Designing a Cognitive Supply Chain Platform Using SAP ERP Solution for Smart Decision Support
DOI:
https://doi.org/10.47604/ijscm.2650Keywords:
Cognitive Computing, Supply Chain Management, SAP ERP, Decision Support, Analytics, Machine Learning, Natural Language ProcessingAbstract
Purpose: This paper aims to discuss how cognitive computing can work with supply chain management and differentiate it from the SAP ERP solutions which improves decision-making activities. It would reveal the potential of using analytics, machine learning techniques, and natural language processing for real-time decision-making.
Methodology: To start with, a critical review of the literature is made and a functional breakdown of SAP ERP is conducted to understand the system's structure and components. Talking about it in detail, the paper explores the chances, threats, and outlooks of integrating cognitive computing into SCM.
Findings: In light of the above findings, it is revealed that integrating cognitive technologies with SAP ERP can enhance supply chain decision-making by equipping managers with complete timely information and possible future outcomes.
Unique Contribution to Theory, Practice and Policy: Considering this, the paper suggests that more studies and implementations of cognitive supply chain platforms connected to the SAP ERP solution will improve decision-making effectiveness and company competitiveness in the modern context.
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References
Noaman, A.Y. and Ahmed, F.F., 2015. ERP systems functionalities in higher education. Procedia Computer Science, 65, pp.385-395.
Aamer, A., Eka Yani, L. and Alan Priyatna, I., 2020. Data analytics in the supply chain management: Review of machine learning applications in demand forecasting. Operations and Supply Chain Management: An International Journal, 14(1), pp.1-13.
Angelopoulos, S., Bendoly, E., Fransoo, J.C., Hoberg, K., Ou, C.X. and Tenhiala, A., 2023. Digital transformation in operations management: Fundamental change through agency reversal. Journal of operations management, Forthcoming.
Araz, O.M., Choi, T.M., Olson, D.L. and Salman, F.S., 2020. Data analytics for operational risk management. Decis. Sci., 51(6), pp.1316-1319.
Barykin, S.Y., Bochkarev, A.A., Kalinina, O.V. and Yadykin, V.K., 2020. Concept for a supply chain digital twin. International Journal of Mathematical, Engineering and Management Sciences, 5(6), pp.1498-1515.
Darvazeh, S.S., Vanani, I.R. and Musolu, F.M., 2020. Big data analytics and its applications in supply chain management. New Trends in the Use of Artificial Intelligence for the Industry, 4, p.175.
Fosso Wamba, S. and Akter, S., 2019. Understanding supply chain analytics capabilities and agility for data-rich environments. International Journal of Operations & Production Management, 39(6/7/8), pp.887-912.
Gupta, S., Modgil, S., Bhushan, B., Sivarajah, U. and Banerjee, S., 2023. Design and Implementation of an IIoT Driven Information System: A Case Study. Information Systems Frontiers, pp.1-15.
Hung, J.L., He, W. and Shen, J., 2020. Big data analytics for supply chain relationship in banking. Industrial Marketing Management, 86, pp.144-153.
Ivanov, D., Dolgui, A., Das, A. and Sokolov, B., 2019. Digital supply chain twins: Managing the ripple effect, resilience, and disruption risks by data-driven optimization, simulation, and visibility. Handbook of ripple effects in the supply chain, pp.309-332.
Ivković, J. and Ivković, J.L., 2023. Conceptual Analysis and Potential Applications of DL NN Transformer and GPT Artificial Intelligence Models for the Transformation and Enhancement of Enterprise Management, EIS/ESS, and Decision Support Integrated Information Systems. In LINK IT> EdTech International Scientific Conference, Belgrade, R. Serbia (pp. 92-102).
Jawad, Z.N. and Balázs, V., 2024. Machine learning-driven optimization of enterprise resource planning (ERP) systems: a comprehensive review. Beni-Suef University Journal of Basic and Applied Sciences, 13(1), p.4.
Koot, M., Mes, M.R. and Iacob, M.E., 2021. A systematic literature review of supply chain decision making supported by the Internet of Things and Big Data Analytics. Computers & industrial engineering, 154, p.107076.
Lee, I. and Mangalaraj, G., 2022. Big data analytics in supply chain management: A systematic literature review and research directions. Big data and cognitive computing, 6(1), p.17.
Liu, J., Chen, M. and Liu, H., 2020. The role of big data analytics in enabling green supply chain management: a literature review. Journal of Data, Information and Management, 2, pp.75-83.
Mageto, J., 2021. Big data analytics in sustainable supply chain management: A focus on manufacturing supply chains. Sustainability, 13(13), p.7101.
Nguyen, A., Lamouri, S., Pellerin, R., Tamayo, S. and Lekens, B., 2022. Data analytics in pharmaceutical supply chains: state of the art, opportunities, and challenges. International Journal of Production Research, 60(22), pp.6888-6907.
Ogbuke, N.J., Yusuf, Y.Y., Dharma, K. and Mercangoz, B.A., 2022. Big data supply chain analytics: ethical, privacy and security challenges posed to business, industries and society. Production Planning & Control, 33(2-3), pp.123-137.
Oliveira, J.B., Jin, M., Lima, R.S., Kobza, J.E. and Montevechi, J.A.B., 2019. The role of simulation and optimization methods in supply chain risk management: Performance and review standpoints. Simulation Modelling Practice and Theory, 92, pp.17-44.
Oncioiu, I., Bunget, O.C., Türkeș, M.C., Căpușneanu, S., Topor, D.I., Tamaș, A.S., Rakoș, I.S. and Hint, M.Ș., 2019. The impact of big data analytics on company performance in supply chain management. Sustainability, 11(18), p.4864.
Raparthi, M., 2021. Blockchain-Based Supply Chain Management Using Machine Learning: Analyzing Decentralized Traceability and Transparency Solutions for Optimized Supply Chain Operations. Blockchain Technology and Distributed Systems, 1(2), pp.1-9.
Seyedan, M. and Mafakheri, F., 2020. Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. Journal of Big Data, 7(1), p.53.
Shafiq, A., Ahmed, M.U. and Mahmoodi, F., 2020. Impact of supply chain analytics and customer pressure for ethical conduct on socially responsible practices and performance: An exploratory study. International Journal of Production Economics, 225, p.107571.
Swink, M., Hu, K. and Zhao, X., 2022. Analytics applications, limitations, and opportunities in restaurant supply chains. Production and Operations Management, 31(10), pp.3710-3726.
Wang, D., Liu, W., Liang, Y. and Wei, S., 2023. Decision optimization in service supply chain: the impact of demand and supply-driven data value and altruistic behavior. Annals of Operations Research, pp.1-22.
Wang, S. and Yan, R., 2022. A global method from predictive to prescriptive analytics considering prediction error for "Predict, then optimize" with an example of low-carbon logistics. Cleaner logistics and supply chain, 4.
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Copyright (c) 2024 Vummadi Jayapal Reddy, Krishna Chaitanaya Raja Hajarath
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