The term Artificial Intelligence (AI) is being used as an umbrella term referring to the digital technologies performing activities, tasks and decisions normally performed by human intelligence (Pomerol, 1997). Recently, the use of AI techniques has been brought to the forefront of attention due to the wide range of organisational operations that could be transformed through multidisciplinary AI approaches based on data sharing, gathering and analytics (Baryannis et al., 2019; Spanaki et al. 2018).
The applications of AI, big data analytics and intelligent processes and practices (through the use of IoT, technology, machine learning techniques, cyber-physical systems, blockchain etc.) in Manufacturing, Production and Operations could pose multiple challenges and managerial implications. The vast range of challenges could span from difficulties in the use and adoption of these applications, identifying the required skills and capabilities for the employees, to a wide variety of productivity and performance problems. There are multiple opportunities but also respective challenges for the supported supply management tasks, therefore the research should support the operations by promoting AI approaches for smart and intelligent operations in multiple industrial sectors, while predicting weaknesses and risks (Sivarajah et al.,2019; Giannakis & Papadopoulos, 2016).
There is emerging anecdotal evidence that the use of AI and data analytics for manufacturing, production can fundamentally reshape the existing operational practices and tasks (Sivarajah et al., 2019; Papadopoulos et al., 2017; Dubey et al., 2019). Various organizations have already applied AI and data analytics for humanitarian operations addressing, healthcare and hunger challenges through early-stage medical diagnosis, identifying agrifood supply chain risk, optimized food distribution, effective crisis response by quickly and accurately forecasting natural disasters (Google AI, 2019;) and also effective food waste management (Irani et al., 2018; Despoudi et al., 2018) . However, scaling up AI usage could have some significant bottlenecks, such as misuse of AI algorithms, privacy breach and data accessibility (Spanaki et al., 2019). The use of AI just like other technological developments comes with its own challenges and risks in a commercial environment that can lead it to being misused, lead to user distrust and raise privacy and ethical concerns.
The special issue aims to promote the research around the area of AI and data sharing in manufacturing, production and operations research. The papers will develop concepts, methods, models and solutions that fit within the scope of the International Journal of Production Research.
Some of the indicative topics include but are not limited to the following:
Papers submitted to the special issue will be subject to the Journal review process and submission guidelines. For further guidance on how to submit your manuscript to this Special Issue, visit our Instructions for Authors page.
Professor Thanos Papadopoulos, University of Kent, UK
Dr Uthayasankar Sivarajah, University of Bradford, UK
Dr Konstantina Spanaki, Loughborough University, UK
Dr Stella Despoudi, Aston Business School, UK
Professor Angappa Gunasekaran, California State University, USA
For more information please visit: https://think.taylorandfrancis.com/international-journal-of-production-research-artificial-intelligence-ai-and-data-sharing-in-manufacturing-production-and-operations-management-research/.