Shipment Exception Management

AI flags missing order data and helps staff correct held shipments before they delay fulfillment.

Business impact

• Historically held orders corrected proactively — Stanley Black & Decker reported that almost 90% of historically held orders were corrected proactively.
• Non-value-added labor hours — Stanley Black & Decker reported more than 35 hours a month of non-value-added labor saved for load planners.

Data requirements

• Order data (tabular) — The system checks orders for missing information.
• Contextual information — The system uses surrounding business context to detect exceptions.
• Business rules — Rules define when an order should be flagged for correction.

AI methods and techniques

• Classification — It identifies held orders that need correction based on contextual information and business rules.
• Rules-based decisioning — Business rules guide which orders are flagged for correction.

Industries

Real-world evidence

1 documented case study on record.

Companies using this: Stanley Black & Decker.