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Adoption of Artificial Intelligence for Improved Supply Chain and Logistic Performance: A Conceptual Insight

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In the evolving landscape of supply chain digitalization, integration, and globalization, there is a growing recognition of the potential of advanced information processing methods like Artificial Intelligence (AI) to enhance supply chain performance (SCP) and logistic performance (LP). Out of sixty articles reviewed, sourced from both conferences and journals, only twenty-four qualified for in-depth synthesis and analysis. This highlights a significant gap in the literature, especially when considering comprehensive reviews on the current and potential impacts of AI on SCP and LP, despite the increasing interest in this domain. Thus, this paper examines the nexus of AI application, SCP and LP. This paper consolidates and synthesize the current available research and provides the basis for further research on the connection between AI, SCP, and LP.
Adele, A. (2022). Future of industry 5.0 in society: human-centric solutions, challenges, and prospective research areas. Journal Cloud Comp 11, 40 (2022).
https://doi.org/10.1186/s13677-022-00314-5.
Balfaqih, H. (2023). Artificial Intelligence in Logistics and Supply Chain Management: A Perspective on Research Trends and Challenges. In: Alareeni, B., Hamdan, A. (eds) Explore Business, Technology Opportunities and Challenges ?After the Covid-19 Pandemic. ICBT 2022. Lecture Notes in Networks and Systems, vol 495. Springer, Cham. https://doi.org/10.1007/978-3-031-08954-1_106
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.
Bhargava, A., Bhargava, D., Kumar, N. V., Sajja, G, S., & Ray, S. (2022). Industrial IoT and AI implementation in vehicular logistics and supply chain management for vehicle mediated transportation systems. Int. Journal System Assur Eng & Management.
Brynjolfsson, E., & A. Mcafee. (2017). The Business of Artificial Intelligence. Harvard Business Review,1–20. https://hbr.org/2017/07/the-business-of-artificial-intelligence.
Chen H, Simoska O, Lim K, Grattieri M, Yuan M, Dong F, Lee YS, Beaver K, Weliwatte S, Gaffne EM, Minteer S.D. (2020). Fundamentals, applications, and future directions of bioelectrocatalysis. Chem Rev, 120(23):12903–12993.
Chen, V. C. H., & Chen, Y. C. (2021). Influence of intellectual capital and integration on operational performance: Big data analytical capability perspective. Journal of Chinese management study, Vol. 16 No. 3, 2022, pp. 551-570.
Chung, S. H. (2021). Applications of smart technologies in logistics and transport: A review. Transportation Research Part E: Logistics and Transportation Review, 153, 102455.
Engelman, R. M., Fracasso, E. M., Schmidt, S. & Zen, A. C. (2017). Intellectual capital, absorptive capacity and product innovation. Management Decision, Vol. 55 No. 3, pp. 474-490.
Erevelles, S., Fukawa, N., & Swayne, L. (2016). BD consumer analytics and the transformation of marketing. Journal of Business Research, Vol. 69 No. 2, pp. 897-904.
Eric-Dossou, P. (2019). Impact of sustainability on the supply chain 4.0 performance. 28th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2018), June 11-14, 2018, Columbus, OH, USA.
Eyo-Udo, N. L. (2024). Leveraging artificial intelligence for enhanced supply chain optimization. Open Access Research Journal of Multidisciplinary Studies, 2024, 07(02), 001–015. Article DOI: https://doi.org/10.53022/oarjms.2024.7.2.0044.
Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision. 57. 10.1108/MD-07-2018-0825.
Fu, W., & Chien, C. F. (2019). UNISON data driven intermittent demand forecast framework to empower supply chain resilience and an empirical study in electronic distribution. International Journal of Computers & Engineering, 135 (2019) 940-949.
Giovanni, P.D. (2020). Smart supply chains with vendor managed inventory, coordination, and environmental performance. European Journal of Operational Research, Available online 11 November 2020.
Govindan, K., Cheng, T. E., Mishra, N., & Shukla, N. (2018). Big data analytics and application for logistics and supply chain management. Transportation Research Part e: Logistics and Transportation Review, 114, 343–349.
Irfan, M. & Wang, M. (2019). Data-driven capabilities, supply chain integration and competitive performance: evidence from the food and beverages industry in Pakistan. British Food Journal, Vol. 121 No. 11, pp. 2708-2729.
Jackson, I., Ivanov, D., Dolgui, A., & Namdar, J. (2024). Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation. International Journal of Production Research, 62:17, 6120-6145, DOI: 10.1080/00207543.2024.2309309.
Jomthanachai, S., Wong, W.P., & Khaw, K.W. (2023). An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance. Computer Econ. https://doi.org/10.1007/s10614-023-10358-7.
Kamble, S. S., Gunasekaran, A., Parekh, H., Mani, V., Belhadi, A., & Sharma, R. (2022). Digital twin for sustainable manufacturing supply chains: Current trends, future perspective, and implementation framework. International Journal of Technological Forecasting & Social Change, 176 (2022)121448.
Kamble, S. S., & Gunasekaran, A. (2020). Big data-driven supply chain performance measurement system: A review and framework for implementation. International Journal of Production Research, 58(1), 65–86.
Kamble, S. S., Gunasekaran, A., Ghadge, A., Raut, R. (2020). A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs-A review and empirical investigation. International Journal Production Econ. 229, 107853.
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. Environ. Prot. 117, 408–425.
Kumar, V., Ramachandran, D. & Kumar, B. (2020). Influence of new-age technologies on marketing: A research agenda. Journal of Business Research, 10.1016/j.jbusres.2020.01.00.
Latif, Z., Lei, W., Latif, S., Pathan, Z. H., Ullah, R. & Jianqiu, Z. (2019). Big data challenges: prioritizing by decision-making process using analytic network process technique. Multimedia Tools and Applications, Vol. 78 No. 19, pp. 27127-27153.
Latif, Z., Pathan, Z.H., Ximei, L., Tunio, M. Z., Jianqiu, Z. & Sadozai, S. K. (2018). A review of policies concerning development of big data industry in Pakistan: development of big data industry in Pakistan. International Conference on Computing, Mathematics and Engineering Technologies – iCoMET 2018.
Li, L., Gong, Y., Wong, Z. Q., Liu, S., & Shaanxi Logistics Group. (2022). Big Data and big disaster: a mechanism of supply chain risk management in global logistics industry. International Journal of Operation & Production Management, Vol. 43 No. 2, 2023 pp. 274-307.
Li, F., Li, Z. G., Wu, J., & Li, T. (2022). A capacity matching model in a collaborative urban public transport system: integrating passenger and freight transportation. International Journal of Production Research, DOI: 10.1080/00207543.2021.1991021.
Liu, H., Wei, S., Ke, W., Wei, K., & Hua, Z. (2016). The configuration between supply chain integration and information technology competency: a resource orchestration perspective. Journal of Operations Management, Vol. 44 No. 1, pp. 13-29.
Lo, S. C., & Chuang, Y. L. (2023). Vehicle Routing Optimization with Cross-Docking Based on an Artificial Immune System in Logistics Management. Mathematics 2023, 11, 811. https://doi.org/10.3390/ math11040811.
Madancian, M., Taherdoost, H., Javadi, M., Khan, I.U., Kalantari, A., Kumar, D. (2024). The Impact of Artificial Intelligence on Supply Chain Management in Modern Business. In: Farhaoui, Y., Hussain, A., Saba, T., Taherdoost, H., Verma, A. (eds) Artificial Intelligence, Data Science and Applications. ICAISE 2023. Lecture Notes in Networks and Systems, vol 838. Springer, Cham. https://doi.org/10.1007/978-3-031-48573-2_82.
Min, H. (2022). Developing a smart port architecture and essential elements in the era of Industry 4.0. International Journal of Maritime & Logistics, 24:189-207. https://doi.org/10.1057/s41278-022-00211-3.
Min, H. (2010). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics-research and Applications. International Journal of Logistics Research and Application. 13. 13-39. 10.1080/13675560902736537.
Mohsen, B. M. (2023). Impact of Artificial Intelligence on Supply Chain Management Performance. Journal of Service Science and Management, 16, 44-58.
Naseer, S., Khawaja, K.F., Qazi, S., Syed, F. & Shamim, F. (2021). How and when information proactiveness leads to operational firm performance in the banking sector of Pakistan? The roles of open innovation, creative cognitive style, and climate for innovation. International Journal of Information Management, Vol. 56, p. 102260.
Negri, E., Fumagalli, L., & Macchi, M. (2017). A review of roles of digital twins in CPS-based production systems. Procedia Manufacturing. 11, 939–948. DOI=10.1016%2fj.promfg.2017.07.198.
Nwagwu, U., Niaz, M., Chukwu, M. U., & Saddiq, F. (2023). The Influence of Artificial Intelligence to Enhancing Supply Chain Performance Under the Mediating Significance of Supply Chain Collaboration in Manufacturing and Logistics Organizations in Pakistan. Traditional Journal of Multidisciplinary Sciences (TJMS), July-December 2023, Vol. 01, No. 02, 29 –40.
Oztemel, E., & Gursev. S. (2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31 (1): 127–182.
Park, K. T., Nam, Y. W., Lee, H. S., Im., S.J., Noh, S.D., Son, J.Y., & Kim, H. (2019). Design and implementation of a digital twin application for a connected micro smart factory International. Journal. Computer Integrated. Manuf. 32 (6), 596–614,
10.1080/0951192X.2019.1599439.
Pessot, E., Zangiacomi, A., Marchiori, I., & Fornasiero, R. (2023). Empowering supply chains with Industry 4.0 technologies to face megatrends. Journal of Business Logistics. https://doi. org/10.1111/jbl.12360.
Rahimi, A., & Alemtabriz, A. (2022). Providing a Model of Leagile Hybrid Paradigm Practices and Its impact on Supply Chain Performance. International Journal of Lean Six Sigma, 13, 1308-1345. https://doi.org/10.1108/IJLSS-04-2021-0073.
Rezaei Aderiani, A., W ?armefjord, K., Soderberg, R., Lindkvist, L. (2019). Developing a selective assembly technique for sheet metal assemblies. International. Journal. Prod. Res. 57 (22), 7174–7188, 10.1080/00207543.2019.1581387.
Richardson, A. (2019). Why AI is Key to Unlock the True Value of Industry 4.0. Available at: https:// kontakt.io/blog/ai-key-unlock-true-value-industry-4-0/.
Richey, R. G., Jr, Chowdhury, S., Davis-Sramek, B., Giannakis, M. & Dwivedi, K., Y. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Wiley Online Library: Downloaded from
https://onlinelibrary.wiley.com/doi/10.1111/jbl.12364.
Richey, R. G., Jr, Morgan, T. R., Lindsey-Hall, K. & Adams, F.G. (2016). A global exploration of big data in the supply chain. International Journal of Physical Distribution and Logistics Management, Vol. 46 No. 8, pp. 710-739.
Silva, P.D., Neto, G.C.D.O., Correia, J. M. F., and Tucci, H. N. P. T. (2021). Evaluation of economic, environmental, and operational performance of the adoption of cleaner production: survey in large textile industries. Journal of Cleaner Production, Vol. 278, p. 123855.
Sisinni, E., Saifullah, A., Han, S., Jennehag, U., & Gidlund, M. (2018). Industrial Internet of Things: challenges, opportunities, and directions. IEEE Transactions on Industrial Informatics, 14 (1): 4724–4734.
Sharma, R., Shishodia, A., Gunasekaran, A., Min, H., & Munim, Z. H. (2022). The role of artificial intelligence in supply chain management: mapping the territory. International Journal of Production Research, 60:24, 7527-7550, DOI: 10.1080/00207543.2022.2029611.
Sharma, S., Gahlawat, V. K., Rahul, K., Mor, R.S., & Malik, M. (2021). Sustainable Innovations in the Food Industry through Artificial Intelligence and Big Data Analytics Logistics. Vol. 5, 66. https://doi.org/survey 10.3390/logistics5040066.
?lusarczyk, B. (2018). Industry 4.0: Are we ready. Polish Journal of Management Studies, 17 (1): 232–248.
Srinivasan, R., & Swink, M. (2018). An investigation of visibility and flexibility as complements to supply chain analytics: an organizational information processing theory perspective. Production and Operations Management, Vol. 27 No. 10, pp. 1849-1867.
Statista (2022). Artificial Intelligence (AI) Adoption Rate in Supply Chain and Manufacturing Businesses Worldwide in 2022 and 2025.
Uhlemann, T. H. J., Schock, C., Lehmann, C., Freiberger, S., & Stenhilper, R. (2017). The digital twin: demonstrating the potential of real time data acquisition in production systems. Procedia Manuf. 9, 113–120.
Walter, S. (2023). AI impacts on supply chain performance: a manufacturing use case study. Discov Artif Intell 3, 18 (2023). https://doi.org/10.1007/s44163-023-00061-9
Wu, Z., Wang, S., Yang, H., & Zhao, X. (2021). Construction of a supply chain financial logistics supervision system based on Internet of Things technology. Math Probl Eng 2021:9980397. https://doi.org/10.1155/2021/9980397.
Yu, W., Chavez, R., Jacobs, M.A. & Feng, M. (2018). Data-driven supply chain capabilities and performance: a resource-based view. Transportation Research Part E: Logistics and Transportation Review, Vol. 114, pp. 371-385.
Zhang, P., Sun, H., Situ, J., Jiang, C., & Xie, D. (2021). Federated transfer learning for IIoT devices with low computing power based on blockchain and edge computing. IEEE Access 9:98630–98638, https://doi.org/10.1109/ACCESS.2021.3095078.
Azman, S. N., Ramli, F., Azami, N., & Rahim, R. A. (2024). Adoption of Artificial Intelligence for Improved Supply Chain and Logistic Performance: A Conceptual Insight. International Journal of Academic Research in Business and Social Sciences, 14(8), 79–92.