Skip to main content
SpringerLink
Log in

Can Industry 5.0 Develop a Resilient Supply Chain? An Integrated Decision-Making Approach by Analyzing I5.0 CSFs

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

Advances in science and technology act as the gatekeepers of a sustainable future where a stable environment helps generate the power for innovation. Supply chains are the messengers of this euphoric future. However, when the messengers and the gatekeepers are not in sync, the flow of information is bound to stop and bring about a chaotic turn of events, the repercussions of which can be felt through the years. The same was the case with the COVID-19 pandemic, where the lack of man–machine collaboration in Industry 4.0 and the inability of firms to advance their supply chains technologically left them exposed and vulnerable to the disruptions created by the pandemic. It was an eye-opener for companies worldwide as the supply chains collapsed and production reached a standstill. Thus, a stance arises to re-evaluate the resilience capabilities of the supply chains and rethink the priorities for achieving sustainable and resilient supply chain practices. We also suggest injecting industry 5.0 technologies to meet the re-assessed priorities. For this, we have identified the criteria and CSFs of supply chain resilience using the PRISMA 2020 statement and subsequently analyzed them using PF-AHP (for finding criteria weights), m-TISM (to interpret the interrelationships of the CSFs), PF-CoCoSo (to rank the CSFs) and sensitivity analysis (to check the robustness). The results suggest cost-effectiveness as the top weighted criteria and disruption awareness as the highest priority CSF for achieving supply chain resilience.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

Data is available on request.

References

  • Agarwal, N., Seth, N., & Agarwal, A. (2020). Modeling supply chain enablers for effective resilience. Continuity & Resilience Review,2(2), 97–110.

    Article  Google Scholar 

  • Akundi, A., Euresti, D., Luna, S., Ankobiah, W., Lopes, A., & Edinbarough, I. (2022). State of Industry 5.0—analysis and identification of current research trends. Applied System Innovation,5(1), 27.

    Article  Google Scholar 

  • Attri, R., Dev, N., & Sharma, V. (2013). Interpretive structural modelling (ISM) approach: An overview. Research Journal of Management Sciences,2319(2), 1171.

    Google Scholar 

  • Bag, S., Gupta, S., Choi, T. M., & Kumar, A. (2021). Roles of innovation leadership on using big data analytics to establish resilient healthcare supply chains to combat the COVID-19 pandemic: A multimethodological study. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2021.3101590

  • Balakrishnan, A. S., & Ramanathan, U. (2021). The role of digital technologies in supply chain resilience for emerging markets’ automotive sector. Supply Chain Management: An International Journal,26(6), 654–671.

    Google Scholar 

  • Belhadi, A., Kamble, S., Jabbour, C. J. C., Gunasekaran, A., Ndubisi, N. O., & Venkatesh, M. (2021). Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technological Forecasting and Social Change,163, 120447.

    Article  Google Scholar 

  • Bhargava, A., Bhargava, D., Kumar, P. N., Sajja, G. S., & Ray, S. (2022). Industrial IoT and AI implementation in vehicular logistics and supply chain management for vehicle mediated transportation systems. International Journal of System Assurance Engineering and Management,13(1), 673–680.

    Google Scholar 

  • Bhatia, M. S., & Kumar, S. (2020). Critical success factors of industry 4.0 in automotive manufacturing industry. IEEE Transactions on Engineering Management,69(5), 2439–2453.

    Article  Google Scholar 

  • Blessley, M., & Mudambi, S. M. (2022). A trade way and a pandemic: Disruption and resilience in the food bank supply chain. Industrial Marketing Management,102, 58–73.

    Article  Google Scholar 

  • Bouaynaya, W. (2020). Characterization of cloud computing reversibility as explored by the DELPHI method. Information Systems Frontiers,22(6), 1505–1518.

    Article  Google Scholar 

  • Boynton, A. C., & Zmud, R. W. (1984). An assessment of critical success factors. Sloan Management Review,25(4), 17–27.

    Google Scholar 

  • Cheng, E. (2022). Here's where China's real estate troubles could spill over. CNBC. Retrieved August 12, 2022, from https://www.cnbc.com/2022/08/12/heres-where-chinas-real-estate-troubles-could-spill-over-.html - 10:40 accessed

  • Chou, C. H., Zahedi, F. M., & Zhao, H. (2014). Ontology-based evaluation of natural disaster management websites. Mis Quarterly,38(4), 997–1016.

    Article  Google Scholar 

  • Choudrie, J., Patil, S., Kotecha, K., Matta, N., & Pappas, I. (2021). Applying and understanding an advanced, novel deep learning approach: A Covid 19, text based, emotions analysis study. Information Systems Frontiers,23(6), 1431–1465.

    Article  Google Scholar 

  • Chowdhury, M. M. H., & Quaddus, M. (2017). Supply chain resilience: Conceptualization and scale development using dynamic capability theory. International Journal of Production Economics,188, 185–204.

    Article  Google Scholar 

  • Christopher Frey, H., & Patil, S. R. (2002). Identification and review of sensitivity analysis methods. Risk Analysis, 22(3), 553–578.

    Article  Google Scholar 

  • Clendenin, J. A. (1997). Closing the supply chain loop: Reengineering the returns channel process. The International Journal of Logistics Management,8(1), 75–86.

    Article  Google Scholar 

  • Crook, T. R., & Combs, J. G. (2007). Sources and consequences of bargaining power in supply chains. Journal of Operations Management,25(2), 546–555.

    Article  Google Scholar 

  • Darko, A., Chan, A. P. C., Ameyaw, E. E., Owusu, E. K., Pärn, E., & Edwards, D. J. (2019). Review of application of analytic hierarchy process (AHP) in construction. International Journal of Construction Management,19(5), 436–452.

    Article  Google Scholar 

  • Davis, K. F., Downs, S., & Gephart, J. A. (2021). Towards food supply chain resilience to environmental shocks. Nature Food,2(1), 54–65.

    Article  Google Scholar 

  • Demir, K. A., Döven, G., & Sezen, B. (2019). Industry 5.0 and human-robot co-working. Procedia Computer Science,158, 688–695.

    Article  Google Scholar 

  • Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., & Papadopoulos, T. (2020). Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. International Journal of Production Research,58(11), 3381–3398.

    Article  Google Scholar 

  • Dumas, J., Sorce, J., & Virzi, R. (1995). Expert reviews: how many experts is enough?. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 39, No. 4, pp. 228–232). SAGE Publications.

  • Fontana, M. E., Vidal, R., Marques, P. C., & Silva, W. D. O. (2023). Priority model for critical success factors in reverse flow inventory management. International Journal of Inventory Research,6(2), 116–134.

    Article  Google Scholar 

  • Galbreath, J. (2005). Which resources matter the most to firm success? An exploratory study of resource-based theory. Technovation,25(9), 979–987.

    Article  Google Scholar 

  • Han, L., Hou, H., Bi, Z. M., Yang, J., & Zheng, X. (2021). Functional requirements and supply chain digitalization in industry 4.0. Information Systems Frontiers, 1–13.

  • Hillman, A. J., Withers, M. C., & Collins, B. J. (2009). Resource dependence theory: A review. Journal of Management,35(6), 1404–1427.

    Article  Google Scholar 

  • Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part e: Logistics and Transportation Review,125, 285–307.

    Article  Google Scholar 

  • Hudnurkar, M., Jakhar, S., & Rathod, U. (2014). Factors affecting collaboration in supply chain: A literature review. Procedia-Social and Behavioral Sciences,133, 189–202.

    Article  Google Scholar 

  • Hugos, M. H. (2018). Essentials of supply chain management. John Wiley & Sons.

    Book  Google Scholar 

  • Humayun, M. (2021). Industrial Revolution 5.0 and the role of cutting edge technologies. International Journal of Advanced Computer Science and Applications, 12(12), 34–57.

  • Iftikhar, A., Purvis, L., Giannoccaro, I., & Wang, Y. (2022). The impact of supply chain complexities on supply chain resilience: The mediating effect of big data analytics. Production Planning & Control, 1–21.

  • Ilbahar, E., Karaşan, A., Cebi, S., & Kahraman, C. (2018). A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Safety Science,103, 124–136.

    Article  Google Scholar 

  • Ivanov, D. (2018). Revealing interfaces of supply chain resilience and sustainability: A simulation study. International Journal of Production Research,56(10), 3507–3523.

    Article  Google Scholar 

  • Jain, V., Kumar, S., Soni, U., & Chandra, C. (2017). Supply chain resilience: Model development and empirical analysis. International Journal of Production Research,55(22), 6779–6800.

    Article  Google Scholar 

  • Jena, J., Sidharth, S., Thakur, L. S., Kumar Pathak, D., & Pandey, V. C. (2017). Total interpretive structural modeling (TISM): Approach and application. Journal of Advances in Management Research,14(2), 162–181.

    Article  Google Scholar 

  • Kamalahmadi, M., & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics,171, 116–133.

    Article  Google Scholar 

  • Kannan, D. (2018). Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process. International Journal of Production Economics,195, 391–418.

    Article  Google Scholar 

  • Karl, A. A., Micheluzzi, J., Leite, L. R., & Pereira, C. R. (2018). Supply chain resilience and key performance indicators: a systematic literature review. Production, 28, 45–67.

  • Kazancoglu, I., Ozbiltekin-Pala, M., Mangla, S. K., Kazancoglu, Y., & Jabeen, F. (2022). Role of flexibility, Agility and responsiveness for sustainable supply chain resilience during COVID-19. Journal of Cleaner Production, 132431.

  • Khan, S., & Haleem, A. (2021). Investigation of circular economy practices in the context of emerging economies: A CoCoSo approach. International Journal of Sustainable Engineering,14(3), 357–367.

    Article  Google Scholar 

  • Lahane, S., & Kant, R. (2021). A hybrid Pythagorean fuzzy AHP–CoCoSo framework to rank the performance outcomes of circular supply chain due to adoption of its enablers. Waste Management,130, 48–60.

    Article  Google Scholar 

  • Lahane, S., & Kant, R. (2021). Evaluating the circular supply chain implementation barriers using Pythagorean fuzzy AHP-DEMATEL approach. Cleaner Logistics and Supply Chain,2, 100014.

    Article  Google Scholar 

  • Landeta, J. (2006). Current validity of the Delphi method in social sciences. Technological Forecasting and Social Change,73(5), 467–482.

    Article  Google Scholar 

  • Li, X., Wu, Q., Holsapple, C. W., & Goldsby, T. (2017). An empirical examination of firm financial performance along dimensions of supply chain resilience. Management Research Review,40(3), 254–269.

    Article  Google Scholar 

  • Liao, H., Qin, R., Wu, D., Yazdani, M., & Zavadskas, E. K. (2020). Pythagorean fuzzy combined compromise solution method integrating the cumulative prospect theory and combined weights for cold chain logistics distribution center selection. International Journal of Intelligent Systems,35(12), 2009–2031.

    Article  Google Scholar 

  • Linstone, H. A., & Turoff, M. (Eds.). (1975). The delphi method (pp. 3–12). Addison-Wesley.

    Google Scholar 

  • Liu, P., Rani, P., & Mishra, A. R. (2021). A novel Pythagorean fuzzy combined compromise solution framework for the assessment of medical waste treatment technology. Journal of Cleaner Production,292, 126047.

    Article  Google Scholar 

  • Longo, F., Padovano, A., & Umbrello, S. (2020). Value-oriented and ethical technology engineering in industry 5.0: A human-centric perspective for the design of the factory of the future. Applied Sciences,10(12), 4182.

    Article  Google Scholar 

  • Lu, Y., Zheng, H., Chand, S., Xia, W., Liu, Z., Xu, X., ... & Bao, J. (2022). Outlook on human-centric manufacturing towards Industry 5.0. Journal of Manufacturing Systems, 62, 612–627.

  • Madsen, D. Ø., & Berg, T. (2021). An exploratory bibliometric analysis of the birth and emergence of industry 5.0. Applied System. Innovation,4(4), 87.

    Google Scholar 

  • Mandal, S. (2012). An empirical investigation into supply chain resilience. IUP Journal of supply chain management, 9(4), 46–59.

  • Mandal, S. (2019). The influence of big data analytics management capabilities on supply chain preparedness, alertness and Agility: An empirical investigation. Information Technology & People,32(2), 297–318.

    Article  Google Scholar 

  • Mandal, S., Sarathy, R., Korasiga, V. R., Bhattacharya, S., & Dastidar, S. G. (2016). Achieving supply chain resilience: The contribution of logistics and supply chain capabilities. International Journal of Disaster Resilience in the Built Environment,7(5), 544–562.

    Article  Google Scholar 

  • Mangla, S. K., Govindan, K., & Luthra, S. (2017). Prioritizing the barriers to achieve sustainable consumption and production trends in supply chains using fuzzy Analytical Hierarchy Process. Journal of Cleaner Production,151, 509–525.

    Article  Google Scholar 

  • Martynov, V. V., Shavaleeva, D. N., & Zaytseva, A. A. (2019). Information technology as the basis for transformation into a digital society and industry 5.0. In 2019 International Conference“ Quality Management, Transport and Information Security, Information Technologies”(IT&QM&IS) (pp. 539–543). IEEE.

  • Meena, A., & Dhir, S. (2021). An analysis of growth-accelerating factors for the Indian automotive industry using modified TISM. International Journal of Productivity and Performance Management,70(6), 1361–1392.

    Article  Google Scholar 

  • Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). Defining supply chain management. Journal of Business Logistics,22(2), 1–25.

    Article  Google Scholar 

  • Mohr, D. C., Spring, B., Freedland, K. E., Beckner, V., Arean, P., Hollon, S. D., ..., & Kaplan, R. (2009). The selection and design of control conditions for randomized controlled trials of psychological interventions. Psychotherapy and psychosomatics, 78(5), 275–284.

  • Mondal, S., & Samaddar, K. (2023). Reinforcing the significance of human factor in achieving quality performance in data-driven supply chain management. The TQM Journal,35(1), 183–209.

    Article  Google Scholar 

  • Nagarajan, S. M., Deverajan, G. G., Chatterjee, P., Alnumay, W., & Muthukumaran, V. (2022). Integration of IoT based routing process for food supply chain management in sustainable smart cities. Sustainable Cities and Society,76, 103448.

    Article  Google Scholar 

  • Nahavandi, S. (2019). Industry 5.0—A human-centric solution. Sustainability,11(16), 4371.

    Article  Google Scholar 

  • Nakatani, J., Tahara, K., Nakajima, K., Daigo, I., Kurishima, H., Kudoh, Y., ... & Moriguchi, Y. (2018). A graph theory-based methodology for vulnerability assessment of supply chains using the life cycle inventory database. Omega, 75, 165–181.

  • Nikookar, E., & Yanadori, Y. (2022). Preparing supply chain for the next disruption beyond COVID-19: Managerial antecedents of supply chain resilience. International Journal of Operations & Production Management,42(1), 59–90.

    Article  Google Scholar 

  • Orhan, E. (2022). The Effects of the Russia-Ukraine War on Global Trade. Journal of International Trade, Logistics and Law,8(1), 141–146.

    Google Scholar 

  • Ozdemir, D., Sharma, M., Dhir, A., & Daim, T. (2022). Supply chain resilience during the COVID-19 pandemic. Technology in Society,68, 101847.

    Article  Google Scholar 

  • Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ..., & Moher, D. (2021). Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. Journal of clinicalepidemiology, 134, 103–112.

  • Pathak, P., Pal, P. R., Shrivastava, M., & Ora, P. (2019). Fifth revolution: Applied AI & human intelligence with cyber physical systems. International Journal of Engineering and Advanced Technology,8(3), 23–27.

    Google Scholar 

  • Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management,20(1), 124–143.

    Article  Google Scholar 

  • Queiroz, M. M., Wamba, S. F., Jabbour, C. J. C., & Machado, M. C. (2022a). Supply chain resilience in the UK during the coronavirus pandemic: A resource orchestration perspective. International Journal of Production Economics,245, 108405.

  • Queiroz, M. M., Ivanov, D., Dolgui, A., & Fosso Wamba, S. (2022b). Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals ofoperations research, 319(1), 1159–1196.

  • Rajesh, R. (2017). Technological capabilities and supply chain resilience of firms: A relational analysis using Total Interpretive Structural Modeling (TISM). Technological Forecasting and Social Change,118, 161–169.

    Article  Google Scholar 

  • Raut, R. D., Narkhede, B., & Gardas, B. B. (2017). To identify the critical success factors of sustainable supply chain management practices in the context of oil and gas industries: ISM approach. Renewable and Sustainable Energy Reviews,68, 33–47.

    Article  Google Scholar 

  • Remko, V. H. (2020). Research opportunities for a more resilient post-COVID-19 supply chain–closing the gap between research findings and industry practice. International Journal of Operations & Production Management,40(4), 341–355.

    Article  Google Scholar 

  • Ribeiro, J. P., & Barbosa-Povoa, A. (2018). Supply Chain Resilience: Definitions and quantitative modelling approaches–A literature review. Computers & Industrial Engineering,115, 109–122.

    Article  Google Scholar 

  • Ruiz-Benítez, R., López, C., & Real, J. C. (2018). The lean and resilient management of the supply chain and its impact on performance. International Journal of Production Economics,203, 190–202.

    Article  Google Scholar 

  • Sabahi, S., & Parast, M. M. (2020). Firm innovation and supply chain resilience: A dynamic capability perspective. International Journal of Logistics Research and Applications,23(3), 254–269.

    Article  Google Scholar 

  • Scholten, K., & Schilder, S. (2015). The role of collaboration in supply chain resilience. Supply Chain Management: An International Journal,20(4), 471–484.

    Article  Google Scholar 

  • Scholten, K., Sharkey Scott, P., & Fynes, B. (2019). Building routines for non-routine events: Supply chain resilience learning mechanisms and their antecedents. Supply Chain Management: An International Journal,24(3), 430–442.

    Article  Google Scholar 

  • Sindhwani, R., Singh, P. L., Behl, A., Afridi, M. S., Sammanit, D., & Tiwari, A. K. (2022a). Modeling the critical success factors of implementing net zero emission (NZE) and promoting resilience and social value creation. Technological Forecasting and Social Change, 181, 121759.

  • Sindhwani, R., Chakraborty, S., Behl, A., & Pereira, V. (2022b). Building resilience to handle disruptions in criticalenvironmental and energy sectors: Implications for cleaner production in the oil and gas industry. Journal of CleanerProduction, 365, 132692.

  • Singh, C. S., Soni, G., & Badhotiya, G. K. (2019). Performance indicators for supply chain resilience: Review and conceptual framework. Journal of Industrial Engineering International,15(1), 105–117.

    Article  Google Scholar 

  • Spekman, R. E., Kamauff, J. W., & Myhr, N. (1998). An empirical investigation into supply chain management: A perspective on partnerships. Supply Chain Management: An International Journal,3(2), 53–67.

    Article  Google Scholar 

  • Sushil, A. (2017). Modified ISM/TISM process with simultaneous transitivity checks for reduced direct pair comparisons. Global Journal of Flexible Systems Management,18(4), 331–351.

    Article  Google Scholar 

  • Sushil, S. (2012). Interpreting the interpretive structural model. Global Journal of Flexible Systems Management,13(2), 87–106.

    Article  Google Scholar 

  • Tan, K. C., Kannan, V. R., & Handfield, R. B. (1998). Supply chain management: supplier performance and firm performance. International Journal of Purchasing & Materials Management, 34(3), 1–23.

  • Tan, W. J., Cai, W., & Zhang, A. N. (2020). Structural-aware simulation analysis of supply chain resilience. International Journal of Production Research,58(17), 5175–5195.

    Article  Google Scholar 

  • Tan, W. J., Zhang, A. N., & Cai, W. (2019). A graph-based model to measure structural redundancy for supply chain resilience. International Journal of Production Research,57(20), 6385–6404.

    Article  Google Scholar 

  • Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics: Research and Applications,9(1), 33–45.

    Article  Google Scholar 

  • Tremblay, M. C., Kohli, R., & Rivero, C. (2023). Data is the new protein: how the commonwealth of virginia built digital resilience muscle and rebounded from opioid and covid shocks. MIS Quarterly, 47(1), 423–454.

  • Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research,169(1), 1–29.

    Article  Google Scholar 

  • Wang, Y., Xu, L., & Solangi, Y. A. (2020). Strategic renewable energy resources selection for Pakistan: Based on SWOT-Fuzzy AHP approach. Sustainable Cities and Society,52, 101861.

    Article  Google Scholar 

  • Wu, L. Y. (2010). Applicability of the resource-based and dynamic-capability views under environmental volatility. Journal of Business Research,63(1), 27–31.

    Article  Google Scholar 

  • Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of Manufacturing Systems,61, 530–535.

    Article  Google Scholar 

  • Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision,57(9), 2501–2519.

    Article  Google Scholar 

  • Yu, W., Jacobs, M. A., Chavez, R., & Yang, J. (2019). Dynamism, disruption orientation, and resilience in the supply chain and the impacts on financial performance: A dynamic capabilities perspective. International Journal of Production Economics,218, 352–362.

    Article  Google Scholar 

  • Zizic, M. C., Mladineo, M., Gjeldum, N., & Celent, L. (2022). From Industry 4.0 towards Industry 5.0: A review and analysis of paradigm shift for the people, organization and technology. Energies,15(14), 5221.

    Article  Google Scholar 

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Indian Institute of Management Sambalpur, Odisha, India

    Rahul Sindhwani

  2. Management Development Institute, Gurgaon, India

    Abhishek Behl

  3. Avgol Nonwovens, Mandideep, India

    Ramandeep Singh

  4. Hull University Business School, University of Hull, Hull, UK

    Sushma Kumari

Authors
  1. Rahul Sindhwani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abhishek Behl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ramandeep Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sushma Kumari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhishek Behl.

Ethics declarations

Conflict of Interest

None.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sindhwani, R., Behl, A., Singh, R. et al. Can Industry 5.0 Develop a Resilient Supply Chain? An Integrated Decision-Making Approach by Analyzing I5.0 CSFs. Inf Syst Front (2024). https://doi.org/10.1007/s10796-024-10486-x

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10796-024-10486-x

Keywords

Search

Navigation