Simulation-Based Prediction

Abstract

Predicting Shortages in a Supply Chain Using Simulation A supply chain shortage is a serious problem that can lead to assembly plant shutdowns. However, predicting such shortages using simulation poses a challenge, because the information necessary to initialize such a supply chain simulation is often only partially observable in many real-world applications. In “Simulation-Based Prediction,” Lim and Glynn investigate this problem. They formulate such a prediction problem as the problem of computing the conditional expectation of the quantity of interest, given the observed state of the system. Simulation can be easily applied to computing such a conditional expectation when the simulation state is fully observed in the real system. Lim and Glynn propose a new simulation methodology appropriate to the many settings in which the observed current state does not fully determine the simulation’s initial state. With the use of such methods, simulation has the potential to more accurately predict upcoming supply chain bottlenecks and to enhance predictions in the many other problem settings where simulation is commonly used.