The Forecast Was Right. The Decision Was Nine Days Late.
Start by asking your team one question: When the forecast changes, how long until the production schedule does?
Supply chain forecasting has improved a great deal over the past decade. Machine learning models catch demand shifts that would have slipped past a planner using only a spreadsheet, and most manufacturers trust their numbers more than they did five years ago.
So why are the same companies still missing orders, paying for rush components and tearing up production schedules they locked down a few days earlier?
The answer usually has nothing to do with the forecast. It has to do with what happens after the forecast changes, and how long it takes for that change to reach every team that has to act on it.
Picture a manufacturer with three plants and two ERP systems. On a Tuesday night, the forecast run flags a jump in demand for a high-margin product family. The model is right.
This story was originally published by SupplyChainBrain. Read the full story at SupplyChainBrain →
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