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Production and Distribution Planning in Danone Waters China Division

Author

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  • Simin Zhang

    (Department of Business Management, Lingnan College, Sun Yat-Sen University, 510275 Guangzhou, China)

  • Haiqing Song

    (Department of Business Management, Lingnan College, Sun Yat-Sen University, 510275 Guangzhou, China)

Abstract

For a logistics manager, the problem of making decisions on production and distribution planning is important but difficult to solve, especially when these decisions involve many products and complicated constraints. This work addresses the development of a decision support system (DSS) and its mathematical model to solve such problems at Danone Waters China Division, one of the China’s largest producers and wholesalers of water and beverages. The DSS is a multistage integration project designed with the objective of integrating operations research tools and techniques into the production and distribution planning process. We first formulate this problem as a mixed-integer program, which involves the production, transportation, and storage costs and the constraint conditions. We then provide customizable options to the Danone Waters managers through simple graphical interfaces. We next develop a method of data collection and preprocessing and solve the model using optimization algorithms. The results show that this approach significantly increases efficiency and reduces total cost. In this paper, we also identify an approach for integrating operations research tools and techniques into the planning process.

Suggested Citation

  • Simin Zhang & Haiqing Song, 2018. "Production and Distribution Planning in Danone Waters China Division," Interfaces, INFORMS, vol. 48(6), pages 578-590, November.
    • Handle: RePEc:inm:orinte:v:48:y:2018:i:6:p:578-590
    DOI: 10.1287/inte.2018.0973
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    References listed on IDEAS

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    Cited by:

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    4. Wang, Julong & Liu, Zhixue & Li, Feng, 2024. "Integrated production and transportation scheduling problem under nonlinear cost structures," European Journal of Operational Research, Elsevier, vol. 313(3), pages 883-904.

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