Big Data Analytics for Sustainable Products: A State-of-the-Art Review and Analysis

Hamed Gholami (), Jocelyn Ke Yin Lee and Ahad Ali
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Hamed Gholami: Mines Saint-Etienne, Université Clermont Auvergne, INP Clermont Auvergne, CNRS, UMR 6158 LIMOS, F-42023 Saint-Etienne, France
Jocelyn Ke Yin Lee: Mines Saint-Etienne, Université Clermont Auvergne, INP Clermont Auvergne, CNRS, UMR 6158 LIMOS, F-42023 Saint-Etienne, France
Ahad Ali: A. Leon Linton Department of Mechanical, Robotics and Industrial Engineering, Lawrence Technological University, Southfield, MI 48075, USA

Sustainability, 2023, vol. 15, issue 17, 1-22

Abstract: Big data analytics, described as the fourth paradigm of science breaking through Industry 4.0 technological development, continues to expand globally as organizations strive to attain the utmost value and sustainable competitive edge. Yet, concerning its contribution to developing sustainable products, there is a need for innovative research due to limited knowledge and uncertainty. This research is hence aimed at addressing (a) how research on big data analytics for sustainable products has evolved in recent years, and (b) how and in what terms it can contribute to developing sustainable products. To do so, this study includes a bibliometric review performed to shed light on the phenomenon gaining prominence. Next, the fuzzy technique for order of preference by similarity to ideal solution, along with a survey, is used to analyze the matter in terms of the respective indicator set. The review’s findings revealed that there has been growing global research interest in the topic in the literature since its inception, and by advancing knowledge in the area, progress toward sustainable development goals 7, 8, 9, 12, and 17 can be made. The fuzzy-based analytical findings demonstrated that ‘product end-of-life management efficiency’ has the highest contributory coefficient of 0.787, followed by ‘product quality and durability’ and ‘functional performance’, with coefficients of 0.579 and 0.523, respectively. Such research, which is crucial for sustainable development, offers valuable insights to stakeholders seeking a deeper understanding of big data analytics and its contribution to developing sustainable products.

Keywords: big data analytics; sustainable products; sustainable development goals; manufacturing sustainability indicator set; bibliometric review; empirical analysis; tableau (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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