Teaching data-driven decision making for inventory analysis with Monte Carlo simulation

Abstract

Inventory management and the ability to make data-driven decisions under uncertainty are two critical components of supply chain management. This brief describes how an Excel-based Monte Carlo simulation of fuel and lottery purchases can be used to teach students to analyze a system with randomness. This exercise can serve as a group or individual assignment to demonstrate the ability of Monte Carlo simulation to estimate solutions involving uncertainty and to teach undergraduate business students how to implement a basic multiperiod fixed-interval inventory policy with order-up-to-levels. The important concepts of cycle service levels and confidence intervals are emphasized as are the proper implementation of one-tailed and two-tailed critical values. Students are encouraged to compare algebraic solutions to their simulation results.