Optimal policies for demand forecasting and inventory management of goods with intermittent demand

Abstract

Demand forecasting is one of the most crucial aspects of inventory management. For intermittent demand, i.e. demand peaks follow several periods of zero or low demands, accurate forecasting is difficult. A framework is developed to support inventory management decision making for intermittent demand. Several forecasting methods and inventory management policies are compared in this framework. Because mathematical models cannot accurately describe the complex system, a simulation model is built. To determine the best combination of a demand forecasting method and an inventory management policy, a continuous tabu search metaheuristic is developed to optimize the simulation model. Depending on the experimental environment, two options for optimal strategies can be distinguished for a reliable supplier. In addition, the robustness of the results to uncertainty on the supply side is investigated.