The Competitive Car Production Game: Teaching Theory of Constraints and Linear Programming

Maud Van den Broeke & Tanja Mlinar

INFORMS Transactions on Education2025https://doi.org/10.1287/ited.2024.0125article
AJG 2ABDC C
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What the paper says

Having observed many students struggling with the topics of theory of constraints (TOC) and modeling optimization problems, we developed the competitive car production game. It is a tangible interactive in-class educational game, in which student teams compete for the highest profit by managing their own production line of (toy) cars. The objective of the game is to teach topics such as TOC, linear programming (LP), production scheduling, and game theory. Our game is flexible in its application: Students can play a basic version (Module 1) and/or an extended version (Module 2). In both modules, students determine the optimal product mix, deciding how many of each type of car to manufacture while considering production and demand constraints. In Module 2, students also have the opportunity to bid in an auction event for extra capacity and must compete for total market demand. Since September 2023, this game has been successfully implemented in various operations management–related courses taught to Bachelor, Master, and MBA students. Our positive experience with teaching the game, combined with students’ evaluations, confirms its value. Supplemental Material: Additional material for Modules 1 and 2 is available at https://doi.org/10.1287/ited.2024.0125 . The Teaching Note is available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .

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https://doi.org/https://doi.org/10.1287/ited.2024.0125

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@article{maud2025,
  title        = {{The Competitive Car Production Game: Teaching Theory of Constraints and Linear Programming}},
  author       = {Maud Van den Broeke & Tanja Mlinar},
  journal      = {INFORMS Transactions on Education},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1287/ited.2024.0125},
}

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0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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