with status "Pending Approval" and no rule engine propagation occurs And approvers are notified via configured channels and the UI shows "Awaiting Approval" When an authorized approver approves the change Then the version status transitions to "Active", the audit trail records approver id and timestamp, and propagation to the rule engine begins

Given a guardrail version is Active When the next order within the guardrail scope is evaluated by the rule engine Then label selection and packing recommendations reflect the guardrail And the change is visible in UI and API within 10 seconds of activation And orders outside the scope remain unaffected And the /rules/current endpoint returns the new version_id for the affected scope

Given there is an Active guardrail version v2 and a previous version v1 When an authorized user triggers Rollback on v2 Then v1 becomes "Active" and v2 becomes "Rolled Back" And an audit log entry records who performed the rollback, timestamp, target version, and reason And the rule engine reverts behavior within 10 seconds And any pending approvals related to v2 are canceled or marked obsolete

Given a valid scenario configuration and a connected user session When the user clicks Run Simulation Then a background job is created within 1 second with a unique job ID and initial status Queued And the UI displays a progress indicator with percentage and ETA that updates at least every 2 seconds And the user can cancel the run; when canceled before completion the job status becomes Canceled, partial results are discarded, and the UI confirms cancellation within 2 seconds When the job completes successfully Then the job status becomes Completed and the UI receives completion via websocket within 2 seconds (with REST fallback within 10 seconds)

Given the scenario builder When the user configures guardrails (weight buffer %, minimum box L×W×H, service locks, dimensional thresholds) and selects a lookback window (7, 14, 30 days, or custom up to 90 days) Then required fields are validated client-side and server-side with inline error messages and disabled Run Simulation until valid And the saved configuration is versioned with timestamp, user, and data snapshot ID And a deterministic hash of all inputs (including data snapshot ID) is generated for caching

Given a completed simulation When results are computed Then the output includes baseline, simulated, absolute delta, and relative delta for: risk rate (mislabel/mis-cartonization), postage spend, SLA adherence (on-time %), and processing time per order And each metric includes a 95% confidence interval displayed as [lower, upper] And any metric whose delta CI spans zero is flagged as Not statistically significant in the UI And values use consistent units (currency in account currency with 2 decimals, percentages with 1 decimal, time in seconds)

Given completed results When the user opens the Impacts tab Then tables are available for Channel, Carrier-Service, and SKU with baseline, simulated, absolute delta, relative delta, and 95% CI And the SKU table defaults to Top 50 by absolute risk delta and supports search, sort on any column, and pagination And filters allow inclusion/exclusion by channel, carrier, and SKU pattern; applying filters updates aggregates within 1 second for cached results And the user can export the current view to CSV with applied filters and visible columns

Given completed results When the user opens Trade-offs Then a scatter plot displays points per selected dimension with X=postage delta (%), Y=risk delta (pp), and point size=volume; quadrants are labeled and counts shown And the user can toggle dimension (Channel, Carrier-Service, SKU) and hover to see baseline/simulated values and 95% CI And the baseline is visually indicated and scales remain consistent across scenarios for accurate visual comparison And the user can pin up to 3 points to compare detailed metrics side-by-side

Given a saved scenario configuration When an identical configuration (same hash and data snapshot) is run within 24 hours Then results are served from cache within 2 seconds and labeled From cache And the user can generate a shareable link with an expiring token; only authenticated users with workspace access can open it When a recipient opens the link Then the scenario configuration and results load; if cache has expired but snapshot is still available, a rerun is queued automatically and the UI shows Restoring results And links expire after 30 days; expired links return a 410 Gone message in the UI with an option to request a new link

Given a completed simulation When the user navigates to the Risk Heatmap Compare panel Then the scenario appears as selectable alongside Baseline and up to 3 other saved scenarios And selecting a scenario updates heatmap cells to show delta badges (↑/↓ with magnitude and significance) relative to baseline And clicking a heatmap hotspot deep-links to the scenario’s per-dimension impacts filtered to that hotspot And the compare selection and view state persist per user across sessions

Given a subscription with configured risk threshold T and scoped dimensions And risk indices are computed on a recurring schedule When a cohort’s risk index exceeds T in two consecutive computation cycles Then an alert is generated within 60 seconds of the second breach And the alert is delivered only to subscribers whose scopes intersect the cohort And no alert is generated if the threshold is not exceeded in consecutive computations

Given a new or updated risk rule is saved And hotspot significance is defined as: (any cohort reaches risk index ≥ T) OR (any cohort’s risk index increases by ≥10 points affecting ≥100 shipments in the last 7 days) When the rule evaluation meets the significance condition within the subscriber’s scope Then an alert is generated within 60 seconds and tagged "New Rule Hotspot" And no alert is generated if significance conditions are not met

Given a user creates subscriptions scoped by warehouse, channel, SKU group, and carrier/service When an alertable event occurs Then only subscribers whose scopes match the event dimensions receive the alert And multiple matching subscriptions for the same user are coalesced into a single delivery per channel And unsubscribed or paused subscriptions receive no alerts

Given an alert is generated When delivered via Slack Then a message is posted to the configured channel with app mention, title, severity, and primary deep link, and the Slack API responds 2xx When delivered via Email Then an email is sent to the configured address with subject prefix "[ParcelPilot Risk Alert]" and payload summary, and the SMTP response is 2xx/OK When delivered via Webhook Then an HTTPS POST is sent with JSON payload and headers X-PP-Signature (HMAC-SHA256) and X-PP-Timestamp, and the endpoint responds 2xx And for any non-2xx response, the system retries with exponential backoff up to 3 attempts and logs outcomes

Given a rate limit of 1 alert per {cohort_id, rule_id, channel} per 15-minute window When multiple qualifying events occur within the window Then only one alert is sent and subsequent ones are suppressed and aggregated into the next payload’s suppressed_count Given deduplication keys of {cohort_id, rule_id, subscription_id, channel} When an identical alert would be emitted within 15 minutes Then it is dropped as a duplicate Given quiet hours are configured from 21:00 to 07:00 in the account’s local timezone When an alertable event occurs during quiet hours Then the alert is queued and delivered at 07:00 with a delayed_due_to_quiet_hours flag And if the event no longer meets criteria by 07:00, the queued alert is discarded

Given an alert is generated Then the payload includes: threshold(s) and actual risk index values, affected cohorts with counts, top 3 root-causes with contribution percentages, and 1–3 recommended guardrails And the payload includes deep links to the heatmap and simulation pre-filtered to the alert’s dimensions and rule context And deep links include signed query params (cohort_id, rule_id, date_range) and open successfully to the correct views And the payload validates against JSON Schema "risk_alert.v1" with all mandatory fields present

Given any alert delivery attempt occurs Then an audit log entry is written with timestamp, correlation_id, subscription_id, channel, recipient, payload_hash, delivery_status, response_code, and retry_count And audit logs are retained for 365 days and searchable by correlation_id and recipient Given a user with appropriate permissions When they create, update, pause/resume, or delete a subscription in User Settings Then changes are validated, applied, and reflected within 1 minute And each change is versioned and recorded in the audit log with actor and before/after diff

Given I am creating a new profile scoped to Store A / Channel Etsy / US-West When I select objective "Minimize Spend" and set SLA variance target to <= 1.0% and risk tolerance to "Low" Then all required fields validate successfully and the Save action becomes enabled Given invalid target values are entered (e.g., SLA variance < 0% or > 100%) When I attempt to save Then an inline error specifies the allowed range and the Save action remains disabled Given risk tolerance must be one of {Low, Medium, High} When I open the risk tolerance selector Then only these options are available and one must be selected before Save is enabled Given all entries are valid When I click Save Then the profile persists with objective type, target metrics, and risk tolerance retrievable via UI and API

Given I add carrier/service exclusions (e.g., exclude "CarrierX Ground") When I save the profile Then the exclusions are persisted and visible on reload and via the profile API Given I set a max label cost rule "OrderValue < $20 -> MaxLabelCost $6.00" When I run a simulation on matching orders Then any recommendation exceeding $6.00 is flagged non-compliant and not proposed Given I define cutoff times per service and warehouse timezone When current time is after the cutoff Then services requiring same-day tender are excluded from recommendations Given hazmat and dimensional rules are enabled (e.g., lithium batteries, oversize thresholds) When SKUs with those attributes are present Then only carrier/services compliant with those attributes are considered

Given I exclude all carriers or all services for a destination When I attempt to save Then a blocking error states "No feasible carriers/services remain" and Save is disabled Given constraints conflict (e.g., MaxLabelCost $5 while requiring 2‑Day Air for 50 lb shipments) When validation runs Then the conflicting rules are identified with field-level messages and Save is disabled until resolved Given I modify any field in the builder When validation triggers Then results appear within 200 ms at the 95th percentile (<= 500 ms worst case under 200 concurrent users) Given all validation errors are resolved When I review the form Then all error indicators clear and Save is enabled without page reload

Given I save changes to an existing profile When the save completes Then a new immutable version is created with an incremented patch number and a required changelog message Given multiple profiles exist for a scope When I set one profile as Active for Store A / Channel B / Region West Then the previous Active becomes Inactive and the assignment is audit-logged with actor and timestamp Given I clone a profile to a new scope When the clone completes Then all objectives and constraints are copied and the new profile starts at version 1.0.0 Given I select a prior profile version When I click Restore Then that version is duplicated as the latest version, preserving full history

Given an active profile is selected for a simulation dataset When Smart Tuner runs Then 100% of proposed rule adjustments and label selections satisfy all hard constraints from the profile Given objectives include SLA variance <= 1.0% and spend minimization When recommendations are produced Then the output includes predicted SLA variance and cost deltas with 95% confidence intervals meeting or exceeding targets Given any potential recommendation violates a constraint When results are compiled Then the item is excluded and a machine-readable reason code is logged Given recommendations are accepted to the rules sandbox When changes are reviewed Then each parameter maps directly to existing rule fields (e.g., service weights, exclusions) with no manual translation required

Given profiles exist at store, channel, and destination scopes When an order arrives for Store A / Channel Etsy / State CA Then the most specific matching profile is applied using precedence: store+channel+destination > store+channel > store > global default Given no specific profile matches an order When processing begins Then the global default profile is applied Given a profile is selected for an order When the shipment is created Then the applied profile ID and version are recorded on the shipment and exposed via logs and API Given user permissions restrict profile visibility by scope When a user without access views profiles Then profiles for other stores/channels are not visible or selectable

Given shipment history is synced from tracking and cost ledger When the analyzer builds aggregates over the last 180 days by (SKU, destination_region, carrier, service, week) Then it computes for every group: delivery_time_distribution (business-day histogram), SLA_miss_variance, claim_rate, return_rate, surcharge_incidence by type, dim_weight_uplift_pct, and seasonal_index with no null metrics And for a stratified sample of 1,000 shipments, re-aggregated counts match production tracking within ±0.2% and cost components match the ledger with mean absolute error ≤ $0.01 per label And any parity breach beyond thresholds emits a parity_failure event with group keys and diff summary

Given lookback_days has a default of 90 and allowed range [30, 365] When lookback_days is set to 180 Then only shipments with ship_date within the last 180 days contribute to aggregates And metadata in outputs records lookback_days=180 Given exponential decay weighting is enabled with half_life_days default 30 When half_life_days is set to 15 on a deterministic test fixture Then weighted mean transit time and variance equal the expected values within ±0.5% Given decay weighting is disabled When recomputing aggregates on the same fixture Then weighted and unweighted metrics are equal within numerical tolerance (|diff| ≤ 1e-6)

Given a SKU with fewer than 20 shipments in the active lookback When aggregates are requested Then the analyzer returns fallback aggregates from the SKU’s category or nearest cluster And sets cold_start_flag=true, confidence_score ≤ 0.40, and fallback_source populated Given a brand-new SKU with 0 shipments When aggregates are requested Then destination- and carrier-level baselines are returned with no nulls, confidence_score ≤ 0.25, and P95 latency ≤ 200 ms Given the SKU reaches 20 or more shipments in the lookback When aggregates are recomputed Then cold_start_flag=false and confidence_score ≥ 0.60

Given an injected anomaly window where SLA miss rate is > 3σ above the trailing 28-day mean for ≥ 12 consecutive hours When anomaly detection runs Then the window is flagged with anomaly_type and excluded from aggregates (weight=0) and listed in anomaly_summary outputs Given anomaly_exclusion=false When aggregates are recomputed Then previously flagged windows are included in the metrics Given a labeled validation set of outages and normal periods When evaluating detection performance Then precision ≥ 95% and recall ≥ 90% over the set

Given cached cubes are warm When executing standard aggregate queries under 50 RPS for 60 seconds Then P95 latency ≤ 150 ms, P99 latency ≤ 300 ms, error rate < 0.1%, and data staleness ≤ 10 minutes Given a cold cache When the first query is executed Then cache warm-up completes within 5 seconds and subsequent queries meet the latency SLAs Given a query that would return > 10,000 rows When requesting results Then pagination is enforced with a stable cursor and totals remain consistent across pages

Given an aggregate group When feature vectors are produced for Smart Tuner Then each record includes: transit_time_histogram, sla_miss_variance, claim_rate, return_rate, surcharge_rate_by_type, dim_weight_uplift_pct, seasonal_index, cost_components, sample_size, confidence_score, data_freshness_ts, schema_version, and provenance_ids Given the JSON Schema for feature vectors When exporting 10,000 records Then 100% validate against the schema and contain no NaN/Inf values Given controlled input fixtures (n=100) with known outcomes When feature vectors are computed Then confidence_score increases monotonically with sample_size and decreases with variance, and numeric values match expected within ±0.5%

Given a historical order set and active objective/constraint configuration When the engine generates rule tweak suggestions Then it returns a list sorted by objective score improvement (or cost reduction when minimizing), tie-breaking by lower risk delta, then higher confidence And each suggestion includes spend delta (absolute and %), SLA variance delta (pp), risk delta (pp), and 95% confidence intervals for each metric And each suggestion includes a plain-language rationale of ≤280 characters And if ≥10 feasible tweaks exist, at least 5 suggestions are returned; otherwise all feasible suggestions are returned And if no feasible suggestions exist, an empty list is returned with reason code NO_FEASIBLE

Given hard constraints from the Objective & Constraint Builder When candidate suggestions violate any hard constraint Then those candidates are excluded from output And the response includes a constraints_summary with names of enforced constraints and count of pruned candidates per constraint And all emitted suggestions meet objective targets in preview (e.g., SLA variance ≤ target) and their 95% CI lower bound does not breach any hard constraint And any suggestion whose CI indicates potential breach of a hard constraint is not emitted

Given a selected suggestion or bundle of suggestions When Apply to Sandbox is invoked Then a validated patch payload referencing rule IDs and version is produced and applies without conflict to the sandbox repository And a re-simulation on the selected validation set runs automatically and returns updated spend, SLA variance, and risk metrics And observed re-simulation deltas are within ±10% relative error of previewed deltas or within ±0.5 pp for variance/risk (whichever is greater) And a rollback patch ID is provided that reverts the sandbox to the prior state And total apply + simulate time is ≤30 seconds for 1,000 orders

Given repository schema version X.Y and rule set R When outputting changes Then the payload conforms to schema X.Y and passes JSON Schema validation And the payload includes change_id, created_at, actor, target_rule_ids, operations, and rollback operations And applying the payload is idempotent (double-apply yields no further changes) And semantic validation passes: referenced rules exist; operations are allowed for their rule types; no orphaned references are created And a dry-run returns a diff summary with counts of adds/updates/deletes

Given current carrier rate data and pack prediction outputs When proposing carrier weights, service eligibility filters, or packaging overrides Then each suggestion is validated for feasibility against rate shopping and pack prediction components with 0 invalid combinations in a 1,000-order test set And any packaging override maps to an available box type and fits predicted dimensions/weights for ≥95% of affected orders And service filter suggestions do not reduce on-time probability below the configured SLA threshold in simulation And each suggestion includes feasibility_check:true with sample size and validation timestamp

Given any emitted suggestion When a user views its details Then a plain-language rationale (≤280 chars) and top 3 drivers with contribution percentages are displayed And the historical window used for estimation and key assumptions are shown And 95% confidence intervals are displayed per metric with the estimation method noted And a trace_id is included that allows reproducing the analysis And no PII or sensitive order content is exposed in rationale, drivers, or logs

Given a workload of 5,000 orders and 30 tweak dimensions When the engine runs Then initial suggestions are produced in ≤15 seconds with p95 latency ≤20 seconds And with a fixed random seed, the top-10 suggestions are identical across runs and all metrics are within ±1% And resource usage stays under 4 CPU cores and 8 GB RAM at p95 during optimization And telemetry is emitted: generation_time_ms, candidate_count, pruned_count, simulation_calls, and error_rate<1%, with logs containing no sensitive data

Given snapshot SNAP-001 (tariffs, rules, objectives), order set O1000, and seed 987654 When the simulator is executed twice under environment Sim-Std-4 with identical inputs Then result.payloadHash (SHA-256) is identical across runs And aggregate KPIs and order-level selections match exactly (monetary values equal within $0.01 rounding)

Given environment Sim-Std-4 (4 vCPU, 8 GB RAM), snapshot SNAP-001, and order set O1000 When the simulator runs with default parallelism Then median wall-clock time across 5 consecutive runs is <= 5 seconds And p95 wall-clock time across those runs is <= 8 seconds And no single run exceeds 10 seconds

Given baseline rules R_base and proposed rules R_prop within snapshot SNAP-001, and order set O1000 When the simulator completes Then the output includes side-by-side KPIs: spend, SLA reliability, risk, and processing throughput And aggregate KPIs equal the sum/average of corresponding order-level fields (money within $0.01, percentages within 0.1 percentage points) And each order diff contains: carrier/service, box size, weight, label cost, expected delivery window, SLA hit/miss, risk score change, and decision rationale And downloadable CSV and JSON reports are produced with record counts equal to the input order count

Given a snapshot export created from SNAP-001 at time T When the snapshot is restored and the same order set O1000 is re-simulated with seed 987654 Then the restored snapshot hash equals the original export hash And the simulation result payloadHash matches the original run And snapshot metadata records carrier tariff versions and ruleset commit identifiers

Given snapshot SNAP-001 and order set O1000 When the simulator is executed with workers=1 and again with workers=8 Then result payloads are identical across worker counts (equal payloadHash) And runtime with workers=8 is <= 50% of the runtime with workers=1 on the same environment And peak memory usage remains <= 6 GB during the workers=8 run

Given objectives specify constraints (e.g., SLA variance < 1%, risk <= R_threshold) and active carrier constraints When the simulator evaluates order set O1000 Then any constraint breach is flagged with type, scope (order/aggregate), threshold, observed value, and affected order IDs And edge cases are flagged for: missing dimensions/weight, no eligible rate, tariff not found, DIM weight applied, service blackout, and pickup cutoff missed And a summary section reports counts per breach/edge type; only triggered types appear with count > 0

Given a user applies Smart Tuner changes via One-Click Sandbox Apply When the sandbox rules commit is saved Then a simulation auto-starts within 1 second and is visible in the UI with status Running And for a 1,000-order sample, results display within 10 seconds with side-by-side KPIs and order/aggregate diffs And the UI shows simulation run ID, seed, snapshot ID, and a Re-simulate control And on failure, an actionable error with run ID and retry option is shown

Given a user with Rules:ApplySandbox permission has selected 1–100 Smart Tuner suggestions and an optional scope (store/channel and/or destination segments) When the user clicks "Apply to Sandbox" Then the system creates a new sandbox ruleset branch named sandbox/{orgId}/{username}/{timestamp} within 3 seconds And only the selected suggestions within the chosen scope are applied (no unselected rules modified) And an automatic simulation starts within 3 seconds of branch creation And a summarized impact forecast is returned within 120 seconds for up to 10,000 historical orders in scope And the forecast includes: spend_delta_abs, spend_delta_pct, SLA_breach_rate_pred, SLA_variance_delta_pct, error_rate_delta_pct, risk_score_delta, confidence_level, sample_size And the UI displays counts of rules added, updated, removed, and affected SKUs And the operation is idempotent for 60 seconds per idempotency_key, preventing duplicate branches

Given a user without Rules:ApplySandbox permission attempts to apply suggestions to sandbox When the request is made via UI or API Then the operation is blocked with HTTP 403 and error_code=RBAC_DENIED And no sandbox branch is created and no simulation is triggered And an audit log entry is recorded with actor, attempted scope, timestamp, and outcome=denied Given a user with Rules:ApplySandbox permission and scope access to the selected store/channel/destination When the user applies suggestions to sandbox Then the request succeeds (HTTP 200) and an audit log entry records actor, scope, branch_id, and outcome=allowed

Given org policy approval.required=true And User A with Rules:ApplySandbox permission initiates an apply When User A submits the apply action Then a Change Request is created in status=PendingApproval and no branch changes are applied yet And User A cannot approve their own Change Request (self-approval blocked) And only approvers in roles {Admin, OpsManager} can approve When an eligible User B approves within 24 hours Then the branch is instantiated, suggestions are applied, simulation auto-starts, and status transitions to Approved->Applied And the audit log links initiator and approver identities and timestamps When the request is rejected or expires after 24 hours Then no branch is created or changes are applied and status=Rejected or Expired

Given selected suggestions may violate constraints (e.g., price_floor, carrier_blocklist, min_SLA, surcharge_ceiling) When pre-apply validation runs Then all suggestions are evaluated against org constraints and rule schema And if any blocking violations exist, the apply is halted with status=ValidationFailed, HTTP 409, and a list of violations {suggestion_id, rule_id, constraint_code, message} And no sandbox branch is created and no simulation is triggered on blocking failures And non-blocking warnings are returned as warnings[] and displayed, but the apply proceeds And the validation result is recorded in the audit log

Given the user selects a scope consisting of one or more stores/channels and/or destination segments When applying suggestions to sandbox Then only rules tagged within the selected scope are created or modified And rules outside the scope remain unchanged (0 modifications) And the simulation dataset is limited to orders matching the scope And the forecast presents segmented results per scope segment and a combined total And attempts to modify outside-scope rules are rejected with HTTP 400 and error_code=SCOPE_VIOLATION

Given a successful sandbox apply and simulation completion When the user opens the change log Then each rule change is listed with: rule_id, field_path, previous_value, new_value, suggestion_id, rationale, user_id, timestamp And a human-readable summary line is shown for each change (e.g., "Increase USPS Zone 5 weight cap from 10lb to 12lb") And totals are shown for rules added/updated/removed And the change log can be exported as CSV and JSON And the log is persisted and queryable for at least 365 days And the audit trail links change log entries to the originating Smart Tuner run and simulation id

Given changes are applied to a sandbox ruleset branch When the apply completes Then the production ruleset version_id remains unchanged before vs. after And no production write events are emitted (0 production mutations) And live order processing continues to use the current production ruleset (verified by 0% difference in rule evaluation paths across a 100-order sample window post-apply) And the "Promote to Production" control remains a separate explicit action and is disabled until validation passes And monitoring emits a "sandbox_apply" event and no "prod_change" events for this operation

Given a user modifies any rule or objective profile in sandbox When they click Save Then a new version is created with a unique immutable version ID and UTC timestamp And the version requires non-empty release notes (minimum 10 characters) And optional tags (1–10) are accepted and validated (alphanumeric, dash/underscore, max 24 chars) And a field-level before/after diff is persisted for every modified field And the new version appears in version history within 2 seconds of save

Given two versions of the same ruleset/profile are selected When the user opens Compare Then only changed fields are shown by default with before/after values side-by-side And the user can toggle to show unchanged fields And nested objects are displayed using dot-path notation for location (e.g., shipping.rules[3].carrier.weightBias) And a downloadable machine-readable JSON Patch file representing the diff is available And permissions are enforced so only users with read access to both versions can compare

Given a production ruleset/profile is at version Vn and a prior version Vn-1 exists When a Release Manager initiates a rollback to Vn-1 and provides a mandatory reason (min 10 chars) Then the system performs an atomic switch with no partial states And the rollback is recorded as a new version Vn+1 with metadata revertedFrom=Vn-1 And the cutover completes within 1 second of confirmation And all subsequent label rating/printing requests use Vn-1 immediately after cutover And an audit log entry captures who, what, when, fromVersion, toVersion, and reason

Given a sandbox version has been marked Ready for Promotion by its author When the author submits a promotion request Then at least one approver other than the author must approve before promotion executes And if policy RequireRecentSimulation(24h)=true, the version must have a linked passing simulation not older than 24 hours And if policy RequireSLAVariance(<1%)=true, linked simulation must show SLA variance under 1% And promotion requires non-empty release notes and at least one tag And upon approval, the version is promoted to production and recorded as the current production version within 2 seconds And all approval/denial actions are captured in the audit log with comments

Given a specific version is selected for export When the user clicks Export Then a single JSON file is downloaded containing schemaVersion, checksum, rules, objective profiles, tags, release notes, and linkage metadata (recommendation and simulation IDs) And the export includes a cryptographic SHA-256 checksum of the payload Given an export file is selected for import into sandbox When the user runs a Dry Run Then a validation report lists conflicts, missing dependencies, and schema errors without changing data When the user confirms Import with a selected conflict policy (Fail, Overwrite, CreateNew) Then the version is imported into sandbox with new IDs where necessary and a mapping report is generated And no production state is modified by Import

Given a Smart Tuner recommendation R is applied to sandbox and saved as changes When the new version V is created Then V stores references to R (recommendationId) and all simulation run IDs used to evaluate R And from R the user can navigate to V, and from V the user can navigate back to R and its simulations And one recommendation may link to multiple versions and one version may link to multiple recommendations And these links are included in export/import and preserved across promotion and rollback

Given any change event occurs (save, compare, revert, export, import, promotion request, approve/deny, promote) When the event completes Then an audit record is appended containing actor, action, timestamp (UTC), entity type, entity ID, fromVersion, toVersion (if applicable), reason/comment, and request origin (IP/user agent) And audit storage is append-only with hash chaining (each record stores hash and previousHash) to provide tamper-evidence And attempts to modify or delete audit entries are rejected with a 403 response and logged And audit logs are filterable by date range, actor, action, and exportable as CSV and JSONL

Given a draft launch scenario with at least one store and one carrier connected, When the user clicks "Run Preflight", Then a checklist is generated and displayed within 10 seconds including only relevant categories for the scenario. Given checklist generation completes, When results are shown, Then each check displays status (Pass/Warning/Blocker), timestamp, and a Retry action for that check. Given a transient error affects a single check, When the user clicks Retry on that check, Then only that check is re-executed and prior Pass results remain intact. Given no configuration changes since last run, When the user opens the checklist within 24 hours, Then cached results are shown with a visible "Cached" indicator and an option to Re-run all.

Given connected carriers (e.g., USPS, UPS, FedEx, DHL, Evri), When the preflight runs, Then each carrier credential is validated via a non-billable API ping and marked Pass/Warning/Blocker per result. Given a credential is expired or invalid, When the check completes, Then it is marked Blocker with last success timestamp, error details, and a deep link to Carrier Credentials for that carrier. Given a carrier API is rate-limited or times out, When validation occurs, Then the check is marked Warning with guidance to retry and does not block enablement if a subsequent retry passes. Given the user clicks Recheck for a carrier, When the validation passes, Then the status updates to Pass without re-running unrelated checks.

Given active shipping rules exist, When the preflight runs, Then 100% of active rules have a mapped carrier service and packaging, otherwise each unmapped rule is listed with a deep link to edit. Given multiple rules target overlapping conditions, When the preflight detects conflicts, Then conflicting rules are flagged with a Blocker and links to both rules for resolution. Given the last 30 days of destinations and order mix, When coverage is computed, Then at least 99% of destination/service combinations by order count are routable; shortfalls are listed with suggested services. Given coverage gaps remain, When fallback rules are evaluated, Then at least one fallback path per warehouse exists; absence of a fallback is flagged as Blocker with link to create one.

Given warehouses are configured, When the preflight runs, Then each warehouse has a default printer assigned and reachable; missing or offline printers are flagged with deep links to Printer Settings. Given carrier/service label format requirements, When label formats are validated, Then each mapping uses a compatible size and format (e.g., 4x6 ZPL/PDF) or is flagged with the required format stated. Given the user clicks Test Print on a warehouse, When the test executes, Then a one-page test label is sent successfully within 5 seconds or a failure reason is displayed. Given thermal printers are selected, When the preflight checks DPI, Then incompatible DPI settings are flagged with steps to correct.

Given Shopify, Etsy, WooCommerce, or eBay stores are connected, When the preflight runs, Then required webhook/subscription endpoints are present with correct scopes per channel. Given webhook endpoints are configured, When a test event is sent, Then the store responds with 2xx within 5 seconds and the receipt is logged; failures include response code and a deep link to Channel Settings. Given retries are configured, When a test fails, Then up to 3 retries with exponential backoff are attempted and the final status recorded in the checklist. Given tracking sync mapping is missing for a channel, When validation runs, Then the check is marked Blocker with a link to enable tracking sync.

Given order history for the last 30 days, When SKU coverage is computed, Then ≥95% of shipped order lines have weight and dimensions present; coverage <95% is flagged with a Warning and a CSV of missing SKUs. Given coverage <90%, When the preflight runs, Then the check is a Blocker unless a fallback packaging rule exists for the affected warehouse(s). Given the user fixes missing SKU data, When the check is re-run, Then coverage recalculates and status updates accordingly within 10 seconds. Given oversized items exist, When detection runs, Then SKUs exceeding carrier max dimensions are listed with suggested services or "Do not auto-pack" flags.

Given preflight results include zero Blockers, When a user with Launch Approver role clicks Sign Off, Then the system records name, timestamp, checklist version hash, and IP in the audit log. Given sign-off is recorded, When the user attempts to Enable the scenario, Then enablement succeeds only if the checklist version hash matches the latest run within the last 24 hours. Given any configuration changes affecting mapped areas occur after sign-off, When the scenario is opened, Then the sign-off is invalidated and a Re-run Preflight requirement is enforced before enablement. Given access control policies, When a non-approver attempts to sign off or enable, Then the action is denied with a clear error message.

Given a draft or scheduled ruleset contains a rule selecting a carrier/service without an active mapping for the rule’s origin and destination scope When the lint job runs from preflight or the Rules Editor Then a finding with code MISSING_MAPPING and severity Error is returned including rule_id, rule_name, carrier, service, origin_ids, destination_scope, and a remediation summary And the finding includes impact_scope.last30d.order_count and a representative example_order_id And a one-click fix "Add mapping" opens the mapping editor pre-filtered to the carrier/service and origin; selecting Save resolves the finding And rollout/scheduling is blocked until the error is resolved or explicitly waived by an Admin with reason and timestamp And the lint completes in ≤2s for rulesets with ≤500 rules

Given two or more rules (active or scheduled) have overlapping conditions that would select different outcomes (carrier/service/parcel template) for the same order When the lint job runs Then a finding with code OVERLAP_CONFLICT is returned with severity Warning if a deterministic explicit priority resolves the overlap, else severity Error And the finding lists involved_rule_ids, conflict_dimensions (e.g., destination, weight, SKU tags), and shows precedence explaining which rule would win And the finding includes impact_scope.last30d.order_count and percent_of_volume And one-click fixes are offered: "Split conditions" to auto-generate mutually exclusive filters, and "Adjust priority" to reorder rules; preview shows resulting diffs before apply And fixes applied create a new draft version linked to the finding and mark the finding resolved

Given a rule’s condition set is fully subsumed by one or more higher-priority rules such that it never matches When the lint job runs Then a finding with code UNREACHABLE_RULE is returned with severity Warning including rule_id, shadowing_rule_ids, and proof via simulated evaluation trace And the finding shows last30d.match_count for the rule equals 0 and includes last_seen_match_at if any within the last 90 days And one-click fixes are available: "Disable rule" (set inactive) and "Narrow conditions" (adds suggested exclusions); both create a new draft version And applying a fix re-runs lint automatically and removes the finding if resolved

Given the ruleset’s priority ordering or fallback references form a cycle (e.g., group A > group B > group A) When the lint job runs Then a finding with code CIRCULAR_PRIORITY is returned with severity Error including the cycle path (ordered list of rule/group identifiers) And rollout/scheduling is blocked until the cycle is broken And a one-click fix "Normalize priorities" proposes a linearized, cycle-free ordering based on current precedence; preview is shown and user confirmation applies the change And re-running lint after fix shows no CIRCULAR_PRIORITY findings

Given two or more rules targeting the same scope have overlapping activation windows that would yield conflicting outcomes When the lint job runs against scheduled changes Then a finding with code TIME_WINDOW_COLLISION is returned with severity Error if outcomes conflict, else Warning if outcomes are identical And the finding contains a timeline visualization data payload (start_at, end_at, rule_ids) and impacted_window_duration And the finding includes projected_impact.order_count for the overlapping window based on last30d hourly distribution And one-click fixes are offered: "Auto-split windows" (adjusts start/end to remove overlap) and "Set window precedence" (adds explicit precedence for overlaps) And fixes generate a new scheduled version and the collision finding is resolved upon successful apply

Given any lint finding is generated When the user opens the finding in the UI or fetches it via API Then the finding includes a human-readable summary ≤280 characters, a detailed description with cause and resolution steps, and deep links to relevant editors and docs And the API returns structured fields: code, severity, rule_ids, message, details, impact_scope, remediation.actions[], and version_id; schema validates against the published OpenAPI spec And clicking a one-click fix prompts a confirmation modal with a diff preview; confirming creates a new draft/scheduled version, posts a success toast, and updates the finding to Resolved And accessibility checks pass: buttons are keyboard-focusable, have ARIA labels, and color contrast meets WCAG AA

Given a user attempts to schedule or roll out a ruleset version When the preflight runs automatically Then rollout is blocked if any Error-severity findings exist, with HTTP 409 and a list of blocking finding codes; Warnings require explicit acknowledgment with a checkbox and reason And the lint results are stored immutably against version_id with checksum and created_at, and are retrievable via API for 30 days And a webhook event rules.lint.completed is emitted with status (pass/warn/fail) within 5s of request for rulesets ≤1,000 rules And a CLI/API endpoint POST /rules/{version_id}/lint returns 200 and completes in ≤5s for ≤1,000 rules with parallelization enabled And rerunning lint on unchanged content returns a cached result with HTTP 200 and header X-Lint-Cache: HIT

Given historical order data for the past 30 days is available When lint identifies a finding tied to rule conditions Then the system computes impact_scope including order_count, percentage_of_total_volume, representative SKUs, and top 3 destinations affected And the computation supports sampling with ±2% error at 95% confidence for datasets >1M orders and labels results as sampled when applicable And impact_scope is displayed consistently in UI and returned by API for each finding; absence of data is explicitly indicated as unknown rather than 0 And performance: computing impact adds ≤1s p95 to total lint time for rulesets ≤1,000 rules

Given a user selects a configuration and a historical date range up to 90 days with ≥1,000 eligible orders When the user starts a dry‑run simulation Then only historical, non‑cancelled, fulfillable orders across all connected stores/warehouses in that range are included And a unique Simulation Run ID is assigned and displayed And processing completes for 10,000 orders in ≤15 minutes and for 50,000 orders in ≤60 minutes And a real‑time progress indicator (0–100%) and final status (Success/Warning/Failed) are shown And a snapshot hash of configuration and rate tables used is stored with the run And re‑running with identical inputs yields identical outputs, including byte‑identical CSV exports

Given the current production rules and carrier/rate settings are captured at simulation start as the baseline When simulated outcomes are computed Then each order output includes: predicted box dimensions, weight, carrier, service, expected cost, expected transit days, and estimated delivery date And the same fields from the baseline are included And per‑order deltas are computed for cost (currency), transit days (days), SLA on‑time probability (percentage points), and error propensity (percentage points) And tie‑breaks between equal‑cost services follow configured priorities and are recorded in a reason_code And orders missing critical data (e.g., SKU weight or mapping) are marked Incomplete, excluded from savings %, and listed in an exceptions report with a cause

Given segment dimensions: store, warehouse, destination region (domestic zone or country), and carrier When results are aggregated Then per‑segment metrics include: order_count, total_baseline_spend, total_sim_spend, savings_amount, savings_pct, avg_sim_cost, avg_sim_transit_days, on_time_sla_pct, and error_propensity_pct And any segment with <30 orders is labeled Low Sample and excluded from savings_pct rollups while included in counts And grand totals reconcile to order‑level totals within 0.1% variance for spend and within 0.1 days for average transit

Given default alert thresholds: cost increase >5%, on‑time SLA decrease >2 percentage points, error propensity increase >1 percentage point When the simulation completes Then the Summary highlights top 5 segments by savings and top 5 by adverse impact And any segment breaching thresholds is flagged with a Risk badge and machine‑readable reason_code in {COST_UP, SLA_DOWN, ERROR_UP} And an overall projected savings % and count of flagged segments are displayed and included in exports And users can override thresholds per run, and the applied thresholds are recorded with the Simulation Run ID

Given a user requests CSV exports for a completed simulation When exports are generated Then two CSV files are produced: orders.csv and segments.csv And orders.csv columns include: order_id, store_id, warehouse_id, destination, baseline_carrier, baseline_service, baseline_box, baseline_weight_oz, baseline_cost, baseline_transit_days, sim_carrier, sim_service, sim_box, sim_weight_oz, sim_cost, sim_transit_days, cost_delta, transit_days_delta, sla_risk_delta_pp, error_propensity_delta_pp, change_note And segments.csv columns include: segment_type, segment_key, orders, baseline_spend, sim_spend, savings_amount, savings_pct, avg_sim_cost, avg_sim_transit_days, on_time_sla_pct, error_propensity_pct, risk_flags And CSVs are UTF‑8, comma‑delimited, RFC4180‑quoted, with US‑locale decimals, and row counts match UI totals exactly And exports complete in ≤30 seconds for 10,000‑order runs and file names include the Simulation Run ID and timestamp

Given the simulation uses the rate‑shopping engine in sandbox mode When the run executes Then no live labels are created, no carrier accounts are charged, no fulfillment webhooks are fired, and no inventory/order status changes occur in connected platforms And all external rate calls use test/sandbox endpoints or headers as required by the carrier And an audit log records: Simulation Run ID, actor, timestamp, sandbox=true, and zero side effects

Given a rollout is configured with 10% exposure targeting Store=S1, Warehouse=W1, Destination Region=US-EAST, Carrier/Service=CarrierX Ground, SKU Set=Set-A And the configuration passes validation When 10,000 eligible orders meeting all target filters are processed over the next 24 hours Then between 9.5% and 10.5% of those eligible orders are routed to the rollout path And 0% of ineligible orders (outside any target filter) are routed to the rollout path And the system records the applied targets, exposure percentage, and effective timestamps on each routed order

Given user- and order-based bucketing is enabled with stable hashing on customer_id with order_id fallback And Order O1 from Customer C9 is bucketed into treatment at 10% When subsequent orders O2 and O3 from C9 arrive during the same rollout Then O2 and O3 are routed consistently to the same bucket result as O1 And when the rollout increases to 25% Then previously bucketed customers remain in their prior assignment (no flip-flop), and new eligibility follows the 25% exposure And bucket assignment remains stable across service restarts for the lifetime of the rollout

Given phases are scheduled as 10% at 2025-09-01 09:00 UTC, 25% at 2025-09-03 09:00 UTC, 50% at 2025-09-05 09:00 UTC When the system clock reaches each phase time Then the active exposure updates within 60 seconds of the scheduled timestamp And an audit event is written with old% → new%, actor=system, and correlation to rollout ID And the timeline view reflects the change within 60 seconds And if the service restarts between phases, the next due phase still executes without manual intervention

Given a rollout is active at 25% exposure When an authorized user selects Pause and confirms Then within 60 seconds 0% of new eligible orders are routed to the rollout path (all are routed to control) And the prior bucket assignments are retained but not used while paused And the UI shows status=Paused with timestamp and actor When the user selects Resume Then the exposure returns to the last configured phase (25%) within 60 seconds And audit logs capture both actions

Given a staged rollout draft with targets, phases, canary cohorts, and bucketing strategy When the user opens Change Review Then a diff view shows all changes versus the current production configuration, including targets, phase schedule, and exposure levels And validation runs for missing mappings, overlapping rules, and target conflicts, listing issues by severity And Activate is disabled until all blocking validation errors are resolved When the user clicks Activate and confirms Then the rollout status becomes Active and the timeline view displays all scheduled phases with correct timestamps

Given a rollout is active at 25% for Store=S1 and Carrier=CarrierY Express And the decision engine integration is enabled When 1,000 eligible orders matching S1 and CarrierY Express are processed Then 25% ±0.5% are routed to the rollout decision path and 75% ±0.5% to control And each order record includes rollout_id, stage, bucket_id, and routing outcome And orders not matching S1 or CarrierY Express are 0% routed to the rollout path And if the decision engine returns an error, the order is routed to control and the error is captured with the order ID

Given a canary cohort is defined by a list of 50 Customer IDs and SKU Set=Set-Canary And the base exposure is 0% When the rollout is activated Then 100% of orders from the canary cohort are routed to the rollout path and 0% of non-cohort orders are routed And cohort membership is pinned so that all subsequent orders from those customers remain in treatment across later phases And removing a customer from the cohort takes effect within 5 minutes for new orders

Given a rollout Stage and Segment (channel, carrier, service, country) When a user defines thresholds for KPIs (label creation failure rate, reprint rate, postage variance vs baseline, exception rate, on‑time proxy) with evaluation window and comparator Then the system validates required fields, value ranges, and prevents overlapping duplicate definitions for the same Stage+Segment+KPI. Given a valid configuration When the user saves it Then the configuration is persisted, versioned, audit‑logged (user, timestamp, diff), and becomes effective for evaluation within 2 minutes.

Given active thresholds When the evaluation job runs every 60 seconds Then each KPI is computed per Stage+Segment over its configured rolling window and breaches are detected within 2 minutes of occurrence. Given no events for a Stage+Segment within a window When the evaluation runs Then the KPI is marked "insufficient data" and no alert is fired.

Given a detected threshold breach When an alert is generated Then Slack, Email, and PagerDuty notifications are sent to the configured destinations within 60 seconds, each including KPI name, current value vs threshold, window, Stage, Segment, timestamp, top contributing dimensions, correlation ID, suggested remediation text, and links to the live dashboard/runbook. Given a notification delivery failure to any destination When retries are attempted Then the system retries with exponential backoff for up to 5 minutes and records final delivery status and error for audit.

Given an alert fired for a KPI and Stage+Segment When the condition persists Then duplicate alerts with the same dedup key are suppressed for the configured cool‑down period (default 30 minutes). Given a persisting breach during cool‑down When the metric severity increases to ≥ 2x the threshold Then a new alert is emitted and marked as an escalation, bypassing cool‑down. Given a cleared condition When the metric remains below threshold for 2 consecutive evaluation intervals Then the alert is re‑armed and may trigger again on the next breach.

Given selected KPIs, thresholds, Stage+Segment, and a historical time range When a preview backtest is run Then the system returns expected alert count, time‑in‑breach, percent of intervals over threshold, and sample alert instances within 60 seconds for a 7‑day window. Given a threshold referencing a baseline When the backtest runs Then the baseline source is applied and the output includes a projected false‑positive rate using control data if provided.

Given a KPI of average postage variance vs baseline When a user selects a baseline source (last 14 days pre‑rollout or control segment) and granularity (SKU, carrier, service) Then the system snapshots the baseline with an effective date and uses it consistently for evaluation and backtesting. Given a new baseline snapshot is published When subsequent evaluations run Then they reference the new snapshot starting on its effective date, and prior alerts retain links to the snapshot used at trigger time.

Given an on‑time performance proxy KPI (e.g., label‑to‑first‑scan p95) When a user defines the percentile, evaluation window, and threshold Then the system computes the proxy per Stage+Segment and triggers alerts when the metric exceeds the threshold. Given delayed carrier scan data When data completeness for the window is below 90% Then the metric is marked as stale and alerts are deferred until completeness recovers.

Given an active rollout with a defined lastKnownGoodConfigId and rollback thresholds (e.g., error_rate >= 5% for 5 consecutive minutes) When a monitoring event with a unique correlationId indicates any configured threshold is breached for the impacted rule set Then 100% of traffic for the impacted rule set is routed to the lastKnownGood configuration within 60 seconds And the rollout progression is paused and marked as RolledBack in the rollout timeline And the rollback record is created with rollbackId, breach correlationId, triggerType = automatic, and affected scope And the system returns success telemetry to the monitoring source acknowledging the correlationId

Given a user with role ReleaseManager or higher provides rolloutId, environment, scope, and a non-empty reason via UI or API When the manual rollback request is submitted and the lastKnownGood configuration exists Then the system initiates rollback within 10 seconds and completes traffic reversion within 60 seconds And the API responds 200 with rollbackId, state (InProgress or Completed), lastKnownGoodConfigId, scope, and initiator userId And the action is recorded in the audit log with timestamp, userId, IP, request payload hash, and diff summary And requests from unauthorized users are rejected with 403 and no state change

Given segments are defined (e.g., channel = Etsy, Shopify; carrier = UPS, USPS) and a rollback is requested for segment = Etsy When the partial rollback is initiated Then at least 95% of requests tagged segment = Etsy are served by the lastKnownGood configuration within 60 seconds And non-target segments experience less than 1% unintended traffic shift during the transition window And the rollout timeline annotates the rollback as scope = segment:Etsy with accurate before/after traffic percentages And monitoring and tracking remain isolated per segment, preserving metrics continuity

Given rollback rate limits are configured as minCooldown = 10 minutes and maxRollbacksPerHourPerRuleSet = 3 When multiple threshold breaches occur for the same rule set within the cooldown window Then no additional rollback is initiated during the cooldown and a Suppressed event is recorded with reason = cooldown And if maxRollbacksPerHourPerRuleSet is reached, further rollbacks are blocked for the remainder of the hour with a Suppressed event logged And the rollout state remains stable (no oscillation) and the suppression is visible in the rollout timeline

Given a rollback has been initiated for (rolloutId, scope, lastKnownGoodConfigId) and is InProgress or Completed When the same rollback request (same idempotencyKey or same (rolloutId, scope, lastKnownGoodConfigId)) is received again within 15 minutes Then the system does not start a new rollback and returns 200 with the original rollbackId and current state And the audit log contains a single state transition for the rollback, with subsequent requests logged as idempotent-replay without state changes

Given any rollback (automatic or manual) occurs When the rollback starts and completes (or fails) Then the rollout timeline displays startTime, endTime (or failureTime), initiator (userId or system), triggerType, scope, previousConfigId, lastKnownGoodConfigId, and result And the audit log records a normalized diff of affected rules/mappings, immutable entryId, and retains entries for at least 365 days And the timeline and audit data are retrievable via API with filters by date range, rolloutId, scope, and initiator within 2 seconds for p95 requests

Given notification channels are configured (email, Slack, webhook) for the environment When a rollback is initiated Then a start notification is sent to all active channels within 30 seconds including rollbackId, triggerType, scope, reason, and lastKnownGoodConfigId And when the rollback completes or fails Then a completion/failure notification is sent within 30 seconds including outcome, duration, and links to timeline and audit entries And duplicate notifications for the same rollbackId and phase are suppressed And webhook deliveries include a signed HMAC header and are retried with exponential backoff up to 3 times on 5xx

Given a change request with risk_level = "High" When the first user with role in ["Approver","Admin","Owner"] approves Then the request status becomes "Pending Second Approval" and activation remains blocked And the same user and the submitter cannot provide the second approval When a different eligible user approves within 72 hours and there are zero rejections Then the request status becomes "Approved" and activation is permitted When any eligible user rejects before final approval Then the request status becomes "Rejected" and activation is blocked

Given a change request with a generated preflight checklist When any checklist item is Incomplete or Fail or conflict_check_status != "Clear" Then Approve and Activate actions are disabled and a tooltip indicates outstanding items When all checklist items have status = "Pass" within the last 24 hours and conflict_check_status = "Clear" Then the Approve action is enabled for eligible approvers

Given any submit, approve, reject, activate, rollback, or edit event occurs on a change request When the event is saved Then an audit record is appended with fields: event_id, event_type, timestamp_utc (ISO 8601), actor_id, actor_email, actor_role, entity_type, entity_id, before, after, diff_summary, reason, request_id, ip, correlation_ids And the audit record cannot be updated or deleted via UI or API (write-once) And any attempt to modify or delete an audit record returns 403 and creates a SecurityAudit event And audit records are retained for >= 7 years

Given a change modifies rules, mappings, or thresholds When viewing the approval modal or the audit log entry Then a structured diff displays added/removed/modified elements with per-field before/after values And nested objects/arrays are diffed using path notation (e.g., rules[3].condition.operator) And the same diff content is stored in and exported with the audit record

Given a change affects shipping rules, mappings, or thresholds When submitting the change for approval Then at least one linked simulation run and one automated test suite with status = "Pass" within the last 72 hours are required And approvers can open linked artifacts directly from the approval screen And if required passing artifacts are missing, submission is blocked with an error indicating what is missing

Given an admin selects a date range and filters for audit records and requests an export When the export is triggered Then CSV and JSON files are generated within 2 minutes containing all matching records with a canonical schema and headers And a SHA-256 checksum and a signed manifest (including filter parameters and generated_at timestamp) are provided And the exported record count equals the on-screen count for the same filters

Given a role "Packer" that is allowed: reprint_label and denied: void_label, edit_weight for Brand Alpha on Channel Shopify And a user U1 assigned to role "Packer" When U1 opens a Brand Alpha Shopify order in the Order Processing UI Then the Void Label control is hidden or disabled with an "Insufficient permissions" message When U1 calls POST /labels/{id}/void for a Brand Alpha Shopify order with their auth token Then the response is 403 Forbidden and the label remains active When U1 uses Reprint Label in the UI for the same order Then the label reprint succeeds and the action completes without permission errors

Given role "Supervisor" permits edit_weight only when workstation_id ∈ {WS-01, WS-02}, warehouse = "West", and shift = Day (06:00–14:00) And user U2 is assigned role "Supervisor" When U2 edits weight from WS-01 in warehouse "West" at 10:00 local time Then the weight change is saved successfully When U2 attempts the same action from WS-99 or warehouse "East" or at 22:00 Then the action is blocked with a message indicating the violated scope (workstation/warehouse/shift) And no weight change is persisted

Given a global template "Ops-Default" that denies override_address for all roles And a Brand Beta template that allows override_address for role "Manager" on Channel eBay And user U3 is assigned role "Manager" When U3 attempts override_address on a Brand Alpha eBay order Then the action is blocked per the global template When U3 attempts override_address on a Brand Beta eBay order Then the override is allowed and the change is saved When the global template later changes override_address to allowed Then Brand Beta's local setting remains in effect and precedence rules continue to allow it for Brand Beta When the Brand Beta override is removed Then the effective permission for Brand Beta reverts to the global template value

Given a CSV import file with 500 permission rules including columns: role, action, brand, channel, warehouse, workstation, shift, allow_deny When Operations uploads the CSV via the matrix UI Then the system validates schema and field values and returns row-level errors for invalid entries And no changes are applied if any validation errors exist When Operations fixes the errors and re-uploads a valid CSV Then all 500 rules are applied and visible in the matrix UI with correct scopes When Operations exports the matrix Then the exported CSV reflects the current effective rules and scopes in a stable, documented column order

Given an SSO group "PP_Ship_Leads" mapped to ParcelPilot role "Shipping Lead" And user U4 is a member of "PP_Ship_Leads" When U4 authenticates via SSO Then U4 is granted the "Shipping Lead" role for that session and can perform actions allowed to that role When U4 is removed from "PP_Ship_Leads" and re-authenticates Then U4 no longer has permissions granted by "Shipping Lead" When a user is in multiple mapped SSO groups Then the effective permissions are the union of the mapped roles When the SSO-to-role mapping is deleted Then affected users lose the mapped role on next authentication

Given user U5 in role "Packer" is allowed reprint_label but denied change_service And a batch is created for 200 orders requesting change_service and reprint_label When U5 runs the batch in the Batch Workflow UI or via API Then all change_service operations are blocked with a PERM_DENIED error per order and none are applied And all reprint_label operations succeed for eligible orders And the batch summary reports counts of successes and permission-denied items consistently between UI and API responses When a user with permission to change_service runs the same batch Then change_service operations succeed for all eligible orders with no permission errors

Given a visual policy rule named "Block HV to Restricted" with conditions: order_value >= 500 AND destination_country in {NG, IR, KP} AND channel in {Shopify, Etsy} and outcome = Block and priority = 1 and status = Active When a user attempts to purchase a label for a matching order in the UI Then the action is blocked, no label is created, and a banner displays: "Blocked by policy: Block HV to Restricted (rule_id, version)" And an audit record is written with rule_id, rule_version, order_id, user_id, action=label_purchase, outcome=blocked, timestamp When a client attempts label purchase via API for a matching order Then the API responds HTTP 403 with error_code=POLICY_BLOCK and includes rule_id and rule_version in the response body And the order is not modified When a batch contains both matching and non-matching orders Then only matching orders are blocked; non-matching are processed; the batch summary reports counts for processed, blocked_by_policy, and failures with rule metadata

Given a policy rule "Weight Edit Step-Up" with conditions: measured_weight_variance_percent > 20 OR measured_weight_variance_absolute > 0.5 lb AND user_role = Picker AND workstation_group = Packing AND time_of_day between 08:00 and 20:00; outcome = Require Step-Up (scan+PIN or SSO); priority = 2; status = Active When a Picker attempts to edit weight in the UI on an order that meets the variance condition Then a step-up prompt is shown and the edit is applied only after successful scan+PIN or successful SSO step-up per policy configuration And the audit log captures rule_id, rule_version, user_id, order_id, action=edit_weight, step_up_method, step_up_result, timestamp When the same edit is attempted via API for a matching order Then the API responds 401 with error_code=STEP_UP_REQUIRED and includes rule_id and rule_version; upon successful step-up and retry, the edit succeeds and is logged with step_up_result=success And if the user lacks base permission to edit weight per the permission matrix Then the attempt is blocked with error_code=PERMISSION_DENIED regardless of the rule outcome

Given a policy rule "Address Override Justification" with conditions: address_validation_confidence < 0.70 AND sales_channel = eBay; outcome = Warn with justification (min_length=20, max_length=250); priority = 3; status = Active When a user attempts to override the shipping address in the UI for a matching order Then a warning dialog explains the risk and requires a justification between 20 and 250 characters before proceeding And the override is applied only if a valid justification is entered; cancel leaves the order unchanged And the justification, rule_id, rule_version, user_id, order_id, and timestamp are stored and viewable in order history When an address override is submitted via API for a matching order without a justification Then the API responds 400 with error_code=JUSTIFICATION_REQUIRED; with a valid justification, the override succeeds and the justification and rule metadata are recorded

Given multiple active rules that can match the same action: R1 (priority=1, outcome=Block), R2 (priority=2, outcome=Warn), R3 (priority=3, outcome=Allow) When an action matches R1, R2, and R3 Then the engine evaluates by ascending priority and applies the first matching rule (R1 Block); no subsequent rules are applied When priorities are reordered so that R2 has priority=0 and R1 has priority=1 Then the engine applies R2 Warn and continues the action; R1 is not evaluated because stop-on-first-match is enabled And the UI and API responses include the applied rule_id and rule_version for traceability And the rule builder UI supports drag-to-reorder priorities and persists the new order; simulations reflect the new precedence

Given a reusable condition set "High Value" defined as order_value >= 500 (version v1) and a condition set "Restricted Countries" defined as destination_country in {NG, IR, KP} (version v1) And a rule "Block HV to Restricted" references High Value v1 AND Restricted Countries v1 with outcome=Block, priority=1 When an operator edits the "High Value" set to order_value >= 600 and publishes version v2 Then the system records a versioned change history (author, timestamp, diff) for the condition set And existing rules remain pinned to referenced versions (v1) until explicitly updated When the operator updates the rule to reference High Value v2 and publishes rule version r2 Then subsequent evaluations use the updated threshold; audit logs show rule_version=r2 and condition_set_versions={High Value:v2, Restricted Countries:v1} And the operator can roll back the rule to r1 or the condition set to v1, with all changes captured in history

Given a rule "Weight Edit Step-Up" configured with a staged rollout: Site=A at 25% of workstations, Site=B at 0% When the rule is activated Then only users on Site=A within the selected 25% of workstations are subject to the rule; users on Site=B are unaffected And the rule builder provides a dry-run simulation that reports the percentage and count of historical actions that would have matched per site before activation When the rollout is increased to 100% for Site=A and then reduced back to 0% Then enforcement adjusts accordingly within 5 minutes, and the rollout changes are recorded with author, timestamp, and notes

Given the permission matrix denies weight edits for role=Viewer and allows weight edits for role=Operator And a policy rule requires step-up for weight edits when measured_weight_variance_percent > 20 When a Viewer attempts to edit weight in the UI or via API Then the attempt is blocked with error_code=PERMISSION_DENIED regardless of policy rules When an Operator edits weight in the UI for a matching order Then the step-up requirement is enforced and logged; on success the edit is saved; on failure the edit is not applied When a batch process attempts actions across multiple orders where some match the rule and others do not Then matching items require and enforce the policy outcome; non-matching proceed; the batch result includes per-order outcomes and rule metadata consistently across UI and API

Given a user is assigned a badge barcode and a PIN and policy requires scan+PIN for "Void Label" with retryLimit=3 and lockoutDuration=5 minutes When the user initiates "Void Label", scans their badge, and enters the correct PIN on the first attempt Then the step-up is accepted, the action proceeds, and a grace window timer starts Given the same policy and user When the user enters an incorrect PIN 3 times within 2 minutes Then the step-up is denied, the UI displays "Too many attempts", a 5-minute lockout is applied for scan+PIN on this workstation for this user, and all attempts are logged with timestamps Given the lockout is active When the user attempts scan+PIN again during the lockout Then input is blocked, the remaining lockout time is shown, no additional attempts are counted, and the event is logged Given a different user on the same workstation When they attempt scan+PIN during the first user’s lockout Then they are not blocked and may authenticate normally

Given policy requires SSO step-up and the IdP is configured for MFA and ACR/AMR enforcement When the user triggers step-up and is redirected to the IdP and completes MFA per IdP policy Then the response (ID token or SAML assertion) is validated for signature, issuer, audience, nonce/in_response_to, notBefore/notOnOrAfter, and includes MFA evidence (required ACR/AMR), and the action proceeds Given the IdP returns a response without MFA or with insufficient ACR/AMR When validation is performed Then the step-up is rejected, the user is re-prompted with guidance to complete MFA, and the attempt is logged with an error code Given a freshness requirement of max_age=300 seconds for the action When the user triggers step-up with an existing IdP session older than 300 seconds Then the client forces re-authentication at the IdP (e.g., prompt=login or max_age) and proceeds only after fresh MFA is completed

Given graceWindow=15 minutes and per-action freshness requirements: Edit Weight=15 minutes, Void Label=5 minutes When the user completes step-up in a modal for Edit Weight Then subsequent Edit Weight modals within 15 minutes do not re-prompt; after 15 minutes a new step-up is required Given the user completes step-up for Void Label When another Void Label is attempted after 6 minutes Then the user is re-prompted due to the 5-minute freshness requirement Given a batch flow processes 50 orders with 3 Void Label actions When the user completes step-up for the first Void Label Then at most one prompt occurs per 5-minute freshness window for Void Label during the batch, and non-sensitive batch steps are not blocked Given the user logs out, changes role, or switches workstation When they initiate a sensitive action Then any existing grace is invalidated and a new step-up is required

Given scanner device D is bound to workstation W1 and not to W2 When a user on W1 performs scan+PIN with D Then the scan is accepted for step-up on W1 Given the same device D When a user on W2 attempts scan+PIN with D Then the step-up is rejected with "Unbound device" and logged as invalid provenance Given an admin re-binds device D to W2 and saves the change When the next scan occurs from D on W2 Then the scan is accepted on W2 and rejected on W1, with binding changes reflected in audit logs within 5 seconds Given a scan originates from an unknown or unregistered HID/USB source When step-up is attempted Then the attempt is denied, a security alert message is shown, and the event is logged with device fingerprint

Given any step-up attempt occurs (scan+PIN or SSO) When the attempt is processed Then an immutable audit record is written with: timestamp (UTC), tenant, user ID, role, action, method (scan+PIN|SSO), result (success|failure|lockout|break-glass), workstation ID, device ID (if scan), IdP issuer (if SSO), ACR/AMR (if SSO), client IP, and correlation ID Given a failed attempt due to retry limit or IdP error When the audit record is written Then the record includes a standardized error code/category and excludes secrets (no PINs, no raw tokens), storing only token identifiers (e.g., JTI) or cryptographic hashes Given an auditor queries by time range and action across 10,000 events When the query is executed Then results include all matching records and return in ≤2 seconds, and records cannot be altered or deleted by non-audit roles

Given SSO step-up is required and the IdP is unreachable (e.g., DNS failure, timeout >10s, HTTP 5xx) When the user initiates a sensitive action Then the action is blocked, a "Identity provider unavailable" message is shown with retry guidance, and no partial/expired assertions are accepted Given a user with Admin Break-Glass permission and a policy requiring reason and second approver PIN When the IdP is unreachable and the admin initiates break-glass Then the system requires a textual reason and approver PIN, records a break-glass audit entry, grants a temporary exception scoped to the specific user and action for ≤15 minutes, and allows the action Given IdP connectivity is restored When the same user initiates another sensitive action Then break-glass does not bypass normal step-up; standard step-up is required Given a non-admin user attempts break-glass When the request is made Then it is denied and logged with an authorization error

Given an embedded page loads the ParcelPilot step-up SDK When it calls stepUp.prompt({ action: "Override Address", freshnessSeconds: 300 }) Then the host renders the native step-up UI, and on success the SDK resolves with a signed, single-use token bound to user, action, role, and workstation, with TTL ≤300s and ≤ configured graceWindow, and ≥128 bits of entropy Given the token is posted to a backend validation endpoint When validation occurs Then the endpoint verifies signature, expiry, action match, user/workstation match, and single-use; on reuse returns 409 and logs a replay attempt; on success returns 200 Given the embedded page runs in an iframe without third-party cookies When stepUp.prompt is invoked Then communication occurs via secure postMessage without leaking secrets to the iframe origin, and the SDK still functions without reliance on third-party cookies

Given a user lacks permission to void labels for the current brand/client, sales channel, and workstation per policy matrix When the user clicks "Void Label" in the UI or calls POST /labels/{labelId}/void via API Then the action is blocked deterministically And the UI shows a standardized modal with title "Action Not Permitted" and actionable guidance referencing policy_id and policy_version And the API returns HTTP 403 with error_code="policy_denied", policy_id, policy_version, correlation_id And a decision record is written with correlation_id, subject_id, action="void_label", resource_id=labelId, context={brand,channel,workstation}, outcome="deny"

Given a user is allowed to override addresses only with step-up per policy When the user edits a shipment address and clicks Save or calls PATCH /shipments/{id}/address via API Then a standardized enforcement modal prompts for step-up with options specified by policy (scan+PIN and/or SSO) And if step-up succeeds within 120 seconds, the change is committed and the UI/API returns success And if step-up fails, times out, or is cancelled, the change is not saved; the UI shows an actionable message and the API returns HTTP 428 with error_code="step_up_required" including challenge metadata And a decision record logs prompt_type="step_up" and result in {"success","failure","timeout","cancelled"} with correlation_id

Given a policy requires justification for weight or dimension edits beyond a configured threshold (e.g., >=5%) When a user attempts to change weight or dimensions exceeding the threshold or changes the package preset Then a standardized modal requires selecting a reason_code from a controlled list and entering justification_text with minimum length 15 characters And the action is blocked until both fields are provided and pass validation; the API returns HTTP 409 with error_code="justification_required" when justification is missing or invalid And upon valid submission, the edit proceeds and the decision record includes reason_code, justification_text_hash, old_values, new_values, correlation_id

Given a batch operation includes items with mixed enforcement needs (e.g., 100 label voids across brands and channels) When the user initiates the batch Then the middleware deduplicates and consolidates prompts into at most one step-up challenge and one justification modal per unique policy requirement And the UI presents a single batched modal with counts by outcome and supports approve/cancel; the API returns a single challenges object and per-item challenge tokens And per-item outcomes are applied deterministically; items without required prompts proceed, denied items remain unchanged with reason; partial success is reported with per-item statuses And P95 decisioning overhead per item is <=35ms and P99 <=70ms for 100-item batches

Given normal load of 200 enforcement evaluations per second per instance Then added decision latency is P95 <=25ms and P99 <=60ms as measured at the middleware boundary Given the upstream policy service experiences 5 consecutive timeouts within 30 seconds When evaluating an action Then the circuit breaker opens for 60 seconds; up to 2 retries with exponential backoff (50ms, 100ms) are attempted per request while closed/half-open And while open, sensitive actions fail closed with HTTP 503 and error_code="policy_service_unavailable" in API and an actionable UI message advising to retry or contact an administrator And outcomes remain deterministic per idempotency_key; repeated requests with the same key produce identical results; logs include fallback_reason and breaker_state with correlation_id

Given identical user role, brand/client, channel, workstation, and policy version When the same sensitive action is attempted from the web app, desktop workstation client, and public API Then the enforcement outcome and required prompts are identical across channels And UI clients use the standardized modal component with consistent copy, primary/secondary actions, and telemetry events And API responses use a standardized schema with status codes: 200 allow, 403 policy_denied, 409 justification_required, 428 step_up_required; body includes error_code, message, policy_id, policy_version, correlation_id, and challenge metadata when applicable

Given any intercepted sensitive action When a decision is made (allow, prompt, require justification, or deny) Then an immutable audit record is written within 1 second containing correlation_id, timestamp, subject_id, action, resource identifiers, context {brand, channel, workstation, client_app}, policy_id, policy_version, decision, prompt_type, latency_ms, idempotency_key, and a deterministic hash of decision inputs And PII fields in logs are masked or hashed per security policy; justification_text is stored as a salted hash with recorded length And logs are queryable via an internal audit API by correlation_id and date range and are retained for at least 12 months

Given a user attempts a gated action (void, address override, weight edit) on an order or label When the action is evaluated by Role Gates Then an audit event is written atomically and immediately upon decision with fields: actor ID, actor display name, role, auth method, step-up method used (scan+PIN or SSO), action type, decision outcome (allow/deny), UTC timestamp (ISO 8601 with ms), workstation/device ID, IP, order ID, label ID (if applicable), before values, after values, policy ID, policy version, matched rule ID, and justification text (if required) And the event has a unique event ID and a SHA-256 content hash And the log store enforces append-only semantics: update/delete operations are blocked and the attempt is itself logged as a security event And reading the event returns the exact stored values and validates the content hash successfully

Given an authorized user opens the Audit Trail view When they apply filters for date range, user, action type, client/brand, channel, and policy (ID or version) and optionally search by order or label ID Then the result set contains only events matching all filters And results default to sort by timestamp desc, with user-selectable asc/desc And pagination supports page sizes of 25/50/100 with accurate total count and page navigation And for datasets up to 500k events under the applied filters, the first page loads within 2 seconds at p95 and subsequent pages within 1.5 seconds at p95 And clearing filters restores the default unfiltered view

Given an authorized user applies filters in the Audit Trail view When they request a CSV export Then the exported file includes only the filtered events and columns aligned to the documented schema with a header row And PII fields are redacted per active redaction policy (e.g., names masked, address lines partially masked, emails hashed) consistently with the in-app view And timestamps are UTC ISO 8601, values are UTF-8 encoded, comma-delimited, with RFC 4180 quoting And exports up to 100k rows complete synchronously; larger exports run asynchronously and provide a downloadable link with expiry and an email/notification upon completion And the export action itself is recorded in the audit trail with actor, filters summary, row count, and file identifier

Given SIEM webhook and/or S3 streaming destinations are configured and verified When new audit events are produced Then events are delivered to the webhook within 60 seconds p95 and to S3 objects within 5 minutes p95 And webhook deliveries are signed (HMAC-SHA256 with shared secret), include an idempotency key, and are retried with exponential backoff for up to 24 hours on failure And S3 objects are written in compressed NDJSON with partitioning by UTC date/hour and include a manifest file; schema version is included per record And destination outages or schema validation failures generate delivery failure metrics and surfaced errors in the admin UI And streaming can be paused/resumed without data loss, and backfill catches up in order by timestamp and idempotency key

Given brand/client-specific retention (e.g., 90/365/730 days) and field-level PII redaction policies are configured When an event reaches its retention horizon Then it is purged from the primary store and optional downstream storage per policy, and the purge operation is logged with counts and ranges And redaction rules are applied at write-time and respected in UI, CSV exports, webhooks, and S3 streams; redacted values are not recoverable via the application And policy changes take effect prospectively and do not unredact historical data; tightening retention schedules future purges, loosening does not resurrect purged data And administrators can run a dry-run report showing records scheduled for purge by date range and policy before execution

Given alert thresholds and recipients are configured (e.g., ≥5 denials by a single user or workstation within 10 minutes; ≥3 overrides without required step-up in 15 minutes) When thresholds are met or exceeded Then alerts are sent to Ops and Security via configured channels (email, Slack, webhook) within 60 seconds, including summary, sample events, and deep links to the audit view And alerts are deduplicated within a suppression window to prevent alert storms and require acknowledgment in the UI And alert triggers and acknowledgments are themselves recorded in the audit trail And administrators can test an alert rule and see the last 24h hit count and current status

Given an investigator is viewing an order or label in ParcelPilot When they open the Audit Trail tab or click "View Audit" from the action menu Then all related gated action events are displayed with links to the exact policy ID and version that governed the decision and to the actor’s profile And the record shows justification text (if required) and the evidence of step-up (e.g., SSO assertion ID or PIN scan reference) And from an audit event, the investigator can navigate to the associated order/label, policy, user, and workstation/device pages And the view supports export of only the currently scoped events and preserves filters in the shared URL

Given an Admin with Policy Manager permission selects a policy version and a date range up to 90 days with filters for brand, client, channel, workstation, and role When they run a historical simulation on at least 10,000 orders Then the system returns counts by decision outcome (allow, warn, step-up, block), the top 20 impacted users/roles, and KPI deltas (estimated voids prevented per week, expected step-ups per shift) within 2 minutes Given identical inputs and the same policy version When the simulation is rerun Then aggregate results match within 0.1% and per-order decisions match exactly Given the simulation completes When the user exports results Then a CSV is generated containing order ID, decision outcome, evaluated rules, rationale, and segment tags Given a policy has no matching orders for the selected segment and date range When the user runs the simulation Then the system completes within 15 seconds and displays a zero-results summary without error

Given dry-run mode is enabled for a policy When a picker or packer opens the live orders queue Then each order line displays a simulated decision badge within 2 seconds and all actions remain enabled Given a user initiates a gated action (void, address override, weight edit) under dry-run When the policy would require a step-up or block Then the action completes normally and a non-blocking toast shows the simulated requirement with a View Walkthrough link Given the policy engine round-trip latency is under 500 ms When the queue refreshes Then the UI remains responsive and badge updates do not exceed one refresh per 5 seconds per order Given dry-run is active When a user operates offline Then no blocking behavior occurs and simulations are queued for logging upon reconnection

Given a simulation summary for a policy and selected segment When predicted blocks exceed 3% of orders or predicted step-ups exceed 20% of scans per shift Then the Enable button is disabled and a Guardrail panel lists exceeded thresholds with required approver role Given an Admin with Approver role provides SSO step-up and justification text of at least 20 characters When thresholds are exceeded Then Enable proceeds and an override event is logged with policy ID, version, thresholds exceeded, approver identity, and timestamp Given thresholds are within limits When the owner clicks Enable Then the policy enables without override and the event is logged and linked to the simulation run ID Given no simulation run exists or the latest run is older than 7 days or based on fewer than 500 orders When the user tries to enable Then the system blocks enablement and requires running simulation first

Given dry-run mode is active When a gated action is attempted Then a decision log is written including policy version, order ID, user ID, role, workstation, segment tags, decision, evaluated conditions, rationale, and timestamp with no mutation to order state Given at least 24 hours of dry-run logs When the user opens the Impact Report Then the report shows pre-policy baseline vs dry-run metrics for voids prevented estimate, step-ups per shift, and time-to-ship with filters for brand, client, channel, workstation, and role Given a policy is enabled from dry-run When viewing reports after 7 days Then pre, dry-run, and post metrics are comparable with trend lines and a simulation vs actual variance percentage Given a report is requested for export When the user clicks Export CSV Then the file downloads within 30 seconds for up to 1,000,000 rows Given retention is configured to 90 days When the oldest logs exceed retention Then logs are purged and aggregates are preserved

Given a valid policy draft exists When the user opens Preview Then at least one sample order is shown for each decision outcome present in the rules (allow, warn, step-up, block) with step-by-step rule evaluation Given brand, client, channel, workstation, and role filters are set When Preview is refreshed Then sample orders and KPI projections update to the selected segment and display the sample set size and last refresh time Given the user clicks Share When generating a share link Then a link valid for 7 days is created with access control restricted to authenticated users with View Policies permission Given an outcome type has no matching historical examples When Preview is loaded Then the system generates a clearly labeled synthetic walkthrough for that outcome

Given the user is editing a policy in the rule builder and the policy validates When they click Simulate Then the system runs a historical simulation on at least 500 orders matching the selected segment and shows results in the builder pane Given no fresh simulation exists for the current policy version When the user views the Enable control Then the control is disabled with a tooltip indicating that a simulation within the last 7 days is required Given any edit changes the policy logic When the draft is saved Then the previous simulation is marked Stale and cannot be used to enable Given the policy is enabled When 7 days of post-rollout data are available Then the system calculates simulation-to-actual variance for step-ups and blocks and flags if variance exceeds 10% with a recommendation to adjust rules

Given an authenticated admin via UI or API When they create a rule with conditions: (order_value > 250 OR hazmat = true OR (cross_border = true AND address_risk >= "medium")) using comparators >, >=, =, != and grouped with parentheses And they assign a numeric priority Then the rule saves successfully and is returned with an ID and priority And rule evaluation respects priority where lower numeric value is higher priority And when two rules have the same priority and both match, the earliest-created rule is deterministically selected And the evaluate API returns matched_rule_id, action, block flag, and rule_version for any match

Given an order that matches a rule whose action is "require supervisor approval" with block=false When a user attempts label purchase Then the system prompts for supervisor approval and allows purchase only after approval is granted And the approval requires a non-empty reason_code from a configurable list and records approver_id, timestamp, and reason_code in the audit log Given an order that matches a rule with block=true When a user attempts label purchase without an approved override Then the purchase is blocked and the UI/API returns a 409 state with decision details and required reason codes And after a supervisor override with a valid reason_code, the purchase proceeds and the override is logged

Given a single fulfillment workflow instance (transaction_id) for an order that matches a risk rule When pick sheet generation, weigh/measure capture, rate selection, and label purchase each query the shared decision service Then each touchpoint receives the same decision (decision_id), matched_rule_id, action, block flag, and rule_version And if rules change mid-workflow, the existing decision persists for that transaction_id; new transactions use the new active rule_version And evaluate p95 latency is ≤ 150 ms and error rate < 0.1% over a 15‑minute window, observable via metrics And one audit decision entry is recorded per transaction_id with references from each touchpoint event

Given a rule configured with condition weight_delta_pct >= 20 OR sku_history_mismatch = true And SKU history baseline is computed from prior shipments per SKU as median weight and dimensions When a picker captures actual package weight and dimensions Then the system computes delta% against baseline and evaluates the rule And if delta% >= 20 or sku_history_mismatch = true, step‑up approval is required; otherwise no prompt is shown And the decision log includes baseline_weight, measured_weight, delta_percent, sku_ids, and mismatch_flags And unit tests cover edge cases at 19.9%, 20.0%, and 20.1% delta and missing baseline data (falls back to no-match unless explicitly configured)

Given a rule version set to Preview When real orders are evaluated Then no step‑up prompts or blocks are enforced; only annotations and metrics are recorded And the evaluate API indicates preview_hit=true with matched_rule_id and rule_version And an admin can run a backtest over the last N (configurable, default 10,000) orders and receive hit_rate, conflict_rate, and estimated impacted shipments And toggling a rule from Preview to Active requires confirmation and records a change reason And preview results are retained for at least 30 days And no operational side effects (holds, blocks, approvals) occur while in Preview

Given any create, update, activate/deactivate, or delete of a rule When the change is saved Then an audit entry is written with actor_id, timestamp, action, before/after diff, and change_reason And rule_version is an incrementing integer; previous versions remain immutable and queryable And rollback creates a new active version whose content matches the selected prior version and logs the rollback linkage And the evaluate API includes the rule_version used in each decision And audit and versions are filterable by date range, actor, and rule_id via API and admin UI

Given API endpoints: POST /rules, GET /rules, PATCH /rules/{id}, POST /evaluate with OpenAPI schema published When an Admin role calls these endpoints with valid payloads Then requests succeed with 2xx and payloads are schema-validated; non-admins receive 403 and unauthenticated requests receive 401 And rate limits of 100 requests/min per API key are enforced with 429 responses when exceeded And conditions support channel in [Shopify,Etsy,WooCommerce,eBay] and carrier in [UPS,USPS,FedEx,DHL] constraints, verified via evaluation And evaluate responses include decision_id, matched_rule_id (or null), action, block flag, rule_version, and a trace of evaluated conditions with true/false outcomes

Given a picker opens a pick task for an order and the client supplies orderId, attemptId, and eventType="pick_task_open" When the client requests a risk decision Then the engine evaluates active rules synchronously and returns a response within 100 ms at P95 (150 ms at P99) under 200 RPS sustained load And the response includes decision in {allow, block}, stepUpRequired in {true, false}, reasons[] (non-empty when decision=block or stepUpRequired=true), codes[] (machine-readable), ruleVersion, decisionId, and correlationId And for orders with no active risk signals, decision=allow and stepUpRequired=false

Given an order with predictedWeight from SKU history and an actualWeight confirmed by scale When |actualWeight - predictedWeight| exceeds the configured relativeDelta% threshold (default 20%) and the absoluteDelta threshold (default 0.25 lb or 0.11 kg) Then the decision returns stepUpRequired=true and includes reasons containing "weight_delta_exceeded" and codes containing "WEIGHT_DELTA" And the decision is deterministic per ruleVersion: the same inputs and ruleVersion yield identical decision, reasons, and codes across retries And with the same orderId+attemptId idempotency key, repeated requests return the same decision payload and decisionId; with a new attemptId, a fresh decision is computed

Given eventType="rate_selection" and a candidate service for an order that may be cross-border, hazmat, or high value When the client requests a risk decision with context including destinationCountry, originCountry, hazmat flag, and orderValue Then if hazmat=true and the selected service is not hazmat-capable, decision=block with reasons including "hazmat_service_incompatible" and codes including "HAZMAT_BLOCK" And if destinationCountry != originCountry and required customs data is missing, stepUpRequired=true with reason "customs_data_missing" and code "CUSTOMS_MISSING" And if orderValue >= configured highValueThreshold, stepUpRequired=true with reason "high_order_value" and code "HIGH_VALUE" And if none of the above apply, decision=allow and stepUpRequired=false

Given eventType="label_purchase" and the most recent decision for the same orderId+attemptId has stepUpRequired=true When the user attempts to purchase a label without presenting a valid step-up auth token Then decision=block with reason "step_up_required" and code "STEP_UP_REQUIRED" When a valid, unexpired step-up auth token tied to the same orderId+attemptId is presented Then decision=allow and the response includes authEventId and code "AUTH_OK" And repeated calls with the same orderId+attemptId are idempotent and return the same decisionId and outcome within 100 ms P95

Given the decision engine is unreachable due to timeout or 5xx error When a decision is requested with a configured fallbackPolicy in {"block","require_step_up","allow"} and a timeout budget T (<= 100 ms) Then the client receives a response within T+20 ms with decision and stepUpRequired derived from fallbackPolicy, fallbackApplied=true, source="fallback", and reasons including "engine_unreachable" with code "ENGINE_UNREACHABLE" And if no fallbackPolicy is configured, the default behavior is decision=block, stepUpRequired=false And the event is logged with correlationId and retriable=true

Given REST endpoint POST /v1/risk/decisions and SDK methods risk.decide() for UI, batch, and partner WMS integrations When called with required fields {orderId, attemptId, eventType, context} and a valid idempotency key Then the response conforms to schema v1.0 containing decision, stepUpRequired, reasons[], codes[], ruleVersion, decisionId, correlationId, fallbackApplied (boolean), source ("engine"|"fallback"), and riskSignals[] And HTTP responses are: 200 on success; 400 with code "INVALID_INPUT" for schema violations; 409 with the original decision body for idempotency key reuse with different payload; 503 when fallback is not allowed and the engine is unavailable And for the same inputs and ruleVersion, repeated invocations across SDKs and REST return identical decisions (byte-for-byte equality of the JSON body except for correlationId)

Given a picker on the desktop Packing view initiates Buy & Print Label for an order with an active risk signal, When the action is initiated, Then an inline modal appears over the current page without a full navigation. Given the inline modal is open, When the approver completes authentication successfully, Then the originally requested action executes within 2 seconds and the modal closes returning focus to the originating control. Given the inline modal is open, When the approver cancels or closes the modal, Then no label is purchased and the pre-modal page state (filters, scroll position, selections, unsaved inputs) remains unchanged. Given form inputs exist on the underlying page, When the modal opens and closes, Then all inputs retain their values and selection state. Given an approval flow completes successfully, Then telemetry records time_to_approve (seconds) and success=true for the session.

Given a risk-triggered action is initiated on a scanner/mobile device, When step-up is required, Then a full-screen prompt displays with tap targets ≥ 44×44 dp and high-contrast theme. Given the full-screen prompt is displayed, When authentication succeeds, Then the interrupted action resumes within 2 seconds and the prompt dismisses. Given device rotation or app backgrounding occurs during the prompt, When the app resumes, Then the prompt state and any entered values are preserved. Given accessibility services are enabled, When the prompt is navigated, Then it is fully operable via hardware keys and screen reader announces labels and errors. Given keypad station mode is active on a shared device, When approval is requested, Then the prompt presents PIN entry sized for the physical keypad and logs the station ID with the approval attempt.

Given SSO/OAuth is enabled for the site, When the approver selects SSO, Then an OAuth 2.0 Authorization Code with PKCE flow is initiated and on success control returns to the step-up prompt. Given SSO is unavailable due to connectivity or IdP outage, When step-up is required, Then PIN and TOTP options are presented as fallbacks without blocking the flow. Given PIN authentication is selected, When an incorrect PIN is entered 3 times (configurable), Then PIN auth is locked for 10 minutes (configurable) and further attempts are blocked with a non-revealing message. Given TOTP authentication is selected, When a valid code within a 30-second window is entered, Then authentication succeeds; when invalid codes are entered more than 2 retries (configurable), Then the attempt fails with a non-revealing error. Given a 90-second inactivity timeout is configured, When no interaction occurs within that period, Then the prompt expires, clears sensitive fields, and displays a timeout message.

Given a user attempts to approve, When their role lacks Approve_StepUp permission for the current site, Then the approve action is blocked and a non-revealing error is shown. Given an approval is attempted, When scope constraints (site, shift, station) are invalid, Then approval is denied and the event is logged with scope_mismatch. Given an approval is submitted, When the form lacks a selected reason code, Then submission is prevented and the reason field is marked as required. Given an approval succeeds, Then an immutable audit entry is created with approver ID, role, scope, order/shipment ID, risk signals, reason code, optional note (≤200 chars), outcome, UTC timestamp, and a signed hash. Given an authorized admin queries the audit API, When filters for date range, risk signal, and outcome are applied, Then only matching entries are returned.

Given the device is offline or unstable, When an approver authenticates via PIN or TOTP, Then a locally signed approval token (JWS) with ≤10-minute expiry is generated and queued with the pending action. Given connectivity is restored, When the queue flushes, Then the backend validates token signature, expiry, and RBAC before executing the deferred action. Given a queued token expires before submission, When the queue attempts delivery, Then the action is not executed and the user is prompted to reauthenticate. Given retries occur due to intermittent connectivity, When duplicate submissions are detected, Then the action executes at most once per order/action key (idempotent). Given SSO is selected while offline, When the prompt detects no network, Then it explains SSO is unavailable and offers PIN/TOTP instead without exiting the flow.

Given step-up authentication fails, When the error is returned, Then the user sees a clear non-sensitive message with options: Retry, Request Supervisor, Cancel. Given the approver selects Cancel, When the modal closes, Then the system restores the pre-action state and records a declined event with reason. Given the failure reason is RBAC, When the message is shown, Then it suggests contacting an authorized supervisor and optionally links to on-call supervisor info if configured. Given ≥2 consecutive failures occur, When Retry is offered, Then a 3-second delay is applied before enabling the Retry button to deter rapid attempts. Given any rejection occurs, Then telemetry records failure_type, attempts_count, and time_to_resolution for the session.

Given a warehouse locale is selected, When the step-up UI appears, Then all strings, dates, numbers, and reason codes display in the selected language and format, including RTL support. Given keyboard-only navigation is used, When traversing the prompt, Then all controls are reachable in logical tab order and have visible focus indicators. Given a screen reader is active, When interacting with the prompt, Then elements expose accessible names, roles, and states, and errors are announced within 500 ms. Given color-blind safe theme is enabled, When errors are shown, Then contrast ratios meet WCAG 2.1 AA and error states are conveyed without relying on color alone. Given the language is switched during the prompt, When the switch occurs, Then the UI updates immediately without loss of state.

Given a batch contains a mix of low-risk and high-risk orders per configured risk rules (value, weight delta, hazmat, cross-border, SKU mismatch) When the user initiates batch pick sheet generation and label purchasing Then labels are purchased and queued for print only for low-risk orders And high-risk orders are excluded from label purchase and moved to the Review Queue for that batch And the pick sheet includes only low-risk orders by default and displays a held-for-review count badge And the segmentation uses the active risk configuration snapshot at batch start time

Given a batch has completed segmentation When the user opens the batch summary Then the summary displays counts for Total Orders, Processed (low-risk), and Held for Review (high-risk) And counts match the underlying order states within ±0 of the database records And the summary lists top risk reasons with per-reason counts And each held order row shows its primary reason and all contributing reasons And the summary includes quick links to Open Review Queue, Bulk Approve, and Export Held Orders CSV for the batch

Given held orders exist in the Review Queue for batch B and a supervisor is authenticated When the supervisor selects held orders and chooses Bulk Approve Then the system records reviewer identity and timestamp and (if configured) prompts for step-up auth And each approved order is re-queued for label purchase And if batch B is still open, approved labels are added to B’s next print job And idempotency ensures no duplicate labels: repeated approvals or retries return the original label ID And audit logs capture before/after states, reason(s), and label IDs for all approved orders

Given batch B has been closed (printed/archived) and held orders remain When any of those held orders are approved Then the system creates a follow-up mini-batch linked to B (e.g., B-1) containing only the newly approved orders And labels for the mini-batch are purchased and queued for print without duplicating any previously purchased labels And the mini-batch inherits shipment settings (carrier/service, ship-from, printer profile) from B unless overridden And the original batch summary updates to show the number moved to the follow-up mini-batch with a link

Given a user with 3PL Supervisor permissions views a batch with held orders When they open the Review Queue UI Then they can filter held orders by risk reason, channel, carrier, destination, and value And they can select one, many, or all results and perform Bulk Approve with a single action And step-up auth is enforced on approve when any order meets the configured step-up policy And the UI disables approve for orders with unresolved hard blocks (e.g., missing hazmat doc) and shows the block reason And post-approval, the UI reflects updated counts within 2 seconds and removes approved items from the held list

Given an API client with scope review_queue:read and review_queue:write When the client calls GET /batches/{id}/held-orders with optional filters Then the API returns paginated held orders including orderId, reasons[], primaryReason, batchId, createdAt, and lock status When the client POSTs /batches/{id}/held-orders/approve with an idempotency key and a list of orderIds Then the API approves eligible orders, purchases labels, and returns per-order results with status (approved, skipped, blocked), labelId (if any), and error (if any) And repeated POSTs with the same idempotency key within 24h return the same labelIds without duplicates And all endpoints enforce role-based access, validate inputs, and respond within p95 ≤ 500 ms under load of 100 RPS

Given a risk evaluation is executed for a shipment, When the decision engine evaluates rules, Then an audit record is created containing non-null fields: event_id, tenant_id, shipment_id, order_id, event_type ("risk_evaluated"), rule_version, input_attributes, decision_outcome, created_at_utc (ISO 8601), source_ip, device_id_or_station_id, actor_user_id (null if system), risk_flags, and sku_history_checksum. Given an approval or override is performed, When the approver submits, Then an audit record is created with event_type ("approved" or "overridden"), approver_user_id, approver_role, comment, override_reason_code, links to prior risk_evaluated event_id, and resulting label_id if generated. Given any audit record is created, When validated, Then all timestamps are UTC ISO 8601 with millisecond precision and write acknowledgment completes within 200 ms p95.

Given any attempt to update or delete an existing audit record via UI or API, When executed by any role, Then the operation is blocked (HTTP 405) and no persisted data changes. Given the audit log integrity is requested, When the verifier endpoint is called, Then a hash-chain proof (prev_hash, hash) validates the last 10,000 events and detects any modification. Given a compliant redaction is requested by a compliance_admin, When approved, Then a redaction tombstone event is appended referencing the original event_id, masking only allowed fields while preserving hash-chain continuity; the original event is no longer retrievable via standard reads.

Given an authorized user opens Audit History, When they filter by any combination of date range, shipment_id, order_id, decision_outcome, rule_version, approver_user_id, risk_flag, device_id, or source_ip, Then the results match the filter and return within 2 seconds p95 for a 30‑day window up to 1M events. Given results are displayed, When the user paginates, Then ordering by created_at desc is stable and no records are skipped or duplicated across pages. Given a result row is opened, When viewing details, Then the full field set and links to shipment, order, label, and prior/next audit events are shown.

Given an authorized user has applied a filter, When CSV export is requested, Then a UTF‑8 CSV with stable headers and UTC ISO 8601 timestamps is generated within 60 seconds for up to 500k rows and the export event is audited with requester, filter summary, row_count, and checksum. Given the same filter, When JSON export is requested, Then a JSON Lines file is produced with one event per line using canonical field names and nulls for empty values, and access to the file enforces RBAC. Given webhooks are configured, When risk.evaluated, risk.approved, or risk.overridden events occur, Then a POST is delivered within 5 seconds p95 with the full payload and X‑Signature (HMAC‑SHA256) header; retries use exponential backoff for up to 24 hours and deliveries are idempotent via event id with visible delivery logs.

Given a tenant retention policy of N months is configured (default 24), When an audit record exceeds N months and is not under legal hold, Then it is permanently purged within 24 hours and is not retrievable via UI, API, exports, or webhooks, and a purge event is recorded. Given legal hold is applied to a shipment or order, When retention would otherwise purge related audit records, Then those records are retained until hold removal, after which purge occurs within 24 hours. Given audit data is stored, When compliance status is queried, Then the system reports encryption at rest is enabled with managed keys and annual rotation; attempts to disable encryption are blocked for all roles.

Given user permissions are enforced, When a user without audit_read attempts to access audit history, Then access is denied with HTTP 403 and no record metadata is leaked. Given a user with audit_read but without audit_export, When they attempt to export, Then the UI hides export options and direct API calls return HTTP 403. Given a user with audit_admin, When they configure retention, manage webhook endpoints, or request a redaction, Then the action succeeds and an administrative audit event is recorded; users without audit_admin are blocked.

Given a shipment_id is entered in the Reconciliation view, When the view loads, Then it displays a timeline linking the order, all risk evaluations, approvals/overrides, and generated label_id(s) with timestamps. Given the reconciliation timeline is displayed, When a linked entity (order, audit event, label) is clicked, Then the user is navigated to its detail view filtered to the related record. Given a required approval is missing for a generated label, When the reconciliation view evaluates consistency, Then the discrepancy is flagged with a clear status and can be exported as CSV or copied via a shareable link.

Given an order triggers Risk Triggers and requires supervisor approval When the order enters the pending-approval state Then eligible on-duty supervisors in the order’s facility receive an in-app toast within 5 seconds And the toast displays order number, merchant, top 3 risk reasons, and SLA countdown And the toast contains a deep link to the exact approval screen And non-approvers and off-duty users do not see the toast And dismissing the toast does not remove the order from the approval queue

Given multiple pending approvals for the same supervisor within a 60-second window When email notifications are generated Then a single consolidated email is sent summarizing all orders in the window (up to 50) And subsequent duplicate emails for the same order are suppressed for 30 minutes unless the approval state changes And each order entry includes a deep link and risk reasons And per-supervisor email rate does not exceed 1 email per minute

Given Slack or Teams is connected and the user is an authorized approver When a pending approval notification is delivered Then the message contains Approve and Deny actions using signed, single-use tokens expiring in 10 minutes And clicking an action updates the order status within 3 seconds and edits the message to show the outcome, actor, and timestamp And unauthorized or expired actions are rejected with a safe error message and no state change And the message includes compact order details, risk reasons, and a deep link

Given a schedule with business hours, holidays, and a rotation roster is configured per facility When an approval is required Then notifications are sent only to on-duty approvers for that facility and channel And outside business hours, notifications route to the on-call approver per rotation And SLA timers honor the configured mode: pause outside hours or continue counting And all time calculations use the facility’s timezone

Given an approval SLA of N minutes and an escalation path is configured When the SLA elapses without action Then the system escalates within 60 seconds to the next approver level across all enabled channels And escalated notifications are marked as Escalation Level n and include the original SLA breach context And previous approvers remain able to act until resolution, with messages updated to reflect the new level And no duplicate escalations occur for the same level

Given auto-expire is enabled with a TTL of M minutes When the TTL elapses without approval or denial Then the request transitions to Approval Expired state and scheduled escalations are canceled And all pending notifications are updated or replaced to indicate expiration and disable actions And chat actions and links reject further attempts with an expired response and no state change And an audit log records expiration time, TTL, and affected recipients

Given I am a user with Analytics access and a dataset exists for the last 30 days When I open Risk Analytics and select channel=All, carrier=All, warehouse=All, date range=Last 30 days Then the dashboard displays, for each selectable dimension (channel, carrier, warehouse), these KPIs: Trigger Rate (%), Rule Hit Distribution (per-rule hits and % of orders), Added Processing Time (median and p90 in seconds), Approval Outcomes (auto-approved, manual-approved, declined counts and rates), Financial Impact (postage savings $, delay cost $, net impact $) And each KPI is visible overall and per selected dimension with totals matching the sum of slices within ±0.5% rounding tolerance And all KPIs load within 3 seconds for up to 100,000 orders

Given filters exist for date range, channel, carrier, warehouse, and rule id When I adjust any single filter Then results update within 2 seconds and an applied-filters summary reflects the selection And when I click a rule in Rule Hit Distribution Then I see a drill-through table of affected orders with columns: order_id, timestamp, rule_id, risk_score, action (actual/shadow), processing_time_added_s, approval_outcome, channel, carrier, warehouse And the table supports pagination (50 rows/page), sorting, CSV export, and row count equals rule hits within ±1

Given shadow mode is enabled for a threshold set When live orders meet a shadowed rule condition Then no step-up auth or shipping hold is triggered for those orders And a shadow event is logged with: order_id, timestamp, rule_id, threshold_version, risk_score, predicted_action, predicted_processing_time_added_s And the dashboard labels these as Shadow and excludes them from live-impact metrics while including them in backtesting datasets And disabling shadow mode stops new shadow events within 1 minute

Given I select a historical window and configure a proposed threshold set T2 while baseline T1 is stored When I run a backtest Then a report returns deltas T2 vs T1 for: Trigger Rate, False Positive Rate, Added Processing Time (median/p90), Approval Outcomes, and Financial Impact And the backtest completes within 10 minutes for 1,000,000 orders and streams progress status at least every 5 seconds And I can download a CSV with per-rule and per-dimension diffs And results are versioned with run_id, inputs checksum, and timestamp for reproducibility

Given at least 10,000 labeled historical orders with approval outcomes exist When I open Recommendations Then I see a prioritized list of rule/threshold changes each with: estimated change in false positive rate (±95% CI), expected net financial impact ($), affected order count, rationale (top contributing signals), and suggested threshold value And each recommendation supports actions: Simulate (runs backtest) and Apply (creates draft threshold set) And applying creates an audit log entry and leaves live thresholds unchanged until explicitly deployed

Given I have selected filters and a report layout When I export the current view Then a file is generated within 30 seconds in CSV and XLSX with headers, applied-filters metadata, and ISO-8601 timestamps in the selected timezone And when I schedule a report for weekdays 07:00 warehouse local time Then recipients receive an email with attachment and dashboard link within 5 minutes of the scheduled time, with delivery retries up to 3 times on failure And scheduled jobs list next run, last run status, and support pause/resume/delete

Given new orders and risk events stream into analytics When I view the dashboard Then a data freshness indicator shows data latency <= 15 minutes And metric totals (orders, rule hits, approvals) reconcile to source event counts within 0.5% over the selected period And financial impact calculations match reference formulas within $0.01 per order

Given a provisioned user with an active badge and a supported device/browser, When the user taps an NFC badge or scans a barcode/QR badge, Then the session is established and the device is bound to the user within 2 seconds of scan detection. Given a bound device with no active session, When a valid badge is scanned, Then the user lands on the ParcelPilot home screen with their mapped role applied and the session start timestamp recorded. Given a device temporarily offline and a user with a valid cached badge token (cacheTTL=8h), When the badge is scanned within the TTL, Then the session starts in offline mode within 2 seconds and an "Offline mode" banner is displayed. Given an unrecognized, disabled, or expired badge, When it is scanned, Then no session is created, a non-enumerating error message is shown within 500ms, and the attempt is rate-limited per device and identity.

Given policy settings maxAttempts=5 per 5-minute window per identity and per device, rateLimit=1 attempt/second/device, and lockoutDuration=15 minutes, When incorrect PINs are entered exceeding maxAttempts, Then the identity is locked out for lockoutDuration and further attempts are blocked with a clear message. Given a valid user PIN, When the PIN is submitted on a supported device/browser, Then the session is established within 2 seconds and the device is bound to the user. Given the device is offline and a user has a valid cached PIN token (cacheTTL=8h from last successful online login), When the PIN is entered, Then the session starts offline within 2 seconds and is marked for server-side verification and token rotation upon reconnect. Given a disabled user or revoked PIN, When a PIN is entered, Then the system denies access with a non-enumerating message and increments the failed-attempt counter respecting rate limits.

Given an IdP (Okta or Microsoft Entra) is configured with OIDC/SAML and the user is entitled, When the user initiates SSO and completes IdP authentication, Then upon callback receipt the ParcelPilot session is established within 2 seconds and the device is bound to the user. Given a successful SSO assertion containing groups/claims, When the session is created, Then ParcelPilot maps the identity to roles per the configured mapping table and enforces those permissions immediately. Given an SSO login, When an IdP requires MFA, Then the flow completes successfully after MFA at the IdP and returns to ParcelPilot without additional prompts beyond device binding. Given an expired or invalid SSO assertion, When callback is received, Then the session is not created and a clear retry prompt is shown with no leakage of identity existence.

Given scanning hardware is unavailable or camera permission is denied, When the login screen loads, Then PIN and SSO options are presented within 200ms and focus moves to the PIN field for immediate input. Given the SSO provider is unreachable or returns 5xx, When a user selects SSO, Then a non-blocking error is shown within 1 second and badge/PIN options are presented unless restricted by policy. Given an admin policy disables a method (e.g., PIN), When the login screen renders, Then the method is hidden or disabled with an accessible explanation and other enabled methods remain available. Given a user completes login via a fallback method, When the session is established, Then the total ParcelPilot processing time for the chosen method remains within 2 seconds on supported devices/browsers.

Given idleTimeout=60s, When there is no user interaction for idleTimeout, Then the session auto-locks at timeout ±1 second and sensitive actions are blocked until re-auth via any enabled method. Given a new user authenticates on a device with an active session for a different user, When the new authentication succeeds, Then the previous session is locked immediately and the device is rebound to the new user. Given an in-progress operation at lock time, When the session auto-locks, Then unsaved work is preserved safely and is resumed only after successful re-auth by the same user or is discarded per policy with explicit user notice.

Given a desktop without a hardware scanner but with a camera, When the user grants camera permission, Then the login screen can scan QR and Code 128 badges with a 95% success rate across 50 test scans under office lighting (≥300 lux) and decodes within 500ms after the code is in frame. Given the user denies camera permission or the camera is unavailable, When scanning is attempted, Then the system displays a clear prompt and offers PIN and SSO alternatives immediately. Given supported browsers (current Chrome, Edge, Firefox, Safari), When camera scanning is used, Then the UI provides a visible framing guide and audible/visual feedback on successful scan and navigates to the next step without additional clicks.

Given the shared‑kiosk login screen, When used with keyboard only and a screen reader, Then all controls are reachable in a logical order, have programmatic names, meet WCAG 2.1 AA contrast (≥4.5:1), and actionable targets are ≥44×44 CSS px. Given any authentication failure (e.g., invalid PIN, disabled badge, SSO error), When the message is displayed, Then it is specific and actionable (e.g., remaining attempts), does not disclose account existence, and is announced via ARIA live region. Given any authentication attempt (success or failure), When it completes, Then an audit record is written with timestamp, anonymized/user ID per policy, device ID, IP (if available), method (Badge, PIN, SSO), outcome, and end-to-end latency, and records are retrievable via the audit API within 5 seconds.

Given a user authenticates successfully on a device with a detectable durable identifier (pref order: MDM Device ID, OS identifier, browser fingerprint) When a session is created Then a device-scoped session token is issued and cryptographically bound to the durable device identifier And the token is stored in secure storage (Keychain/Keystore or HttpOnly SameSite=Strict cookie) and not accessible via client-side JS And all API calls require the token and a matching device identifier; mismatches return 401 with error code DEVICE_BINDING_MISMATCH and are audit-logged And the audit log records user ID, device ID, auth method, timestamp, and IP for the binding event

Given user U has an active bound session on device A and policy setting maxConcurrentDevices=1 When U attempts to start a session on device B Then device B is shown a blocking prompt with options: Handoff or Cancel And selecting Cancel denies login on device B and logs AUDIT_CONCURRENCY_BLOCKED And at no time do two active sessions for U exist simultaneously (verified by querying active sessions) Given user U has maxConcurrentDevices=2 and exactly one active session When U starts a session on a second device Then the second session is allowed and both sessions remain active Given user U has maxConcurrentDevices=2 and already has two active sessions When U attempts to start a third session Then the attempt is denied with error code CONCURRENCY_LIMIT_EXCEEDED and audit log entry is created

Given user U has an active session on device A and initiates login on device B choosing Handoff When U confirms Handoff with a second-factor (badge rescan or PIN) within 30 seconds Then device A session is terminated within 5 seconds, device B session becomes active, and both devices display transition to the correct state And device A receives a toast "Session handed off to <device B name>" and returns to the locked screen And the audit log contains a single HANDOFF event linking oldSessionId and newSessionId with device IDs and timestamps And any in-flight requests from device A after termination receive 401 SESSION_REVOKED

Given an active session on device D When the OS user is switched or the device is locked/unlocked to a different OS account Then the session token is invalidated immediately and the app shows the lock screen; next action requires re-authentication Given the app is reinstalled or app data is cleared on device D When the app is opened Then any server-side token previously issued to device D is invalidated and cannot be replayed; first API call without a fresh login returns 401 TOKEN_INVALIDATED Given the device’s MDM compliance status changes (unenrolled or non-compliant) and a webhook is received When the webhook is processed Then all active tokens bound to that device are revoked within 10 seconds and actions are blocked until compliance is restored; AUDIT_MDM_REVOCATION is logged

Given a user is actively bound to the device When navigating to any app screen, pick/pack view, print queue, or scan modal Then the active user’s full name and user code are displayed in the header and action modals within 1 second of load And the display updates within 1 second on session change or handoff and is readable by screen readers (aria-label includes user name and "active user") And the identity badge is visible on all screens without scrolling and cannot be hidden by user settings And the lock state visibly changes the header to "Locked — <user name>" when the session is locked

Given an active bound session on device D When initiating a print (label or pick sheet) or recording a scan Then the request payload includes the bound user ID and device ID and is signed by the device-scoped token And the backend validates token binding and rejects actions from other devices/tabs with 401 DEVICE_BINDING_MISMATCH; no print or scan is executed And all print jobs and scan events persist the operator user ID and device ID in audit trails; printed labels include operator initials if enabled by policy Given two browser tabs on the same device and the session is locked or handed off in tab 1 When tab 2 attempts a print/scan using a stale token Then the action is blocked, the user is prompted to re-authenticate, and AUDIT_GHOST_ACTION_BLOCKED is recorded

Given an active session and idleTimeout=120 seconds When there is no qualifying activity (scan, print, pick confirm, navigation) for 120 seconds Then the app locks, obscures sensitive data, and requires re-authentication to resume; an idle-lock event is audit-logged And qualifying activity resets the idle timer without re-authentication, minimizing repeated prompts Given a handoff is initiated from another device for the same user When the handoff completes Then the original device locks immediately and prevents further actions until re-authentication

Given an admin with permission selects a site and role When they set a session time-box duration and idle threshold and save Then the values are validated against allowed ranges and persisted And new sessions for that site and role use the saved values And sessions for other sites/roles are unaffected And role-specific values override site defaults; if no role value exists, the site default applies

Given a user starts a session Then a persistent UI element displays remaining session time And the countdown updates at least once per second And when remaining time is at or below the configured warning threshold, the indicator changes state (e.g., color) and optional alert is triggered And the indicator remains visible across app screens and orientation changes

Given an active session When no user interaction occurs for the configured idle threshold Then the session locks and displays the lock screen within 2 seconds Given an active session When the app is backgrounded or the device sleeps or a network change occurs or the device is docked/undocked Then the session locks and displays the lock screen within 2 seconds And the current work state is checkpointed at lock

Given a session has auto-locked and the elapsed lock time is within the configured grace window When the same user scans their badge or enters their PIN Then the session unlocks within 2 seconds without full re-auth and returns to the prior screen Given a session has auto-locked and the elapsed lock time exceeds the configured grace window When the user attempts to re-enter Then full re-authentication is required before resuming

Given the user is mid-workflow (e.g., picking, packing, label purchase) with unsaved inputs When a lock is triggered by any configured event Then the workflow state, entered fields, selections, and scan buffers are persisted And upon successful re-entry within the grace window, the workflow resumes at the exact step with no data loss And if re-auth occurs after the grace window, the user is offered to restore the saved draft state

Given pending batch jobs (pick sheets, label queue) exist or are in progress When a lock occurs Then all pending and in-flight jobs are queued atomically with unique identifiers And no job is executed while the session is locked And after re-entry or relogin, the user can review and resume the queued jobs exactly once And no duplicate prints or labels are produced; completed and failed counts match the pre-lock state

Given a session has a configured duration and a warning threshold When remaining time reaches the warning threshold Then the user receives a prominent warning without interrupting work When remaining time reaches zero Then the session auto-locks immediately, shows a time-box expiration message, and blocks further actions And within the grace window the user may re-enter via badge/PIN; after the window, full re-auth is required

Given a device is active or locked with User A bound, no sensitive operation in progress, and pick list PL-123 with carton CT-45 selected When User B authenticates by scanning a badge or entering a valid PIN/SSO Then the system validates policy and transfers only permitted context (PL-123, CT-45) to User B, closes User A’s session, and presents a transfer summary requiring a single acknowledgment And any context not permitted is excluded and clearly labeled as "Not Transferred" in the summary with reasons And transient changes eligible per policy are finalized before transfer; remaining transient changes are rolled back and listed in the summary And the handoff completes within 3 seconds of successful authentication And no additional prompts are shown beyond the incoming authentication and the single summary acknowledgment

Given a label purchase is in progress on the device When any new user attempts handoff via scan or PIN/SSO Then the handoff is blocked until the purchase completes or fails And a blocking message states "Handoff blocked: Label purchase in progress" and shows the order/shipment ID And no session or context changes occur and the existing session remains active/locked as before And the attempt is audit-logged with outgoing user (if bound), incoming user ID, timestamp, and operation=blocked

Given User B lacks permission for the active context per policy rules (pick list PL-123, carton CT-45) When User B authenticates to take over the device Then the handoff is denied and the device remains bound to User A with the screen locked And all transient changes not finalized by User A are rolled back atomically to the last committed state And a denial message shows "Access denied" with policy rule ID and contact/override instructions And the denial is audit-logged with both users, policy rule ID, and affected context IDs

Given the active context is classified high-risk per policy When User B initiates handoff Then dual confirmation is required: User A confirms with scan/PIN and User B confirms with scan/PIN within 30 seconds And if dual confirmation is not completed within 30 seconds, the handoff is canceled and the device remains with User A’s session locked And the outcome (confirmed/canceled/timeout) is audit-logged with both users and context IDs

Given a successful handoff to User B Then User A’s session is closed and can no longer perform actions without re-authentication And User B is bound to the device/station until the configured idle timeout or the next handoff And if the transfer summary is not acknowledged within 15 seconds, the device auto-locks and reverts to the pre-handoff state

Given any handoff event occurs (success, blocked, denied, canceled, timeout, error) Then an immutable audit record is created with device/station ID, outgoing user (if any), incoming user, timestamps (start and end), outcome, transferred context IDs before/after, and reason codes And the record is visible in the audit UI within 5 seconds and includes a correlation ID And audit records are filterable by date range, user, device, and outcome

Given a network or service error occurs during context transfer When User B initiates handoff Then the system aborts the handoff, rolls back any transient changes, and keeps or restores User A’s session locked And a clear error message is shown with a correlation ID and retry option And no partial context is transferred and inventory/carton/pick list state remains consistent And retrying within 60 seconds creates a single additional audit record linked by the same correlation chain and produces no duplicate side effects

Given an active Scan-Bound session bound to user U and the policy marks rate override, address edit, weight change, label void, refund, and reprint as sensitive, When U initiates any of those actions, Then the system displays a step-up verification prompt that accepts badge scan or PIN entry and does not present a full login screen. Given U provides a valid badge or correct PIN for U in the current session, When verification succeeds, Then the requested action executes and the session remains active. Given U provides an invalid badge or incorrect PIN, When verification fails, Then the action is not executed and an error message is shown.

Given the policy cooldown is set to 5 minutes and U successfully completes step-up at T0 on Device D, When U performs another sensitive action on Device D at T0+3 minutes, Then no step-up prompt is shown and the action executes. Given the policy cooldown is 5 minutes and U completed step-up at T0 on Device D, When U performs a sensitive action at T0+6 minutes, Then a step-up prompt is shown. Given U has an active cooldown on Device D, When U attempts a sensitive action on a different device D2, Then a step-up prompt is shown on D2.

Given U lacks permission to perform rate override and the policy allows temporary elevation, When U initiates rate override, Then a step-up prompt appears with a mandatory Reason field. Given U enters a non-empty reason and successfully verifies via badge or PIN, When elevation is granted, Then U is elevated only to the minimum role required for the action and the action executes. Given the elevation TTL is policy-configured to 10 minutes, When the TTL expires or the session ends, Then the elevation is automatically revoked. Given the Reason field is empty, When U attempts to verify, Then elevation is denied and the action is not executed.

Given a cooldown or temporary elevation is active, When the device auto-locks due to inactivity, Then the cooldown is cleared and the temporary elevation is revoked; the next sensitive action requires step-up. Given a cooldown or temporary elevation is active, When the session is handed off and rebound to a different user, Then the previous user’s cooldown and elevation do not apply to the new user. Given a cooldown is active, When the user signs out, Then the cooldown is cleared.

Given label void is marked as requiring carrier-side integrity and the device is offline, When U attempts label void, Then the system denies the action and displays "Online verification required". Given label void requires carrier-side integrity and the carrier verification service is unreachable, When U attempts label void, Then the system denies the action and displays a reason indicating carrier service is unavailable. Given label void requires carrier-side integrity and the network and service are available, When U completes step-up verification, Then the action executes only after server-side verification succeeds; cached/offline credentials are not accepted.

Given any sensitive action attempt (approved or denied), When the attempt completes, Then an audit record exists containing timestamp (UTC), user ID, session ID, device ID, action type, target ID, outcome (approved/denied), method (badge/PIN), policy version, cooldown_applied (true/false), elevation_applied (true/false), reason text if provided, and carrier_transaction_id when applicable. Given a denied attempt due to invalid credentials, offline state, missing reason, or carrier unavailability, When the attempt completes, Then the audit record includes a standardized reason_code reflecting the denial cause. Given audit logging is enabled, When querying the admin audit log by session ID or action ID, Then the corresponding record(s) are retrievable and immutable.

Given the default policy, Then the sensitive actions list includes rate override, address edit, weight change, label void, refund, and reprint, and the policy defines a cooldown duration and an elevation TTL. Given an administrator updates the policy to add or remove a sensitive action, When a user next attempts that action, Then the step-up requirement reflects the updated policy without requiring application restart. Given an administrator updates the cooldown duration or elevation TTL, When a subsequent step-up occurs, Then the new durations are enforced for that session.

Given a user authenticates via badge scan or PIN/SSO to start a scan‑bound session, When the session begins, Then the system generates a globally unique session_id and persists it for the session lifetime. Given an event of type scan, edit, rate_selection, label_purchase, void, print, or system_action occurs during the session, When the event is recorded, Then the identical session_id is attached to the event record. Given the device auto‑locks and the same user unlocks within the configured session timeout, When new events occur, Then the session_id remains unchanged. Given a device handoff occurs and a different user authenticates, When new events occur, Then a new session_id is generated and used, and no subsequent events carry the prior session_id. Given the device is offline during the session, When events sync, Then the original session_id is preserved on all synced events and their ordering is preserved by event sequence.

For every event, include device_id, user_id, user_role, auth_method, station_id, app_version, ip_address, geo_location (if available), and server_received_at and event_created_at timestamps in ISO 8601 UTC with millisecond precision. Given any change of IP, network, or device within a session, When an event is recorded, Then the new metadata values are captured on that event. Given events are recorded within a session, When validating, Then event_created_at timestamps are non‑decreasing; if device clock skew is detected, Then server_received_at is present and used for ordering. Given device_id cannot be determined, When an event is recorded, Then a deterministic fallback fingerprint is used and flagged with device_id_confidence=low.

For every event, compute payload_hash = SHA‑256 over the canonicalized action payload and store it immutable. For every event after the first in a session, compute event_hash = SHA‑256(prev_event_hash || payload_hash || metadata) and store prev_event_hash; for the first event, store genesis_hash. Given any stored event or payload is altered, When chain verification runs, Then verification fails and returns the index/id of the first invalid link. Given a verification API is called with a session_id, When processing, Then the API returns status=valid|invalid and a signed receipt including a checksum over all event_hash values. Given public key verification is performed, When validating an event’s signature, Then the signature verifies against the published public key; otherwise the event is rejected for write.

Given the UI or API is queried by session_id, user_id, order_id, device_id, event_type, or time window, When the query executes on a site with <=100k events in the last 30 days, Then p95 response time <= 2s and correct results are returned. Given pagination parameters limit and cursor, When listing events, Then results are stable, cursor‑based, and next_cursor is provided until exhaustion. Given an export is requested for a time window or filter, When processing completes, Then a downloadable CSV and NDJSON are produced including all fields, payload_hash, event_hash, and prev_event_hash. Given an export is produced, When delivered, Then it includes a chain_verification status and a file‑level SHA‑256 checksum; the export action itself is logged as an event. Given RBAC policies, When a user without permission attempts search/export, Then access is denied with 403 and a security event is logged.

Given a webhook destination is configured with a signing secret, When events occur, Then batched webhook deliveries include event_id, session_id, payload_hash, event_hash, prev_event_hash, and metadata, and contain an HMAC‑SHA256 signature header. Given transient delivery failures occur, When retrying, Then exponential backoff retries for at least 24 hours with idempotency keys and no re‑ordering within a batch. Given the SIEM pull API is called with a since cursor or timestamp, When events are available, Then the API streams NDJSON with at‑least‑once semantics and supports rate limiting headers. Given a consumer acknowledges receipt, When the system records the ack, Then checkpoint cursors are advanced and visible in admin UI. Given webhook or API schema versions change, When a vN request is made, Then backward‑compatible fields are present; breaking changes appear only in new versions.

Given a site retention policy (e.g., 90/180/365 days) is configured, When events exceed their retention age, Then they are purged by an automated job that writes a purge_summary audit event with counts and time range. Given a legal hold is applied on a user, order, session, or time window, When retention jobs run, Then held records are excluded from purge until the hold is removed. Given a user attempts to shorten retention below a previously configured value, When saving, Then a confirmation with justification is required and the change is versioned and logged. Given a purge is scheduled, When N days before purge (default 7), Then admins receive a notice with an option to export affected records. Given WORM storage is enabled, When writing events, Then records are append‑only and cannot be updated or hard‑deleted by any API.

Given any user attempts to delete or edit an event record via UI or API, When the request is processed, Then the system returns 403 Forbidden and logs a security event with session_id. Given database‑level access is attempted via application pathways, When a delete or update is issued against the audit log, Then the operation is blocked by policy and recorded by a guardrail event. Given backups are executed, When stored, Then backups are immutable for the duration of retention and verified daily with checksum; integrity check failures alert on‑call within 5 minutes. Given break‑glass access is initiated, When multi‑party approval (2 of 3 approvers) is obtained, Then a time‑bound access token is issued, scoped read‑only, and all actions during the window are logged with a special flag.

Given I am a Policy Admin and open Create Policy for site "East DC" and role "Packer" When I set Allowed Auth Methods to ["Badge","PIN"], Session TTL to 8h, Idle Threshold to 10m, Step‑up Policies to ["PurchaseLabel"], Concurrency Rule to "Max 1 session per user per site", Offline Allowance to 15m, and IdP Mapping to "Okta" and click Save Then the policy is saved with a unique policy_id and version, appears in the policy list and via GET /v1/policies, and all saved values match my inputs Given Session TTL = 30m and Idle Threshold = 45m When I click Save Then saving is blocked and I see an inline error "Idle threshold must be less than session TTL" and the API returns 422 with code idle_gt_ttl Given Allowed Auth Methods = ["SSO"] and Offline Allowance > 0m When I click Save Then saving is blocked with error "Offline allowance must be 0 when only SSO is enabled" and 422 with code offline_requires_offline_capable_auth Given Offline Allowance = 90m and Session TTL = 60m When I click Save Then saving is blocked with error "Offline allowance must be less than or equal to session TTL" and 422 with code offline_gt_ttl Given Concurrency Rule "Max sessions per user per site" is set to -1 When I click Save Then saving is blocked with error "Concurrency must be a non‑negative integer" and 422 with code invalid_concurrency Given I enter an unknown Step‑up policy name "FooBar" When I click Save Then saving is blocked with error "Unrecognized step‑up policy" and 422 with code unknown_stepup Given a new site "North DC" is created When I view its Policies tab (UI) or GET /v1/policies?site=North%20DC (API) Then a default policy exists seeded from the Safe Defaults preset (catalog v1) with TTL=8h, Idle=10m, Offline=0m, Concurrency=1, Allowed Auth Methods including Badge, PIN, and SSO, and IdP Mapping unset, and it passes validation

Given I open the policy editor for site "East DC" and role "Picker" When I select the preset "Handheld (v1.0)" and click Apply Then all policy fields are populated exactly to the preset catalog values for "Handheld (v1.0)", an Unsaved Changes indicator appears, and differences versus current values are highlighted Given I applied preset "Packing Station (v1.0)" When I override Idle Threshold from the preset value to 3m and Save Then the policy saves with Idle Threshold=3m while all other fields remain at preset values, and the audit log records preset_applied=true, preset_name="Packing Station", preset_version="v1.0", and overrides={"idle_threshold":"3m"} Given I apply a preset with values that would violate validation (e.g., preset Idle >= TTL due to my prior TTL override) When I click Save Then the save is blocked with specific validation errors and the UI suggests "Revert to preset value" for offending fields

Given I select 50 policies across sites ["East DC","West DC"] and roles ["Picker","Packer"] in the Policy Console When I choose Bulk Edit, set Idle Threshold=5m and Session TTL=1h, and click Validate Then I see a preview showing Success=48, Blocked=2 with specific reasons (e.g., offline_gt_ttl), and no changes are yet committed Given the same selection and preview When I click Apply Then the system updates the 48 valid policies atomically per policy (all fields for a policy succeed or none), leaves the 2 invalid policies unchanged, returns a bulk_operation_id, and displays a summary with counts and IDs; the audit log contains one bulk entry plus child entries per updated policy referencing bulk_operation_id Given some targeted policies are concurrently edited by another admin When I Apply the bulk update with If‑Match ETags Then updates with stale ETags are skipped with 412 Precondition Failed and reported in the summary; no partial field updates occur within a single policy

Given active Scan‑Bound sessions exist across sites When I open the Real‑Time Sessions view Then I see a table with columns: user_id, display_name, role, device_id, device_type (handheld/station), site, location_zone, ip, start_time, last_activity_at, current_activity, policy_id, ttl_expires_at, step_up_state; the list auto‑refreshes at least every 5s and reflects creations/updates/terminations within 5s Given sessions from multiple sites When I filter by site="East DC", role="Packer", and user contains "ana" Then only matching sessions remain and the total count reflects the filtered set; sorting by last_activity_at desc changes row order accordingly Given my admin scope is limited to site="East DC" When I open the Real‑Time Sessions view Then I cannot see sessions from other sites and API calls to list them return 403 forbidden

Given a selected active session with device online When I click Lock, enter reason "Policy update" and optional message "Please re‑scan badge", and confirm Then the device receives a lock command and displays the message within 3s, the session state becomes Locked, further sensitive actions are blocked until re‑auth per policy, and an audit record is written with action=lock, reason, message, actor, and timestamp Given a selected active session with device online When I click Terminate, enter reason and optional message, and confirm Then the session is ended within 3s, tokens are invalidated, the device shows the message, the session disappears from the active list within 5s, and an audit record is written with action=terminate Given a selected session is offline and offline allowance > 0 When I issue Terminate Then the command is queued with status=Pending Delivery, visible in the session detail, and it is delivered and enforced within 5s of the device reconnecting; if the device does not reconnect within the allowance window, the session is auto‑terminated at ttl_expires_at Given I lack the permission session:write When I attempt to lock or terminate a session Then the UI disables the action and the API returns 403 forbidden

Given I have OAuth2 token with scopes policy:read policy:write session:read session:write When I call GET /v1/policies?site=East%20DC&role=Packer Then I receive 200 with a paginated list matching the UI grid and JSON schema policy.v1 Given I create or update a policy via POST /v1/policies or PUT /v1/policies/{id} with the same values that pass in the UI When I send the request with If‑Match (for updates) Then I receive 201/200 and the saved resource matches field‑for‑field with the UI; invalid combinations return 422 with structured error codes identical to UI validation Given I call POST /v1/sessions/{id}:lock with Idempotency‑Key=abc123 and a JSON body {reason, message} When I retry the same request within 24h Then I receive the same result (status and response body) and no duplicate audit entries are created Given I attempt to update a policy without policy:write scope When I call PUT /v1/policies/{id} Then I receive 403 forbidden; exceeding rate limits returns 429 with Retry‑After header

Given I create, update, apply a preset to, or bulk‑edit a policy via UI or API When the operation completes Then an immutable audit event is recorded with fields: event_id, actor_id, actor_type (user/api_token), site, role, policy_id, action (create|update|delete|preset_apply|bulk_update), reason (optional), before, after, preset_name/version (if any), bulk_operation_id (if any), timestamp (UTC ISO8601), ip; events are append‑only with a previous_hash to provide a verifiable chain Given audit events exist When I filter by site="East DC", actor_id, action, and time range Then results are returned within 2s for up to 10k events and can be exported as CSV or NDJSON; the export includes a header row (CSV) and preserves field types (NDJSON) Given I request audit logs via API When I call GET /v1/audit/policies?site=East%20DC&page_size=500&cursor=... Then I receive 200 with stable pagination, and events are retained for at least 365 days; attempts to modify or delete audit events are rejected with 405 method not allowed

Given a shipment is in picked-up state and a protected action Void Label is initiated And User A is authenticated with permission Void:Initiate When User A scans and selects a valid reason code Then the system creates a pending approval capturing shipment/order id, action type, reason code, user A id, session id, device id, station id, and timestamp And the UI disables execution of the change and indicates pending second approval When User B scans within the configured approval window and is distinct from User A by account, session, and device And User B has permission Void:Approve Then the system commits the void, cancels the carrier label, and updates shipment state And the audit log records both scans with all captured metadata And the shipment timeline shows a single Void Completed event

Given a pending approval created by User A from session S and device D When a second scan is attempted by the same user account as User A Then the system rejects the scan with error Second scan must be a different user And the pending approval remains pending and no changes are committed When a second scan is attempted from a different account but from the same session S or device D Then the system rejects the scan with error Second scan must be a different device/session And the pending approval remains pending and no changes are committed

Given workspace rule Second approval must be Supervisor is enabled And a protected action Duty Term Flip is initiated and the first scan by User A is accepted When User B performs the second scan and holds the Supervisor role and permission DutyTerms:Approve Then the change is committed and the audit log indicates supervisor approval When User B performs the second scan without the Supervisor role Then the system rejects with error Supervisor approval required And no changes are committed and the pending approval remains until timeout or valid supervisor scan

Given a pending approval exists with timeout window T configured When T elapses without a valid second scan Then the system automatically expires the pending approval and restores the original state And the UI shows Approval expired and clears pending indicators And subsequent API confirm attempts for the expired approval return an explicit Expired error When a valid second scan occurs before T elapses Then the approval proceeds and the expiration timer is cleared

Given a client attempts to commit a protected action via API with only a first‑scan approval reference Then the API responds 403 Forbidden indicating a second approval is required When the client submits a second‑scan confirm referencing the same approval And the second scan is from a distinct user identity and device And both users satisfy role and permission checks for the action Then the API responds 200 OK and commits the change And the response includes approval id, action id, user a id, user b id, and committed at timestamp When the second‑scan confirm is retried after commit Then the API responds 409 Already approved and no duplicate side effects occur

Given the first scan is accepted Then within 500 ms the workstation plays the configured audible cue once And a yellow banner Pending second approval with a countdown timer is displayed And action controls are disabled at the station When the second scan is accepted Then within 500 ms a success chime plays and the banner turns green with Change committed And pending indicators are cleared When any validation fails including same user, invalid permission, or expired approval Then an error tone plays and a red banner shows the specific error message And no partial changes are applied

Given a pending approval exists for a protected action When duplicate first‑scan messages are received from User A due to retries Then only one pending approval record exists for the action When multiple second‑scan events are received for the same pending approval Then at most one commit occurs And subsequent second‑scan attempts receive Already approved and no side effects And carrier calls and shipment state transitions execute exactly once

Given an admin defines an active Two-Scan policy scoped to a specific warehouse, channel, carrier, shipment value threshold, destination (domestic/international), action type, SKU/HS category, and order age When a shipment/action meets all configured conditions Then the system flags the action as Two-Scan Required and blocks completion until approvals are satisfied Given a shipment/action that does not meet the policy conditions When the action is attempted Then the action proceeds without requiring Two-Scan Given multiple active policies match a shipment/action When evaluating requirements Then the system applies the most restrictive outcome (Two-Scan required if any matched policy requires it) and records the matched policy IDs in the audit log

Given a policy requires Two-Scan with Supervisor as the second approver When User A completes the first scan Then any attempt by User A to complete the second scan is rejected with an error indicating a distinct user is required Given the second scan is attempted by a user without the Supervisor role under such a policy When they scan Then the system rejects the attempt and prompts for a Supervisor Given the second scan is completed by a Supervisor distinct from the first approver within the allowed time window When they confirm Then the action is approved and unblocked, and both user IDs and roles are recorded in the audit log

Given a policy sets the maximum time between scans to 15 minutes When the second scan occurs more than 15 minutes after the first Then the approval session expires, the action remains blocked, and a new first scan is required Given the second scan occurs within 15 minutes of the first When it is confirmed Then the approval completes and the action proceeds Given an approval session expires When viewing the approval state Then the UI/API indicates Expired with timestamps and the policy version in the audit log

Given a policy is configured to apply only during business hours (e.g., Mon–Fri 08:00–18:00 warehouse local time) When a targeted action occurs within business hours and matches the policy Then Two-Scan is required and enforced Given the same policy and a targeted action occurs outside business hours When the action is attempted Then the policy does not enforce Two-Scan and the audit log notes bypass reason Business Hours Not Applicable Given a shipment is flagged as a Test Order and the policy exceptions include Test Orders When a targeted action is attempted Then Two-Scan is bypassed and the audit log records the exception type and policy ID

Given an admin configures a reason code catalog with active codes and optional required notes When the first approver scans Then they must select a valid reason code (and enter notes if required) before proceeding Given the second approver scans When confirming Then they must select a reason code (which may differ from the first) and any required notes; otherwise the confirmation is blocked Given the reason code catalog is updated (add, deactivate, edit labels) When changes are saved Then updates apply to new approval sessions only, previous sessions retain the original catalog version for audit traceability

Given Policy v2 is created in Draft status When sandbox testing is run against a selected historical order set Then the system reports which actions would require Two-Scan under v2 versus the current version, including counts and match reasons Given v2 is set to a gradual rollout of 10% per warehouse When eligible shipments are processed Then 10% are governed by v2 and 90% by the current version, with the applied policy version recorded per action Given rollout is increased to 100% and v2 is promoted to Active When activation occurs Then the previous version is archived and audit visibility shows version history and the activation timestamp

Given a matching active policy requires Two-Scan When a critical change is initiated from the shipment detail view, batch tools, or public API Then the system creates an approval session, blocks the change, and displays/returns a standardized Two-Scan Required state including approval_session_id and required roles Given the first scan is initiated via API by User A and the second via API by User B (distinct) When both scans complete within the allowed time window Then the change is applied and the API returns success with audit IDs and policy version Given a batch includes shipments with mixed requirements When the batch is executed Then non-requiring items complete, requiring items move to a Pending Approvals queue, and a summary shows counts by outcome and reasons

Given a protected action requiring two distinct scans When both scans are completed and the action is committed Then the system writes exactly one append‑only audit entry linked to the previous entry via previous_hash and entry_hash And the entry includes: action_type, channel, pre_change, post_change, reason_code, policy_version, requester_user_id, requester_role, requester_timestamp, requester_device_id, requester_ip, approver_user_id, approver_role, approver_timestamp, approver_device_id, approver_ip, commit_timestamp, workstation_id And attempts to modify or delete any audit entry via UI, API, or DB migration interfaces are blocked with 403/READ‑ONLY errors and a separate audit event is recorded And recomputing entry_hash = H(previous_hash || canonical_payload) matches the stored entry_hash

Given audit entries exist for multiple users, actions, channels, and dates When a user filters by user_id, action_type, channel, and a UTC date range Then only matching entries are returned, sorted by commit_timestamp desc by default And the result count and page totals are accurate for page sizes 25, 50, and 100 And each row displays entry_id, action_type, requester_user_id, approver_user_id, commit_timestamp, channel, and an icon/link to view full diffs And the detail view renders the complete pre_change and post_change diffs without truncation for payloads up to 256 KB within 2 seconds for datasets up to 50k entries

Given a user has applied any combination of filters to the audit log When the user exports to CSV or JSON Then the file contains exactly these headers/keys in order for CSV and present for JSON: entry_id, previous_hash, entry_hash, action_type, channel, pre_change, post_change, reason_code, policy_version, requester_user_id, requester_role, requester_timestamp, requester_device_id, requester_ip, approver_user_id, approver_role, approver_timestamp, approver_device_id, approver_ip, commit_timestamp, workstation_id, status And the export reflects only the filtered result set and preserves UTC timestamps in ISO‑8601 with Z suffix And CSV is UTF‑8 with RFC 4180 quoting; JSON is UTF‑8, pretty=false, one object per array element And exports up to 250k rows stream without timeout and include a SHA‑256 checksum file of the content And unauthorized users receive 403 and no file is produced

Given webhook delivery is enabled with a configured endpoint and shared secret When a new audit entry is committed Then ParcelPilot POSTs a JSON payload of the entry to the endpoint within 5 seconds with headers X‑PP‑Signature (HMAC‑SHA256 over body using the secret), X‑PP‑Timestamp, and Idempotency‑Key And receivers can verify the signature and timestamp (±5 min clock skew) to accept And transient 4xx/5xx responses trigger exponential backoff retries for up to 24 hours with at‑least‑once delivery semantics And duplicate deliveries carry the same Idempotency‑Key And delivery outcomes (success/failure, last_attempt_timestamp, last_status) are visible in an admin log, with failures raising an alert

Given an account admin configures an audit retention period in days When the retention is set below the system minimum or applicable policy minimum Then the change is rejected with a validation error and no update occurs When retention is valid and saved Then the change is recorded in the audit log with old_value, new_value, actor, timestamp, and policy_version And entries newer than the current retention window cannot be deleted by any user or API; delete attempts return 403 and are themselves audited And a nightly job permanently purges entries older than the retention window, emitting a summary audit event (range, count) And exports remain available for all entries within the retention window

Given a protected action configured for two‑scan approval and optional supervisor‑only enforcement When the requester performs the first scan and the approver performs the second scan Then the system validates that requester_user_id != approver_user_id And, if supervisor‑only is enabled, approver_role is in the configured supervisor roles; otherwise the second scan is rejected and audited And the committed audit entry captures both scans with distinct user IDs, roles, timestamps, device IDs, IPs, and the policy_version that evaluated the rule And if the second scan is not completed within the configured timeout, an aborted audit entry is recorded with status=aborted and no post_change is applied

Given a critical action enters a pending state after the first scan by User A And eligible approvers are determined by current policy When the pending state is created Then in-app notifications (toast + inbox item) appear for all eligible users within 3 seconds And an email is sent to each eligible user within 60 seconds containing action summary, expiry time, and one-click Accept and Decline links And a Slack message is sent to each eligible user (DM or configured channel) within 15 seconds containing action summary, expiry time, and one-click Accept and Decline buttons And deep links/buttons carry a signed single-use token that expires at action expiry and prevents replay And notifications are not sent to ineligible users and are suppressed for opted-out channels per user settings And all notification deliveries and failures are recorded with timestamp and channel in the audit log

Given a pending action is visible to multiple eligible approvers When Approver B clicks Claim Then the action is locked to Approver B and displays "Claimed by B" to all users within 2 seconds And Accept/Decline controls are disabled for non-claimants with an explanation tooltip And Approver B can Release claim, after which the lock clears and controls re-enable for others within 2 seconds And a Supervisor can Reassign the claim to another eligible approver; both users receive notifications of the change And if no decision is made by the claimer within 5 minutes, the claim auto-expires and the item returns to unclaimed state without changing the original approval expiry And all claim/assign/release events are captured in the audit log with actor, timestamp, and reason

Given a pending action has a 15-minute time-to-live (TTL) When any approver or supervisor views the item in-app Then a countdown timer shows server-synchronized remaining time, updating at least once per second And emails and Slack messages display the absolute expiry timestamp in the recipient’s local timezone When the TTL elapses without an Accept Then the system expires the pending action within 10 seconds, reverts to the pre-pending state, and prevents further one-click actions via stale links And the initiator and eligible approvers receive an expiry notification in all enabled channels And an audit log entry records Expired status with timestamps, initiator, eligible recipients, and reason = "Timeout"

Given an escalation rule is configured to escalate after 10 minutes without decision And an on-duty supervisor roster is active When a pending action reaches 10 minutes without Accept or Decline Then escalation notifications are sent to on-duty supervisors only, with an Escalated badge and the ability to Claim/Accept/Decline And previously notified approvers retain visibility; their controls remain enabled unless policy overrides on escalation And escalation respects schedule windows and role permissions and does not reset expiry by default And if a supervisor completes the action, all channels reflect the final outcome within 3 seconds And all escalation attempts and notifications are logged with target list and delivery results

Given a supervisor opens the Approvals Queue view When items are created, claimed, reassigned, accepted, declined, escalated, or expired Then the queue reflects changes within 2 seconds without manual refresh, including counts, state badges, and assignees And the supervisor can filter by action type, station, sales channel, age, claimed status, and assignee; filters apply within 500 ms And sorting by age and priority is available and stable across refreshes And selecting an item opens a detail pane with full audit trail and one-click actions when permitted by role And real-time updates do not overwrite the supervisor’s current selection or applied filters

Given an eligible approver chooses Decline on a pending action When the approver submits Decline with an optional reason (required if policy enforces), limited to 280 characters Then the system cancels the pending action, reverts any provisional effects within 10 seconds, and blocks downstream processing dependent on the change And the initiator receives Declined notifications across enabled channels including the provided reason And stale Accept links/buttons are rejected with an "Already Declined" message and logged as no-ops And the audit log records decision, approver identity, reason (if provided), timestamps, and policy reference

Given approval policies scope eligibility by action type, warehouse, station, sales channel, and role When a pending action is created Then only users matching the policy receive notifications and see one-click controls in app, email, and Slack And one-click actions validate permissions at execution; if the user lacks permission or scope, the action is blocked and the attempt is logged with a 403-equivalent outcome And cross-tenant users never receive notifications or gain access through shared channels And channel delivery preferences (e.g., email off, Slack on) are honored per user and policy And policy changes affect only newly created pending items unless an admin triggers re-evaluation for existing items

Given a batch of N orders (N >= 2) where N <= configured batch_limit and all items share the same action type (e.g., Bulk Void, Address Correction) and the same destination type (all domestic or all international) and no high-value or other risk flags are present And user A is authenticated and completes the first scan to initiate the batch approval When user B (user_id != user A) completes the second scan within the configured approval window Then the system approves the entire batch with a single two-scan and applies the action to all items And the system rejects any attempt where user B equals user A with an error "Second scan must be a different user" And the success summary reports 100% processed with counts matching N And audit entries are written for both batch and each item including batch_id, action_type, item_ids, user_ids [A,B], timestamps, and result=success

Given a batch containing at least one anomalous item (e.g., mix of international and domestic, item value >= high_value_threshold, duty term flip, or differing action types) And user A completes the first scan When user B performs the batch-level second scan Then the system requires per-item secondary scans only for items flagged as anomalies and blocks batch completion until each flagged item receives a per-item second scan by a user different from the first-scan user And non-flagged items are approved by the batch-level second scan without additional scans And the UI displays a list of flagged items with reasons and remaining count, updating in real time as items are confirmed or excluded And the final summary shows separate counts for auto-approved vs per-item-approved items

Given a batch review for a multi-order address correction or duty term change When the review screen loads Then the risk summary displays category counts (international mix, high-value, duty term change, address country change, hazardous) that exactly match the underlying flagged items And each item row displays per-field diffs of pending changes (e.g., street1, city, postal_code, country, incoterm, declared_value) with old -> new values and currency for monetary fields And exporting the review as CSV or JSON reproduces identical diffs and risk flags for all items And any item with no change shows "No diff" and is excluded by default from approval

Given a batch containing both anomalous and non-anomalous items When the user selects "Split flagged items" in the review UI Then the system creates a new batch containing all flagged items and leaves the original batch with only homogeneous items, updating both batch summaries and IDs And when the user selects specific items and chooses "Exclude" Then those items are removed from the current batch without altering their underlying order/shipment state and are listed as Excluded with reasons And all split and exclusion actions are logged with initiating user, timestamps, original batch_id, new batch_id (if any), and before/after item lists

Given configured batch_limit = 1000 and a batch of 1000 items under representative load When the batch review is opened Then first contentful rendering occurs within 1.5s and full review render completes p95 <= 3.0s, p99 <= 5.0s And pagination or virtualized scrolling is used so UI remains responsive with frame rate >= 50 FPS during scroll And when a homogeneous batch receives the second scan Then batch approval completes p95 <= 7.0s, p99 <= 12.0s with zero timeouts and error rate < 0.5% And peak memory usage attributable to the operation remains < 500 MB and server CPU utilization < 80% And operations exceeding 2.0s display a progress indicator with current counts processed/remaining

Given any batch action attempt (approve, reject, split, exclude) completes When audit records are written Then batch-level and item-level logs include: batch_id, correlation_id, action_type, requested_by, approver(s), roles, timestamps, item_ids, per-item diffs, risk summary, decision, and error details if any And records are immutable, signed with a hash of the batch contents, and store the policy version used And logs are retrievable by batch_id or correlation_id within 2 seconds and exportable as JSON within 60 seconds for batches up to the batch_limit And a recomputed content hash matches the stored hash, otherwise an integrity alert is raised

Given organization policy "Two-Scan: Supervisor Required" is enabled for critical batch actions (e.g., post-pickup voids, cross-country address edits, duty term flips) And user A (non-supervisor) completes the first scan When user B attempts the second scan Then the system accepts the second scan only if user B has role=Supervisor and user B != user A And the system rejects second scans from non-supervisors with message "Supervisor approval required" and rejects scans from the same user with "Distinct users required" And audit logs include the policy ID and role information used in the decision

Given a station with a registered USB HID barcode scanner and the approval dialog focused When an approver scans a valid ParcelPilot credential code (QR or Code 128) Then the system decodes and validates within 300 ms, plays a success tone, and displays a green confirmation with masked identity Given an invalid, expired, or unrecognized code is scanned When processed Then the system responds within 500 ms with an error tone, red banner stating the reason, and no approval is recorded Given the first approval has been captured When the same HID device provides input for the second approval Then the system rejects it with "Second approval must be from a different device/session" and logs the attempt Given a scan includes known HID prefixes/suffixes When received Then the system normalizes input and successfully parses supported codes Given any scan attempt occurs When logging the event Then the audit entry includes timestamp, station ID, device fingerprint, resolved user ID (if valid), and outcome; raw credential secrets are never persisted

Given a workstation with an available camera When a user initiates camera scanning Then the app requests permission once, shows a live preview, and decodes supported codes (QR, Data Matrix, Code 128) at 10–60 cm within 800 ms under 100–500 lux Given camera permission is denied or no camera is present When scanning is initiated Then the system offers immediate fallback to USB HID or username+PIN without blocking Given a successful camera scan occurs When decoded Then the same success/error tones and visual indicators as HID are used, and torch/autofocus controls are available where supported Given camera scanning is in use When processing frames Then image data stays on-device; only decoded payloads are handled by the app

Given a user presents a ParcelPilot mobile app QR credential When it is scanned by HID or camera Then the payload signature is validated against server or local cache per policy, is within its validity window, and maps to an active user Given a mobile QR has been revoked or rotated When scanned Then validation fails with "Credential revoked/rotated" within 500 ms and the attempt is audited Given network connectivity is available When a valid mobile QR is scanned Then the local cache for that user is refreshed within 1 s without blocking the approval outcome

Given a station without an available scanner When an approver selects "Use username + PIN" Then the username is entered, the PIN input is obscured, and submission is only allowed with a 4–8 digit PIN Given incorrect credentials are entered When attempts are made Then rate limiting applies: maximum 5 failed attempts per user and per station in 15 minutes with exponential backoff (10s, 30s, 60s, 120s), and the UI displays remaining wait time Given the failure threshold is exceeded When further attempts occur Then the user or station is locked for 15 minutes, an alert/audit record is generated, and other users may still authenticate on that station Given credentials are correct When submitted Then acceptance feedback (tone + green confirmation) is shown within 300 ms and one approval is recorded

Given organization policy AllowCachedApprovals with TTL 24 hours When the station is offline Then approvals succeed only if each approver’s credential exists in the encrypted local cache and is not older than 24 hours; stale or missing cache entries cause rejection with a clear message Given organization policy RequireOnlineApprovals When the station is offline Then all approval attempts are blocked with "Approvals require online verification" and no partial approval state is stored Given offline approvals were captured under AllowCachedApprovals When connectivity is restored Then all offline approval events are synced to the server within 60 seconds using original timestamps and any conflicts are flagged for review Given local caches exist on a station When stored at rest Then they are encrypted and hardware-bound; an admin cache clear takes immediate effect and disables offline approvals until refreshed

Given a critical change requires Two‑Scan Approvals When the first approval is captured Then a 5‑minute window starts for the second approval; after 5 minutes the first approval expires and is removed Given the second approval is attempted When the same user ID, same session ID, or same device fingerprint as the first approval is detected Then the attempt is rejected with an explanatory message and the violation is audited Given supervisor‑only second approver rules are enabled When the second approval is scanned Then validation passes only if the user has the Supervisor role; otherwise it is rejected Given two distinct users from distinct devices/sessions approve within the window When both validations pass Then the critical change is executed and a single audit record links both approvals with user IDs, device fingerprints, station IDs, timestamps, and outcome

Given an administrator selects a set of users When Generate Badges is invoked Then a printable PDF (A4/Letter) is produced within 10 seconds containing scannable QR codes, user names, roles, and layout safe for common label printers Given an administrator rotates a user’s QR secret When the rotation is confirmed Then new QR payloads become valid immediately and old payloads become invalid within 5 minutes across all stations; attempts using old payloads are rejected and audited Given a bulk rotation is performed When completed Then no active approval session is terminated, unaffected users continue uninterrupted, and scanning newly printed badges works without requiring user re-login Given provisioning or rotation actions occur When auditing Then entries include admin ID, affected users, action type, timestamp, and reason; raw secrets are never displayed or logged

Given a user initiates an override of dimensions, weight, carrier/service, or shipping cost in batch, single-order, or scan-to-pack flows When the Override Reason Capture modal opens Then the primary action to proceed is disabled until a reason code is selected and all required validations for that code (note length, required photo) pass And attempting to continue, print, or complete packing without a valid submission is blocked with an inline error message and no state change to the order And Cancel closes the modal and discards the override, restoring the system recommendation And upon valid submission, the override is applied and the user is returned to the prior flow state without losing selection or scroll position

Given an override type is known (dimensions, weight, carrier/service, cost) When the modal renders Then only reason codes tagged for that override type are displayed And typing in the search input filters results within 150 ms per keystroke And Up/Down moves the active selection, Enter selects the highlighted code, Tab navigates through focusable fields, and Escape cancels the modal And a No results message appears when the filter returns zero codes And the first visible code is focused by default unless a default code is configured, in which case the default is focused

Given a reason code configured as Note required with minimum 15 characters When the user submits with a note under 15 non-whitespace characters Then submission is blocked and a validation message indicates the remaining characters required Given a reason code configured as Photo required When the user attempts to submit without a photo or with an unsupported format Then submission is blocked and a validation message indicates accepted formats and size limits And accepted photo formats are JPG and PNG up to 10 MB, with a single attachment permitted And notes accept up to 2000 characters, preserve line breaks, and trim leading/trailing whitespace

Given the modal is open When the user attaches a photo from camera or file picker Then a thumbnail preview and filename are displayed with an option to remove or replace the photo And the app respects EXIF orientation so previews display correctly And the attachment upload is deferred until submission; removing the photo cancels any pending upload And on successful submission the attachment is linked to the override event and retrievable via attachment ID

Given a valid submission When the user submits the modal Then an immutable audit log record is persisted within 1 second containing: event ID, order ID, order number, list of item SKUs and quantities, override type, pre- and post- values, selected reason code ID and label, note, photo attachment ID (if any), lane, client, workstation ID, user ID, and UTC timestamp (ms precision) And the record is retrievable by order ID and event ID and passes schema validation (all required fields non-null) And an analytics event ReasonOverrideSubmitted is emitted within 2 seconds with the same payload plus environment metadata (tenant ID, app version) and maintains order relative to the audit log event And PII in the analytics payload is limited to user ID and workstation ID; customer shipping addresses are excluded

Given the workstation is offline at the time of submission When the user submits a valid modal Then the submission is queued locally with all fields and attachment data, the user may continue their flow, and a visible banner indicates the count of pending submissions And queued submissions survive app reloads and workstation restarts And upon reconnection, queued items auto-sync in FIFO order; on success the banner decrements; on failure an error is shown with Retry and Discard options And idempotency is enforced via a client-generated UUID to prevent duplicate events on retry

Given a default reason code is configured for the detected override type and its validation rules require no note or photo When the modal opens during batch, single-order, or scan-to-pack flows Then the default reason is preselected and focus is on the primary action And the user can submit and continue with at most one keystroke (Enter) beyond the existing flow And invoking the modal and submitting does not deselect other orders in a batch or change the current scan-to-pack session state

Given I am an authenticated admin and select a specific client, warehouse, and workflow scope When I create a reason category and a reason code with applicability (weight/dimensions ranges, carrier/service list, address attributes, cost override), flags (note required, photo required), display order, active state, and descriptions Then the category and code are persisted with all fields, appear in the configured display order, and are retrievable via UI and API in that scope And validation rejects missing required fields, invalid ranges (e.g., min>max), or unknown carriers/services And codes cannot be hard-deleted if referenced by any historical event; attempting delete returns a blocked error and suggests deactivation instead And deactivated codes no longer appear in selectable lists but remain queryable for reporting and history

Given a reason code exists with an active version When I create a new version with an effective start date/time in the future Then the system prevents overlapping effective windows, requires a change note, and stores the new version in history And only the version whose effective window includes now() is considered current in selection APIs/UI And an audit trail records who changed what and when, and version history is viewable and exportable And attempting to edit a historical version creates a new version instead of mutating the stored historical record

Given localization strings are required for the default locale When I add localized labels and descriptions for additional locales (e.g., en-US, es-ES) Then the system validates locale codes, enforces presence of default locale, and stores all translations per version And selection and read APIs return the label for the requested locale with fallback to default when missing And export/import includes all locale strings with their locale codes per version

Given API consumers need to manage the taxonomy programmatically When they call endpoints to list, create, update, deactivate, and view version history filtered by client/warehouse/workflow Then responses are paginated, filterable, and include all fields (applicability, flags, display order, active state, effective dates, localization, version metadata) And optimistic concurrency is enforced via ETag/version; stale updates return 409 And invalid payloads return 400 with field-level errors; violations of referential integrity return 422; unauthorized calls return 403

Given an admin needs to bulk edit the taxonomy for a scope When they export the current taxonomy Then the system produces CSV and JSON files that round-trip all fields including versions and localizations When they import a modified file in dry-run mode Then the system performs full validation, reports row/field errors and potential collisions, and makes no changes When they import in apply mode with a valid file Then changes are applied atomically (all-or-nothing), with a summary of created/updated/deactivated records and any new versions created

Given a seed library of best-practice reason categories and codes is provided When an admin chooses a scope and selects seeds to import Then the system previews the items, detects duplicates by stable key, and only creates missing items with effective now() And imported seeds are tagged as seeded, can be edited or versioned later, and include localization provided by the library And re-running the import is idempotent and does not create duplicates

Given historical override events must not change when labels or flags are updated later When a user records an override event selecting a reason code and providing required note/photo Then the event stores the reason code ID, the localized label text and flags as a snapshot at event time And when the reason code is renamed, re-localized, re-ordered, versioned, or deactivated later Then the historical event continues to display the original snapshot values in UI and API, and attempts to backfill or retroactively alter snapshots are rejected

Given a user is performing an override and opts to attach evidence photos When the user captures via device camera or uploads from desktop up to three images in JPEG, PNG, or HEIC formats Then the client compresses each image on-device to ≤ 2 MB and ≤ 3000 px on the longest side before upload And any file that exceeds limits or is in an unsupported format is blocked with an inline error explaining allowed formats and limits And selecting a fourth image is prevented with a message indicating the 3-photo limit And if no photos are attached, the override flow continues without error

Given an attached photo contains a shipping label or text When the system processes the image for sensitive data Then all detected barcode regions (1D and 2D) and PII patterns (names, phone numbers, emails, street addresses, tracking numbers) are blurred or masked before the image is persisted And only the redacted version is stored and displayed; the original unredacted image is never written to persistent storage And the redacted preview is shown in the UI before submission And at 200% zoom, redacted regions are not human-readable And redaction processing completes within 3 seconds per image on a typical workstation/mobile device

Given an evidence photo is saved Then it is stored in object storage with server-side encryption at rest (AES-256 or equivalent) And access to the asset requires a time-limited signed URL that expires in ≤ 10 minutes from issuance And only users with the RBAC permission "view_evidence" can request signed URLs; others receive HTTP 403 And all asset accesses (create, view, delete) are audit-logged with user ID, override ID, timestamp, and IP And direct bucket paths are not publicly accessible

Given a photo is attached during an override When the upload completes Then the photo record stores the EXIF DateTimeOriginal value if present; otherwise records the server upload timestamp And the asset is linked to the override event ID, reason code, actor user ID, and created-at timestamp in the database And the stored metadata is retrievable via API and visible in the UI details panel

Given one or more evidence photos have been attached When the override details panel is displayed Then redacted thumbnails (≤ 256 px longest side, ≤ 50 KB) are rendered within 1.5 seconds on a 10 Mbps connection And clicking a thumbnail opens a viewer that supports pan and zoom up to 400% without pixelation beyond the redaction masks And the viewer loads the full-size redacted image via a signed URL without exposing the direct storage path

Given a client retention policy is configured (e.g., 90 days) When an asset reaches its retention age Then a scheduled purge deletes the file from object storage and removes its database linkage within 24 hours And any subsequent access attempts return HTTP 404 and previously issued signed URLs are invalid And an immutable audit log entry records the purge with asset ID, client ID, and timestamp

Given a user begins uploading one or more evidence photos When network connectivity is interrupted during transfer Then the uploader retries with exponential backoff and resumes from the last confirmed chunk without restarting the upload And if connectivity is restored within 24 hours, the upload completes successfully without user re-selection of files And progress indicators reflect chunked progress and retries And duplicate uploads caused by retries do not create duplicate assets in storage or the database

- Given a user with Analytics:View in Tenant A and override events across lanes/SKUs/clients/users/types, When they open the Reason Trends dashboard and set a date range, Then metrics include only Tenant A events within the inclusive range using the tenant’s reporting timezone. - Given the selection, When the dashboard loads, Then for each chosen grouping it shows: total overrides, unique orders affected, rate per 100 fulfilled orders (overrides/orders*100 rounded to 2 decimals), top reasons sorted by count, and estimated impact (sum postage_delta; sum processing_time_delta_seconds). - Given events on multiple days, When the user selects Daily/Weekly/Monthly interval, Then the time series buckets by that interval and displays delta vs the previous equivalent interval as absolute and percent with directional indicators. - Given no events match filters, When the dashboard loads, Then an empty state appears with “No override events” and KPIs show 0. - Performance: For up to 100k events in range, initial render P90 <= 4s and subsequent filter change P90 <= 2s. - Calculations: Rates use fulfilled order counts from the same filters and interval; events without impact fields are excluded from impact sums and counted in an “impact_unknown” metric.

- Filters available for date range, lane, SKU, client, user, override type, and reason code; multi-select and search supported; filters apply consistently across all widgets and exports. - When baseline = Previous Period, Same Period Last Year, or Custom Saved Baseline, Then each KPI and chart shows baseline value and delta (absolute and percent) with tooltips describing denominators; NA shown when baseline denominator is zero. - When filters are modified, Then baseline recalculates within 1s from cached data and retains the same filter set unless changed. - A Clear All action resets to tenant default (Last 7 days, no additional filters).

- When a user clicks a chart point/bar/row, Then a detail view opens filtered to that cohort and date range. - Each row shows: event_id, timestamp_utc, order_id, shipment_id, lane, sku, client_id, user_id, override_type, reason_code, reason_note (<=500 chars), postage_delta, processing_time_delta_seconds, and evidence thumbnail(s) if present. - Clicking a thumbnail opens full-resolution media in a modal with download; access requires Evidence:View permission; without it, a masked placeholder is shown. - Detail view supports sort (default timestamp desc), pagination (page size 50), and CSV export of the filtered set; all respect current filters and permissions. - Rows include deep links to Order and Shipment pages opening in a new tab with context preserved.

- A user can save the current dashboard state (filters, groupings, interval, visual choices) as a Saved View; name length 3–50 chars; unique per user; validation errors shown inline. - Saved Views are Private by default; owner/Admin can share with roles or specific tenant users; recipients get read-only access; only owner/Admin can modify. - Applying a Saved View restores the exact state in under 1s (from cache) and updates the URL with a shareable view id. - A user can set one Saved View as personal default; the dashboard loads it on first open; users can unset/change defaults. - Create/update/delete of Saved Views are recorded in audit logs with actor and timestamp.

- From any table/chart, user can export Aggregates CSV and Details CSV; rows reflect active filters; timestamps exported in UTC ISO 8601; numeric fields use dot decimal. - Aggregates CSV columns: dimensions, date_bucket (if applicable), overrides_count, unique_orders, rate_per_100_orders, top_reason, postage_impact_total, processing_time_impact_total_seconds. - Details CSV columns: event_id, timestamp_utc, tenant_id, order_id, shipment_id, lane, sku, client_id, user_id, override_type, reason_code, reason_note, evidence_urls, postage_delta, processing_time_delta_seconds. - Evidence URLs are signed tenant-scoped links with >=24h TTL; unauthorized access attempts are denied. - API endpoints provide equivalent datasets with cursor pagination, analytics:read scope (OAuth/JWT), rate limit 60 req/min, and P95 <= 1s for pages <=10k rows; OpenAPI docs published. - Exports >1M rows run asynchronously; completion notifications are sent; exported row counts match UI counts exactly.

- Users can schedule a digest from a Saved View selecting channel (Email/Slack), frequency (daily/weekly/monthly), send time, and timezone. - Digest content includes: total overrides, rate per 100 orders, top 5 reasons with counts and WoW/MoM deltas, and impact totals; includes deep link back to the Saved View. - Slack digests use Block Kit with a compact table; Emails render correctly in dark/light mode and meet WCAG AA for contrast; all images include alt text. - Delivery SLO: 95% of digests sent within 10 minutes of scheduled time; failures auto-retry up to 3 times with exponential backoff; outcomes visible in an Activity log. - Permission checks at send time ensure all recipients belong to the tenant and have Analytics:View; otherwise sending is blocked with an explicit error. - Users can pause/resume/cancel schedules; changes apply before the next run; actions are audit logged.

- All queries, exports, and evidence links are scoped by tenant_id; cross-tenant requests via UI or API return no data; automated tests verify Tenant B cannot access Tenant A data. - RBAC: Viewer sees aggregates only; Analyst sees details and evidence; Admin manages shares/schedules; UI reflects capabilities and APIs enforce scopes/scopes are validated. - Data freshness: dashboards reflect events within 15 minutes (P95) of occurrence; a visible freshness indicator shows last updated timestamp. - Integration: data is stored/queried through the existing analytics stack using the platform’s standard auth, catalog, and query engine; lineage is captured in the existing metadata catalog; no new external credentials are required. - Reliability: dashboard P95 load <=4s for 100k events, API uptime >=99.9% monthly; incidents reported via the existing status page.

Given an override is submitted that changes one or more shipment fields (e.g., weight, dimensions, package_type, carrier, service, rate) When the event is written Then the record includes event_id, client_id, shipment_id, actor_id, actor_role, source_workflow (enum), device_id or user_agent, pre_values and post_values for all changed fields, reason_code_id, reason_label_snapshot, freeform_note, attachment_ids (0..n), created_at with timezone and >= microsecond precision And reason_code_id references an active reason code at time of event And reason_label_snapshot equals the reason label at time of event, unaffected by later changes And attachments (if any) store content_type in [image/jpeg, image/png, application/pdf] and size <= 10 MB each with SHA-256 checksum And writes without required fields are rejected with 400 and no record is created

Given any existing override event When a non-supervisor attempts to update any field on the event Then the operation is rejected with 403 and the stored event remains byte-for-byte unchanged When a supervisor adds a follow-up note Then a new immutable note row is appended with note_id, event_id, actor_id, created_at (tz, microsecond), and content And attempts to edit or delete a follow-up note are rejected (except via GDPR deletion workflow) And an audit entry is created for each rejected attempt with actor_id and timestamp

Given parallel creation of 1,000,000 events across 200 concurrent workers When events are persisted Then each event_id is unique (0 collisions) and conforms to UUIDv7 or ULID format And event_ids are monotonically increasing within the same worker process And a database unique constraint on event_id prevents duplicates And on collision, the client retries and succeeds within 3 attempts without partial writes

Given a dataset of >=50,000,000 events for >=500 clients When querying by client_id and created_at between T1 and T2 ordered by created_at desc Then p95 latency <= 800 ms and p99 <= 1.5 s When filtering additionally by source_workflow, actor_id, or sku_id Then p95 latency <= 1.2 s And explain plans confirm usage of compound indexes on (client_id, created_at desc), (client_id, source_workflow, created_at), (client_id, actor_id, created_at), (client_id, sku_id, created_at) And pagination supports stable, seek-based paging via (created_at, event_id)

Given an event is created Then it is published to the event stream within 2 seconds And delivered to the data warehouse with end-to-end p95 <= 2 minutes and p99 <= 5 minutes And delivery is at-least-once with idempotent deduplication on event_id And per-shipment_id ordering is preserved And failures are retried with exponential backoff up to 24 hours and alerting triggers if lag > 10 minutes

Given a client retention policy of N days (30 <= N <= 1825) When an event’s age exceeds N days Then raw event payloads (pre_values, post_values, notes, attachments) are purged while derived aggregates remain And purge jobs run daily and produce an auditable report of deleted counts by client When a client updates retention policy Then the new policy takes effect within 24 hours

Given a GDPR erasure request scoped to a data subject or shipment When the deletion job runs Then all freeform notes, attachment binaries, and attachment metadata that may contain personal data are irreversibly removed from primary storage within 72 hours And aggregated metrics (counts/sums by reason, lane, SKU, client, user) remain intact without retaining any PII And the change is propagated to the data warehouse within 24 hours and to searchable indexes within 24 hours And a non-PII audit log records request_id, actor, scope, timestamps, and outcomes And any future re-ingestion from backups re-applies the deletion before making data queryable

Given a user without the "Override:Shipment" permission in Client X, Warehouse Y, Workflow Step Z When the user attempts to initiate an override in that context (UI or API) Then the override action is blocked in the UI and the API returns 403 RBAC_DENIED with context details And label purchase remains disabled until a permitted user proceeds And an audit log entry is recorded for the denied attempt with user, role, context, and timestamp Given a user with the "Override:Shipment" permission scoped to Client X, Warehouse Y, Workflow Step Z When the user initiates an override in that exact context Then the override controls are enabled and the API allows the request to proceed to validation And the authorization decision reflects the user’s current role assignments on each request (no cached stale grants) Given an admin removes the permission from the user’s role When the user performs the next authorization-checked action in that context Then the system denies the action without requiring user logout or service restart

Given a policy that requires a reason code and note for overrides in Client A / Warehouse B / Step C When a user attempts to confirm an override Then the system requires selection of a valid reason code from the configured list and a non-empty note (1–500 chars) And the confirm action is disabled in the UI and the API returns 422 VALIDATION_ERROR with fields [reasonCode, note] until both are provided And upon submission, the audit log stores reasonCode, note, user, context, and timestamp Given an API client submits an override request without a reason code or with an invalid code When the request is processed Then the response is 422 with error REASON_REQUIRED or REASON_INVALID and no label is purchased Given an admin updates the available reason codes When a user opens the override dialog or calls the reasons endpoint Then the latest list is presented and only those codes are accepted

Given a policy that requires photo evidence for overrides in Client A / Warehouse B / Step C When a user attempts to confirm an override Then the system blocks confirmation until a photo is uploaded (PNG or JPEG, <=5 MB) And the UI shows a thumbnail preview and the API accepts a multipart/file or signed-upload reference And the audit log stores a secure reference to the photo, file type, size, user, context, and timestamp Given an API client submits an override without required photo When the request is processed Then the response is 422 with error PHOTO_REQUIRED and no label is purchased Given a photo upload fails validation (type/size) When the user submits Then the system displays a clear validation error and prevents continuation

Given a user with the "Bypass:Enforcement" supervisor permission When they initiate a bypass for the current shipment in Client A / Warehouse B / Step C Then they must enter a justification (1–500 chars) and a duration between 5 and 60 minutes And upon confirmation, the system records the bypass with scope, user, justification, start/end timestamps, and reviewer fields in the audit log Given a bypass is active for the current shipment When the user proceeds to purchase a label without providing otherwise required reason/photo Then the system permits the purchase within the bypass scope and window And displays (UI) or returns (API) metadata indicating BYPASS_APPLIED=true with expiry time Given the bypass duration elapses or the label is purchased (whichever comes first) When any further override action is attempted Then the bypass no longer applies and requirements are enforced again And an audit entry is recorded for bypass expiry Given a user without supervisor permission attempts to create a bypass When they submit the request Then the system denies the action with 403 RBAC_DENIED and logs the attempt

Given policies exist at client-level, warehouse-level, and workflow-step-level When evaluating requirements for a shipment in Client A / Warehouse B / Step C Then the effective policy is the union of applicable policies such that the most restrictive requirement is enforced (e.g., if any requires photo, photo is required) And the effective policy is displayed in the UI policy preview and available via API for the given context Given an admin changes a policy at any level When a new override session begins for the affected context Then the updated effective policy is applied to that session and recorded with a policy version in the audit log Given conflicting policy settings across levels When the system computes the effective policy Then the computed output is deterministic and identical across UI and API for the same inputs

Given required inputs (reason, note, photo) are missing per effective policy When a user attempts to purchase a label via UI Then the purchase button remains disabled and inline validation identifies the missing fields Given required inputs are missing per effective policy When a client attempts to purchase a label via API Then the system returns 422 with specific error codes [REASON_REQUIRED, NOTE_REQUIRED, PHOTO_REQUIRED] and no label is created Given all required inputs are provided and the user has permission When the label purchase is submitted via UI or API Then the purchase succeeds and the audit log contains the inputs, user, context, policy version, and outcome

Given audit entries for supervisor bypasses exist When an admin opens the Bypass Report and filters by date range, client, warehouse, and workflow step Then the report displays totals by user and overall counts, with columns: user, bypass count, average duration, first/last bypass timestamps And the counts reconcile with underlying audit entries for the same filters Given the admin exports the report When they choose CSV export Then the system generates a CSV with the same rows and columns as the on-screen report Given no bypasses match the selected filters When the report is run Then the system displays zero results with no errors

Given a packer triggers a reason-code override in scan-to-pack When the Reason Codes modal is opened Then the modal becomes interactive within ≤200 ms on reference devices and networks (p95 ≤250 ms, p99 ≤300 ms as measured by telemetry) And no network request blocks initial render; deferred content uses placeholders within ≤75 ms And opening the modal does not drop input events; the next keystroke is processed within ≤50 ms

Given a default reason rule is configured for the current context When a barcode mapped to the default reason is scanned or the mapped hotkey is pressed Then the default reason is preselected before user input And pressing Enter submits the modal with the preselected reason and required note, totaling ≤1 additional keystroke post-scan And ESC cancels without submission And HID scanners that append Enter still result in a single interaction to submit

Given a user interacts with the modal using keyboard only When the modal opens Then initial focus is set on the reason list And Tab/Shift+Tab navigate all interactive elements in logical order; Arrow keys move between reasons; Space toggles selection; Ctrl+Enter submits; ESC closes without submit And a visible focus indicator is present on every focusable element with contrast ≥3:1 against adjacent colors And screen readers announce modal title, selected state, reason count, and errors; labels and roles meet WCAG 2.1 AA

Given the account locale is set to a supported language (e.g., en-US, es-ES, fr-FR) When the modal, tooltips, and errors render Then 100% of user-visible strings are localized for that locale with correct pluralization and formatting And no truncated/overflowing text at 320px width or when system font scaling is 200% And missing keys fall back to en-US and are logged to telemetry once per session

Given the device is offline or connectivity is intermittent When the user provides required reason, note, and optional photo and presses submit Then the submission is enqueued locally within ≤50 ms and the packing workflow can proceed without blocking And the UI shows a non-blocking queued state with retry status; no modal re-entry is required And background sync retries with exponential backoff (initial 5 s, max 5 min) up to 12 hours, deduplicates by client ID, and preserves attachments up to 5 MB per item And upon reconnect, 99% of queued submissions succeed without user action; failures surface a dismissible alert with a one-click manual retry

Given telemetry is enabled When users open and submit the Reason Codes modal Then events are emitted for modal_open, modal_interactive, submit_attempt, submit_success, submit_fail, time_in_modal_ms, attachment_bytes, retry_count, offline_queued with session, user, client, SKU, and lane IDs (pseudonymized; no PII in payload) And 95% of events are delivered within 2 minutes; sampling rate ≥95% for performance metrics And dashboards show p95 modal_interactive ≤250 ms and submit_success rate ≥99.5% (online) and ≥98% (including offline queued within 12 hours)

Given a ledger chain for tenant T, entity_type "shipment", entity_id S with 10,000 contiguous events When the client calls POST /api/ledger/verify with {tenantId:T, entityType:"shipment", entityId:S, fromIndex:0, toIndex:9999} Then the API responds 200 within 300 ms and body.valid=true and body.startIndex=0 and body.endIndex=9999 and body.endHash equals record[9999].hash When any single record in the range is modified out-of-band Then the same call returns 200 and body.valid=false and body.firstMismatchIndex equals the first tampered index and body.proof includes previousHash and computedHash for that index When fromIndex>toIndex or the range exceeds head Then the API returns 400 with error.code="INVALID_RANGE" When toIndex equals the head index and the head advances during verification Then verification uses a consistent snapshot and returns a result for the originally requested range

Given active chains receiving events continuously When 10,000 new events accrue for a chain or 5 minutes elapse since the last snapshot (whichever occurs first) Then a chain-root snapshot is created and persisted within 60 seconds and contains {tenantId, entityType, entityId, snapshotIndex, rootHash, previousRootHash, createdAt, version} When GET /api/ledger/snapshots/latest?tenantId=...&entityType=...&entityId=... Then response 200 includes the most recent snapshot with ETag and rootHash length equals the algorithm digest length When POST /api/ledger/snapshots/verify with {snapshotId} Then the service recomputes the root over the snapshot range and returns valid=true within 2 minutes for ranges up to 1,000,000 events

Given a record with canonicalEventId X for chain C does not yet exist When POST /api/ledger/events with {chain:C, canonicalEventId:X, payload} Then the service appends exactly one new record with chainIndex=headIndex+1 and returns 201 with chainIndex and hash When the same request (same canonicalEventId X, same chain C) is retried up to 50 concurrent times Then only one record is appended and all responses return 200/201 with the identical chainIndex, hash, and createdAt and no duplicate records exist When a request omits canonicalEventId Then the service returns 400 with error.code="MISSING_CANONICAL_EVENT_ID" When attempting to reuse canonicalEventId X for a different payload on chain C Then the service returns 409 with error.code="EVENT_ID_CONFLICT" and no new record is appended

Given a workload of 1,000 events/sec sustained for 10 minutes across 100 concurrent chains with bursts to 5,000 events/sec for 60 seconds When the system processes events with write-ahead logging enabled Then end-to-end ledger write latency is P95<=8 ms and P99<=20 ms per event and error rate <0.01% and zero lost or reordered events are observed When label generation requests run concurrently at 200 req/sec Then additional P95 latency attributable to ledger writes is <=10 ms and no request exceeds a 2 s SLA When a node crashes during ingestion Then committed events are durable (RPO=0) and recovery completes within 30 seconds and ingestion resumes without gaps or duplicates

Given a 3-node cluster ingesting 1,500 events/sec When the cluster scales to 6 nodes Then sustainable throughput increases to >=2,700 events/sec (>=1.8x) and per-chain ordering is preserved (chainIndex increments by 1 with no gaps) and cross-tenant isolation remains intact When rebalancing moves partitions Then per-chain appends remain linearizable and no successful write is persisted out of order When verifying chains that span multiple partitions Then range verification completes successfully within <=300 ms for a 10,000-event range

Given existing records in a chain When a client attempts PUT/PATCH/DELETE on any ledger record endpoint Then the service returns 405 or 409 and no data is modified When a privileged operator simulates direct storage mutation bypassing the service Then the next verification over the affected range returns valid=false with firstMismatchIndex pointing to the mutated record When reading any record Then the record includes immutable fields {tenantId, entityType, entityId, chainIndex, canonicalEventId, previousHash, hash, timestamp} and any attempt to change them via write APIs is rejected with 409