Step 1 — Event Log Extraction & Correlation The Connector agent retrieves 90 days of event data from Manhattan WMS (1.8M task records), the Honeywell scan middleware (12.4M barcode events), the LMS (340,000 associate-task assignments), and SAP ERP (127,000 sales orders). The Extractor processes 2,100 shift handoff notes and 870 exception escalation emails, extracting 4,300 structured events capturing informal process context — equipment downtime, carrier no-shows, temporary zone closures — invisible in WMS logs. | Step 4 — Cycle Count Discrepancy Root Cause The Analyst mines 34,000 cycle count results against the full transaction chain for each location. It identifies that 44% of discrepancies correlate with put-away override events (associate directed to Location A, scanned at Location B). Another 23% correlate with partial-case replenishment in zones where the unit-of-measure configuration allows both eaches and cases in the same location. The remaining discrepancies distribute across receiving count errors (18%), unrecorded damage (9%), and system timing issues (6%). Zone C3 has 3.1x the average discrepancy rate, driven almost entirely by the put-away override pattern. |
Step 2 — Warehouse Execution Flow Discovery The Analyst reconstructs the put-away → pick → pack → ship flow across 127,000 orders. It discovers 43 distinct execution variants where operations assumed 3 (standard, expedited, hazmat). The dominant happy path accounts for only 52% of orders. The most costly variant — multi-zone split pick with pack station overflow and staged loading — affects 11% of orders but consumes 28% of total labor hours. Wait time between pick completion and pack initiation averages 47 minutes during peak shifts, accounting for 34% of total order cycle time. | Step 5 — SLA Conformance Analysis The Policy agent evaluates all 127,000 orders against their SLA windows. Same-day SLA conformance is 89.2% (target: 95%). The Analyst decomposes breaches: 38% caused by pick queue wait during afternoon peak (labor shortage between 2–4 PM), 24% by replenishment-driven pick delays (empty forward locations), 19% by pack station saturation (single bottleneck station handling all multi-item orders), and 12% by carrier cutoff misses (staged but not loaded before 5:30 PM pickup). Multi-zone orders have 2.4x the SLA breach rate of single-zone orders. |
Step 3 — Replenishment Variant Analysis The Analyst mines 89,000 replenishment events and discovers 8 execution variants. Only 41% of replenishments are triggered by the designed min/max threshold. Emergency replenishment (picker finds empty location) accounts for 27%, creating an average 14-minute pick delay per occurrence. The platform traces 62% of emergency replenishments to a timing mismatch: replenishment tasks are released 35 minutes after pick waves on average, but the fastest pickers reach forward locations within 12 minutes — systematically outrunning the replenishment cycle. | Step 6 — Intelligence Synthesis & Action The Orchestrator synthesizes findings: the replenishment timing mismatch alone accounts for an estimated 3,400 SLA breaches per quarter and 1,200 excess labor hours in emergency replenishment. The Actor generates three action packages: (1) replenishment wave pre-release 45 minutes before pick wave for high-velocity SKUs, (2) labor rebalancing recommendation shifting 2 associates from morning receiving to afternoon picking, (3) slotting optimization work orders for 47 SKUs with the highest put-away override rates. Total analysis time: under 5 hours vs. weeks of manual observation studies. |