A Review of Digital Transformation and Industry 4.0 in Supply Chain Management for Small and Medium-sized Enterprises

Authors

  • Sushil Kumar Sahoo Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India Author https://orcid.org/0000-0002-8551-7353
  • Shankha Shubhra Goswami Department of Mechanical Engineering, Abacus Institute of Engineering and Management, Hooghly, West Bengal, India Author https://orcid.org/0000-0002-0033-3089
  • Shouvik Sarkar Department of Mechanical Engineering, Abacus Institute of Engineering and Management, Hooghly, West Bengal, India Author
  • Soupayan Mitra Department of Mechanical Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, West Bengal, India Author https://orcid.org/0000-0001-6953-7189

DOI:

https://doi.org/10.31181/sems1120237j

Keywords:

Industry 4.0, Supply Chain Management, Technology Integration, Internet of Things, Big Data Analytics

Abstract

 The introduction of Industry 4.0, which includes a suite of advanced technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence, and cyber-physical systems, has substantially altered supply chain dynamics in recent years. Digital Transformation and Industry 4.0 have emerged as pivotal paradigms reshaping the landscape of supply chain management (SCM) across industries. This study begins with an overview of classic SCM concepts and progresses to the contemporary digital era. We investigate the fundamental principles of digital transformation in SCM, emphasizing its drivers and accompanying benefits. Following that, this study delves into small and medium-sized enterprises (SMEs) and discusses their integration inside SCM systems. Through a systematic analysis of literature and case studies, this review synthesizes key methodologies, challenges, and opportunities. It highlights the importance of tailored approaches encompassing technology adoption roadmaps, pilot projects, workforce upskilling, and cyber security measures. The review also underscores the critical role of future research in developing standardized frameworks and best practices to empower SMEs in harnessing the transformative potential of the digital age within their supply chains. This study also discusses digital transformation initiatives, their impact on SCM performance, and their role in improving supply chain resilience. Finally, this research article investigates the obstacles and barriers faced during digital transformation programs, as well as future trends and consequences for practitioners and researchers.

Downloads

Download data is not yet available.

References

Jafarzadeh-Ghoushchi, S., Asghari, M., Mardani, A., Simic, V., & Tirkolaee E.B. (2023). Designing an efficient humanitarian supply chain network during an emergency: A scenario-based multi-objective model. Socio-Economic Planning Sciences, 90, 101716. https://doi.org/10.1016/j.seps.2023.101716.

Ghasemi, P., Goodarzian, F., Simic, V., & Tirkolaee, E.B. (2023). A DEA-Based Simulation-Optimization Approach to Design a Resilience Plasma Supply Chain Network: A Case Study of the COVID-19 Outbreak. International Journal of Systems Science: Operations & Logistics, 10(1), 2224105. https://doi.org/10.1080/23302674.2023.2224105.

Soori, M., Arezoo, B., & Dastres, R. (2023). Internet of things for smart factories in industry 4.0, a review. Internet of Things and Cyber-Physical Systems. https://doi.org/10.1016/j.iotcps.2023.04.006.

Birkel, H., Hohenstein, N. O., & Hähner, S. (2023). How have digital technologies facilitated supply chain resilience in the covid-19 pandemic? An exploratory case study. Computers & Industrial Engineering, 109538. https://doi.org/10.1016/j.cie.2023.109538.

Sawik, B. (2023). Space Mission Risk, Sustainability and Supply Chain: Review, Multi-Objective Optimization Model and Practical Approach. Sustainability, 15(14), 11002. https://doi.org/10.3390/su151411002.

Yenugula, M., Sahoo, S., & Goswami, S. (2023). Cloud computing in supply chain management: Exploring the relationship. Management Science Letters, 13(3), 193-210. https://doi.org/10.5267/j.msl.2023.4.003.

Guntuka, L., Corsi, T. M., & Cantor, D. E. (2023). Recovery from plant-level supply chain disruptions: supply chain complexity and business continuity management. International Journal of Operations & Production Management. https://doi.org/10.1108/IJOPM-09-2022-0611.

Ryalat, M., ElMoaqet, H., & AlFaouri, M. (2023). Design of a smart factory based on cyber-physical systems and Internet of Things towards Industry 4.0. Applied Sciences, 13(4), 2156. https://doi.org/10.3390/app13042156.

Hrouga, M., & Sbihi, A. (2023). Logistics 4.0 for supply chain performance: perspectives from a retailing case study. Business Process Management Journal. https://doi.org/10.1108/BPMJ-03-2023-0183.

Javaid, M., Haleem, A., & Singh, R. P. (2023). A study on ChatGPT for Industry 4.0: Background, Potentials, Challenges, and Eventualities. Journal of Economy and Technology. https://doi.org/10.1016/j.ject.2023.08.001.

Pfohl, H. C. (2023). Strategic Logistics Planning. In Logistics Management: Conception and Functions (pp. 77-168). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-66564-0_4.

Kashem, M. A., Shamsuddoha, M., Nasir, T., & Chowdhury, A. A. (2023). Supply Chain Disruption versus Optimization: A Review on Artificial Intelligence and Blockchain. Knowledge, 3(1), 80-96. https://doi.org/10.3390/knowledge3010007.

AL-Shboul, M. D. A. (2023). Better understanding of triadic supply chain relationships through power dynamics in manufacturing firms context: evidence from case study approach from a developed country. Journal of Manufacturing Technology Management. https://doi.org/10.1108/JMTM-01-2023-0019.

Zhang, H. N. (2023). Coupling System Dynamics Model of Cross Border Logistics and Ecological Environment Based on the Sustainable Perspective of Global Value Chain. Sustainability, 15(17), 13099. https://doi.org/10.3390/su151713099.

Xiao, J., Zhang, W., & Zhong, R. Y. (2023). Blockchain-enabled cyber-physical system for construction site management: A pilot implementation. Advanced Engineering Informatics, 57, 102102. https://doi.org/10.1016/j.aei.2023.102102

Helmold, M. (2023). Lean Production as Part of QM. In Virtual and Innovative Quality Management Across the Value Chain: Industry Insights, Case Studies and Best Practices (pp. 127-137). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-30089-9_11.

Lee, J., & Lim, H. C. (2023). Expanding Overseas, Becoming Multinational, and Moving Up the Value Chain: Three Waves of Globalization in the Korean Apparel Industry. In Knitting Asia, Weaving Development: Globalization of the Korean Apparel Industry (pp. 25-52). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-3764-6_2.

Sahoo, S. K., & Goswami, S. S. (2023). A comprehensive review of multiple criteria decision-making (MCDM) Methods: advancements, applications, and future directions. Decision Making Advances, 1(1), 25-48. https://doi.org/10.31181/dma1120237.

De, S., & Kar, A. K. (2023). Exploring IoT Applications in Industry 4.0—Insights from Review of Literature. IoT, Big Data and AI for Improving Quality of Everyday Life: Present and Future Challenges: IOT, Data Science and Artificial Intelligence Technologies, 15-38. https://doi.org/10.1007/978-3-031-35783-1_2.

Mughal, Y. H., Nair, K. S., Arif, M., Albejaidi, F., Thurasamy, R., Chuadhry, M. A., & Malik, S. Y. (2023). Employees’ Perceptions of Green Supply-Chain Management, Corporate Social Responsibility, and Sustainability in Organizations: Mediating Effect of Reflective Moral Attentiveness. Sustainability, 15(13), 10528. https://doi.org/10.3390/su151310528.

Fernando, Y., Al-Madani, M. H. M., & Shaharudin, M. S. (2023). COVID-19 and global supply chain risks mitigation: systematic review using a scientometric technique. Journal of Science and Technology Policy Management. https://doi.org/10.1108/JSTPM-01-2022-0013.

Razak, G. M., Hendry, L. C., & Stevenson, M. (2023). Supply chain traceability: A review of the benefits and its relationship with supply chain resilience. Production Planning & Control, 34(11), 1114-1134. https://doi.org/10.1080/09537287.2021.1983661.

Unal, P., Albayrak, Ö., Jomâa, M., & Berre, A. J. (2022). Data-driven artificial intelligence and predictive analytics for the maintenance of industrial machinery with hybrid and cognitive digital twins. In Technologies and Applications for Big Data Value (pp. 299-319). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-78307-5_14.

Maheshwari, P., Kamble, S., Belhadi, A., Venkatesh, M., & Abedin, M. Z. (2023). Digital twin-driven real-time planning, monitoring, and controlling in food supply chains. Technological Forecasting and Social Change, 195, 122799. https://doi.org/10.1016/j.techfore.2023.122799.

El Jaouhari, A., Alhilali, Z., Arif, J., Fellaki, S., Amejwal, M., & Azzouz, K. (2022). Demand forecasting application with regression and iot based inventory management system: a case study of a semiconductor manufacturing company. International Journal of Engineering Research in Africa, 60, 189-210. https://doi.org/10.4028/p-8ntq24.

Enrique, D. V., Lerman, L. V., de Sousa, P. R., Benitez, G. B., Santos, F. M. B. C., & Frank, A. G. (2022). Being digital and flexible to navigate the storm: How digital transformation enhances supply chain flexibility in turbulent environments. International Journal of Production Economics, 250, 108668. https://doi.org/10.1016/j.ijpe.2022.108668.

Nguyen, D. K., Broekhuizen, T., Dong, J. Q., & Verhoef, P. C. (2023). Leveraging synergy to drive digital transformation: A systems-theoretic perspective. Information & Management, 60(7), 103836. https://doi.org/10.1016/j.im.2023.103836.

Alabi, T. M., Aghimien, E. I., Agbajor, F. D., Yang, Z., Lu, L., Adeoye, A. R., & Gopaluni, B. (2022). A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems. Renewable Energy, 194, 822-849. https://doi.org/10.1016/j.renene.2022.05.123.

Sahoo, S. K., & Choudhury, B. B. (2023). Wheelchair Accessibility: Bridging the Gap to Equality and Inclusion. Decision Making Advances, 1(1), 63-85. https://doi.org/10.31181/dma1120239.

Tuukkanen, V., Wolgsjö, E., & Rusu, L. (2022). Cultural values in digital transformation in a small company. Procedia Computer Science, 196, 3-12. https://doi.org/10.1016/j.procs.2021.11.066.

Fatima, Z., Tanveer, M. H., Waseemullah, Zardari, S., Naz, L. F., Khadim, H., & Tahir, M. (2022). Production plant and warehouse automation with IoT and industry 5.0. Applied Sciences, 12(4), 2053. https://doi.org/10.3390/app12042053.

Hammad, M., Jillani, R. M., Ullah, S., Namoun, A., Tufail, A., Kim, K. H., & Shah, H. (2023). Security Framework for Network-Based Manufacturing Systems with Personalized Customization: An Industry 4.0 Approach. Sensors, 23(17), 7555. https://doi.org/10.3390/s23177555.

Sahoo, S. K., & Choudhury, B. B. (2023). Challenges and opportunities for enhanced patient care with mobile robots in healthcare. Journal of Mechatronics and Artificial Intelligence in Engineering. https://doi.org/10.21595/jmai.2023.23410.

Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2022). Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review. Annals of Operations Research, 1-28. https://doi.org/10.1007/s10479-022-04983-y.

Tsolakis, N., Schumacher, R., Dora, M., & Kumar, M. (2023). Artificial intelligence and blockchain implementation in supply chains: a pathway to sustainability and data monetisation?. Annals of Operations Research, 327(1), 157-210. https://doi.org/10.1007/s10479-022-04785-2.

Wong, L. W., Tan, G. W. H., Ooi, K. B., Lin, B., & Dwivedi, Y. K. (2022). Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis. International Journal of Production Research. https://doi.org/10.1080/00207543.2022.2063089.

Kumar, P., Sharma, S. K., & Dutot, V. (2023). Artificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation. International Journal of Information Management, 69, 102598. https://doi.org/10.1016/j.ijinfomgt.2022.102598.

Sahoo, S. K., & Choudhury, B. B. (2023). A Review of Methodologies for Path Planning and Optimization of Mobile Robots. Journal of Process Management and New Technologies, 11(1-2), 122-140. https://doi.org/10.5937/jpmnt11-45039.

Kumar, P. S., Petla, R. K., Elangovan, K., & Kuppusamy, P. G. (2022). Artificial Intelligence Revolution in Logistics and Supply Chain Management. Artificial Intelligent Techniques for Wireless Communication and Networking, 31-45. https://doi.org/10.1002/9781119821809.ch3.

Muna, N., Yasa, N., Ekawati, N., & Wibawa, I. (2022). A dynamic capability theory perspective: borderless media breakthrough to enhance SMEs performance. International Journal of Data and Network Science, 6(2), 363-374. https://doi.org/10.5267/j.ijdns.2022.1.001.

Al-Banna, A., Ashraf, Z., Yaqot, M., & Menezes, B. (2023). Interconnectedness between Supply Chain Resilience, Industry 4.0, and Investment. Logistics, 7(3), 50. https://doi.org/10.3390/logistics7030050.

Downloads

Published

2023-10-18

Issue

Vol. 1 No. 1 (2023): Spectrum of Engineering and Management Sciences

Section

Articles

License

Copyright (c) 2023 CC Attribution-NonCommercial-NoDerivatives 4.0

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Sahoo, S. K., Goswami, S. S. ., Sarkar, S. ., & Mitra, S. . (2023). A Review of Digital Transformation and Industry 4.0 in Supply Chain Management for Small and Medium-sized Enterprises. Spectrum of Engineering and Management Sciences, 1(1), 58-72. https://doi.org/10.31181/sems1120237j