Automated Inventory Monitoring System
AIMS was an enterprise-level system I built to monitor inventory health across a large retail store network in real time. Instead of waiting for problems to surface, it proactively identified issues, notified the right people, and provided guided resolution paths — all automatically.
Problem detection
The system monitored 23+ inventory problem scenarios by correlating data across 10 interconnected databases:
- Negative on-hand balances paired with past-due purchase orders or unreceived transfers
- Large cycle count variances (unit and dollar-based)
- Low cycle count participation and accuracy
- Purchase orders past due 7+ months, never released, or created empty
- SKU/balance mismatches between systems
- Large transaction outliers in receipts, adjustments, transfers, and returns-to-vendor
- Out-of-stock items marked incorrectly
- Closed locations with outstanding inventory issues
When a problem was detected, the system sent targeted email notifications with the problem description, resolution steps, and reference materials — contextual guidance tailored to each scenario type.
Store performance scoring
AIMS evaluated every store across six weighted categories:
- Negative on-hands — quantity affected, percentage of SKUs, and absolute balance percentages
- Past-due purchase orders — weighted by age, so older issues carried heavier penalties
- Unreceived transfers — also age-weighted to reflect real business impact
- Cycle count participation — days submitted vs. days required
- Cycle count accuracy — variance-based buckets with weighted distribution
- Category SKU sales — percentage of unit sales under managed categories
The overall store score was a weighted average across all six categories, giving operations teams a single metric to identify underperforming locations.
Architecture
The system used a hub-and-spoke design — a central processing database connected to 10 satellite data sources covering inventory balances, purchase orders, transfers, returns, cycle counts, sales metrics, and transaction errors. It fed downstream dashboards for both operations and accounting teams.
The result
AIMS transformed inventory management from reactive to proactive. It reduced stockouts, improved vendor relationships by catching late receipts and outstanding payments early, enforced compliance with inventory procedures, and gave leadership a data-driven view of store performance across the entire network.