Generative AI for enhanced operations and supply chain management

Khaoula Khlie ,Zoubida Benmamoun ,Ikhlef Jebbor ,Driss Serrou

doi:10.24294/jipd.v8i10.6637

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Generative AI for enhanced operations and supply chain management

Khaoula Khlie

Zoubida Benmamoun

Ikhlef Jebbor

Driss Serrou

Journal of Infrastructure Policy and Development 2024, 8(10), 6637; https://doi.org/10.24294/jipd.v8i10.6637

Submitted：24 May 2024

Accepted：13 Jun 2024

Published：27 Sept 2024

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Abstract

In this paper, we will provide an extensive analysis of how Generative Artificial Intelligence (GenAI) could be applied when handling Supply Chain Management (SCM). The paper focuses on how GenAI is more relevant in industries, and for instance, SCM where it is employed in tasks such as predicting when machines are due for a check-up, man-robot collaboration, and responsiveness. The study aims to answer two main questions: (1) What prospects can be identified when the tools of GenAI are applied in SCM? Secondly, it aims to examine the following question: (2) what difficulties may be encountered when implementing GenAI in SCM? This paper assesses studies published in academic databases and applies a structured analytical framework to explore GenAI technology in SCM. It looks at how GenAI is deployed within SCM and the challenges that have been encountered, in addition to the ethics. Moreover, this paper also discusses the problems that AI can pose once used in SCM, for instance, the quality of data used, and the ethical concerns that come with, the use of AI in SCM. A grasp of the specifics of how GenAI operates as well as how to implement it successfully in the supply chain is essential in assessing the performance of this relatively new technology as well as prognosticating the future of generation AI in supply chain planning.

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References

Akbari, M., & Do, T. N. A. (2021). A systematic review of machine learning in logistics and supply chain management: current trends and future directions. Benchmarking: An International Journal, 28(10), 2977–3005. https://doi.org/10.1108/bij-10-2020-0514

Ashcroft, S. (2023). How might ChatGPT help Supply Chains. Supply Chain Digital; BizClick: London, UK.

Aydın, Ö., & Karaarslan, E. (2022). OpenAI ChatGPT Generated Literature Review: Digital Twin in Healthcare. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4308687

Bahrini, A., Khamoshifar, M., & Abbasimehr, H., et al. (2023). ChatGPT: Applications, Opportunities, and Threats. 2023 Systems and Information Engineering Design Symposium (SIEDS). https://doi.org/10.1109/sieds58326.2023.10137850

Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. Journal of AI, 7(1), 52–62. https://doi.org/10.61969/jai.1337500

Bandi, A., Adapa, P. V. S. R., & Kuchi, Y. E. V. P. K. (2023). The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges. Future Internet, 15(8), 260. https://doi.org/10.3390/fi15080260

Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1). https://doi.org/10.1007/s12525-023-00680-1

Benmamoun, Z., Fethallah, W., & Ahlaqqach, M., et al. (2023). Butterfly Algorithm for Sustainable Lot Size Optimization. Sustainability, 15(15), 11761. https://doi.org/10.3390/su151511761

Benmamoun, Z., Hachimi, H., & Amine, A. (2018). Comparison of inventory models for optimal working capital; case of aeronautics company. International Journal of Engineering, 31(4), 605-611.

Bozkurt, A. (2023). Generative artificial intelligence (AI) powered conversational educational agents: The inevitable paradigm shift. Asian Journal of Distance Education, 18(1).

Brand, J., Israeli, A., & Ngwe, D. (2023). Using GPT for Market Research. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4395751

Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530–1534. https://doi.org/10.1126/science.aap8062

Burger, B., Kanbach, D. K., & Kraus, S., et al. (2023). On the use of AI-based tools like ChatGPT to support management research. European Journal of Innovation Management, 26(7), 233–241. https://doi.org/10.1108/ejim-02-2023-0156

Cao, Y.-J., Jia, L.-L., & Chen, Y.-X., et al. (2019). Recent Advances of Generative Adversarial Networks in Computer Vision. IEEE Access, 7, 14985–15006. https://doi.org/10.1109/access.2018.2886814

Castelvecchi, D. (2016). Can we open the black box of AI? Nature, 538(7623), 20–23. https://doi.org/10.1038/538020a

Cooper, G. (2023). Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence. Journal of Science Education and Technology, 32(3), 444–452. https://doi.org/10.1007/s10956-023-10039-y

Dhudasia, M. B., Grundmeier, R. W., & Mukhopadhyay, S. (2021). Essentials of data management: an overview. Pediatric Research, 93(1), 2–3. https://doi.org/10.1038/s41390-021-01389-7

Diianni, P., & De Girloamo, W. (2021). BMW i Ventures Invests in Inventory Optimization Software Company Verusen to Fuel Intelligent, Connected Supply Chains. BMW USA News. Available online: https://www.bmwusanews.com/newsrelease.do?id=3685 (accessed on 18 April 2023).

Dogru, T., Line, N., & Mody, M., et al. (2023). Generative Artificial Intelligence in the Hospitality and Tourism Industry: Developing a Framework for Future Research. Journal of Hospitality & Tourism Research. https://doi.org/10.1177/10963480231188663

Dwivedi, Y. K., Hughes, L., & Ismagilova, E., et al. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

Dwivedi, Y. K., Kshetri, N., & Hughes, L., et al. (2023a). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Dwivedi, Y. K., Pandey, N., & Currie, W., et al. (2023b). Leveraging ChatGPT and other generative artificial intelligence (AI)-based applications in the hospitality and tourism industry: practices, challenges and research agenda. International Journal of Contemporary Hospitality Management, 36(1), 1–12. https://doi.org/10.1108/ijchm-05-2023-0686

Eason, G., Noble, B., & Sneddon, I. N. (1955). On certain integrals of Lipschitz-Hankel type involving products of Bessel functions. Phil. Trans. Roy. Soc. London, A247, 529–551.

Ferreira, K. J., Lee, B. H. A., & Simchi-Levi, D. (2016). Analytics for an Online Retailer: Demand Forecasting and Price Optimization. Manufacturing & Service Operations Management, 18(1), 69–88. https://doi.org/10.1287/msom.2015.0561

Filippi, S. (2023). Measuring the Impact of ChatGPT on Fostering Concept Generation in Innovative Product Design. Electronics, 12(16), 3535. https://doi.org/10.3390/electronics12163535

Fisher, J. A. (2023). Centering the Human: Digital Humanism and the Practice of Using Generative AI in the Authoring of Interactive Digital Narratives. In: International Conference on Interactive Digital Storytelling. Cham: Springer Nature Switzerland. pp. 73-88.

Fui-Hoon Nah, F., Zheng, R., & Cai, J., et al. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277–304. https://doi.org/10.1080/15228053.2023.2233814

Garvey, M. D., Carnovale, S., & Yeniyurt, S. (2015). An analytical framework for supply network risk propagation: A Bayesian network approach. European Journal of Operational Research, 243(2), 618–627. https://doi.org/10.1016/j.ejor.2014.10.034

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Govindan, K., Rajendran, S., & Sarkis, J., et al. (2015). Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. Journal of Cleaner Production, 98, 66–83. https://doi.org/10.1016/j.jclepro.2013.06.046

Harmon, P., & King, D. (1985). Expert systems: Artificial intelligence in business. John Wiley & Sons, Inc.

Hassani, H., & Silva, E. S. (2024). Predictions from Generative Artificial Intelligence Models: Towards a New Benchmark in Forecasting Practice. Information, 15(6), 291. https://doi.org/10.3390/info15060291

He, R., Li, X., & Chen, G., et al. (2020). Generative adversarial network-based semi-supervised learning for real-time risk warning of process industries. Expert Systems with Applications, 150, 113244. https://doi.org/10.1016/j.eswa.2020.113244

Hendriksen, C. (2023). Artificial intelligence for supply chain management: Disruptive innovation or innovative disruption? Journal of Supply Chain Management, 59(3), 65–76. https://doi.org/10.1111/jscm.12304

Jacobs, F. R., & Richard, B. C. (2018). Operations and supply chain management. McGraw-Hill.

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695. https://doi.org/10.1007/s12525-021-00475-2

Jebbor, I., Benmamoun, Z., & Hachimi, H. (2023). Optimizing Manufacturing Cycles to Improve Production: Application in the Traditional Shipyard Industry. Processes, 11(11), 3136. https://doi.org/10.3390/pr11113136

Jebbor, I., Raouf, Y., Benmamoun, Z., & Hachimi, H. (2024). Process Improvement of Taping for an Assembly Electrical Wiring Harness. In: Sheu, S. H. (editors). Industrial Engineering and Applications – Europe. ICIEA-EU 2024. Lecture Notes in Business Information Processing. Springer, Cham. https://doi.org/10.1007/978-3-031-58113-7_4.

Kalasani, R. R. (2023). An Exploratory Study of the Impacts of Artificial Intelligence and Machine Learning Technologies in the Supply Chain and Operations Field. Diss. University of the Cumberlands.

Khaoula, K., & Abdullah, A. (2013). Redesigning the hospital supply chain for enhanced performance using a lean methodology. Int. J. Ind. Eng, 12, 917-927.

Khlie, K., & Abouabdellah, A. (2017a). Integrating lean and six sigma for the securement of the medication circuit. 2017 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA). https://doi.org/10.1109/logistiqua.2017.7962889

Khlie, K., & Abouabdellah, A. (2017b). Identification of the patient requirements using lean six sigma and data mining. International Journal of Engineering, 30(5), 691–699.‏

King, M. R. (2023). A Conversation on Artificial Intelligence, Chatbots, and Plagiarism in Higher Education. Cellular and Molecular Bioengineering, 16(1), 1–2. https://doi.org/10.1007/s12195-022-00754-8

Krichen, S., & Jouida, S. B. (2015). Supply Chain Management and its Applications in Computer Science. John Wiley and Sons, Inc. https://doi.org/10.1002/9781119261469

Kühl, N., Schemmer, M., & Goutier, M., et al. (2022). Artificial intelligence and machine learning. Electronic Markets, 32(4), 2235–2244. https://doi.org/10.1007/s12525-022-00598-0

Kumar, A., Gupta, N., & Bapat, G. (2023). Who is making the decisions? How retail managers can use the power of ChatGPT. Journal of Business Strategy, 45(3), 161–169. https://doi.org/10.1108/jbs-04-2023-0067

Kumar, A., Shankar, R., & Aljohani, N. R. (2020). A big data driven framework for demand-driven forecasting with effects of marketing-mix variables. Industrial Marketing Management, 90, 493–507. https://doi.org/10.1016/j.indmarman.2019.05.003

Kumar, V., Rajan, B., & Venkatesan, R., et al. (2019). Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing. California Management Review, 61(4), 135–155. https://doi.org/10.1177/0008125619859317

Lal, K., Ballamudi, V. K. R., & Thaduri, U. R. (2018). Exploiting the Potential of Artificial Intelligence in Decision Support Systems. ABC Journal of Advanced Research, 7(2), 131–138. https://doi.org/10.18034/abcjar.v7i2.695

Lee, H., Cha, J., & Kwon, D., et al. (2020). Hosting AI/ML Workflows on O-RAN RIC Platform. 2020 IEEE Globecom Workshops (GC Wkshps). https://doi.org/10.1109/gcwkshps50303.2020.9367572

Li, M., Bao, X., & Chang, L., et al. (2022). Modeling personalized representation for within-basket recommendation based on deep learning. Expert Systems with Applications, 192, 116383. https://doi.org/10.1016/j.eswa.2021.116383

Li, S., Guo, Z., & Zang, X. (2023). Advancing the production of clinical medical devices through ChatGPT. Annals of Biomedical Engineering, 1–5.

Lund, B. D., Wang, T., & Mannuru, N. R., et al. (2023). ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology, 74(5), 570–581. https://doi.org/10.1002/asi.24750

Mahata, G. C., De, S. K., Bhattacharya, K., & Maity, S. (2023). Three-echelon supply chain model in an imperfect production system with inspection error, learning effect, and return policy under fuzzy environment. International Journal of Systems Science: Operations & Logistics, 10(1), 1962427. https://doi.org/10.1080/23302674.2021.1962427

Mani, V., Delgado, C., & Hazen, B., et al. (2017). Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain. Sustainability, 9(4), 608. https://doi.org/10.3390/su9040608

Meriton, R., Bhandal, R., & Graham, G., et al. (2020). An examination of the generative mechanisms of value in big data-enabled supply chain management research. International Journal of Production Research, 59(23), 7283–7310. https://doi.org/10.1080/00207543.2020.1832273

Mohan Mewari, M., & Kamath, G. (2021). 17 Remarkable Use Cases of AI in the Manufacturing Industry. Birlasoft. Available online: https://www.birlasoft.com/articles/17-usecases-of-ai-in-manufacturing (accessed on 18 April 2023).

Murris, K. (2023). ChatGPT, care and the ethical dilemmas entangled with teaching and research in the early years. European Early Childhood Education Research Journal, 31(5), 673–677. https://doi.org/10.1080/1350293x.2023.2250218

Myers, K. L., & Berry, P. M. (1999). At the Boundary of Workflow and AI. In: Proc. AAAI 1999 Workshop on Agent-Based Systems in The Business Context.

Nikolic, S., Daniel, S., Haque, R., et al. (2023). ChatGPT versus engineering education assessment: a multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity. European Journal of Engineering Education, 48(4), 559–614. https://doi.org/10.1080/03043797.2023.2213169

Patterson, D. (1990). Introduction to artificial intelligence and expert systems. Prentice-Hall, Inc.

Pokhrel, S., & Banjade, S. R. (2023). AI Content Generation Technology based on Open AI Language Model. Journal of Artificial Intelligence and Capsule Networks, 5(4), 534–548. https://doi.org/10.36548/jaicn.2023.4.006

Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241, 108250. https://doi.org/10.1016/j.ijpe.2021.108250

Pukkila, M. (2023). Exploring the Power of ChatGPT: An Opportunity for Supply Chain Transformation. Gartner: Stamford, CT, USA.

Rane, N. (2023a). ChatGPT and Similar Generative Artificial Intelligence (AI) for Smart Industry: Role, Challenges and Opportunities for Industry 4.0, Industry 5.0 and Society 5.0. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4603234

Rane, N. (2023b). Role of ChatGPT and Similar Generative Artificial Intelligence (AI) in Construction Industry. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4598258

Richey, R. G., Chowdhury, S., Davis‐Sramek, B., et al. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532–549. https://doi.org/10.1111/jbl.12364

Ritala, P., Ruokonen, M., & Ramaul, L. (2023). Transforming boundaries: how does ChatGPT change knowledge work? Journal of Business Strategy, 45(3), 214–220. https://doi.org/10.1108/jbs-05-2023-0094

Saheb, T., Dehghani, M., & Saheb, T. (2022). Artificial intelligence for sustainable energy: A contextual topic modeling and content analysis. Sustainable Computing: Informatics and Systems, 35, 100699. https://doi.org/10.1016/j.suscom.2022.100699

Samtani, S., Zhu, H., & Padmanabhan, B., et al. (2023). Deep Learning for Information Systems Research. Journal of Management Information Systems, 40(1), 271–301. https://doi.org/10.1080/07421222.2023.2172772

Schmittner, C., & Abdelkader Magdy, S. (2023). Overview Of AI Standardization, 31st Interdisciplinary Information Management Talks: New Challenges for ICT and Management. IDIMT, 143–149.

Spaniol, M. J., & Rowland, N. J. (2023). AI‐assisted scenario generation for strategic planning. FUTURES & FORESIGHT SCIENCE, 5(2). https://doi.org/10.1002/ffo2.148

Stancati, M., & Schechner, S. (2023). ChatGPT banned in Italy over data privacy concerns. The Wall Street Journal.

Syntetos, A. A., Babai, Z., & Boylan, J. E., et al. (2016). Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research, 252(1), 1–26. https://doi.org/10.1016/j.ejor.2015.11.010

Ufuk, F. (2023). The Role and Limitations of Large Language Models Such as ChatGPT in Clinical Settings and Medical Journalism. Radiology, 307(3). https://doi.org/10.1148/radiol.230276

Votto, A. M., Valecha, R., & Najafirad, P., et al. (2021). Artificial Intelligence in Tactical Human Resource Management: A Systematic Literature Review. International Journal of Information Management Data Insights, 1(2), 100047. https://doi.org/10.1016/j.jjimei.2021.100047

wael AL-khatib, A. (2023). Drivers of generative artificial intelligence to fostering exploitative and exploratory innovation: A TOE framework. Technology in Society, 75, 102403. https://doi.org/10.1016/j.techsoc.2023.102403

Wessel, M., Adam, M., & Benlian, A., et al. (2023). Generative AI and its transformative value for digital platforms. Journal of Management Information Systems.

Winston, P. H. (1984). Artificial intelligence. Addison-Wesley Longman Publishing Co., Inc.

Zhang, D., Li, W., & Niu, B., et al. (2023). A deep learning approach for detecting fake reviewers: Exploiting reviewing behavior and textual information. Decision Support Systems, 166, 113911. https://doi.org/10.1016/j.dss.2022.113911

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