Ton de Kok "Forecasting, Lot Sizing, Safety Stocks and Empirical Validity"

Over many decades the fields of forecasting, lot sizing and inventory management have developed in parallel. These fields are the foundation for today’s planning systems as embedded in Advanced Planning and Scheduling (APS) systems. The field of forecasting has a very strong empirical basis, as its methodology is derived to a large extent from mathematical statistics. The field of inventory management is rooted in the mathematical analysis of stochastic processes. Lot sizing is a well-developed branch of combinatorial optimization and MILP. In text books on Operations Research and Operations Management, separate chapters are devoted to these fields. As the real-life problem concerns the use of forecasts, inventory control rules and lot sizing mechanisms for multiple items in a multi-echelon inventory system, heuristics have been implemented in APS systems. Typically, a wide range of forecasting methods are offered, some lot sizing heuristics, and basic safety stock formulas derived under some service measure assumption.

In this lecture we discuss the caveats of this pragmatic approach, typically resulting in a discrepancy between target customer service and actual customer service. We provide an explanation for this phenomenon and propose a framework under which under the assumption of linear holding and penalty costs, we can derive the optimal parameters of all end-item control policies with only two long-run simulations, irrespective of the forecasting method and lot sizing mechanisms. We discuss possible extensions to setting parameters for control of upstream items in the multi-item multi-echelon setting.

Ton de Kokis a Full Professor at the School of Industrial Engineering at Eindhoven University of Technology and Director of CWI in Amsterdam. His research concerns the optimization of operational business processes under uncertainty in the context of supply chain management, transportation management and production management. His work has been implemented in many different industries, ranging from transportation, process industries to capital goods industries. The empirical validity of the models and their analysis has provided clear evidence of the importance of stationary stochastic models.