How to Reduce Error: Avoid Making Overrides Under Pressure

Vyan.ai tracks overrides made to the touchless forecast to learn when sales / marketing / demand planners are successful with their overrides. Vyan.ai engine continually learns from such overrides which add value (reduce error). Vyan.ai also develops a classification model based on override size, direction, etc. to alert Demand Managers when overrides made by Demand Planners are likely to destroy value (increase error).

Typical demand planning solution reports on forecast error after the fact, when it’s already too late. The alerting capability is limited to such past-facing error reporting or at most a comparative evaluation when forecasts are short of targets / too volatile cycle over cycle / too diverse across various stakeholders. This is all good baby steps, but it does nothing to predict the likely error in consensus demand signal and the cost of such forecast error.

Vyan.ai predicts forecast error and reports on likely errors along with the confidence level in the alert prediction with a full explanation with supporting evidence: where past overrides of similar size and direction have hurt value at the SKU/location/week level or based on the override track record from the human in question (e.g., bias to lift forecasts to chase business targets: what we would like the sales to be as opposed to what we think most likely scenario is for sales in future).