International Journal of Production Research
The Interplay Between Artificial Intelligence, Production Systems, and Operations Management Resilience
Professor Samuel Fosso Wamba, TBS Business School, France, Email: firstname.lastname@example.org [principle guest editor]
Professor Maciel M. Queiroz, Paulista University – UNIP and Mackenzie Presbyterian University, Sao Paulo, Brazil
Professor Eric Ngai, The Hong Kong Polytechnic University, Hong Kong, China
Professor Frederick J. Riggins, North Dakota State University, Fargo, USA
Professor Ygal Bendavid, Université du Québec à Montréal - UAQM, Montréal, Canada
About the special issue
The emergence, adoption, and popularity of cutting-edge technologies have captured different industries' attention worldwide (Ivanov et al., 2020b; Queiroz, Fosso Wamba, De Bourmont, et al., 2020). In this vein, traditional areas deal with by the International Journal of Production Research, including manufacturing, industrial engineering, operations research, and management science, have been profoundly affected in recent years by such technologies. One example of highly disruptive technology is artificial intelligence (AI) (Ivanov et al., 2020a; Chien et al., 2020) which can also be integrated with other emerging technologies (Rodríguez-espíndola et al., 2020; Sahu et al., 2020). Consequently, employing such technologies can remodel the production systems, operations management, and supply networks (Queiroz, Fosso Wamba, Machado, et al., 2020).
In this outlook, unprecedented advances in computer power, which have notably leveraged and reinvigorated new applications and possibilities (Fosso Wamba et al., 2020), have recently given rise to a new development step for AI. As a result, AI approaches like machine learning (ML), deep learning (DL), neural networks (NN), fuzzy logic (FL), natural language processing (NLP), algorithms approaches, among others, are steadily showing their ability to transform the entire production systems and operations management paradigms (Sahu et al., 2020), as well as the various relationships across the supply network. For example, maintenance in relation to production internal operations could be performed by distance, depending on the complexity, by a world-class worker or by robots, which in turn can handle machine setup time and optimized quality inspection. The use of IoT and drones can highly contribute to optimizing inventory checking, operations picking through augmented and virtual reality (AR/VR), among other benefits; the production planning and control can operate by dynamic scheduling, considering real-time information integrating AI and digital twin (Dolgui et al., 2020). The relationship with suppliers, transportation workers, and customers could benefit from the external operations landscape. For example, chatbots help in follow-up and automated order and freight quote and traceability by integrating blockchain technologies, real-time customer feedback, etc.
Furthermore, when considering and integrating complex and unpredictable environmental events (e.g., pandemic outbreaks, humanitarian crisis, natural disasters, etc.), it clearly appears that the production systems and operations management are constantly challenged not only in the pursue of their operations but also for support the organization's survivability, and when possible, generate improvements, efficiency, and performance. In this outlook, highly disruptive and exceptional events can ramp-up demand (Kim et al., 2020) and/or generate stockout products (Geunes & Su, 2020). As a result, the production systems and operations management perspectives are completely challenged not to stop the production flow and to deplete society with products.
However, little is known about these AI features, technologies, their integration with other leading-edge technologies, the challenges, and benefits of the aforementioned complex and unpredictable environmental events, taking into account the resilience of the production systems and operations management (Dwivedi et al., 2019; Fosso Wamba et al., 2020; Ivanov et al., 2020b; Queiroz, Ivanov, et al., 2020). There is, therefore, a need for production systems, operations management, and supply networks to rely on innovative strategies and technologies to survive, improve their adaptability, and, if possible, be more agile, flexible, and resilient in this new configuration (Ivanov & Dolgui, 2020). It thus becomes clear that the integration of AI with other cutting-edge technologies in production systems, operations management, and supply networks (Baryannis et al., 2019; Koh et al., 2019) can significantly improve their resilience.
Given the AI possibilities for production systems and operations management in this new era of complex environmental events, this Special Issue invites scholars, industry-practitioners, and all those directly or indirectly involved in production systems and operations management activities to submit their best research. We are interested in papers that provide substantial theoretical, managerial, and social contributions. A preference may be given to studies using empirical approaches (quantitative, qualitative, or mixed-methods), but papers presenting frameworks, new theory, secondary/archival data, simulation, MCDM, optimization/algorithms techniques, etc., are welcome, and better still if they integrate an empirical approach.
Potential topics include, but are not limited to:
Organization's and workers' AI capabilities for production systems activities.
AI approaches to predict and minimize the effects of unpredictable environmental events.
Drivers of AI adoption and implementation in production systems during pandemic outbreak events.
AI technologies for production scheduling and control during uncertain demand provoked by environmental events.
AI techniques for predicting ramp-up and shortages in the supply network.
The role of AI in adapting the production systems and operations management during a prolonged crisis.
AI combined with other leading-edge technologies (i.e., blockchain, IoT, big data, AR/VR, 3D printing, quantum, and edge computing, etc.).
The role of AI and digital twin applications in production and operations management resilience.
AI models generate and improve production systems agility, flexibility, responsiveness and performance during complex environmental events.
AI models for effectively integrate the network members into the production and operations management to achieve resilience.
AI approaches to support and leverage circular economy production models.
Authors should follow the instructions described in the IJPR's Instructions for Authors and submit their full papers electronically through the journal's online manuscript submission platform, at https://mc.manuscriptcentral.com/tprs by selecting "The Interplay Between Artificial Intelligence, Production Systems, and Operations Management Resilience."
Deadline for Submission of Manuscripts: 28 February 2022.
Final Decision Due: 30 June 2022.
Tentative Publication Date: 30 December 2022.
Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202. https://doi.org/10.1080/00207543.2018.1530476
Dolgui, A., Ivanov, D., & Sokolov, B. (2020). Reconfigurable supply chain: the X-network. International Journal of Production Research, 58(13), 4138–4163. https://doi.org/10.1080/00207543.2020.1774679
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, August, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Fosso Wamba, S., Bawack, R. E., Guthrie, C., Queiroz, M. M., & Carillo, K. D. A. (2020). Are we preparing for a good AI society? A bibliometric review and research agenda. Technological Forecasting & Social Change, Forthcoming.
Geunes, J., & Su, Y. (2020). Single-period assortment and stock-level decisions for dual sales channels with capacity limits and uncertain demand. International Journal of Production Research, 58(18), 5579–5600. https://doi.org/10.1080/00207543.2019.1693648
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915. https://doi.org/10.1080/00207543.2020.1750727
Ivanov, D., Tang, C. S., Dolgui, A., Battini, D., & Das, A. (2020a). Researchers' perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1798035
Ivanov, D., Tang, C. S., Dolgui, A., Battini, D., & Das, A. (2020b). Researchers' perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management. International Journal of Production Research, 1–24. https://doi.org/10.1080/00207543.2020.1798035
Kim, T., Glock, C. H., & Emde, S. (2020). Production planning for a ramp-up process in a multi-stage production system with worker learning and growth in demand. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1798034
Koh, L., Orzes, G., & Jia, F. (2019). The fourth industrial revolution (Industry 4.0): technologies disruption on operations and supply chain management. International Journal of Operations and Production Management, 39, 817–828. https://doi.org/10.1108/IJOPM-08-2019-788
Queiroz, M. M., Fosso Wamba, S., De Bourmont, M., & Telles, R. (2020). Blockchain adoption in operations and supply chain management: empirical evidence from an emerging economy. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1803511
Queiroz, M. M., Fosso Wamba, S., Machado, M. C., & Telles, R. (2020). Smart production systems drivers for business process management improvement: An integrative framework. Business Process Management Journal, 26(5), 1075–1092. https://doi.org/10.1108/BPMJ-03-2019-0134
Queiroz, M. M., Ivanov, D., Dolgui, A., & Fosso Wamba, S. (2020). Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03685-7
Rodríguez-espíndola, O., Chowdhury, S., & Beltagui, A. (2020). The potential of emergent disruptive technologies for humanitarian supply chains : the integration of blockchain , Artificial Intelligence and 3D printing. International Journal of Production Research, 0(0), 1–21. https://doi.org/10.1080/00207543.2020.1761565
Sahu, C. K., Young, C., & Rai, R. (2020). Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1859636