Skip to main content
Log in

Leveraging Smart Supply Chain and Information System Agility for Supply Chain Flexibility

Information Systems Frontiers Aims and scope Submit manuscript

Abstract

Global businesses are leveraging their analytical capabilities to develop competence over others. This study uses Organization Information Processing Theory (OIPT) in context to explain the relationship between the smart supply chain and information system flexibility to achieve an overall greater supply chain flexibility. Also, this shows that correct deployment of information processing leads to better diffusion of information throughout the system necessary for making the supply chain more adaptive in nature. This study extends the application of OIPT theory and a better understanding of analytical data processing and theoretically grounded guidance to managers in order to achieve a higher degree of flexibility in dynamic conditions. The Partial Least Square Method based on Structural Equation Modeling is used to empirically test the theoretical framework. Results from the analysis of 150 respondents indicate the strong relationship between the components of the smart supply chain and information systems agility. The research shows a positive relationship between the characteristics of smart supply chain management and modules of information system flexibility which leads to the achievement of a high level of supply chain flexibility.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

References

  • Ahmed, E., Yaqoob, I., Abaker, I., Hashem, T., Khan, I., Ibrahim, A., & Vasilakos, A. V. (2017). The role of big data analytics in internet of things. Computer Networks, 129, 459–471. https://doi.org/10.1016/j.comnet.2017.06.013.

    Article  Google Scholar 

  • APICS (2015). Supply chain operations reference model: Quick reference guide, Revision 11.0. http://www.apics.org/docs/default-source/scor-p-toolkits/apics-scc-scor-quick-reference-guide.pdf?sfvrsn=2. Accessed 20 Dec 2018

  • Arshinder, K. A., & Deshmukh, S. G. (2008). Supply chain coordination: Perspectives, empirical studies and research directions. International Journal of Production Economics, 115(2), 316–335. https://doi.org/10.1016/j.ijpe.2008.05.011.

    Article  Google Scholar 

  • Astrachan, C. B., Patel, V. K., & Wanzenried, G. (2014). A comparative study of CB-SEM and PLS-SEM for theory development in family firm research. Journal of Family Business Strategy, 5(1), 116–128.

    Article  Google Scholar 

  • Avittathur, B., & Swamidass, P. (2007). Matching plant flexibility and supplier flexibility: Lessons from small suppliers of US manufacturing plants in India. Journal of Operations Management, 25(3), 717–735.

    Article  Google Scholar 

  • Batty, M. (2013). Big data, smart cities and city planning. Dialogues in Human Geography, 3(3), 274–279. https://doi.org/10.1177/2043820613513390.

    Article  Google Scholar 

  • Beamon, B. M. (1999). Measuring supply chain performance. International Journal of Operations & Production Management, 19(3), 275–292. https://doi.org/10.1108/01443579910249714.

    Article  Google Scholar 

  • Beck, K. (2000). Extreme programming explained: Embrace change. Boston: Addison-Wesley.

    Google Scholar 

  • Bendavid, Y., & Cassivi, L. (2010). Bridging the gap between RFID/EPC concepts, technological requirements and supply chain e-business processes. Journal of Theoretical and Applied Electronic Commerce Research, 5(3), 1–16. https://doi.org/10.4067/S0718-18762010000300002.

    Article  Google Scholar 

  • Ben-Daya, M., Hassini, E., & Bahroun, Z. (2017). Internet of things and supply chain management: A literature review. International Journal of Production Research, 7543(November), 1–24. https://doi.org/10.1080/00207543.2017.1402140.

    Article  Google Scholar 

  • Bensaou, M., & Venkatraman, N. (1995). Configurations of interorganizational relationships: A comparison between U.S. and Japanese automakers. Management Science, 41(9), 1471–1492.

    Article  Google Scholar 

  • Bowles, M., & Lu, J. (2014). Technological forecasting and social change removing the blinders: A literature review on the potential of nanoscale technologies for the management of supply chains. Technological Forecasting and Social Change, 82, 190–198. https://doi.org/10.1016/j.techfore.2013.10.017.

    Article  Google Scholar 

  • Bozionelos, N., & Singh, S. K. (2017). The relationships of emotional intelligence with task and contextual performance: More than it meets the linear eyes. Personality and Individual Differences, 116, 206–211.

    Article  Google Scholar 

  • Butner, K. (2010). The smarter supply chain of the future. Strategy and Leadership, 38(1), 22–23. https://doi.org/10.1108/10878571011009859.

    Article  Google Scholar 

  • Cai, S., Jun, M., & Yang, Z. (2010). Implementing supply chain information integration in China: The role of institutional forces and trust. Journal of Operations Management, 28(3), 257–268.

    Article  Google Scholar 

  • Carr, A. S., & Pearson, J. N. (1999). Strategically managed buyer-seller relationships and performance outcomes. Journal of Operations Management, 17(5), 497–519.

    Article  Google Scholar 

  • Chan, A. T. L., Ngai, E. W. T., & Moon, K. K. L. (2017). The effects of strategic and manufacturing flexibilities and supply chain agility on firm performance in the fashion industry. European Journal of Operational Research. Elsevier B.V., 259(2), 486–499. https://doi.org/10.1016/j.ejor.2016.11.006.

    Article  Google Scholar 

  • Charles, A., Lauras, M., & Wassenhove, L. V. (2010). A model to define and assess the agility of supply chains: Building on humanitarian experience. International Journal of Physical Distribution & Logistics Management, 40(8/9), 722–741. https://doi.org/10.1108/09600031011079355.

    Article  Google Scholar 

  • Chatterjee, S., Kar, A. K., & Gupta, M. P. (2018). Success of IoT in smart cities of India: An empirical analysis. Government Information Quarterly, 35(3), 349–361. https://doi.org/10.1016/j.giq.2018.05.002.

    Article  Google Scholar 

  • Cho, V., & Chan, A. (2015). An integrative framework of comparing SaaS adoption for core and non-core business operations: An empirical study on Hong Kong industries. Information Systems Frontiers, 17(3), 629–644.

    Article  Google Scholar 

  • Chopra, S. & Meindl, P. (2013). Supply chain management: Strategy, planning and operations, 5th ed. Pearson.

  • Christopher, M., & Holweg, M. (2011). “Supply chain 2.0”: Managing supply chains in the era of turbulence. International Journal of Physical Distribution and Logistics Management, 41(1), 63–82. https://doi.org/10.1108/09600031111101439.

    Article  Google Scholar 

  • Clegg, S. R., Hardy, C., & Nord, W. R. (Eds.). (1995). Handbook of organization studies (pp. 440–458). Thousand Oaks: Sage.

    Google Scholar 

  • Closs, D. J., Speier, C., & Meacham, N. (2011). Sustainability to support end-to-end value chains: The role of supply chain management. Journal of the Academy of Marketing Science 39(1), 101–116. https://doi.org/10.1007/s11747-010-0207-4.

  • Cockburn, A. (2001), Agile Software Development, Addison Wesley Longman, Glen View, IL.

  • Cooper, M. C., Douglas, M. L., & Janus, D. P. (1997). Supply chain management: More than a new name for logistics. International Journal of Logistics Management, 8(1), 1–14.

    Article  Google Scholar 

  • Council of Supply Chain Management Professionals (2005). Supply Chain Management/ Logistics Management Definitions. http://www.cscmp.org/Downloads/Resources/glossary03.pdf. Accessed 20 Dec 2018

  • Daft, R. L., & Lengel, R. H. (1986). Organizational information requirements, media richness and structural design. Management Science, 32(5), 554–571.

    Article  Google Scholar 

  • Das, K. (2011). Integrating effective flexibility measures into a strategic supply chain planning model. European Journal of Operational Research, 211(1), 170–183.

    Article  Google Scholar 

  • Drury-Grogan, M. L. (2014). Performance on agile teams: Relating iteration objectives and critical decisions to project management success factors. Information and Software Technology, 56(5), 506–515. https://doi.org/10.1016/j.infsof.2013.11.003.

    Article  Google Scholar 

  • Dubey, R., & Bag, S. (2013). Exploring the dimensions of sustainable practices: An empirical study on Indian manufacturing firms. International Journal of Operations and Quantitative Management, 19(2), 123–146.

    Google Scholar 

  • Dubey, R., Gunasekaran, A., Papadopoulos, T., Childe, S. J., Shibin, K. T., & Fosso Wamba, S. (2017a). Sustainable supply chain management: Framework and further research directions. Journal of Cleaner Production, 142, 1119–1130.

    Article  Google Scholar 

  • Dubey, R, Gunasekaran, A, Childe, S.J., Papadopoulos, T., Luo, Z, Fosso-Wamba, S. & Roubaud, D. (2017b). Can big data and predictive analytics improve social and environmental sustainability? Technological Forecasting and Social Change, In Press. https://doi.org/10.1016/j.techfore.2017.06.020.

  • Duclos, L. K., Vokurka, R. J., & Lummus, R. R. (2003). A conceptual model of supply chain flexibility. Industrial Management and Data Systems, 103(6), 446–456.

    Article  Google Scholar 

  • Dwivedi, Y. K., Janssen, M., Slade, E. L., Rana, N. P., Weerakkody, V., Millard, J., & Snijders, D. (2017). Driving innovation through big open linked data (BOLD): Exploring antecedents using interpretive structural modelling. Information Systems Frontiers, 19, 197–212. https://doi.org/10.1007/s10796-016-9675-5.

    Article  Google Scholar 

  • El-Kassar, A. & Singh, S.K. (2018). Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices. Technological Forecasting and Social Change, (Accepted & in Press), https://doi.org/10.1016/j.techfore.2017.12.016.

  • Fang, S., Xu, L., Zhu, Y., Liu, Y., Liu, Z., Pei, H., Yan, J., & Zhang, H. (2015). An integrated information system for snowmelt flood early-warning based on internet of things. Information Systems Frontiers, 17(2), 321–335. https://doi.org/10.1007/s10796-013-9466-1.

    Article  Google Scholar 

  • Fescioglu-Unver, N., Choi, S. H., Sheen, D., & Kumara, S. (2015). RFID in production and service systems: Technology, applications and issues. Information Systems Frontiers, 17(6), 1369–1380. https://doi.org/10.1007/s10796-014-9518-1.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Galbraith, J. R. (1973). Designing complex organizations. MA: Addison-Wesley.

    Google Scholar 

  • Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces (Providence), 4(3), 28–36. https://doi.org/10.1287/inte.4.3.28.

    Article  Google Scholar 

  • Galbraith, J. R. (1977). Organization design. MA: Addison-Wesley.

    Google Scholar 

  • Gill, A. Q., Henderson-Sellers, B., & Niazi, M. (2016). Scaling for agility: A reference model for hybrid traditional-agile software development methodologies. Information Systems Frontiers, 20, 1–27. https://doi.org/10.1007/s10796-016-9672-8.

    Article  Google Scholar 

  • Grover, P., & Kar, A. K. (2017). Big data analytics: A review on theoretical contributions and tools used in literature. Global Journal of Flexible Systems Management, 18(3), 203–229. https://doi.org/10.1007/s40171-017-0159-3.

    Article  Google Scholar 

  • Gupta, Y. P., & Somers, T. M. (1992). The measurement of manufacturing flexibility. European Journal of Operational Research, 60(2), 166–182.

    Article  Google Scholar 

  • Gupta, S., Kumar, S., Singh, S. K., Foropon, C., & Chandra, C. (2018a). Role of cloud ERP on the performance of an organization: Contingent resource-based view perspective. The International Journal of Logistics Management, 29(2), 659–675. https://doi.org/10.1108/IJLM-07-2017-0192.

    Article  Google Scholar 

  • Gupta, S., Kar, A. K., Baabdullah, A., & Al-khowaiter, W. A. A. (2018b). Big data with cognitive computing: A review for the future. International Journal of Information Management, 42, 78–89. https://doi.org/10.1016/j.ijinfomgt.2018.06.005.

    Article  Google Scholar 

  • Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis (6th ed.). Uppersaddle River: Pearson Prentice Hall.

    Google Scholar 

  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–151.

    Article  Google Scholar 

  • Hair, J.F., Hult, T., Ringle, C.M. & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM), Sage, 2nd Edition, ISBN: 9781483377445.

  • Harris, M. L., Hevner, A. R., & Collins, R. W. (2009). Controls in flexible software development controls in flexible software development. Communications of the Association for Information Systems, 24(June), 757–776.

    Google Scholar 

  • Heide, J. B., & John, G. (1990). Alliances in industrial purchasing: The determinants of joint action in buyer-supplier relationships. Journal of Marketing Research, 27(1), 24–36.

    Article  Google Scholar 

  • Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., & Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182–209.

    Article  Google Scholar 

  • Highsmith, J. (2002). Agile software development ecosystems. Addison-Wesley Professional. Vol 13.

  • Hill, R. M., & Omar, M. (2006). Another look at the single-vendor single-buyer integrated production-inventory problem. International Journal of Production Research, 44(4), 791–800. https://doi.org/10.1080/00207540500334285.

    Article  Google Scholar 

  • Huber, G. P. (1990). A theory of the effects of advanced information technologies on organizational design, intelligence and decision making. The Academy of Management Review, 15(1), 47–71.

    Article  Google Scholar 

  • Hyer, N. L., & Brown, K. A. (1999). The discipline of real cells. Journal of Operations Management, 17(5), 557–574.

    Article  Google Scholar 

  • Janssen, M., & van den Hoven, J. (2015). Big and open linked data (BOLD) in government: A challenge to transparency and privacy? Government Information Quarterly, 32(4), 363–368. https://doi.org/10.1016/j.giq.2015.11.007.

    Article  Google Scholar 

  • Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2012). Benefits, adoption barriers and myths of open data and open government. Information Systems Management, 29(4), 258–268. https://doi.org/10.1080/10580530.2012.716740.

    Article  Google Scholar 

  • Kaur, H., & Singh, S. P. (2017). Flexible dynamic sustainable procurement model. Annals of Operations Research, 273, 1–41. https://doi.org/10.1007/s10479-017-2434-2.

    Article  Google Scholar 

  • Kock, N. (2016). Non-normality propagation among latent variables and indicators in PLS-SEM simulations. Journal of Modern Applied Statistical Methods, 15(1), 299–315.

    Article  Google Scholar 

  • Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546–580.

    Article  Google Scholar 

  • Koo, C., Ricci, F., Cobanoglu, C., & Okumus, F. (2017). Special issue on smart, connected hospitality and tourism. Information Systems Frontiers, 19(4), 699–703. https://doi.org/10.1007/s10796-017-9776-9.

    Article  Google Scholar 

  • Laaksonen, T., Jarimo, T., & Kulmala, H. I. (2009). Cooperative strategies in customer-supplier relationships: The role of interfirm trust. International Journal of Production Economics, 120(1), 79–87. https://doi.org/10.1016/j.ijpe.2008.07.029.

    Article  Google Scholar 

  • Lee, H. (2004). The triple-a supply chain. Harvard Business Review., 82(10), 102–112.

    Google Scholar 

  • Liu, R., & Kumar, A. (2011). Leveraging information sharing to configure supply chains. Information Systems Frontiers, 13(1), 139–151. https://doi.org/10.1007/s10796-009-9222-8.

    Article  Google Scholar 

  • Liu, G. J., Shah, R., & Schroeder, R. G. (2006). Linking work design to mass customization: A sociotechnical systems perspective. Decision Sciences, 37(4), 519–545.

    Article  Google Scholar 

  • Lukić, J., Radenković, M., Despotović-Zrakić, M., Labus, A., & Bogdanović, Z. (2017). Supply chain intelligence for electricity markets: A smart grid perspective. Information Systems Frontiers, 19(1), 91–107.

    Article  Google Scholar 

  • Majeed, M. A. A., & Rupasinghe, T. D. (2017). Internet of things (IoT) embedded future supply chains for industry 4.0: An assessment from an ERP-based fashion apparel and footwear industry. International Journal of Supply Chain Management, 6(1), 25–40.

    Google Scholar 

  • Manders, J. H. M., Caniëls, M. C. J., & Ghijsen, P. W. T. (2017). Supply chain flexibility: A systematic literature review and identification of directions for future research. The International Journal of Logistics Management, 28(4), 964–1026.

    Article  Google Scholar 

  • Mani, V., Gunasekaran, A., Papadopoulos, T., Hazend, B., & Dubey, R. (2016). Supply chain social sustainability for developing nations: Evidence from India. Resources, Conservation and Recycling, 111, 42–52.

    Article  Google Scholar 

  • Modi, S. B., & Mabert, V. A. (2007). Supplier development: Improving supplier performance through knowledge transfer. Journal of Operations Management, 25(1), 42–64. https://doi.org/10.1016/j.jom.2006.02.001.

    Article  Google Scholar 

  • Moon, K. K-L., Yi, C. Y., & Ngai, E. W. T. (2012). An instrument for measuring supply chain flexibility for the textile and clothing companies. European Journal of Operational Research, 222 (2), 191–203, https://doi.org/10.1016/j.ejor.2012.04.027

  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill Inc.

    Google Scholar 

  • Peng, D. X., Heim, G. R., & Mallick, D. N. (2014). Collaborative product development: The effect of project complexity on the use of information technology tools and new product development practices. Production and Operation Management, 23(8), 1421–1438.

    Article  Google Scholar 

  • Plachkinova, M., Vo, A., Bhaskar, R., & Hilton, B. (2018). A conceptual framework for quality healthcare accessibility: A scalable approach for big data technologies. Information Systems Frontiers, 20, 289–302. https://doi.org/10.1007/s10796-016-9726-y.

    Article  Google Scholar 

  • Popovič, A., Hackney, R., Tassabehji, R., & Castelli, M. (2018). The impact of big data analytics on firms’ high value business performance. Information Systems Frontiers, 20, 209–222. https://doi.org/10.1007/s10796-016-9720-4.

    Article  Google Scholar 

  • Primo, M. A. M., & Amundson, S. D. (2002). An exploratory study of the effects of supplier relationships on new product development outcomes. Journal of Operations Management., 20(1), 33–52.

    Article  Google Scholar 

  • Rhee, S. H., Bae, H., & Choi, Y. (2007). Enhancing the efficiency of supply chain processes through web services. Information Systems Frontiers, 9(1), 103–118. https://doi.org/10.1007/s10796-006-9020-5.

    Article  Google Scholar 

  • Slack, N. (1983). Flexibility as a manufacturing objective. International Journal of Operations & Production Management, 3(3), 4–13. https://doi.org/10.1108/eb054696.

    Article  Google Scholar 

  • Srinivasan, R., & Swink, M. (2017). An investigation of visibility and flexibility as complements to supply chain analytics: Organizational information processing theory perspective. Production and Operations Management, 27, 1–19. https://doi.org/10.1111/poms.12746.

    Article  Google Scholar 

  • Stephens, S. (2001). Supply chain operations reference model version 5.0: A new tool to improve supply chain efficiency and achieve best practice. Information Systems Frontiers, 3(4), 471–476. https://doi.org/10.1023/A:1012881006783.

    Article  Google Scholar 

  • Sushil. (2015). Creating flexibility through technological and attitudinal change. Global Journal of Flexible Systems Management, 16(4), 309–311. https://doi.org/10.1007/s40171-015-0112-2.

    Article  Google Scholar 

  • Tan, K. H., Wong, W. P., & Chung, L. (2016). Information and knowledge leakage in supply chain. Information Systems Frontiers, 18(3), 621–638. https://doi.org/10.1007/s10796-015-9553-6.

    Article  Google Scholar 

  • Tellis, G. J., Yin, E., & Bell, S. (2009). Global consumer innovativeness: Cross-country differences and demographic commonalities. Journal of International Marketing, 17(2), 1–22.

    Article  Google Scholar 

  • Tiwari, A. K., Tiwari, A., & Samuel, C. (2015). Supply chain flexibility: A comprehensive review. Management Research Review, 38(7), 767–792. https://doi.org/10.1108/MRR-08-2013-0194.

    Article  Google Scholar 

  • Trentin, A., Forza, C., & Perin, E. (2012). Organization design strategies for mass customisation: An information-processing-view perspective. International Journal of Production Research, 50(14), 3860–3877. https://doi.org/10.1080/00207543.2011.597790.

    Article  Google Scholar 

  • Tripp, J. F., Riemenschneider, C., & Thatcher, J. B. (2016). Development as work redesign. Journal of the Association for Information Systems, 17(4), 267–307.

    Article  Google Scholar 

  • Venkatesh, A. (2008). Digital home technologies and transformation of households. Information Systems Frontiers, 10(4), 391–395. https://doi.org/10.1007/s10796-008-9097-0.

    Article  Google Scholar 

  • Vickery, S., Canlantone, R., & Droge, C. (1999). Supply chain flexibility. An empirical study. Journal of Supply Chain Management, 35(1), 16–24.

    Article  Google Scholar 

  • Vickery, S. K., Jayaram, J., Droge, C., & Calantone, R. (2003). The effects of an integrative supply chain strategy on customer service and financial performance: An analysis of direct versus indirect relationships. Journal of Operations Management, 21(5), 523–539. https://doi.org/10.1016/j.jom.2003.02.002.

    Article  Google Scholar 

  • West, D., Grant, T, Gerush, M., & D’Silva, D. (2010). Agile development: Mainstream adoption has changed agility. Forrester Research.

  • Wong, C. W. Y., Lai, K., Cheng, T. C. E., & Lun, Y. H. V. (2015). The role of IT-enabled collaborative decision making in inter-organizational information integration to improve customer service performance. International Journal of Production Economics, 159, 56–65. https://doi.org/10.1016/j.ijpe.2014.02.019.

    Article  Google Scholar 

  • Wu, Z., Choi, T. Y., & Rungtusanatham, M. J. (2010). Supplier-supplier relationships in buyer-supplier-supplier triads: Implications for supplier performance. Journal of Operations Management, 28(2), 115–123. https://doi.org/10.1016/j.jom.2009.09.002.

    Article  Google Scholar 

  • Yu, W., Jacobs, M. A., Salisbury, W. D., & Enns, H. (2013). The effects of supply chain integration on customer satisfaction and financial performance: An organizational learning perspective. International Journal of Production Economics, 146(1), 346–358. https://doi.org/10.1016/j.ijpe.2013.07.023.

    Article  Google Scholar 

  • Zhang, Q., Vonderembse, M. A., & Lim, J.-S. (2003). Manufacturing flexibility: Defining//analyzing relationships among competence, capability, customer satisfaction. Journal of Operations Management, 21(2), 173–191.

    Article  Google Scholar 

  • Zhou, H., & Benton, W. C. (2007). Supply chain practice and information sharing. Journal of Operations Management, 25(6), 1348–1365. https://doi.org/10.1016/j.jom.2007.01.009.

    Article  Google Scholar 

  • Zhou, K., T. Liu, & L. Zhou. (2015). Industry 4.0: Towards future industrial opportunities and challenges. In Proceedings of 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Zhangjiajie, China, 2147–2152.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zongwei Luo.

Additional information

Publisher’s Note

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

Appendices

Appendix 1

Table 9 Operationalization of Constructs

Appendix 2

Table 10 Combined loadings and cross-loadings

Appendix 3

Table 11 Indicator weights

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, S., Drave, V.A., Bag, S. et al. Leveraging Smart Supply Chain and Information System Agility for Supply Chain Flexibility. Inf Syst Front 21, 547–564 (2019). https://doi.org/10.1007/s10796-019-09901-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-019-09901-5

Keywords

Navigation