How to delight everyone (by Reducing Cost of Forecast Error)
Most Businesses have to rely on Demand Forecasts as usually the Total Replenishment Lead Time (time needed to buy raw materials, produce, and ship) is quite a bit more than the Order Lead Time (time the customer is willing to wait post placing the order).
Most Businesses also calculate Forecast Error to understand how often they are over-forecasting (Forecast higher than Sales resulting into excess inventory) or under-forecasting (Forecast lower than Sales resulting into Lost Sales if Safety Stock is fully consumed or Lost Margin if Production or Transportation needs to be expedited).
Most Businesses have no robust way to understand the economic impact of Forecast Error though, what is referred to as Cost of Forecast Error. There are multiple drivers of waste here: Inventory Carrying Cost for Excess Inventory, Obsolescence Costs (in case of inventory write-offs and in worst case including additional Disposal Costs), Lost Margin (in case of Lost Sales), Margin Erosion (in case of Production / Transportation Expediting, which inflates the Cost To Serve), the risk to future margin as any On Time In Full (OTIF) delivery issues also erode customer trust and impact future orders.
Most Integrated Business Planning and Demand Forecasting software providers pitch Forecast Error reduction through better AI/ML Forecast Models, better focus on key products through ABC/XYZ segmentation, and better collaboration through cloud based spreadsheet capabilities aided by high forecast error alerts. While the Supply Chain Benefits of reducing Forecast Error are intuitively present, most players do not have a robust linkage between Forecast Error Reduction and the economic value of this error reduction: to be able to justify the business case for AI/ML based demand forecasting or a cloud-based IBP package enabling higher maturity in your S&OP processes.
Future is certain to be uncertain, and hence all promised benefits in terms of forecast error reduction have to be properly analyzed through sensitivity analytics: what is the incremental benefit of an additional 1% reduction in forecast error. This is tied to not just Demand Forecasting process, but also the Inventory Planning process, as Forecast Error and Safety Stock Recommended are not linearly related.
Vyan.ai team recommends to execute What-If Scenario Planning in an automated manner to understand the full range of Cost of Forecast Error reduction benefits based on different service levels, different levels of forecast error, different lead times, % of demand that can still be captured through product substitution, choices between expediting production or transportation, etc.
Vyan.ai can also help to understand Forecastability of your demand to understand the maximum reduction likely in your Forecast Error. This helps in target setting and understanding the likely range of reduction in the Cost of Forecast Error.
You can reach out to Vyan team at info@vyan.ai to learn more about AI-powered Demand Forecasting Best Practices and to see Vyan.ai in action. Our team can also conduct a Proof of Value with your data, where we conclusively prove the forecast error reduction potential compared to your forecast performance baseline. We help you understand the Cost Of Forecast Error (COFE), which gives you a data-driven business case for Demand Planning Transformation powered by vyan.ai.
