In the fast-moving retail world, inventory accuracy is non-negotiable. Your stock data drives replenishment decisions, customer satisfaction, and sales performance. Yet, even with advanced ERP and WMS systems, inventory exceptions and mismatches between recorded and actual stock are inevitable.

Traditionally, these exceptions have been reactive challenges. By the time a problem is spotted through a customer complaint, a stock audit, or a supplier dispute, the financial and operational damage is already done. AI changes that.

The Hidden Cost of Inventory Exceptions

Inventory discrepancies lead to:

• Lost Sales – Products marked “in stock” but missing from shelves result in disappointed customers.
• Overstocking – Inaccurate counts can trigger unnecessary replenishment orders.
• Increased Waste – Overstock leads to markdowns, spoilage, or obsolescence.
• Operational Disruptions – Teams waste hours reconciling records instead of focusing on growth.
• Supplier Conflicts – Delivery mismatches strain vendor relationships and delay payments.

Even small percentage errors compound across thousands of SKUs, quietly eroding profit margins.

Traditional Exception Handling: Slow and Manual

A typical pre-AI workflow involves:

1. Detection – Exception identified via cycle counts, customer reports, or supplier communication.
2. Investigation – Staff manually check ERP, WMS, POS, and delivery records.
3. Root Cause Analysis – Attempt to determine the source: delivery error, theft, or data entry mistake.
4. Correction – Adjust system records and reconcile with physical counts.

This process is slow, reactive, and prone to recurring issues.

AI-Driven Exception Handling

AI and Large Language Models (LLMs) enable a proactive approach that detects, explains, and resolves exceptions in near real time.

How It Works:

1. Data Integration

AI continuously ingests data from multiple systems: ERP, WMS, POS, supplier feeds, and IoT devices such as shelf sensors or RFID readers.

2. Anomaly Detection

Machine learning algorithms flag mismatches instantly, such as:

• • Negative stock levels
  • Variances beyond tolerance limits
  • Data inconsistencies between ERP and WMS
  • Suspicious patterns suggesting shrinkage

3. Contextual Understanding with LLMs

LLMs analyze the issue and provide a clear, plain-language explanation

4. Automated or Guided Resolution

• • Automatic Fixes – For low-risk mismatches, AI updates records instantly.
  • Human-Approved Actions – Complex discrepancies are sent to staff with recommended solutions.

5. Continuous Learning

The AI adapts over time, improving accuracy in detecting and diagnosing issues.

Benefits of AI Exception Handling

• Faster Resolution – From days to minutes
• Higher Inventory Accuracy – Reducing stock-outs and overstock situations
• Improved Customer Satisfaction – Accurate availability data across channels
• Lower Operational Costs – Less manual investigation and reconciliation work
• Better Supplier Coordination – Faster, data-backed dispute resolution