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Part of the book series: Studies in Computational Intelligence ((SCI,volume 935))

Abstract

Nowadays, artificial intelligence (AI) is becoming a more effective digital domain promised to facilitate immediate access to information and effective decision making in ever-increasing business environments. While big data analytics for organizational renewal has increasingly received interest from data analytics scholars. Despite the increasing adoption of big data analytics for decision making, relatively little is know about how data management capabilities lead to better data insights for supply chain sustainability and circular economy. The researchers understand the extensive use of big data analytics and artificial intelligence among firms as an essential and necessary tool for shaping the future of the supply chain 4.0 industry. This chapter discusses the role of AI applications for the success of a supply chain in the big data era. From a holistic perspective, today, manufacturers, particularly those with global operations and presence, are under enormous pressure to keep up with the continuous growth of disruptive innovative procurement models. This has open doors for the firms to aggressively seek out big data management capabilities to improve operational efficiencies and to innovate the process. This chapter provides a better understanding related to the application of data analytics in the supply chain context. The research issues are classified into different categories, including big data management and machine learning, a business case for the supply chain and innovation in supply using data. This study also presents machine learning data analysis steps.

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Awan, U., Kanwal, N., Alawi, S., Huiskonen, J., Dahanayake, A. (2021). Artificial Intelligence for Supply Chain Success in the Era of Data Analytics. In: Hamdan, A., Hassanien, A.E., Razzaque, A., Alareeni, B. (eds) The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success. Studies in Computational Intelligence, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-62796-6_1

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