Optimal supply chain design with product family : a cloud-based framework with real-time data consideration

Ali, SI; Ali, A; Muhammed, M; Christie, M

Abstract

When the product family (PF) and the supply chain designs (SCD) are aligned and integrated, original equipment manufacturers (OEM) are more likely to improve their operational performance. In this paper, we propose a novel approach, which demonstrates how both the product and the supply chain can simultaneously be designed based on real-time data. At the heart of the proposed model is the utilisation of a cloud-based management system comprising of three steps. In the first step, a generic bill of materials is modelled to design a set of product families using “AND” and “OR” nodes. In the second step, a cloud-based framework is designed to manage real-time costs viz. echelons. In the third step, a mixed integer linear programming model is then applied, which optimizes the SCD based on real-time costs. We use a metaheuristic method based on Genetic Algorithm (GA) to solve the optimization problem. We further illustrate the model using power transformer numerical example. Then the critical parameters of GA are examined to determine the best settings. We believe that the proposed SCD is an intelligent and expert management system, which can facilitate effective decision-making support by taking into account real-time cost data. This is particularly important when there are uncertain and volatile market conditions.

Citation

Ali, S., Ali, A., Muhammed, M., & Christie, M. (2021). Optimal supply chain design with product family : a cloud-based framework with real-time data consideration. Computers and Operations Research, 126, 105112. https://doi.org/10.1016/j.cor.2020.105112

• Journal Article Type: Article
• Acceptance Date: Sep 25, 2020
• Online Publication Date: Oct 10, 2020
• Publication Date: Feb 1, 2021
• Deposit Date: Oct 13, 2020
• Publicly Available Date: Apr 10, 2022
• Journal: Computers and Operations Research
• Print ISSN: 0305-0548
• Publisher: Elsevier
• Volume: 126
• Pages: 105112
• DOI: https://doi.org/10.1016/j.cor.2020.105112
• Publisher URL: https://doi.org/10.1016/j.cor.2020.105112
• Related Public URLs: http://www.journals.elsevier.com/computers-and-operations-research/