AI-Driven Workflow for Inventory Discrepancy Investigation

Manual variance investigation can stall month-end close by days as staff manually comb transaction logs and video footage. This workflow automates evidence gathering and correlation, delivering a ranked list of probable causes (e.g., mispick, receiving error, theft) within minutes. This reduces reconciliation cycle time by 60-80%, enabling faster, more accurate financial reporting and freeing accounting staff for higher-value analysis.

Instead of discovering systemic theft or process failures months later, this workflow acts as a continuous audit. By fusing WMS transaction data with access control logs and surveillance footage timestamps, it identifies patterns—like repeated variances linked to a specific operator or shift—and alerts security and operations leadership. This shifts loss prevention from reactive to predictive, potentially reducing annual shrink by 15-25% through early intervention.

Inventory control teams spend up to 70% of their time on forensic data gathering for discrepancies. This workflow automates that repetitive data fusion, allowing analysts to focus on approving the AI's findings, implementing corrective actions, and improving underlying processes. It effectively multiplies the investigative capacity of your existing team, turning them from data hunters into process engineers who drive permanent accuracy gains.

For regulated industries (pharma, food, aerospace) or public companies, inventory accuracy is audited rigorously. This workflow creates a complete, timestamped audit trail for every variance investigation—automatically documenting the data sources queried, the analysis performed, and the recommended resolution. This dramatically reduces audit preparation time and provides defensible, evidence-based records for internal and external auditors.

Chronic discrepancies are symptoms of broken processes. By systematically categorizing root causes (e.g., 'incorrect putaway' vs. 'mispick'), this workflow generates structured data on operational failure points. This enables data-driven decisions to retrain staff, adjust WMS configurations, or redesign workflows, leading to a continuous improvement cycle that elevates baseline inventory accuracy and reduces future variance volume.

Unreliable inventory records force operations to carry excess safety stock as a buffer against phantom stockouts, tying up working capital. By rapidly resolving discrepancies and improving record accuracy, this workflow provides confidence in the WMS data. This allows planners to safely reduce safety stock levels by 5-15%, freeing cash and warehouse space while maintaining the same service level.

The workflow triggers automatically when a cycle count or audit scan produces a variance outside a configurable tolerance. An initial triage agent assesses the severity based on SKU value, variance quantity, and location risk score, routing high-risk cases for immediate investigation and lower-risk items into a batched review queue. This replaces manual log review and prioritization, ensuring critical losses are addressed first.

A dedicated investigation agent orchestrates parallel queries across fragmented systems: it retrieves the complete transaction history (receipts, picks, adjustments) from the WMS, pulls access logs and badge-in data from physical security systems, and requests relevant video footage timestamps from the VMS. This agent normalizes timestamps and entity IDs (user, pallet, cart) to build a unified timeline of events around the variance, eliminating days of manual data gathering.

Using the fused timeline, an LLM-powered analyst agent reviews the evidence against a library of common failure modes (e.g., mispick, theft, receiving error, system lag). It generates and ranks probable root-cause hypotheses with confidence scores, citing supporting evidence from the logs or footage. For high-confidence conclusions (e.g., 'mispick at station B12'), it can auto-generate the inventory adjustment and route it for approval, closing the loop without human intervention.

For medium-confidence hypotheses or high-value discrepancies, the workflow creates a structured case file in a platform like ServiceNow or Jira. It presents the agent's findings, supporting evidence clips, and a recommended action (adjustment, write-off, security review) to a loss prevention or inventory control manager. The integrated approval gate allows the manager to review, request more info, approve the action, or escalate, with all decisions fed back to train the analysis models.

Upon approval, the workflow executes the final action through system APIs: posting the inventory adjustment to the ERP (e.g., SAP, Oracle), updating the case status, and notifying relevant stakeholders. Every step of the investigation—from initial trigger to final transaction—is logged in an immutable audit trail with full data provenance, creating a defensible record for financial audits and compliance reporting (SOX, ISO).

Built on an orchestration framework like LangGraph or Apache Airflow, the workflow integrates via APIs with the WMS, physical security information management (PSIM) system, video management system (VMS), and ERP. A vector database stores historical case embeddings to improve hypothesis scoring over time. Deployment typically involves a 3-4 week pilot in a single DC, focusing on integrating the data pipelines and tuning confidence thresholds before network-wide rollout.