Control-Relevant Demand Forecasting for Tactical Decision-Making in Semiconductor Manufacturing Supply Chain Management

Abstract

Forecasting highly uncertain demand signals is an important component for successfully managing inventory in semiconductor supply chains. 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 for a tactical inventory management policy based on model predictive control (MPC). The success of the method hinges on a control-relevant prefiltering operation applied to demand estimation data that emphasizes a goodness-of-fit in regions of time and frequency most important for achieving desired levels of closed-loop performance. A multiobjective 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 drawn from the final stage of a semiconductor manufacturing supply chain.