Journal of Optimization and Supply Chain Management

Journal of Optimization and Supply Chain Management

Strategic Integration of Industry 4.0 in the Electronics Sector: An Integrated Fuzzy DEMATEL and Fuzzy COBRA Approach

Document Type : Research Paper

Authors
Md. Mohibul M Islam 1
Mahmodur Rahman 1
Aizizul Haque Raza 2
1 Department of Industrial & Production Engineering, Rajshahi University of Engineering & Technology, Rajshahi-6204, Bangladesh
2 Supply chain management, Kiam Metal Industries Limited, Kushtia 7000, Bangladesh
10.22034/ISS.2026.9730.1060
Abstract
Objective: The Fourth Industrial Revolution (Industry 4.0) is transforming global manufacturing through advanced digital technologies; however, its implementation in developing economies remains constrained by structural and institutional challenges. In Bangladesh, where the electronics sector plays a significant role in economic growth, the adoption of Industry 4.0 technologies is hindered by organizational, infrastructural, and regulatory barriers. This study systematically identifies and prioritizes key obstacles to Industry 4.0 implementation in Bangladesh’s electronics industry and proposes effective mitigation strategies.
Methods: An integrated fuzzy multi-criteria decision-making (MCDM) framework is developed by combining fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) with the fuzzy Comprehensive Distance-Based Ranking (COBRA) method. Expert evaluations and field data were used to examine the causal interrelationships among fourteen critical barriers and to rank six alternative strategies. Data reliability and internal consistency were assessed using dispersion analysis and Cronbach’s alpha. The robustness of the proposed framework was further validated through a comparative analysis with established MCDM techniques, including fuzzy TOPSIS, fuzzy COPRAS, and fuzzy MARCOS, as well as Spearman’s rank correlation.
Results: The findings reveal that inadequate infrastructure, environmental concerns related to energy consumption and electronic waste, and challenges in adopting new business models are the most influential barriers. Regulatory and government support, strategic business alignment, awareness development, and enhanced stakeholder collaboration emerge as the most effective mitigation strategies.
Conclusion: This study presents a novel hybrid MCDM framework that integrates analytical rigor with practical insights, providing policymakers and industry leaders with an evidence-based roadmap for advancing Industry 4.0 adoption in emerging economies.
Keywords
industry 4.0
application challenges
mitigation strategy
electronic industry
hybrid multi-criteria decision modeling
References
Abdullah, F. M., Al-Ahmari, A. M., & Anwar, S. (2023). A hybrid fuzzy Multi-Criteria Decision-Making model for evaluating the influence of industry 4.0 technologies on manufacturing strategies. Machines, 11(2), 310. https://doi.org/10.3390/machines11020310
Akyuz, E., &Celik, E. (2015). A fuzzy DEMATEL method to evaluate critical operational hazards during gas freeing process in crude oil tankers. Journal of Loss Prevention in the Process Industries, 38, 243–253. https://doi.org/10.1016/j.jlp.2015.10.006
Arnold, C., Kiel, D., & Voigt, K. (2016). HOW THE INDUSTRIAL INTERNET OF THINGS CHANGES BUSINESS MODELS IN DIFFERENT MANUFACTURING INDUSTRIES. International Journal of Innovation Management, 20(08), 1640015. https://doi.org/10.1142/s1363919616400156
Arroyabe, M. F., Arranz, C. F., De Arroyabe, I. F., & De Arroyabe, J. C. F. (2023). The effect of IT security issues on the implementation of industry 4.0 in SMEs: Barriers and challenges. Technological Forecasting and Social Change, 199, 123051. https://doi.org/10.1016/j.techfore.2023.123051
Bag, S., Gupta, S., & Kumar, S. (2020). Industry 4.0 adoption and 10R advance manufacturing capabilities for sustainable development. International Journal of Production Economics, 231, 107844. https://doi.org/10.1016/j.ijpe.2020.107844 
Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2020). Industry 4.0 technologies assessment: A sustainability perspective. International Journal of Production Economics, 229, 107776. https://doi.org/10.1016/j.ijpe.2020.107776
Bleicher, J., & Stanley, H. (2017). Digitization as a catalyst for business model innovation a three-step approach to facilitating economic success. https://journals.riseba.eu/index.php/jbm/article/view/82
Büchi, G., Cugno, M., & Castagnoli, R. (2019). Smart factory performance and Industry 4.0. Technological Forecasting and Social Change, 150, 119790. https://doi.org/10.1016/j.techfore.2019.119790
Çalık, A. (2020). 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. https://doi.org/10.1007/s00500-020-05294-9
Chang, S., Chang, H., & Lu, M. (2021). Evaluating Industry 4.0 Technology Application in SMES: Using a Hybrid MCDM approach. Mathematics, 9(4), 414. https://doi.org/10.3390/math9040414
Cugno, M., Castagnoli, R., &Büchi, G. (2021). Openness to Industry 4.0 and performance: The impact of barriers and incentives. Technological Forecasting and Social Change, 168, 120756. https://doi.org/10.1016/j.techfore.2021.120756
Fitzgerald, M., Kruschwitz, N., Bonnet, D., & Welch, M. (2013, October 7). Embracing digital technology. MIT Sloan Management Review. https://sloanreview.mit.edu/projects/embracing-digital-technology/
Florescu, A., &Barabas, S. A. (2020). Modeling and simulation of a flexible manufacturing System—A basic component of industry 4.0. Applied Sciences, 10(22), 8300. https://doi.org/10.3390/app10228300
Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210, 15–26. https://doi.org/10.1016/j.ijpe.2019.01.004
Hajiagha, S. H. R., Hashemi, S. S., &Zavadskas, E. K. (2013). A COMPLEX PROPORTIONAL ASSESSMENT METHOD FOR GROUP DECISION MAKING IN AN INTERVAL-VALUED INTUITIONISTIC FUZZY ENVIRONMENT. Technological and Economic Development of Economy, 19(1), 22–37. https://doi.org/10.3846/20294913.2012.762953
Herceg, I. V., Kuč, V., Mijušković, V. M., &Herceg, T. (2020). Challenges and driving forces for Industry 4.0 implementation. Sustainability, 12(10), 4208. https://doi.org/10.3390/su12104208
Horváth, D., & Szabó, R. Z. (2019). Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities? Technological Forecasting and Social Change, 146, 119–132. https://doi.org/10.1016/j.techfore.2019.05.021
Hoyer, C., Gunawan, I., &Reaiche, C. H. (2020). The Implementation of Industry 4.0 – A systematic literature review of the key factors. Systems Research and Behavioral Science, 37(4), 557–578. https://doi.org/10.1002/sres.2701
Ing, T. S., Lee, T., Chan, S., Alipal, J., & Hamid, N. A. (2019). An overview of the rising challenges in implementing industry 4.0. Shu Ing | International Journal of Supply Chain Management. https://doi.org/10.59160/ijscm.v8i6.4192
Islam, M. M., & Arakawa, M. (2022a). Hesitant fuzzy-based integrated multi-criteria group decision-making model for supplier selection. Journal of Advanced Mechanical Design Systems and Manufacturing, 16(4), JAMDSM0034. https://doi.org/10.1299/jamdsm.2022jamdsm0034
Islam, M. M., & Arakawa, M. (2022b). Integrated multi-criteria group decision-making model for supplier selection in an uncertain environment. Cogent Engineering, 9(1). https://doi.org/10.1080/23311916.2022.2079220
Islam, M. M., & Arakawa, M. (2023). Development of an integrated scenario-based stochastic rolling-planning multistage logistics model considering various risks. Heliyon, 9(11), e22289. https://doi.org/10.1016/j.heliyon.2023.e22289
Javaid, M., Khan, S., Haleem, A., &Rab, S. (2022). Adoption of modern technologies for implementing industry 4.0: an integrated MCDM approach. Benchmarking an International Journal, 30(10), 3753–3790. https://doi.org/10.1108/bij-01-2021-0017
Kadir, B. A., &Broberg, O. (2020). Human well-being and system performance in the transition to industry 4.0. International Journal of Industrial Ergonomics, 76, 102936. https://doi.org/10.1016/j.ergon.2020.102936
Kamble, S. S., Gunasekaran, A., & Sharma, R. (2018). Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry. Computers in Industry, 101, 107–119. https://doi.org/10.1016/j.compind.2018.06.004
Kamble, S. S., Gunasekaran, A., &Gawankar, S. A. (2018). Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408–425.
Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2017, July 13). Achieving digital maturity. MIT Sloan Management Review. https://sloanreview.mit.edu/projects/achieving-digital-maturity/
Kaya, İ., Erdoğan, M., Karaşan, A., &Özkan, B. (2020). Creating a road map for industry 4.0 by using an integrated fuzzy multicriteria decision-making methodology. Soft Computing, 24(23), 17931–17956. https://doi.org/10.1007/s00500-020-05041-0
Krstić, M., Agnusdei, G. P., Miglietta, P. P., &Tadić, S. (2022). Evaluation of the smart reverse logistics development scenarios using a novel MCDM model. Cleaner Environmental Systems, 7, 100099. https://doi.org/10.1016/j.cesys.2022.100099
Kumar, P., Brar, P. S., Singh, D., &Bhamu, J. (2022). Fuzzy AHP approach for barriers to implement LSS in the context of Industry 4.0. International Journal of Productivity and Performance Management, 72(9), 2559–2583. https://doi.org/10.1108/ijppm-12-2021-0715
Kumar, R., Singh, R. K., & Dwivedi, Y. K. (2020). Application of industry 4.0 technologies in SMEs for ethical and sustainable operations: Analysis of challenges. Journal of Cleaner Production, 275, 124063. https://doi.org/10.1016/j.jclepro.2020.124063
Kumar, S., Suhaib, M., &Asjad, M. (2020). Industry 4.0: complex, disruptive, but inevitable. Management and Production Engineering Review. https://doi.org/10.24425/mper.2020.132942
Kumar, V., Vrat, P., & Shankar, R. (2021). Prioritization of strategies to overcome the barriers in Industry 4.0: a hybrid MCDM approach. OPSEARCH, 58(3), 711–750. https://doi.org/10.1007/s12597-020-00505-1
Lah, M. S. C. (2019). Triangular Fuzzy Number Generator (TriGen). International Journal of Advanced Trends in Computer Science and Engineering, 8(1.3), 300–305. https://doi.org/10.30534/ijatcse/2019/5681.32019
Lasi, H., Fettke, P., Kemper, H., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242. https://doi.org/10.1007/s12599-014-0334-4
León-García, O. A., &Bermúdez-Segura, M. Y. (2021). Barreras y fuerzasimpulsoras de la implementación de la Industria 4.0 en las organizaciones: un estado del arte. Revista De Investigación Desarrollo E Innovación, 11(3), 451–466. https://doi.org/10.19053/20278306.v11.n3.2021.13343
Luthra, S., &Mangla, S. K. (2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117, 168–179. https://doi.org/10.1016/j.psep.2018.04.018
Machado, C. G., Winroth, M., Carlsson, D., Almström, P., Centerholt, V., &Hallin, M. (2019). Industry 4.0 readiness in manufacturing companies: challenges and enablers towards increased digitalization. Procedia CIRP, 81, 1113–1118. https://doi.org/10.1016/j.procir.2019.03.262
Manda, M. I., &Dhaou, S. B. (2019). Responding to the challenges and opportunities in the 4th Industrial Revolution in developing countries. In ICEGOV ’19: Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance. https://doi.org/10.1145/3326365.3326398
Mim, J. F., Islam, M. M., & Raza, A. H. (2025). A hybrid MCDM approach for unveiling ChatGPT’s effect on students’ learning. Computers and Education Artificial Intelligence, 100457. https://doi.org/10.1016/j.caeai.2025.100457
Mim, J. F., & Rahman, M. (2025). Assessing the development barriers for f-commerce circular supply chain by using best-worst method. Journal of Future Sustainability, 6(1), 15–22. https://doi.org/10.5267/j.jfs.2026.1.002
Moktadir, M. A., Ali, S. M., Kusi-Sarpong, S., & Shaikh, M. a. A. (2018). Assessing challenges for implementing Industry 4.0: Implications for process safety and environmental protection. Process Safety and Environmental Protection, 117, 730–741. https://doi.org/10.1016/j.psep.2018.04.020
Müller, J. M., Buliga, O., & Voigt, K. (2018). Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0. Technological Forecasting and Social Change, 132, 2–17. https://doi.org/10.1016/j.techfore.2017.12.019
Nascimento, D. L. M., Alencastro, V., Quelhas, O. L. G., Caiado, R. G. G., Garza-Reyes, J. A., Rocha-Lona, L., & Tortorella, G. (2018). Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context. Journal of Manufacturing Technology Management, 30(3), 607–627. https://doi.org/10.1108/jmtm-03-2018-0071
Ozdemir, Y. S. (2022). A spherical fuzzy Multi-Criteria Decision-Making model for industry 4.0 performance measurement. Axioms, 11(7), 325. https://doi.org/10.3390/axioms11070325
Pandya, D., & Kumar, G. (2022). Applying Industry 4.0 technologies for the sustainability of small service enterprises. Service Business, 17(1), 37–59. https://doi.org/10.1007/s11628-022-00507-7
Patalas-Maliszewska, J., &Losyk, H. (2022). An approach to maintenance sustainability level assessment integrated with Industry 4.0 technologies using Fuzzy-TOPSIS: A real case study. Advances in Production Engineering & Management, 17(4), 455–468. https://doi.org/10.14743/apem2022.4.448
Rahman, M., & Islam, M. M. (2024). Digital Transformation of supply Chain Management by Adopting Industry 4.0 Technology in Bangladesh: An Integrated Novel MCDM Approach. In 7th Bangladesh Conference on Industrial Engineering and Operations Management. https://doi.org/10.46254/ba07.20240069
Rahman, M., Sarker, S., & Chowdhury, R. A. (2024). An MCGDM approach for addressing challenges for Industry 4.0 adaptation in heavy duty flooring solution industries of Bangladesh. In 7th Bangladesh Conference on Industrial Engineering and Operations Management. https://doi.org/10.46254/ba07.20240256
Raj, A., Dwivedi, G., Sharma, A., De Sousa Jabbour, A. B. L., &Rajak, S. (2019). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics, 224, 107546. https://doi.org/10.1016/j.ijpe.2019.107546
Rayens, M. K., & Hahn, E. J. (2000). Building consensus using the Policy Delphi method. Policy Politics & Nursing Practice, 1(4), 308–315. https://doi.org/10.1177/152715440000100409
Ross, T. J. (1994). Fuzzy Logic with Engineering Applications. http://202.159.8.148:8001/~digilib/index.php?p=show_detail&id=12583
Sadeghi-Niaraki, A. (2020). Industry 4.0 Development Multi-Criteria Assessment: an integrated fuzzy DEMATEL, ANP and VIKOR methodology. IEEE Access, 8, 23689–23704. https://doi.org/10.1109/access.2020.2965979
Sedgwick, P. (2014). Spearman’s rank correlation coefficient. BMJ, g7327. https://doi.org/10.1136/bmj.g7327
Stock, T., Obenaus, M., Kunz, S., & Kohl, H. (2018). Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential. Process Safety and Environmental Protection, 118, 254–267. https://doi.org/10.1016/j.psep.2018.06.026
Stock, T., &Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP, 40, 536–541. https://doi.org/10.1016/j.procir.2016.01.129
Tayal, A., Agrawal, S., & Yadav, R. (2024). ARank-FSC: Assessment and ranking of Industry 4.0 enablers using fuzzy SWARA and fuzzy COPRAS in Indian construction. Intelligent Decision Technologies, 18(2), 663–683. https://doi.org/10.3233/idt-240459
Torbacki, W. (2021). A hybrid MCDM model combining DANP and PROMETHEE II methods for the assessment of cybersecurity in industry 4.0. Sustainability, 13(16), 8833. https://doi.org/10.3390/su13168833
Trappey, A. J., Trappey, C. V., Govindarajan, U. H., Chuang, A. C., & Sun, J. J. (2016). A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0. Advanced Engineering Informatics, 33, 208–229. https://doi.org/10.1016/j.aei.2016.11.007
Travaglioni, M., Ferazzoli, A., Petrillo, A., Cioffi, R., De Felice, F., &Piscitelli, G. (2020). Digital manufacturing challenges through open innovation perspective. Procedia Manufacturing, 42, 165–172. https://doi.org/10.1016/j.promfg.2020.02.066
Veza, I., Mladineo, M., &Gjeldum, N. (2015). Managing innovative production network of smart factories. IFAC-PapersOnLine, 48(3), 555–560. https://doi.org/10.1016/j.ifacol.2015.06.139
Vinodh, S., & Wankhede, V. A. (2020). Application of fuzzy DEMATEL and fuzzy CODAS for analysis of workforce attributes pertaining to Industry 4.0: a case study. International Journal of Quality & Reliability Management, 38(8), 1695–1721. https://doi.org/10.1108/ijqrm-09-2020-0322
Yazdani, M., Pamucar, D., Chatterjee, P., &Torkayesh, A. E. (2021). “A multi-tier sustainable food supplier selection model under uncertainty.” Operations Management Research, 15(1–2), 116–145. https://doi.org/10.1007/s12063-021-00186-z
Yıldızbaşı, A., &Ünlü, V. (2020). Performance evaluation of SMEs towards Industry 4.0 using fuzzy group decision making methods. SN Applied Sciences, 2(3). https://doi.org/10.1007/s42452-020-2085-9
Yüksel, H. (2020). An empirical evaluation of industry 4.0 applications of companies in Turkey: The case of a developing country. Technology in Society, 63, 101364. https://doi.org/10.1016/j.techsoc.2020.101364
Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/s0019-9958(65)90241-x
Zheng, T., Ardolino, M., Bacchetti, A., &Perona, M. (2020). The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review. International Journal of Production Research, 59(6), 1922–1954. https://doi.org/10.1080/00207543.2020.1824085
Journal of Optimization and Supply Chain Management
Volume 2, Issue 4
Autumn 2025
Pages 306-351