A novel plithogenic TOPSIS- CRITIC model for sustainable supply chain risk management

Abstract The trend of considering supply chain sustainability with an absence of attention to sustainability risks may disturb the business future. The role of risk management is concentrated in identifying and analyse the influence of loss to business, social and environment, get ready by coverage budget, and derive strategies to protect supply chain sustainability against these risks. Risk management assists the company’s performance to be more confident in supply chain sustainability decisions. The extent of the risk is based on the organization’s magnitude, so the sustainable supply chain risk management strategies of large firms require to be more advanced. The purpose of this research is the estimation of sustainable supply chain risk management (SSCRM). The proposed methodology in this paper is a combination of plithogenic multi-criteria decision-making approach based on the Technique in Order of Preference by Similarity to Ideal Solution (TOPSIS) and Criteria Importance Through Inter-criteria Correlation (CRITIC) methods. In order to evaluate the proposed model, we present a real-world case study of the Telecommunications Equipment Company. The results show the importance of each criterion to evaluate SSCRM and the ranking of the three telecommunications equipment categories.

[1]  Orrin Cooper,et al.  An orders-of-magnitude AHP supply chain risk assessment framework , 2016 .

[2]  José Arnaldo Barra Montevechi,et al.  The role of simulation and optimization methods in supply chain risk management: Performance and review standpoints , 2019, Simul. Model. Pract. Theory.

[3]  Florentin Smarandache,et al.  Plithogeny, Plithogenic Set, Logic, Probability, and Statistics , 2018, ArXiv.

[4]  Merve Er Kara,et al.  A data mining-based framework for supply chain risk management , 2019, Comput. Ind. Eng..

[5]  Florentin Smarandache,et al.  A Hybrid Plithogenic Decision-Making Approach with Quality Function Deployment for Selecting Supply Chain Sustainability Metrics , 2019, Symmetry.

[6]  Lucila Maria de Souza Campos,et al.  Performance evaluation of green suppliers using entropy-TOPSIS-F , 2019, Journal of Cleaner Production.

[7]  C. Speier,et al.  Sustainability to support end-to-end value chains: the role of supply chain management , 2011 .

[8]  F. Smarandache Extension of Soft Set to Hypersoft Set, and then to Plithogenic Hypersoft Set , 2018 .

[9]  J. Cullen,et al.  The role of customer awareness in promoting firm sustainability and sustainable supply chain management , 2019, International Journal of Production Economics.

[10]  T. Papadopoulos,et al.  Supply chain sustainability: A risk management approach , 2016 .

[11]  Donya Rahmani,et al.  Sustainability risk management in the supply chain of telecommunication companies: A case study , 2018, Journal of Cleaner Production.

[12]  Henry Y. K. Lau,et al.  Hotel selection using a modified TOPSIS-based decision support algorithm , 2019, Decis. Support Syst..

[13]  Qifeng Yang,et al.  Research on financial risk management model of internet supply chain based on data science , 2019, Cognitive Systems Research.

[14]  Stuart Orr,et al.  The role of supply chain orientation in achieving supply chain sustainability , 2019, International Journal of Production Economics.

[15]  B. B. Zaidan,et al.  Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017 , 2019, Comput. Oper. Res..

[16]  Yashuai Li,et al.  Risk propagation mechanisms and risk management strategies for a sustainable perishable products supply chain , 2019, Comput. Ind. Eng..

[17]  A. Leiras,et al.  Social supply chain risk management: A taxonomy, a framework and a research agenda , 2019, Journal of Cleaner Production.

[18]  Ming Xu,et al.  Supply chain sustainability risk and assessment , 2019, Journal of Cleaner Production.

[19]  Ming-Lang Tseng,et al.  Multicriteria analysis of sustainable development indicators in the construction minerals industry in China , 2015 .

[20]  Adis Puška,et al.  Use of fuzzy logic for measuring practices and performances of supply chain , 2018 .

[21]  F. Smarandache Plithogenic Set, an Extension of Crisp, Fuzzy, Intuitionistic Fuzzy, and Neutrosophic Sets - Revisited , 2018 .

[22]  Mohammad Yazdi,et al.  Risk assessment based on novel intuitionistic fuzzy-hybrid-modified TOPSIS approach , 2018, Safety Science.

[23]  M. O'Sullivan,et al.  Agribusiness supply chain risk management: A review of quantitative decision models , 2017, Omega.

[24]  Seyed Amin Seyed Haeri,et al.  Development of supply chain risk management approaches for construction projects: A grounded theory approach , 2019, Comput. Ind. Eng..

[25]  Cory Searcy,et al.  Assessing sustainability in the supply chain: A triple bottom line approach , 2015 .

[26]  G. Mavrotas,et al.  Determining objective weights in multiple criteria problems: The critic method , 1995, Comput. Oper. Res..