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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hamed Gholami & Jocelyn Ke Yin Lee & Ahad Ali, 2023. "Big Data Analytics for Sustainable Products: A State-of-the-Art Review and Analysis," Sustainability, MDPI, vol. 15(17), pages 1-22, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12758-:d:1223265
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    References listed on IDEAS

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    1. Norhazrina Jamil & Hamed Gholami & Muhamad Zameri Mat Saman & Dalia Streimikiene & Safian Sharif & Norhayati Zakuan, 2020. "DMAIC-based approach to sustainable value stream mapping: towards a sustainable manufacturing system," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 33(1), pages 331-360, January.
    2. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    3. L. Thiruvarasu Letchumanan & Hamed Gholami & Noordin Mohd Yusof & Nor Hasrul Akhmal Bin Ngadiman & Anas A. Salameh & Dalia Štreimikienė & Fausto Cavallaro, 2022. "Analyzing the Factors Enabling Green Lean Six Sigma Implementation in the Industry 4.0 Era," Sustainability, MDPI, vol. 14(6), pages 1-15, March.
    4. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    5. Mikalef, Patrick & Boura, Maria & Lekakos, George & Krogstie, John, 2019. "Big data analytics and firm performance: Findings from a mixed-method approach," Journal of Business Research, Elsevier, vol. 98(C), pages 261-276.
    6. Kristoffersen, Eivind & Blomsma, Fenna & Mikalef, Patrick & Li, Jingyue, 2020. "The smart circular economy: A digital-enabled circular strategies framework for manufacturing companies," Journal of Business Research, Elsevier, vol. 120(C), pages 241-261.
    7. Bernat, Stefan & Karabag, Solmaz Filiz, 2019. "Strategic alignment of technology: Organising for technology upgrading in emerging economy firms," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 295-306.
    8. Judit Nagy & Judit Oláh & Edina Erdei & Domicián Máté & József Popp, 2018. "The Role and Impact of Industry 4.0 and the Internet of Things on the Business Strategy of the Value Chain—The Case of Hungary," Sustainability, MDPI, vol. 10(10), pages 1-25, September.
    9. El-Kassar, Abdul-Nasser & Singh, Sanjay Kumar, 2019. "Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 483-498.
    10. Buddhika M. Hapuwatte & I. S. Jawahir, 2021. "Closed‐loop sustainable product design for circular economy," Journal of Industrial Ecology, Yale University, vol. 25(6), pages 1430-1446, December.
    11. Opresnik, David & Taisch, Marco, 2015. "The value of Big Data in servitization," International Journal of Production Economics, Elsevier, vol. 165(C), pages 174-184.
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