Supply Chain Management in the Era of Industrial Revolution 4.0 in MSMEs
DOI:
https://doi.org/10.71154/jmw9hx24Keywords:
Industrial Revolution 4.0, MSMEs, supply chain managementAbstract
The advent of the Fourth Industrial Revolution (IR 4.0) has significantly transformed supply chain management (SCM), particularly within Micro, Small, and Medium Enterprises (MSMEs). This literature review aims to synthesize recent research on the integration of IR 4.0 technologies in SCM for MSMEs, highlighting both opportunities and challenges. By examining peer-reviewed journal articles indexed in Scopus, this review provides a comprehensive overview of the current state of knowledge, identifies research gaps, and suggests future research directions. The integration of Industry 4.0 (IR 4.0) technologies into supply chain management (SCM) offers transformative potential for micro, small, and medium-sized enterprises (MSMEs), enhancing efficiency, reducing costs, and improving transparency. This literature review examines the adoption of key IR 4.0 technologies—such as IoT, big data analytics, AI, and blockchain within MSME supply chains, highlighting the opportunities and challenges encountered. Successful case studies from diverse sectors illustrate the practical benefits and strategies for overcoming adoption barriers. Despite the advantages, MSMEs face unique challenges, including financial constraints, lack of technical expertise, and cybersecurity risks. Future research should focus on scalable, cost-effective solutions tailored to MSMEs, addressing technological and human factors, and developing robust cybersecurity measures. Additionally, exploring supportive government policies and sustainable business models integrating IR 4.0 technologies is crucial. This review provides a comprehensive understanding of the current landscape and offers directions for future research, aiming to facilitate the digital transformation of MSMEs and enhance their competitiveness and sustainability in the global market.
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Bag, S., Wood, L. C., Xu, L., & Dhamija, P. (2021). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, 169, 105548.
Beier, G., Ullrich, A., Niehoff, S., Reißig, M., & Habich, M. (2020). Industry 4.0: How it is defined from a sociotechnical perspective and how much sustainability it includes–A literature review. Journal of Cleaner Production, 259, 120856.
Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of Things and supply chain management: a literature review. International Journal of Production Research, 57(15-16), 4719-4742.
Bouwman, H., Nikou, S., & Reuver, M. de. (2019). Digitalization, business models, and SMEs: How do business model innovation practices improve performance of digitalizing SMEs? Telecommunications Policy, 43(9), 101828.
Brinch, M. (2018). Understanding the value of big data in supply chain management and its business processes. International Journal of Operations & Production Management, 38(7), 1589-1614.
Casino, F., Dasaklis, T. K., & Patsakis, C. (2019). A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics and Informatics, 36, 55-81.
Cankaya, S. Y., & Sezen, B. (2019). Effects of green supply chain management practices on sustainability performance. Journal of Manufacturing Technology Management, 30(1), 98-121.
Ciarapica, F. E., Bevilacqua, M., & Mazzuto, G. (2016). Performance analysis of supply chain for perishable products: a case study. International Journal of Production Research, 54(17), 5038-5051.
Chiarini, A., & Kumar, M. (2021). Lean implementation in European manufacturing SMEs during the economic downturn: barriers and challenges for operational excellence. Journal of Manufacturing Technology Management, 32(1), 174-188.
Choi, T.-M., Wallace, S. W., & Wang, Y. (2020). Big Data Analytics in Operations Management. Production and Operations Management, 27(10), 1783-1787.
de Sousa Jabbour, A. B. L., Jabbour, C. J. C., Foropon, C., & Godinho Filho, M. (2018). When titans meet–Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors. Technological Forecasting and Social Change, 132, 18-25.
Dhamija, P., Bag, S., & Gupta, S. (2020). Role of artificial intelligence in operations environment: a review and bibliometric analysis. TQM Journal, 32(4), 869-896.
Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture. British Journal of Management, 30(2), 341-361.
Francisco, K., & Swanson, D. (2018). The supply chain has no clothes: Technology adoption of blockchain for supply chain transparency. Logistics, 2(1), 2.
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.
Ghobakhloo, M., & Iranmanesh, M. (2020). Digital transformation success under Industry 4.0: A strategic guideline for manufacturing SMEs. Journal of Manufacturing Technology Management, 31(4), 687-709.
Ghobakhloo, M., Iranmanesh, M., & Maroufkhani, P. (2020). Digital transformation success under Industry 4.0: A strategic guideline for manufacturing SMEs. Journal of Manufacturing Technology Management, 31(4), 687-709.
Ghobakhloo, M., Iranmanesh, M., Vilkas, M., & Grybauskas, A. (2020). Industry 4.0, innovation, and sustainable development: A systematic review and a roadmap to sustainable innovation. Business Strategy and the Environment, 29(8), 2745-2765.
Gunasekaran, A., Papadopoulos, T., Dubey, R., & Wamba, S. F. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317.
Guo, Z. X., Wong, W. K., Leung, S. Y. S., & Li, M. (2017). A hybrid intelligent system for to predict and optimize clothing sales. Engineering Applications of Artificial Intelligence, 59, 164-177.
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. International Journal of Production Research, 58(10), 2904-2915.
Ivanov, D., Dolgui, A., Sokolov, B., Werner, F., & Ivanova, M. (2019). A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. International Journal of Production Research, 57(12), 3860-3883.
Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International Journal of Operations & Production Management, 37(1), 10-36.
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.
Maroufkhani, P., Wagner, R., & Wan Ismail, W. K. (2020). Entrepreneurial ecosystems: a systematic review. Journal of Small Business and Enterprise Development, 27(2), 201-221.
Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0 maturity models: implications for small and medium-sized enterprises (SMEs). Journal of Manufacturing Systems, 49, 194-214.
Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018). The industrial management of SMEs in the era of Industry 4.0. International Journal of Production Research, 56(3), 1118-1136.
Müller, J. M., Buliga, O., & Voigt, K. I. (2018). Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0. Technological Forecasting and Social Change, 132, 2-17.
Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., & Fosso-Wamba, S. (2017). The role of Big Data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142, 1108-1118.
Queiroz, M. M., Telles, R., & Bonilla, S. H. (2020). Industry 4.0 and digital supply chain capabilities: A framework for understanding digitalization challenges and opportunities. Benchmarking: An International Journal, 27(2), 757-774.
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117-2135.
Schumacher, A., Erol, S., & Sihn, W. (2016). A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP, 52, 161-166.
Schwab, K. (2016). The Fourth Industrial Revolution. World Economic Forum.
Srinivasan, R., & Swink, M. (2018). An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: A Dynamic Capabilities Perspective. Production and Operations Management, 27(10), 1849-1867.
Sony, M., & Naik, S. (2020). Critical factors for the successful implementation of Industry 4.0: a review and future research directions. Production Planning & Control, 31(10), 799-815.
Strange, R., & Zucchella, A. (2017). Industry 4.0, global value chains and international business. Multinational Business Review, 25(3), 174-184.
Tjahjono, B., Esplugues, C., Ares, E., & Pelaez, G. (2017). What does industry 4.0 mean to supply chain? Procedia Manufacturing, 13, 1175-1182.
Tortorella, G. L., & Fettermann, D. (2018). Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies. International Journal of Production Research, 56(8), 2975-2987.
Tortorella, G. L., Giglio, R., & van Dun, D. H. (2019). Industry 4.0 adoption as a moderator of the impact of lean production practices on operational performance improvement. International Journal of Operations & Production Management, 39(6/7/8), 860-886.
Wamba, S. F., Akter, S., & de Bourmont, M. (2020). Quality dominant logic in big data analytics and firm performance. Industrial Marketing Management, 90, 48-57.
Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.
Zhang, Y., Ren, S., Liu, Y., & Si, S. (2019). A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. Journal of Cleaner Production, 226, 1084-1091.
Zheng, T., Ardolino, M., Bacchetti, A., & Perona, M. (2020). The impacts of Industry 4.0: a descriptive survey in the Italian manufacturing sector. Journal of Manufacturing Technology Management, 31(5), 1085-1115.
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