Control-relevant demand forecasting for management of a production-inventory system

Forecasting highly uncertain demand signals is an important component for successfully managing inventory. We present a control-relevant approach to the problem that tailors a forecasting model to its end-use purpose, which is to provide forecast signals to a tactical inventory management policy based on Model Predictive Control (MPC). The success of the method hinges on a control-relevant prefiltering operation that emphasizes goodness-of-fit in the frequency band most important for achieving desired levels of closed-loop performance. A multi-objective formulation is presented that allows the supply chain planner to generate demand forecasts that minimize inventory deviation, starts change variance, or their weighted combination when incorporated in an MPC decision policy. The benefits obtained from this procedure are demonstrated on a case study where the estimated demand model is based on a AutoRegressive (AR) process.