Supply chains generate large amounts of data and involve many decisions — ideal ground for AI.
Applications
- Demand planning: forecasting needs across products and locations.
- Inventory optimisation: balancing stock levels and service.
- Route optimisation: planning deliveries around traffic, time windows and capacity.
- Warehouse automation: picking robots, slotting optimisation and vision-based inspection.
- Estimated arrival times: predicting delays.
- Supplier risk monitoring: scanning news and data for disruptions.
- Document processing: invoices, customs and shipping documents.
Combining Methods
Machine learning predictions often feed into optimisation algorithms that make decisions under constraints.
Challenges
- Data scattered across partners.
- Disruptions unlike historical data.
- Integration with existing systems.
Getting Value
Target decisions with measurable costs, such as fuel, stock-outs and expediting, and pilot before scaling.