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Big Data Analytics for Supply Chain Transformation: A Systematic Literature Review Using SCOR Framework Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Efficient Supplier Selection in the Era of Industry 4.0

Authors : Deepanshu Nayak, Meenu Singh, Millie Pant, Sunil Kumar Jauhar

Published in: Digital Transformation and Industry 4.0 for Sustainable Supply Chain Performance

Publisher: Springer International Publishing

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Abstract

With the advent of fast computers, availability of data, and development of sophisticated algorithms, every sector is undergoing a process of rapid advancement. Industries utilize advanced technologies to digitize various phases of the supply chain (SC) for better production and enhanced customer experience. Supplier selection is a fundamental part of supply chain management (SCM) and has an immense scope of exploiting emerging technologies like IoT (Internet of Things), big data analytics, cloud computing (CC), etc. The present study provides brief research and a comparison of the conventional methods or models accessible in literature and the role of Industry 4.0.

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previous chapter Customer Relationship Management in the Digital Era of Artificial Intelligence
next chapter A Comparative Approach for Sustainable Supply Chain Finance to Implement Industry 4.0 in Micro-, Small-, and Medium-Sized Enterprises
Literature
Abdel-Baset, M., Chang, V., Gamal, A., & Smarandache, F. (2019). An integrated neutrosophic ANP and VIKOR method for achieving sustainable supplier selection: A case study in importing field. Computers in Industry, 106, 94–110.CrossRef
Büyüközkan, G., & Göçer, F. (2019). A novel approach integrating AHP and COPRAS under Pythagorean fuzzy sets for digital supply chain partner selection. IEEE Transactions on Engineering Management.
Çalık, A. (2021). A novel Pythagorean fuzzy AHP and fuzzy TOPSIS methodology for green supplier selection in the Industry 4.0 era. Soft Computing, 25(3), 2253–2265.CrossRef
Chen, Z., Ming, X., Zhou, T., & Chang, Y. (2020). Sustainable supplier selection for smart supply chain considering internal and external uncertainty: An integrated rough-fuzzy approach. Applied Soft Computing, 87, 106004.CrossRef
Ghadimi, P., Wang, C., Lim, M. K., & Heavey, C. (2019). Intelligent sustainable supplier selection using multi-agent technology: Theory and application for industry 4.0 supply chains. Computers & Industrial Engineering, 127, 588–600.CrossRef
Gupta, H., & Barua, M. K. (2017). Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production, 152, 242–258.CrossRef
Hasan, M. M., Jiang, D., Ullah, A. S., & Noor-E-Alam, M. (2020). Resilient supplier selection in logistics 4.0 with heterogeneous information. Expert Systems with Applications, 139, 112799.CrossRef
Janvier-James, A. M. (2012). A new introduction to supply chains and supply chain management: Definitions and theories perspective. International Business Research, 5(1), 194–207.
Jayant, A., Gupta, P., Garg, S. K., & Khan, M. (2014). TOPSIS-AHP based approach for selection of reverse logistics service provider: A case study of mobile phone industry. Procedia Engineering, 97, 2147–2156.CrossRef
Kaya, S. K., & Aycin, E. (2021). An integrated interval type 2 fuzzy AHP and COPRAS-G methodologies for supplier selection in the era of industry 4.0. Neural Computing and Applications, 1–21.
Li, Y. L., Ying, C. S., Chin, K. S., Yang, H. T., & Xu, J. (2018). Third-party reverse logistics provider selection approach based on hybrid-information MCDM and cumulative prospect theory. Journal of Cleaner Production, 195, 573–584.CrossRef
Memari, A., Dargi, A., Jokar, M. R. A., Ahmad, R., & Rahim, A. R. A. (2019). Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems, 50, 9–24.CrossRef
Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2017). A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Materials & Design, 121, 237–253.CrossRef
Özbek, A., & Yildiz, A. (2020). Digital supplier selection for a garment business using interval type-2 fuzzy topsis. Textile and Apparel, 30(1), 61–72.
Prakash, C., & Barua, M. K. (2015). Integration of AHP-TOPSIS method for prioritizing the solutions of reverse logistics adoption to overcome its barriers under fuzzy environment. Journal of Manufacturing Systems, 37, 599–615.CrossRef
Prakash, C., & Barua, M. K. (2016). A combined MCDM approach for evaluation and selection of third-party reverse logistics partner for Indian electronics industry. Sustainable Production and Consumption, 7, 66–78.CrossRef
Sachdeva, N., Shrivastava, A. K., & Chauhan, A. (2019). Modeling supplier selection in the era of industry 4.0. Benchmarking: An International Journal.
Senthil, S., Srirangacharyulu, B., & Ramesh, A. (2014). A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics. Expert Systems with Applications, 41(1), 50–58.CrossRef
Shakourloo, A., Kazemi, A., & Javad, M. O. M. (2016). A new model for more effective supplier selection and remanufacturing process in a closed-loop supply chain. Applied Mathematical Modelling, 40(23–24), 9914–9931.MathSciNetCrossRefMATH
Sinha, A. K., & Anand, A. (2018). Development of sustainable supplier selection index for new product development using multi criteria decision making. Journal of Cleaner Production, 197, 1587–1596.CrossRef
Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to compromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231.CrossRef
Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941–2962.CrossRef
Xu, Z., Qin, J., Liu, J., & Martínez, L. (2019). Sustainable supplier selection based on AHPSort II in interval type-2 fuzzy environment. Information Sciences, 483, 273–293.CrossRef
Yin, Y., Stecke, K. E., & Li, D. (2018). The evolution of production systems from industry 2.0 through industry 4.0. International Journal of Production Research, 56(1–2), 848–861.CrossRef
Zekhnini, K., Cherrafi, A., Bouhaddou, I., Benghabrit, Y., & Garza-Reyes, J. A. (2020). Supplier selection for smart supply chain: An adaptive fuzzy-neuro approach.
Metadata
Title
Efficient Supplier Selection in the Era of Industry 4.0
Authors
Deepanshu Nayak
Meenu Singh
Millie Pant
Sunil Kumar Jauhar
Copyright Year
2023
Book
Digital Transformation and Industry 4.0 for Sustainable Supply Chain Performance

Print ISBN: 978-3-031-19710-9

Electronic ISBN: 978-3-031-19711-6

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-19711-6_9