Teaching data‐driven decision making for inventory analysis with Monte Carlo simulation
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....
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| Published in | Decision sciences journal of innovative education |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
08.10.2024
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| Online Access | Get full text |
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| Summary: | 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. |
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| ISSN: | 1540-4595 1540-4609 |
| DOI: | 10.1111/dsji.12328 |