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System dynamics analysis for an Internet-of-Things-enabled production logistics system

Author

Listed:
  • Ting Qu
  • Matthias Thürer
  • Junhao Wang
  • Zongzhong Wang
  • Huan Fu
  • Congdong Li
  • George Q. Huang

Abstract

A production logistics system is often subject to high operational dynamics due to large working areas, frequent resource interactions, long operation periods and intensive human involvement. Researchers have applied system dynamics to design the structure of statistically robust systems which accommodate common dynamics. Yet this approach begins to lose its feasibility because dynamics anticipation and statistics are becoming more difficult in ever more competitive markets and adjustments to system structure typically incur high costs. In response, this study explores how a robust information structure can be designed and real-time control schemes for controlling the dynamics inherent to real-life systems applied. Motivated by the wide application of industrial Internet-of-Things (IoT) systems, this paper investigates the typical production logistic execution processes and adopts system dynamics to design cost-effective IoT solutions. The internal and external production logistic processes are first investigated separately. Using sensitivity analysis, the optimal IoT solutions are evaluated and analysed to provide guidance on IoT implementation. Internal and external production logistic processes are then combined into an integrated structure to offer a generic system dynamics approach. This research does not only enhance the use of system dynamics, but also presents a quantitative IoT system analysis approach.

Suggested Citation

  • Ting Qu & Matthias Thürer & Junhao Wang & Zongzhong Wang & Huan Fu & Congdong Li & George Q. Huang, 2017. "System dynamics analysis for an Internet-of-Things-enabled production logistics system," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2622-2649, May.
  • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:9:p:2622-2649
    DOI: 10.1080/00207543.2016.1173738
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    References listed on IDEAS

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    Cited by:

    1. Ahmed Zainul Abideen & Veera Pandiyan Kaliani Sundram & Jaafar Pyeman & Abdul Kadir Othman & Shahryar Sorooshian, 2021. "Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics," Logistics, MDPI, vol. 5(4), pages 1-22, November.
    2. Morett, Emilio & Tappia, Elena & Melacini, Marco, 2021. "Scheduling mobile robots in part feeding systems," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 129-149, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. Ahmed Zainul Abideen & Jaafar Pyeman & Veera Pandiyan Kaliani Sundram & Ming-Lang Tseng & Shahryar Sorooshian, 2021. "Leveraging Capabilities of Technology into a Circular Supply Chain to Build Circular Business Models: A State-of-the-Art Systematic Review," Sustainability, MDPI, vol. 13(16), pages 1-26, August.
    4. Masoud Zafarzadeh & Magnus Wiktorsson & Jannicke Baalsrud Hauge, 2021. "A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective," Logistics, MDPI, vol. 5(2), pages 1-32, April.
    5. Pan, Yanghua & Zhong, Ray Y. & Qu, Ting & Ding, Liqiang & Zhang, Jun, 2024. "Multi-level digital twin-driven kitting-synchronized optimization for production logistics system," International Journal of Production Economics, Elsevier, vol. 271(C).

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