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What are the implicit assumptions behind measuring Forecast Accuracy as a Supply Chain KPI?
Companies measuring Forecast Accuracy as a KPI make a couple of implicit assumptions and assume a certain mindset.
The first one is about the nature of demand being deterministic. If it wasn’t deterministic in the medium term horizon, why would we estimate it 45-75 days in advance and hold it firm? Of course, companies do expect some variations when they promise to supply up to 10-25% uptick in demand.
The second assumption is about our ability to respond to disruptions. If the demand gets disrupted, we will incur stockouts and we can’t do much about it.
While this assumption does give peace of mind at the time of forecasting, it also results in firefighting more often than not, when the rubber meets the road.
These two assumptions take our internal supply chain capability development efforts in a particular direction, which is unfortunately non-agile. Any efforts to improve agility get side tracked. If you do have Forecast Accuracy as a KPI, check it out for yourself. Do you have demand sensing, lead time improvement, batch size reduction, quick changeovers, faster response in the improvement list?
If we want to make our supply chain more agile, my recommendation would be to de-prioritise Forecast Accuracy as a KPI and focus on improving internal capabilities to meet demand disruptions.