Inventory Shrinkage Investigation Agent
Detects unexplained inventory losses across warehouses and stores, correlating transaction data to isolate likely causes and prioritize investigation effort.
Shrinkage stems from many overlapping causes, including theft, damage, administrative error, vendor fraud, and receiving discrepancies, but most systems only report a net variance without explaining why
Investigators typically discover shrinkage during periodic cycle counts, long after the transactions that caused it, making root-cause tracing difficult and evidence stale
Manually cross-referencing point-of-sale, receiving, transfer, and cycle-count data across systems to isolate a likely cause is time-consuming and rarely done for every discrepancy
High-value or high-frequency shrinkage patterns can hide within a large volume of small variances unless the data is prioritized systematically
The agent continuously reconciles inventory transactions across receiving, point-of-sale, transfers, returns, and cycle counts to detect variances beyond expected tolerance. It correlates each variance against contextual signals, such as shift schedules, employee access logs, vendor delivery patterns, and known damage or markdown events, to generate a ranked likely-cause hypothesis for each discrepancy. High-priority cases are packaged with supporting evidence and routed to loss prevention investigators, while low-risk administrative variances are auto-resolved with a documented explanation. The agent tracks resolved case outcomes to continuously refine its cause-prediction model.
Detect Variance
- Reconcile transactions across POS, receiving, transfers, and counts
- Flag variances exceeding tolerance thresholds
- Aggregate variance by SKU, location, and time window
- Rank variances by dollar impact and frequency
Correlate Context
- Cross-reference variances against shift and access logs
- Check for known damage, markdown, or promotional events
- Compare against vendor delivery discrepancy history
- Identify repeat-location or repeat-SKU patterns
Route and Investigate
- Auto-resolve low-risk administrative variances with documentation
- Package high-priority cases with supporting evidence
- Route cases to loss prevention by priority and location
- Recommend interview or camera-review targets where applicable
Resolve and Learn
- Track investigation outcomes and confirmed root causes
- Update the cause-prediction model with resolved case data
- Report shrinkage trends by cause category and location
- Recommend process or control changes to prevent recurrence