International Journal of Production Research

The emergence of Large Language Models (LLMs) and generative AI (GenAI) has significantly impacted various domains, pushing the boundaries of what's possible with artificial intelligence (Vaswani et al, 2017). Thus, the motivation for the special issue is proposed around LLM and AI systems and their transformative potential of these technologies for supply chain management (SCM) (Li et al., 2023) and manufacturing facilities (Makatura et al., 2023).

Proposing ideas for the integration of GenAI with SCM involves leveraging LLM enhance decision-making, operational efficiency, and automation within supply chains. Contributions made by Richey et al., (2023) and Fosso Wamba et al., (2023) demonstrates the efficacy of generative techniques in mitigating data scarcity issues, particularly in the manufacturing sector, by facilitating dynamic pricing strategies and optimizing production processes through the creation of synthetic datasets. GenAI can be used to simulate complex supply chain scenarios, enabling organizations to identify bottlenecks and test potential solutions without the risk of real-world implementation. Additionally, predictive analytics powered by LLMs could offer more accurate demand forecasting, while generative models could assist in designing optimized logistics networks.

Fosso Wamba et al. (2023) provides insights into the projects and perceptions of ChatGPT and generative artificial intelligence (Gen-AI) in operations and supply chain management (OSCM). Jackson et al. (2024) introduced a practical framework for both practitioners and researchers to identify where and how AI and GAI can be applied in OSCM, focusing on decision-making enhancement, process optimization, investment prioritization, and skills development. Also, they explored key AI capabilities such as learning, perception, prediction, interaction, adaptation, and reasoning and their impact on 13 distinct SCOM decision-making areas, including demand forecasting, inventory management, supply chain design, and risk management.

In logistics, the paramount necessity of route planning and optimization is met through generative models that simulate diverse scenarios and learn from historical data (Feuerriegel et al., 2024). Studies by M. Hadi Baaj et al. (1991) highlights the effectiveness of these models in optimizing transportation efficiency by generating optimal routes. Anomaly detection within logistics data, crucial for maintaining supply chain resilience, benefits from generative AI's capability to identify irregularities and potential disruptions, as explored in the work of Feuerriegel et al. (2024). This adaptive technology, exhibiting prowess in supply chain simulation and decision support, establishes itself as a pivotal tool for offering advancements in predictive analytics, optimization strategies, and risk management navigating complexities in both manufacturing and logistics.

In conclusion, this novel intersection of GenAI with SCM and manufacturing presents a rich area for exploration and innovation. As this field continues to evolve, it will be crucial for researchers and practitioners to stay informed about the latest developments and contribute to the growing body of knowledge on the application of AI in supply chain and manufacturing domains. Additionally, Gen AI can be applied in solving various problems related to manufacturing, logistics, and supply chain, including response supply chain against bioattacks (Simchi-Levi et al., 2019), vehicle routing with stochastic demand (Ledvina et al., 2022), dynamic pricing and inventory control (Chen et al., 2022), identifying risks and mitigating disruptions by analyzing time-to-recover (TTR) and time-to-survive (TTS) (Simchi-Levi et al., 2015).

We thus propose to organize a special issue for the International Journal of Production Research on the applications of generative AI in logistics supply chain and manufacturing processes in this new era.

The special issue aims to address the following, but not limited to potential areas:

1. Utilizing Generative AI systems for simulating complex scenarios in supply chain and manufacturing processes
2. Implementing Large Language Models (LLMs) frameworks for accurate demand forecasting and effective warehouse planning
3. Accelerating the product design and prototyping process in manufacturing with generative AI models
4. Employing LLM for predictive maintenance modeling in manufacturing facilities
5. Improving quality control processes techniques in manufacturing with AI
6. Optimizing inventory management strategies using insightful Gen-AI systems
7. Implementing LLM systems for real-time production scheduling in smart manufacturing environments

References:

• Chen, B., Simchi-Levi, D., Wang, Y. and Zhou, Y. (2022). Dynamic pricing and inventory control with fixed ordering cost and incomplete demand information. Management Science, 68(8), pp.5684-5703.
• Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative AI. Business and Information Systems Engineering, 66(1), 111–126. https://doi.org/10.1007/s12599-023-00834-7
• Fosso Wamba, S., Queiroz, M. M., Chiappetta Jabbour, C. J., & Shi, C. (Victor). (2023). Are both generative AI and ChatGPT game changers for 21st-Century operations and supply chain excellence? International Journal of Production Economics, 265(August), 109015. https://doi.org/10.1016/j.ijpe.2023.109015
• Fosso Wamba, S., Guthrie, C., Queiroz, M. M., & Minner, S. (2023). ChatGPT and generative artificial intelligence: an exploratory study of key benefits and challenges in operations and supply chain management. International Journal of Production Research, 1-21.
• 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, 1-26.
• Ledvina, K., Qin, H., Simchi-Levi, D. and Wei, Y. (2022). A new approach for vehicle routing with stochastic demand: Combining route assignment with process flexibility. Operations Research, 70(5), pp.2655-2673.
• Li, B., Mellou, K., Zhang, B., Pathuri, J. and Menache, I. (2023). Large language models for supply chain optimization. arXiv preprint arXiv:2307.03875.
• Hadi Baaj and Hani S. Mahmassani. (1991). An AI-Based Approach for Ransit Route, 25, 187–209.
• Makatura, L., Foshey, M., Wang, B., HähnLein, F., Ma, P., Deng, B., Tjandrasuwita, M., Spielberg, A., Owens, C.E., Chen, P.Y. and Zhao, A. (2023). How Can Large Language Models Help Humans in Design and Manufacturing?. arXiv preprint arXiv:2307.14377.
• Richey, R. G., Chowdhury, S., Davis-Sramek, B., Giannakis, M., & Dwivedi, Y. K. (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
• Simchi-Levi, D., Schmidt, W., Wei, Y., Zhang, P.Y., Combs, K., Ge, Y., Gusikhin, O., Sanders, M. and Zhang, D. (2015). Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces, 45(5), pp.375-390.
• Simchi-Levi, D., Trichakis, N. and Zhang, P.Y. (2019). Designing response supply chain against bioattacks. Operations Research, 67(5), pp.1246-1268.
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.

Submission Deadline (Publication Schedule):

The deadline for submission: November 15^th, 2024

First round reviews: January 15^th, 2025

Final decision: April 15^th, 2025 (online publication 20 days later)

Publication in print: in the second half of 2025.

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