Forecasting in supply chains is notoriously complex and will never be accurate, as predictions inherently are. One size doesn’t fit all use cases, as no single model consistently delivers the best results across all products, locations, and time horizons. Some models excel with seasonality, others handle noisy data better, and advanced deep learning models uncover nonlinear patterns—but only if the data is robust. So, how do businesses choose the right model without endless manual tuning? The answer lies in a mix-and-match forecasting method.
Understanding the Concept
Mix-and-match forecasting allows planners to adjust model selection and, in some cases, automate it by evaluating multiple algorithms and assigning the best performer to each forecasting run/cycle. Think of it as building a championship team: different players bring different strengths, and the right combination wins. Similarly, mix-and-match is a repository of purpose-built models that lets you select the most suitable one for the job, given business priorities and time horizons.
Why and how the mix and match works
The stakes for precise forecasting have never been higher. With artificial intelligence (AI) and machine learning (ML) acceleration, deploying an inappropriate model will quickly lead to poor forecast accuracy; a poor forecast can cascade into inventory shortages, misaligned capacity planning and dissatisfied customers. Mix and match addresses this by automating model evaluation, reducing planner workload, and improving accuracy—without requiring specialized data science skills.
At its core, mix and match utilizes an intelligent, omnipresent semantic network architecture. This is where forecasting configuration is defined—inputs, outputs, and horizons—in addition to core evaluations of models using KPIs like Mean Squared Error (MSE). For each run, one model is used per node and planning horizon. Then in subsequent forecasting run, models may be reevaluated and chosen differently as data conditions evolve.
