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 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
08.10.2024
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| Online Access | Get full text |
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| 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. |
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| AbstractList | 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. |
| Author | Taylor, Daniel Belford, Matthew Pritchard, Alan |
| Author_xml | – sequence: 1 givenname: Alan orcidid: 0000-0002-8978-3704 surname: Pritchard fullname: Pritchard, Alan organization: Department of Marketing and Logistics, Max M. Fisher College of Business The Ohio State University Columbus Ohio USA – sequence: 2 givenname: Daniel surname: Taylor fullname: Taylor, Daniel organization: Department of Marketing and Supply Chain Management, Jerry S. Rawls College of Business Texas Tech University Lubbock Texas USA – sequence: 3 givenname: Matthew surname: Belford fullname: Belford, Matthew organization: Department of Marketing and Supply Chain Management, Jerry S. Rawls College of Business Texas Tech University Lubbock Texas USA |
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| Cites_doi | 10.1111/j.1937-5956.2000.tb00319.x 10.1287/ited.2018.0200 10.1111/j.1540-4609.2008.00179.x 10.1287/mnsc.37.4.409 10.1016/j.iree.2013.04.001 10.1108/eb054720 10.1080/01621459.1949.10483310 10.1111/dsji.12236 10.2307/41164105 10.1111/dsji.12230 10.1111/j.1745-493X.2004.tb00170.x 10.1111/j.1540-4609.2009.00247.x 10.1111/j.1540-4609.2008.00178.x 10.1111/j.1540-4609.2007.00151.x 10.1111/j.1540-4609.2012.00365.x 10.1080/08832323.2019.1622502 10.1111/dsji.12091 10.1111/dsji.12002 10.1111/dsji.12001 10.1287/mnsc.35.3.321 10.1111/j.1540-4609.2010.00261.x 10.1111/j.1937-5956.2009.01109.x 10.1111/dsji.12100 10.1016/0925-5273(95)00103-4 10.1108/13598540210447782 10.1287/mnsc.12.6.B170 10.1016/j.renene.2014.11.059 10.1118/1.4889165 10.2307/20713644 |
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