Predicting Smart Supply Chain Performance Using Big Data Analytics: A PLS-SEM and Machine Learning Hybrid Approach

This study proposes a hybrid approach integrating Partial Least Squares Structural Equation Modeling (PLS-SEM) and Machine Learning (ML) techniques to predict Smart Supply Chain Management (SmSCM) performance based on Big Data Analytics (BDA) adoption. While previous studies validated behavioral models, this research advances predictive capabilities by leveraging both structural path analysis and data-driven classification. The conceptual model is grounded in the UTAUT2 framework, incorporating constructs such as Performance Expectancy, Effort Expectancy, Facilitating Conditions, Price Value, Perceived Risk, Technology Readiness, and Trust. Data collected from 309 Malaysian manufacturing firms were first analysed using PLS-SEM to confirm causal relationships and model reliability. Subsequently, supervised learning models which are Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbours (KNN). These models were applied to predict SmSCM performance classes (High vs Low) using behavioral and readiness indicators as input features. Results indicate that combining PLS-SEM with ML enhances explanatory and predictive power, with SVM outperforming other classifiers at 70.66% accuracy using entropy-informed features. This study demonstrates the potential of hybrid analytics to guide data-driven decision-making in Industry 4.0 supply chains. It contributes both theoretically and practically by offering a validated, predictive framework for BDA-driven supply chain transformation

Big Data Analytics; Smart Supply Chain; Technology Adoption

1. Aronoff, S., (1989). Geographic Information Systems: A Management Perspective. Ottawa: WDL Publications. [Google Scholar] [Crossref]

2. Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2022). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 24(1), 1–16. https://doi.org/10.1007/s10796-021-10141-5 [Google Scholar] [Crossref]

3. Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2020). Empirical examination of UTAUT2 in the context of mobile payment adoption. Information Systems Frontiers, 22(1), 293–314. https://doi.org/10.1007/s10796-018-9862-7 [Google Scholar] [Crossref]

4. Alshamaila, Y., Papagiannidis, S., & Li, F. (2013). Cloud computing adoption by SMEs in the UK: A multi-perspective framework. Journal of Enterprise Information Management, 26(3), 250–275. https://doi.org/10.1108/17410391311325225 [Google Scholar] [Crossref]

5. Choi, T.-M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1883. https://doi.org/10.1111/poms.12838 [Google Scholar] [Crossref]

6. Chong, A. Y.-L., Ch’ng, E., Liu, M. J., & Li, B. (2017). Predicting consumer product demands via Big Data: The roles of online promotional marketing and online reviews. International Journal of Production Research, 55(17), 5142–5156. https://doi.org/10.1080/00207543.2017.1292063 [Google Scholar] [Crossref]

7. Frederico, G. F., Garza-Reyes, J. A., Anosike, A., & Kumar, V. (2021). Supply Chain 4.0: Concepts, maturity, and research agenda. Supply Chain Management: An International Journal, 26(2), 262–282. https://doi.org/10.1108/SCM-09-2020-0454 [Google Scholar] [Crossref]

8. Gupta, S., Modgil, S., & Gunasekaran, A. (2020). Big data in lean six sigma: A review and future research directions. International Journal of Production Research, 58(3), 947–969. https://doi.org/10.1080/00207543.2019.1598599 [Google Scholar] [Crossref]

9. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications. [Google Scholar] [Crossref]

10. Hazen, B. T., Skipper, J. B., Ezell, J. D., & Boone, C. A. (2016). Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda. Computers & Industrial Engineering, 101, 592–598. https://doi.org/10.1016/j.cie.2016.06.030 [Google Scholar] [Crossref]

11. Ivanov, D., Dolgui, A., & Sokolov, B. (2022). Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”. Transportation Research Part E: Logistics and Transportation Review, 160, 102676. https://doi.org/10.1016/j.tre.2022.102676 [Google Scholar] [Crossref]

12. Kshetri, N. (2021). Blockchain and trust in supply chain management. In Blockchain and supply chain management (pp. 3–23). Springer. https://doi.org/10.1007/978-3-030-73659-4_1 [Google Scholar] [Crossref]

13. Kshetri, N. (2021). Blockchain and trust in supply chain management. In Blockchain and supply chain management (pp. 3–23). Springer. https://doi.org/10.1007/978-3-030-73659-4_1 [Google Scholar] [Crossref]

14. Liao, Y., Deschamps, F., Loures, E. D. F. R., & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0: A systematic literature review and research agenda. International Journal of Production Research, 55(12), 3609–3629. https://doi.org/10.1080/00207543.2017.1308576 [Google Scholar] [Crossref]

15. Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for SMEs. Journal of Manufacturing Systems, 49, 194–214. https://doi.org/10.1016/j.jmsy.2018.10.005 [Google Scholar] [Crossref]

16. Ngai, E. W. T., & Gunasekaran, A. (2020). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110. https://doi.org/10.1016/j.ijpe.2016.03.014 [Google Scholar] [Crossref]

17. Ooi, K. B., Lee, V. H., Tan, G. W. H., Hew, T. S., & Hew, J. J. (2018). Cloud computing in manufacturing: The next industrial revolution in Malaysia? Expert Systems with Applications, 93, 376–394. https://doi.org/10.1016/j.eswa.2017.10.009 [Google Scholar] [Crossref]

18. Sanders, N. R., & Wagner, S. M. (2021). Big data and supply chain management: A review and research agenda. Journal of Business Logistics, 42(1), 81–105. https://doi.org/10.1111/jbl.12264 [Google Scholar] [Crossref]

19. Schoenherr, T., & Speier‐Pero, C. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), 120–132. https://doi.org/10.1111/jbl.12082 [Google Scholar] [Crossref]

20. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189 [Google Scholar] [Crossref]

21. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified Theory of Acceptance and Use of Technology 2: A theoretical model extension. MIS Quarterly, 40(1), 283–301. https://doi.org/10.25300/MISQ/2016/40.1.06 [Google Scholar] [Crossref]

22. Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2020). How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246. https://doi.org/10.1016/j.ijpe.2014.12.031 [Google Scholar] [Crossref]

23. Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2022). Big data analytics in logistics and supply chain management: A predictive analytics perspective. Transportation Research Part E: Logistics and Transportation Review, 114, 210–225. https://doi.org/10.1016/j.tre.2016.04.007 [Google Scholar] [Crossref]

24. Wichmann, P., Brintrup, A., Baker, S., Woodall, P., & McFarlane, D. (2020). Extracting supply chain maps from news articles using deep neural networks. International Journal of Production Research, 58(17), 5320–5336. https://doi.org/10.1080/00207543.2019.1671629 [Google Scholar] [Crossref]

25. Zailani, S., Jeyaraman, K., Vengadasan, G., & Premkumar, R. (2012). Sustainable supply chain management in Malaysia: Key drivers and performance outcomes. International Journal of Operations & Production Management, 32(9), 984–1011. https://doi.org/10.1108/01443571211265684 [Google Scholar] [Crossref]

26. Zhou, K., Liu, T., & Zhou, L. (2020). Industry 4.0: Towards future industrial opportunities and challenges. International Journal of Production Research, 58(6), 1922–1940. https://doi.org/10.1080/00207543.2019.1672905 [Google Scholar] [Crossref]

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