Smart Supply Chains- A Futuristic Business Scenario
Main Article Content
Abstract
Supply chains have transitioned from simple logistic functions to futuristic supply chains, having traveled the journey from mere coordination of production and dispatch duties to become the essence of business setups. Information and Technology have changed supply chain management's infrastructure and working mindsets. Agility, reliability, and efficiency have become synonymous with supply chain outcomes due to the alignment of Technology and intelligence. In this chapter, concepts of Industry 4.0 and Logistic 4.0 or Smart supply chains are discussed to understand their evolving trends, needs, and relevance in changing global business setups. This research covers the need and steps of automation, operative and strategic aspects, and actual and potential application of automation in supply chains.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Alicke, K., Rexhausen, D., and Seyfert, A., Supply Chain 4.0 in consumer goods, McKinsey & Company, Berlin, 2016.
Ali, S. S., and Rajbir Kaur. Exploring the Impact of Technology 4.0 Driven Practice on Warehousing Performance: A Hybrid Approach" Mathematics 10, no. 8: 1252, 2022. https://doi.org/10.3390/math10081252
Ali, S. S., Kaur, R. and Sh. Khan Evaluating sustainability initiatives in warehouse for measuring sustainability performance: An emerging economy perspective, Annals of Operations Research. 2021a, https://doi.org/10.1007/s10479-021-04454-w
Ali, S. S., Kaur, R., Gupta, H., Ahmad, Z.; and Elnaggar, G. Determinants of an Organization’s Readiness for Drone Technologies Adoption, IEEE Transactions on Engineering Management, DOI: 10.1109/TEM.2021.3083138 ,2021b
Ali, S. S., Kaur , Deka, S. , Jaswal,M. , Ahmad , Z. and AlSulami, H. . Feasibility of drone integration as last mile delivery , Emerald Emerging Markets Case Studies, 9,3, 1-17, 2019https://doi.org/10.1108/EEMCS-06-2018-0150 .
Attaran, M., RFID: an enabler of supply chain operations, Supply Chain Management: An International Journal, 12( 4),249-257, 2007.
B. Cortés, A. Boza, D. Pérez, L. Cuenca, Internet of things applications on supply chain management, Int. J. Comput. Electr. Autom. Control Inf. Eng. 9 (2015) 2204–2209.
B.T. Hazen, J.B. Skipper, J.D. Ezell, C.A. Boone, Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda, Comput. Ind. Eng. 101 592–598, 2016.
Brettel, M., Friederichsen, N., Keller, M., and Rosenberg, M., Ho virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective, International Journal of Information and Communication Engineering, 8(1),37-44, 2014.
Davenport, R.T.H., and Bean, R.,. Big Companies Are Embracing Analytics, but Most Still Don't Have a Data‐Driven Culture. Harvard Business Review, 2018.
G. Wang, A. Gunasekaran, E.W.T. Ngai, T. Papadopoulos, Big data analytics in logistics and supply chain management: certain investigations for research and applications, Intern. J. Prod. Econ. 176 98–110, 2016.
Galindo, L. D, The Challenges of Logistics 4.0 for the Supply Chain Management and the Information Technology. Norwegian University of Science and Technology, 96, 2016.
Gunasekaran, T. Papadopoulos, R. Dubey, S.F. Wamba, S.J. Childe, B. Hazen et al., Big data and predictive analytics for supply chain and organizational performance, J. Bus. Res. 70 ,308–317,2017.
Harford, T. (2014), “Big data: are we making a big mistake?”, available at: www.ft.com/intl/cms/s/2/21a6e7d8-b479-11e3- a09a-00144feabdc0.html (accessed 11 October 2019).
Huang, T. and Van Mieghem, J.A., Clickstream data and inventory management: model and empirical analysis, Production and Operations Management,23(3), 333-347, 2014.
Iansiti, M., and Lakhani, K.R. 2017. The Truth about Blockchain.” Harvard Business Review 95 (1) 118– 27.
Ivanov, D., Dolgui, A., Sokolov, B., Werner, F., and Ivanova, M., A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0, International Journal of Production Research, 54(2) 386-402, 2016. 14
J. Oyekan, V. Prabhu, A. Tiwari, V. Baskaran, M. Burgess, R. Mcnally, Remote real-time collaboration through synchronous exchange of digitised human– workpiece interactions, Future Gener. Comput. Syst. 67 (2017) 83–93, doi:
Kache, F., and Seuring, S., 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, 2017.
Khan, S.; Singh, R.; Haleem, A.; Dsilva, J.; Ali, S.S. Exploration of Critical Success Factors of Logistics 4.0: A DEMATEL Approach. Logistics , 6, 13. 2022, doi: 10.3390/logistics6010013
Klumpp, M., Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements, International Journal of Logistics Research and Applications, vol. 21 (3), 224-242, 2018.
Kranz, M. 2017. Success with the Internet of Things Requires More Than Chasing the Cool Factor.” Havard Business Review.
Krivda, C., RFID after compliance: integration and payback, Business Week, December 20, 2004.
Lee J, Bagheri B, Kao HA (2014) Recent advances and trends of cyber-physical systems and big data analytics in industrial informatics. In Proceedings of International Conference on Industrial Informatics (INDIN), 217–229.
Lee, J., Kao, H-A., and Yang, S., Service innovation and smart analytics for Industry 4.0 and big data environment, product service systems and value creation, Proceedings of the 6th CIRP Conference on Industrial Product- Service Systems, vol. 3, no. 8, pp. 3-8, 2014.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A.H. (2011), Big Data: The Next Frontier for Innovation, Competition, and Productivity,
McKinsey & Company, New York, NY, available at: www. mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation (accessed 9 November 2019).
Rossom, P., Big data analytics, TDWI Best Practice Report, Fourth Quarter 2011, TDWI, 2011.
Schiemann, J, Logistics 4.0 - How autonomous are self-managed processes? Axit Connecting Logistics, 16, 2016
Shu, J. and Barton, R. (2012), “Managing supply chain execution: monitoring timeliness and correctness via individualized trace data”, Production and Operations Management,21( 4), 715- 729.
iciliano, B., and Khatib, O. Editors. 2016. Springer handbook of robotics, 2nd Ed. Heidelberg, Germany: Springer.
Tan, K.H., Zhan, Y., Ji, G., Ye, F., and Chang, C.,(2015). Harvesting Big Data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph, International Journal of Production Economics, vol. 165, pp. 223-233.
Thiesse, F., Floerkemeier, C., Harrison, M., Michahelles, F., and Roduner, C., Technology, standards, and real-world deployments of the EPC network, IEEE Internet Computing, vol. 13, no. 2, pp. 36-43, 2009.
Trappey, A. J. C., Trappey, C. V., Fan, C-Y., Hsu, A. P. T., Li, X-K., and Lee, I. J. Y., IoT Patent Roadmap for Smart Logistic Service Provision in the Context of Industry 4.0, Journal of the Chinese Institute of Engineers, 40(7), 593-602, 2017.
Tu, M., An exploratory study of Internet of Things (IoT) adoption intention in logistics and supply chain management: A mixed research approach, The International Journal of Logistics Management, 29(1),131-151, 2018.
Tu, M., Lim, M. K., and Yang, M-F., IoT-based production logistics and supply chain system – Part 1: Modeling IoT- based manufacturing supply chain, Industrial Management & Data Systems, 118(1), 65-95, 2018a.
Tu, M., Lim, M. K., and Yang, M-F., IoT-based production logistics and supply chain system – Part 2: IoT-based cyber-physical system: a framework and evaluation, Industrial Management & Data Systems,118(1), 96-125, 2018b.
Wamba, F.S., Akter, S., Edwards, A., Chopin, G., and Gnanzou, D., (2015). 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.
Wamba, F.S., and Boeck, H., Enhancing information flow in a retail supply chain using RFID and the EPC network: a proof-of-concept approach, Journal of Theoretical and Applied Electronic Commerce Research, 3(1),92-105, 2008.
Wang S, Wan J, Zhang D, Li D, Zhang. Towards smart factory for industry 40: A self-organized multi-agent system with big data-based feedback and coordination. Comput Net 101,158– 168,2016.
Wang, L., Törngren, M., and Onori, M., Current status and advancement of cyber-physical systems in manufacturing, Journal of Manufacturing Systems, 37(2), 517-527, 2015.
Wang, S., Wan, J., Li, D., and Zhang, C., Implementing smart factory of Industries 4.0: An outlook, International Journal of Distributed Sensor Network, 2016.
Witkowski, K., Internet of Things, big data, Industry 4.0 – Innovative solutions in logistics and supply chain management, Proceedings of the 7th International Conference on Engineering, Project, and Production Management, 182,763-769, 2017.
Wu, L., Yue, X., Jin, A., and Yen, D.C., Smart supply chain management: a review and implications for future research, The International Journal of Logistics Management, 27(2), 395-417, 2016.
Zhang Y, Qian C, Lv J, Liu Y (2017) Agent and cyber-physical system based self adaptive intelligent shopfloor. IEEE Trans Indust Inf 13(2):737–744