Sustainable supply chain network design problem: Using the integrated BWM, TOPSIS, possibilistic programming, and ε-constrained methods

Lahri, Vijay; Shaw, Krishnendu; Ishizaka, Alessio

doi:10.1016/j.eswa.2020.114373

Cited by 72 publications

(27 citation statements)

References 65 publications

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“…Mehar approach was suggested to solve the distance problem in the supply chain network, by using the lexicographic method to compare interval-valued Pythagorean fuzzy numbers (Bhatia et al 2021 ). To establish a sustainable supply chain network Lahri et al ( 2021 ) proposed a multi-objective possibilistic integer linear programming model using the integrated possibilistic programming, TOPSIS, BWM, and Epsilon (ε) constraint method. Thus, we proposed the following propositions: Proposition 23: Use of AI technique to minimize the risks for two-echelon resilient supply chain network design.…”

Section: Emerging Research Themes Of Ai and Supply Chain Resiliencymentioning

confidence: 99%

Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research

et al. 2021

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The challenging situations and disruptions that occurred due to the outbreak of the COVID-19 pandemic have created a severe need for supply chain resiliency (SCR). There has been a growing interest among researchers to investigate the resiliency in supply chain operations to overcome risks and disruptions and to achieve successful project management. The supply chain of every business requires innovative projects to accomplish competitive advantage in the market. This study was conducted to identify the significance of artificial intelligence (AI) for creating a sustainable and resilient supply chain, and also to provide optimum solutions for supply chain risk mitigation. A systematic literature review has been conducted to examine the potential research contribution or directions in the field of AI and SCR. In total, 162 articles were shortlisted from the SCOPUS database in the chosen field of research. Structural Topic Modeling (STM), a big data-based approach, was employed to generate several thematic topics of AI in SCR based on the shortlisted articles, and all topics were discussed. Furthermore, the bibliometric analysis was conducted using R-package to investigate the research trends in the area of AI in SCR. Based on the conducted review of literature, a research framework was proposed for AI in SCR that will facilitate researchers and practitioners to improve technological development in supply chain firms. The purpose is to combat sudden risks and disruptions so that project management will perform well Post COVID-19. The study will be also helpful for future researchers and practitioners to identify research directions based on existing literature covered in this paper in the field of SCR. Future research directions are proposed for AI-enabled resilient supply chain management. This study will also provide several implications for supply chain managers to achieve the required resilience in their supply chains post COVID-19 by focusing on the elements of the proposed research framework.

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Section: Emerging Research Themes Of Ai and Supply Chain Resiliencymentioning

confidence: 99%

Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research

et al. 2021

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“…There are several reasons for selecting TOPISIS. To begin with, it is straightforward to use, yet it provides comparable results with more complex MCDM methods (Lahri et al, 2021 ). It also has the universality characteristic which makes it familiar to many researchers and practitioners (De Souza et al, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

Supplier selection to support environmental sustainability: the stratified BWM TOPSIS method

et al. 2022

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Organisations need to develop long-term strategies to ensure they incorporate innovation for environmental sustainability (IES) to remain competitive in the market. This can be challenging given the high level of uncertainty regarding the future (e.g., following the COVID pandemic). Supplier selection is an important decision that organisations make and can be designed to support IES. While the literature provides various criteria in the field of IES strategies, it does not identify the criteria which can be utilised to assist organisations in their supplier selection decisions. Moreover, the literature in this field does not consider uncertainty related to the occurrence of possible future events which may influence the importance of these criteria. To address this gap, this paper develops a novel criteria decision framework to assist supplier evaluation in organisations, taking into consideration different events that may occur in the future. The framework that combines three decision-making methods: the stratified multi-criteria decision-making method, best worst method, and technique for order of preference by similarity to ideal solution. The framework, proposed in this paper, can also be adopted to enable effective and sustainable decision making under uncertainty in various fields.

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“…Since raw materials and components are purchased from suppliers, their activities affect SC sustainability [3,33]. Many researchers have focused on supplier selection, proposing sustainability criteria to assess sustainability levels [34] and mainly focusing on environmental issues associated with the concept of green sourcing and green SC [35][36][37][38]. Relatively few papers have considered social criteria [39].…”

Section: Procurement and Sustainable Development Practicesmentioning

confidence: 99%

SDG-Oriented Supply Chains: Business Practices for Procurement and Distribution

et al. 2022

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This paper aims at analyzing sustainable practices that can be implemented within supply chains, linking them to Sustainable Development Goals (SDGs) in order to show the performance achieved by SDG-oriented supply chains. Sustainable practices were collected from the repository “Business Process Framework for Sustainability” owned by the Department of Industrial Engineering of the University of Salerno (Italy). By using literature research and descriptive statistics, there emerges a positive relationship between sustainable development and SC performance enhancement. From the study of the practices that can be implemented for cooperation activities with upstream and downstream supply chain partners, it emerges that companies have many opportunities of both supporting the 2030 Agenda and enhancing their market and organizational performance. The research demonstrates that both procurement and distribution processes could be reengineered by implementing sustainable approaches considering all three dimensions of sustainability. Differences emerge in terms of number of opportunities, depending on motivations for implementing them, industry and supply chain processes, performance achieved, and SDGs pursued.

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Sustainable supply chain network design problem: Using the integrated BWM, TOPSIS, possibilistic programming, and ε-constrained methods

Cited by 72 publications

References 65 publications

Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research

Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research

Supplier selection to support environmental sustainability: the stratified BWM TOPSIS method

SDG-Oriented Supply Chains: Business Practices for Procurement and Distribution

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