Abstract
Artificial intelligence’s value has increased in recent years. Artificial intelligence (AI) backed by big data analytics has expanded over the past few years. According to reports and reviews, artificial intelligence structured on large volumes of data analytics and information and communications technology has the potential to greatly improve supply chain performance; however, research into the reasons why companies engage in manufacturing activities and the novel artificial intelligent systems is limited. It is in this regard that this study has been carried out. To this end, several theoretical approaches have been proposed as explanations for how manufacturing businesses generate valuable resources and worker skills to impose innovation and enhance circular economy proficiency. The goal of this study is to gain approval for an intellectual concept that explains how institutional pressures on resources affect the implementation of big data in artificial intelligence, as well as its influence on sustainable manufacturing and the model of production and consumption proficiency when regulating the effects of industrial flexibility and industry effectiveness. We believe that if companies want to see a meaningful return on their AI efforts, they must fill this gap and promote AI capability. It is on this central aim that this study will expose and encourage research into this area; moreover, it hopes to create awareness among new industrial facilities of the essence of implementing AI features to boost any form of manufacturing and fabrication process.
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The authors wish to acknowledge the financial support offered by Afe Babalola University Ado Ekiti for the payment of article publication charges (APC).
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Ikumapayi, O.M., Laseinde, O.T., Ogedengbe, T.S., Afolalu, S.A., Ogundipe, A.T., Akinlabi, E.T. (2023). An Insight into AI and ICT Towards Sustainable Manufacturing. In: Bindhu, V., Tavares, J.M.R.S., Vuppalapati, C. (eds) Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems . Lecture Notes in Electrical Engineering, vol 977. Springer, Singapore. https://doi.org/10.1007/978-981-19-7753-4_21
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