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Decentralized modular production to increase supply chain efficiency in chemical markets

Author

Listed:
  • Tristan Becker

    (RWTH Aachen University)

  • Bastian Bruns

    (Ruhr University Bochum)

  • Stefan Lier

    (South Westphalia University of Applied Sciences)

  • Brigitte Werners

    (Ruhr University Bochum)

Abstract

In the chemical industry, shortened product life cycles and greater differentiation of customer demand increase challenges to efficiently meet specific customer requirements. Thus, production systems with high flexibility are required. One innovative production concept that meets this requirement is decentralized, small-scale modular production which offers significantly more flexibility in the tactical configuration of the production network. Corresponding production plants are assembled from standardized apparatus modules in transportation containers, hereby enabling a fast relocation of modular plants and adjustments of the production process. Therefore, modular plants can be operated close to customers or suppliers, which supports local sourcing strategies and a reduction in delivery costs. In this paper, we analyze the advantages of these modular production systems for a case from the specialty chemicals industry. Respective advantages arise especially from a technically flexible design of parallel process lines, autonomous production and local sourcing. In order to evaluate economic efficiency and network configuration of modular production networks, an efficient mathematical formulation for the optimization is proposed. This formulation includes a new way to model relocations of modules. We apply this model to a case based on real data from the chemical industry. As a result of this application we come to three technical and managerial conclusions. Firstly, technical designs with parallel process lines improve flexibility and efficiency compared to mono processes. Secondly, autonomous production increases economic efficiency in contrast to staffed production and finally, local sourcing offers significant cost reduction potential compared to central sourcing.

Suggested Citation

  • Tristan Becker & Bastian Bruns & Stefan Lier & Brigitte Werners, 2021. "Decentralized modular production to increase supply chain efficiency in chemical markets," Journal of Business Economics, Springer, vol. 91(6), pages 867-895, August.
    • Handle: RePEc:spr:jbecon:v:91:y:2021:i:6:d:10.1007_s11573-020-01019-4
    DOI: 10.1007/s11573-020-01019-4
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    References listed on IDEAS

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    1. Ekici, Ali, 2013. "An improved model for supplier selection under capacity constraint and multiple criteria," International Journal of Production Economics, Elsevier, vol. 141(2), pages 574-581.
    2. Melo, M.T. & Nickel, S. & Saldanha-da-Gama, F., 2009. "Facility location and supply chain management - A review," European Journal of Operational Research, Elsevier, vol. 196(2), pages 401-412, July.
    3. Martínez-Costa, Carme & Mas-Machuca, Marta & Benedito, Ernest & Corominas, Albert, 2014. "A review of mathematical programming models for strategic capacity planning in manufacturing," International Journal of Production Economics, Elsevier, vol. 153(C), pages 66-85.
    4. Hsu, Chaug-Ing & Li, Hui-Chieh, 2009. "An integrated plant capacity and production planning model for high-tech manufacturing firms with economies of scale," International Journal of Production Economics, Elsevier, vol. 118(2), pages 486-500, April.
    5. Becker, Tristan & Lier, Stefan & Werners, Brigitte, 2019. "Value of modular production concepts in future chemical industry production networks," European Journal of Operational Research, Elsevier, vol. 276(3), pages 957-970.
    6. Sanjay Dominik Jena & Jean-François Cordeau & Bernard Gendron, 2015. "Dynamic Facility Location with Generalized Modular Capacities," Transportation Science, INFORMS, vol. 49(3), pages 484-499, August.
    7. Masoud Esmaeilikia & Behnam Fahimnia & Joeseph Sarkis & Kannan Govindan & Arun Kumar & John Mo, 2016. "Tactical supply chain planning models with inherent flexibility: definition and review," Annals of Operations Research, Springer, vol. 244(2), pages 407-427, September.
    8. Tsiakis, Panagiotis & Papageorgiou, Lazaros G., 2008. "Optimal production allocation and distribution supply chain networks," International Journal of Production Economics, Elsevier, vol. 111(2), pages 468-483, February.
    9. Hein, Fanny & Almeder, Christian, 2016. "Quantitative insights into the integrated supply vehicle routing and production planning problem," International Journal of Production Economics, Elsevier, vol. 177(C), pages 66-76.
    10. Masoud Esmaeilikia & Behnam Fahimnia & Joeseph Sarkis & Kannan Govindan & Arun Kumar & John Mo, 2016. "A tactical supply chain planning model with multiple flexibility options: an empirical evaluation," Annals of Operations Research, Springer, vol. 244(2), pages 429-454, September.
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    Cited by:

    1. Allen, R. Cory & Avraamidou, Styliani & Butenko, Sergiy & Pistikopoulos, Efstratios N., 2024. "Solution strategies for integrated distribution, production, and relocation problems arising in modular manufacturing," European Journal of Operational Research, Elsevier, vol. 314(3), pages 963-979.

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    More about this item

    Keywords

    Small-scale modular production; Chemical industry; Network optimization; Decentralized production network; Supply chain;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • L23 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Organization of Production
    • L65 - Industrial Organization - - Industry Studies: Manufacturing - - - Chemicals; Rubber; Drugs; Biotechnology; Plastics
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology

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