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Operations performance considering demand coverage scenarios for individual products and products families in supply chains

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  • Alhawari, Omar I.
  • Süer, Gürsel A.
  • Bhutta, M. Khurrum S.

Abstract

In supply chains, businesses compete to meet customer requirements by leveraging their competitive operational capabilities. In this paper, the manufacturer in its supply chain, faces uncertain market demand environment. Based on the products, operations and demand information, the manufacturer makes decisions to design its manufacturing system that saves time and effort in the production process. The methodology followed in this paper includes four phases. In Phase 1, the manufacturer benefits from the grouping of the similar products into families to save time and effort in the production and eventually the related machines are formed accordingly. In Phase 2, the decision of manufacturing system layout or design, considering a stochastic customer demand, is made including cellular manufacturing system design. In Phase 3, the expected profits generated by the system designed to meet the demand of product families are determined and optimal design is selected accordingly. In Phase 4, the problem of determining the optimal profits and quantities of the individual products in families, considering multi-demand coverage levels, is tackled using a proposed mathematical model, then results are analyzed. The results showed that similar products are grouped into four product families using p-median mathematical model and machine cells are formed accordingly. Consequently, the cellular manufacturing systems is designed for each family and the decision on the optimal number of cells is made based on the maximum expected profits generated by the system designed. Further, the problem of finding the optimal profits by individual products in families is studied considering three demand coverage probability scenarios: non-demand coverage restriction, only lower bound-demand coverage restriction and both lower and upper bounds-demand coverage restriction. Maximum profits are generated when the decision does not include any restriction on the demand; however, the product that has the lowest processing time is produced and sold where other products are not. This may leave the decision maker with either keep-the-winner perspective or not depending on the policy implemented in the competition process. Better decisions are made when more information is shared about the customer requirements.

Suggested Citation

  • Alhawari, Omar I. & Süer, Gürsel A. & Bhutta, M. Khurrum S., 2021. "Operations performance considering demand coverage scenarios for individual products and products families in supply chains," International Journal of Production Economics, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:proeco:v:233:y:2021:i:c:s0925527320303613
    DOI: 10.1016/j.ijpe.2020.108012
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    References listed on IDEAS

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    1. Talluri, Srinivas & Baker, R. C., 2002. "A multi-phase mathematical programming approach for effective supply chain design," European Journal of Operational Research, Elsevier, vol. 141(3), pages 544-558, September.
    2. Aidin Delgoshaei & Ahad Ali, 2020. "A Hybrid Ant Colony Optimization and Simulated Annealing Algorithm for Multi-Objective Scheduling of Cellular Manufacturing Systems," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 11(3), pages 1-40, July.
    3. Sarkis, Joseph & Zhu, Qinghua & Lai, Kee-hung, 2011. "An organizational theoretic review of green supply chain management literature," International Journal of Production Economics, Elsevier, vol. 130(1), pages 1-15, March.
    4. Gokhan Egilmez & Bulent Erenay & Gürsel A. Süer, 2019. "Hybrid cellular manufacturing system design with cellularisation ratio: an integrated mixed integer nonlinear programming and discrete event simulation approach," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 32(1), pages 1-24.
    5. Ata Allah Taleizadeh & Kannan Govindan & Nasim Ebrahimi, 2020. "The effect of promotional cost sharing on the decisions of two-level supply chain with uncertain demand," Annals of Operations Research, Springer, vol. 290(1), pages 747-781, July.
    6. Aalaei, Amin & Davoudpour, Hamid, 2017. "A robust optimization model for cellular manufacturing system into supply chain management," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 667-679.
    7. Ali Mohtashami & Alireza Alinezhad & Amir Hossein Niknamfar, 2020. "A fuzzy multi-objective model for a cellular manufacturing system with layout designing in a dynamic condition," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 34(4), pages 514-543.
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