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Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain

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  • Ehsan Badakhshan
  • Paul Humphreys
  • Liam Maguire
  • Ronan McIvor

Abstract

The bullwhip effect (BWE) is a phenomenon, which is caused by ineffective inventory decisions made by supply chain members. In addition to known inefficiencies caused by the bullwhip effect within a supply chain product flow, such as excessive inventory, it can also lead to inefficiencies in cash flow such as the cash flow bullwhip (CFB). The CFB reduces the efficiency of the supply chain (SC) through heterogeneous distribution of cash among supply chain members. This paper aims to decrease both the BWE and the CFB across a SC through applying a simulation-based optimisation approach, which integrates system dynamics (SD) simulation and genetic algorithms. For this purpose, cash flow modelling is incorporated into the SD structure of the beer distribution game (BG) to develop the CFB function. A multi objective optimisation model is then integrated with the SD-BG simulation model. Finally, a genetic algorithm (GA) is applied to determine the optimal values for the inventory, supply line, and financial decision parameters. Results show that the proposed integrated framework leads to efficient liquidity management in the SC in addition to cost management.

Suggested Citation

  • Ehsan Badakhshan & Paul Humphreys & Liam Maguire & Ronan McIvor, 2020. "Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 58(17), pages 5253-5279, September.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:17:p:5253-5279
    DOI: 10.1080/00207543.2020.1715505
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    Cited by:

    1. Badakhshan, Ehsan & Ball, Peter, 2023. "A simulation-optimization approach for integrating physical and financial flows in a supply chain under economic uncertainty," Operations Research Perspectives, Elsevier, vol. 10(C).
    2. Preil, Deniz & Krapp, Michael, 2022. "Bandit-based inventory optimisation: Reinforcement learning in multi-echelon supply chains," International Journal of Production Economics, Elsevier, vol. 252(C).
    3. Dargnies, Marie-Pierre & Hakimov, Rustamdjan & Kübler, Dorothea, 2022. "Aversion to hiring algorithms: Transparency, gender profiling, and self-confidence," Discussion Papers, Research Unit: Market Behavior SP II 2022-202, WZB Berlin Social Science Center.
    4. Deniz Preil & Michael Krapp, 2022. "Artificial intelligence-based inventory management: a Monte Carlo tree search approach," Annals of Operations Research, Springer, vol. 308(1), pages 415-439, January.
    5. Edward G. Anderson & David R. Keith & Jose Lopez, 2023. "Opportunities for system dynamics research in operations management for public policy," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1895-1920, June.

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