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Using Entropy Metrics to Analyze Information Processing Within Production Systems: The Role of Organizational Constraints

Author

Listed:
  • Frits van Merode

    (Care and Public Health Research Institute (CAPHRI), Maastricht University, 6200 MD Maastricht, The Netherlands
    Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands)

  • Henri Boersma

    (Care and Public Health Research Institute (CAPHRI), Maastricht University, 6200 MD Maastricht, The Netherlands
    Department of Radiology & Nuclear Medicine, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands)

  • Fleur Tournois

    (Department of Gynecology and Obstetrics, Maastricht University Medical Centre+, Maastricht University, 6229 HX Maastricht, The Netherlands)

  • Windi Winasti

    (Elisabeth-TweeSteden Ziekenhuis, 5022 GC Tilburg, The Netherlands)

  • Nelson Aloysio Reis de Almeida Passos

    (Department of Computer Science, University of Pisa, 56126 Pisa, Italy
    Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy)

  • Annelies van der Ham

    (Care and Public Health Research Institute (CAPHRI), Maastricht University, 6200 MD Maastricht, The Netherlands
    Rhythm, 1014 AX Amsterdam, The Netherlands)

Abstract

Background : The literature on measuring the complexity of production systems employs the graph and information theory. This study analyzes these systems and their coordination under varying states of control, with a focus on the probability of unfavorable events and their temporal characteristics. Methods : Coordination systems are represented as temporal networks, using entropy and node influence metrics. Two case studies are presented: a factory operating under the principles of the Toyota Production System (TPS) with adjacent (local) coordination and andon (global) coordination and a university obstetrics clinic with only adjacent (local) coordination. Results : Adjacent coordination leads to zero entropy in 38.40% of all situations in the TPS example, contrasted to 76.62% in the same system with andon coordination. Degree centrality of nodes outside of zero-entropy situations exhibits higher average and maximum values in andon coordination networks, compared to those with adjacent coordination in TPS. Entropy values in the university obstetric clinic range from 0.92 to 2.23, average degrees vary between 3 and 4.08, and maximum degrees range from 7 to 9. Conclusions : Coordination systems modeled as temporal networks capture the evolving nature of centralizing and decentralizing coordination in production systems.

Suggested Citation

  • Frits van Merode & Henri Boersma & Fleur Tournois & Windi Winasti & Nelson Aloysio Reis de Almeida Passos & Annelies van der Ham, 2025. "Using Entropy Metrics to Analyze Information Processing Within Production Systems: The Role of Organizational Constraints," Logistics, MDPI, vol. 9(2), pages 1-24, March.
    • Handle: RePEc:gam:jlogis:v:9:y:2025:i:2:p:46-:d:1620661
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    References listed on IDEAS

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    1. Satyendra Kumar Sharma & Praveen Ranjan Srivastava & Ajay Kumar & Anil Jindal & Shivam Gupta, 2023. "Supply chain vulnerability assessment for manufacturing industry," Annals of Operations Research, Springer, vol. 326(2), pages 653-683, July.
    2. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry, 2023. "Efficient resilience portfolio design in the supply chain with consideration of preparedness and recovery investments," Omega, Elsevier, vol. 117(C).
    3. Dmitry Ivanov, 2024. "Two views of supply chain resilience," International Journal of Production Research, Taylor & Francis Journals, vol. 62(11), pages 4031-4045, June.
    4. Tobias Mönch & Arnd Huchzermeier & Peter Bebersdorf, 2021. "Variable takt times in mixed-model assembly line balancing with random customisation," International Journal of Production Research, Taylor & Francis Journals, vol. 59(15), pages 4670-4689, August.
    5. Nye, David E., 2013. "America's Assembly Line," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262018715, December.
    6. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    7. Lin, Yun Hui & Wang, Yuan & Lee, Loo Hay & Chew, Ek Peng, 2021. "Consistency matters: Revisiting the structural complexity for supply chain networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
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