SCM 4.0: Navigating the Impact of Industry 4.0 on Supply Chain Management through Digitalization and Technology Integration
Main Article Content
Abstract
The fourth industrial revolution, referred to as Industry 4.0, has profoundly reshaped supply chain management, necessitating a vital digital transition known as SCM 4.0. This research investigates the implications of Industry 4.0 on supply chain management and identifies the drivers compelling organizations to modernize their methodologies. It; examines the influence of digital technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and cloud computing, in cultivating supply chains that are increasingly resilient, agile, and attuned to customer demands, while also illustrating the shortcomings of traditional practices in meeting modern market expectations. The study articulates the benefits associated with the adoption of SCM 4.0, emphasizing that this transition has the potential to realize substantial advantages, including a projected 30% decrease in operational costs, a 75% reduction in lost sales, a potential decrease of up to 75% in inventory levels, and enhancements in demand forecast accuracy of 30% to 50% through the implementation of predictive analytics. The findings underscore that digital supply chains not only bolster operational efficiencies but also enable organizations to better align with current market requirements, thereby providing a competitive advantage in an increasingly complex business environment. In summary, this paper contributes to the existing body of knowledge by scrutinizing the effects of Industry 4.0 technologies on supply chain management, addressing the limitations of traditional mechanisms, and offering a critical assessment of the strategic adaptations required for the successful adoption of SCM 4.0. These insights highlight the need for organizations to embrace digitalization and integrate advanced technologies to succeed within today's rapidly evolving market landscape
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References
] Z. Suleiman, S. Shaikholla, D. Dikhanbayeva, E. Shehab, and A. Turkyilmaz, “Industry 4.0: Clustering of concepts and characteristics,” Cogent Engineering, 2022.
] S. Grabowska and S. Saniuk, “Business models in the industry 4.0 environment—Results of web of science bibliometric analysis,” J. Open Innov. Technol. Mark. Complex., vol. 8, no. 1, 2022.
] B. Tjahjono, C. Esplugues, E. Ares, and G. Pelaez, “What does industry 4.0 mean to supply chain?,” Procedia Manuf., vol. 13, pp. 1175–1182, 2017.
] H. Fatorachian and H. Kazemi, “Impact of Industry 4.0 on supply chain performance,” Prod. Plan. Control, vol. 32, no. 1, pp. 63–81, 2021.
] E. D. Zamani, C. Smyth, S. Gupta, and D. Dennehy, “Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review,” Ann. Oper. Res., vol. 327, no. 2, pp. 605–632, 2023.
] K. Zekhnini, A. Cherrafi, I. Bouhaddou, Y. Benghabrit, and J. A. Garza-Reyes, “Supply chain management 4.0: a literature review and research framework,” Benchmarking Int. J., vol. 28, no. 2, pp. 465–501, 2021.
] H. Jafari, H. Ghaderi, M. Malik, and E. Bernardes, “The effects of supply chain flexibility on customer responsiveness: the moderating role of innovation orientation,” Prod. Plan. Control, vol. 34, no. 16, pp. 1543–1561, 2023.
] M. Ben-Daya, E. Hassini, and Z. Bahroun, “A conceptual framework for understanding the impact of Internet of things on supply chain management,” Int. J. Prod. Res., vol. 57, no. 15–16, pp. 4719–4742, 2019.
] M. Al-Talib, W. Y. Melhem, A. I. Anosike, J. A. Garza Reyes, S. P. Nadeem, and A. Kumar, “Achieving resilience in the supply chain by applying IoT technology,” Procedia CIRP, vol. 91, pp. 752–757, 2020.
] I. Alsolbi, F. H. Shavaki, R. Agarwal, G. K. Bharathy, S. Prakash, and M. Prasad, “Big data optimisation and management in supply chain management: A systematic literature review,” Artif. Intell. Rev., vol. 56, no. S1, pp. 253–284, 2023.
] P. S. Kang, X. Wang, J. Y. Son, and M. Jat, Service 4.0: Technology-enabled Customer-centric Supply Chains. Springer Nature, 2024.
] W. Chang and Y. Lin, “The effect of lead-time on supply chain resilience performance,” Asia Pac. Manag. Rev., vol. 24, no. 4, pp. 298–309, 2019.
] K. Chirumalla, L. Leoni, and P. Oghazi, “Moving from servitization to digital servitization: Identifying the required dynamic capabilities and related micro-foundations to facilitate the transition,” J. Bus. Res., vol. 158, p. 113668, 2023.
] M. V. Shree, V. Dhinakaran, V. Rajkumar, P. B. Ram, M. D. Vijayakumar, and T. Sathish, “Effect of 3D printing on supply chain management,” Mater. Today Proc., vol. 21, pp. 958–963, 2020.
] A. Derossi, B. Bhandari, K. van Bommel, M. Noort, and C. Severini, “Could 3D food printing help to improve the food supply chain resilience against disruptions such as caused by pandemic crises?,” Int. J. Food Sci. Technol., vol. 56, no. 9, pp. 4338–4355, 2021.
] R. Lacroix, R. W. Seifert, and A. Timonina-Farkas, “Benefiting from additive manufacturing for mass customization across the product life cycle,” Oper. Res. Perspect., vol. 8, p. 100201, 2021.
] Y. Feng, K. H. Lai, and Q. Zhu, “Green supply chain innovation: Emergence, adoption, and challenges,” Int. J. Prod. Econ., vol. 248, p. 108497, 2022.
] E. E. Agu, T. V. Iyelolu, C. Idemudia, and T. I. Ijomah, “Exploring the relationship between sustainable business practices and increased brand loyalty,” Int. J. Manag. Entrep. Res., vol. 6, no. 8, pp. 2463–2475, 2024.
] Y. Wang, Y. Yang, Z. Qin, Y. Yang, and J. Li, “A literature review on the application of digital technology in achieving green supply chain management,” Sustainability, vol. 15, no. 11, p. 8564, 2023.
] S. Chauhan, R. Singh, A. Gehlot, S. V. Akram, B. Twala, and N. Priyadarshi, “Digitalization of supply chain management with industry 4.0 enabling technologies: a sustainable perspective,” Processes, vol. 11, no. 1, p. 96, 2022.
] A. Raj, A. A. Mukherjee, A. B. De Sousa Jabbour, and S. K. Srivastava, “Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned,” J. Bus. Res., vol. 142, pp. 1125–1139, 2022.
] C. Swift, V. Guide, and S. Muthulingam, “Does supply chain visibility affect operating performance? Evidence from conflict minerals disclosures,” J. Oper. Manag., 2019.
] K. Govindan, D. Kannan, T. Jørgensen, and T. Nielsen, “Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence,” Transp. Res. Part E Logist. Transp. Rev., 2022.
] B. N. Pasi, S. K. Mahajan, and S. B. Rane, “Smart supply chain management: a perspective of industry 4.0,” Supply Chain Manag., vol. 29, no. 5, pp. 3016–3030, 2020.
] S. Khan, S. Tomar, M. Fatima, and M. Z. Khan, “Impact of artificial intelligent and industry 4.0 based products on consumer behaviour characteristics: A meta-analysis-based review,” Sustain. Oper. Comput., vol. 3, pp. 218–225, 2022.
] A. Rejeb, S. Simske, K. Rejeb, H. Treiblmaier, and S. Zailani, “Internet of Things research in supply chain management and logistics: A bibliometric analysis,” Internet Things, vol. 12, p. 100318, 2020.
] K. Pal, “Internet of things impact on supply chain management,” Procedia Comput. Sci., vol. 220, pp. 478–485, 2023.
] E. Frazzon, C. Rodriguez, M. Pereira, M. Pires, and I. Uhlmann, “Towards Supply Chain Management 4.0,” Braz. J. Oper. Prod. Manag., 2019.
] P. Helo and Y. Hao, “Artificial intelligence in operations management and supply chain management: An exploratory case study,” Prod. Plan. Control, vol. 33, no. 16, pp. 1573–1590, 2022.
] F. D. Weber and R. Schütte, “State-of-the-art and adoption of artificial intelligence in retailing,” Digit. Policy Regul. Gov., vol. 21, no. 3, pp. 264–279, 2019.
] E. M. Nyckel, “Ahead of Time: The Infrastructure of Amazon’s Anticipatory Shipping Method,” 2021.
] Y. K. Lingam, “The role of Artificial Intelligence (AI) in making accurate stock decisions in E-commerce industry,” Int. J. Adv. Res. Ideas Innov. Technol., vol. 4, no. 3, pp. 2281–2286, 2018.
] V. Soumpenioti and A. Panagopoulos, “AI Technology in the Field of Logistics,” in Proc. 2023 18th Int. Workshop Semant. Soc. Media Adapt. Pers., 2023.
] H. Meisheri, N. Sultana, M. Baranwal, V. Baniwal, S. Nath, and S. Verma, “Scalable multi-product inventory control with lead time constraints using reinforcement learning,” Neural Comput. Appl., 2021.
] P. Dutta, T. Choi, S. Somani, and R. Butala, “Blockchain technology in supply chain operations: Applications, challenges and research opportunities,” Transp. Res. Part E Logist. Transp. Rev., 2020.
] K. R. Patel, “Enhancing global supply chain resilience: Effective strategies for mitigating disruptions in an interconnected world,” BULLET: J. Multidiscip. Sci., vol. 2, no. 1, pp. 257–264, 2023.
] T. K. Dasaklis, T. G. Voutsinas, G. T. Tsoulfas, and F. Casino, “A systematic literature review of blockchain-enabled supply chain traceability implementations,” Sustainability, vol. 14, no. 4, p. 2439, 2022.
] U. J. Munasinghe and M. N. Halgamuge, “Supply chain traceability and counterfeit detection of COVID-19 vaccines using novel blockchain-based Vacledger system,” Expert Syst. Appl., vol. 228, p. 120293, 2023.
] E. A. Maroun, J. Daniel, and B. Fynes, “Adoption of blockchain technology in supply chain transparency: Australian manufacturer case study,” in Proc. 10th Eur. Decis. Sci. Inst. (EDSI), 2019.
] B. Esmaeilian, J. Sarkis, K. Lewis, and S. Behdad, “Blockchain for the future of sustainable supply chain management in Industry 4.0,” Resour. Conserv. Recycl., 2020.
] P. K. Wan, L. Huang, and H. Holtskog, “Blockchain-enabled information sharing within a supply chain: A systematic literature review,” IEEE Access, vol. 8, pp. 49645–49656, 2020.
] D. Kumar, “Blockchain-based solution for Demand Forecasting in Supply chain,” in Proc. 2021 1st Int. Conf. Adv. Comput. Future Commun. Technol. (ICACFCT), pp. 217–224, Dec. 2021.
] M. Mylrea and S. N. G. Gourisetti, “Blockchain for supply chain cybersecurity, optimization and compliance,” in Proc. 2018 Resilience Week (RWS), pp. 70–76, Aug. 2018.
] M. Sharma and P. Kumar, “Adoption of Blockchain Technology: A Case Study of Walmart,” in Blockchain Technol. Appl. Digit. Mark., 2021.
] S. Selvathasan, Integrating Blockchain in Supply Chain Management, 2023.
] R. K. Ray, F. R. Chowdhury, and M. R. Hasan, “Blockchain Applications in Retail Cybersecurity: Enhancing Supply Chain Integrity, Secure Transactions, and Data Protection,” J. Bus. Manag. Stud., vol. 6, no. 1, pp. 206–214, 2024.
] P. Centobelli, R. Cerchione, and L. Cricelli, “Innovation in the supply chain and big data: a critical review of the literature,” Eur. J. Innov. Manag., 2022.
] A. Gordon, “Internet of things-based real-time production logistics, big data-driven decision-making processes, and industrial artificial intelligence in sustainable cyber-physical manufacturing systems,” J. Self-Gov. Manag. Econ., vol. 9, no. 3, pp. 61–73, 2021.
] A. H. Ikevuje, D. C. Anaba, and U. T. Iheanyichukwu, “Optimizing supply chain operations using IoT devices and data analytics for improved efficiency,” Magna Sci. Adv. Res. Rev., vol. 11, no. 2, pp. 070–079, 2024.
] T. Nguyen, Z. H. O. U. Li, V. Spiegler, P. Ieromonachou, and Y. Lin, “Big data analytics in supply chain management: A state-of-the-art literature review,” Comput. Oper. Res., vol. 98, pp. 254–264, 2018.
] M. Anshari, M. N. Almunawar, S. A. Lim, and A. Al-Mudimigh, “Customer relationship management and big data enabled: Personalization & customization of services,” Appl. Comput. Informatics, vol. 15, no. 2, pp. 94–101, 2019.
] L. de Assis Santos and L. Marques, “Big data analytics for supply chain risk management: research opportunities at process crossroads,” Bus. Process Manag. J., vol. 28, no. 4, pp. 1117–1145, 2022.
] M. M. D. Medeiros and A. C. G. Maçada, “Competitive advantage of data-driven analytical capabilities: the role of big data visualization and of organizational agility,” Manag. Decis., vol. 60, no. 4, pp. 953–975, 2022.
] S. Mhaskey, “Exploring Cloud Computing Adoption in Supply Chain Management: Key Drivers and Challenges,” Int. J. Comput. Trends Technol., 2024.
] F. Calignano and V. Mercurio, “An overview of the impact of additive manufacturing on supply chain, reshoring, and sustainability,” Cleaner Logist. Supply Chain, vol. 7, 2023.
] W. M. Patrick, P. I. Anselemo, R. Ronoh, and S. Mbugua, “Impact of predictive analytics of big data in supply chain management on decision-making,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 8, no. 4, pp. 225–238, 2022.
] B. Hieu and B. Uyen, “Cloud Computing: The Key To Better Supply Chain Management – A Brief Review,” J. Multidiscip. Eng. Sci. Technol., 2021.
] B. Gammelgaard and K. Nowicka, “Next generation supply chain management: the impact of cloud computing,” J. Enterp. Inf. Manag., vol. 37, no. 4, pp. 1140–1160, 2024.
] M. Giannakis, K. Spanaki, and R. Dubey, “A cloud-based supply chain management system: effects on supply chain responsiveness,” J. Enterp. Inf. Manag., vol. 32, no. 4, pp. 585–607, 2019.
] C. Bagnoli, A. Albarelli, S. Biazzo, G. Biotto, and G. Marseglia, “Digital Business Models for Industry 4.0: How Innovation and Technology Shape the Future of Companies,” Future Bus. Finance, 2022.
] D. Pantoja-Navajas, “Unlock supply chain value with data and AI,” AWS Blogs, 2024.
] N. Das, J. Bangani, H. Hasson, A. Singh, and C. Bapat, “New Amazon Forecast API that creates up to 40% more accurate forecasts and provides explainability,” AWS Blogs, 2021.
] M. Tahiduzzaman, M. Rahman, S. K. Dey, M. S. Rahman, and S. M. Akash, “Big data and its impact on digitized supply chain management,” IJRDO-J. Bus. Manag., 2017.
] O. Olaniyi, A. Abalaka, and S. Olabanji, “Utilizing Big Data Analytics and Business Intelligence for Improved Decision-Making at Leading Fortune Company,” J. Sci. Res. Rep., 2023.
] A. AIX, “Case Study: Walmart’s AI-Enhanced Supply Chain Operations,” AI Expert Netw., 2023.
] P. Musani, “Decking the aisles with data: How Walmart's AI-powered inventory system brightens the holidays,” Walmart Glob. Tech-Art., 2023.
] B. Tan, J. Yan, S. Chen, and X. Liu, “The Impact of Blockchain on Food Supply Chain: The Case of Walmart,” Smart Blockchain, 2018.
] A. AIX, “BMW Group uses Blockchain to drive supply chain transparency,” BMW Grp. - Art., 2020.
] AES, “Thyssenkrupp Presents AI “alfred” to Manage Their Global Logistics Network,” AEC Bus. - Blog, 2019.
] K. Lawal, “Leveraging Technology for Enhanced Supply Chain Resilience,” IRE J., 2024.