Forecasting, supplier risk, and exception handling are good AI candidates, but only when the underlying data is in order.
Supply chain teams have used statistical forecasting for years. What is changing is that AI can now help with the next step: deciding what to do when a forecast or shipment goes wrong.
Typical use cases include short-term demand forecasting, monitoring suppliers for risk, and suggesting options when an order is late. In each case the model is only as good as the data behind it.
In our experience the main obstacle is not the model. Many companies cannot quickly answer basic questions such as what inventory is in transit right now. Until that data is reliable, AI recommendations will not be trusted.
We suggest starting small: one site, one supplier group, or one type of decision. Build a clean data feed for it, then add forecasting or recommendations on top.
Measure results in terms the business already uses, such as on-time delivery, inventory levels, and the time it takes to resolve an exception.
