Scope of Big Data analytics in green supply chain management: a review

Автор: Singh S., Gandhi M.K., Kumar A.

Журнал: Cardiometry @cardiometry

Рубрика: Original research

Статья в выпуске: 22, 2022 года.

Бесплатный доступ

In the modern era, the specialists working in the supply chain arenas are engulfed with enormous amounts of data, which has made them think out of the box and probe more into the sources of the data and techniques of analyzing and organizing the unstructured data. The new bud scholars and professionals have endorsed big Data Analytics (BDA) lately as a decisive green supply chain management facilitator. Research in this particular area is still to be explored to the fullest. The findings of the research are still in the introductory stages. Our study comprises an organized literature review of 42 significant papers published in the previous 18 years, which performs thorough reasoning and outlines three types of GSCM field: green product innovation, reverse logistics, and green procurement. The study presents the scope of BDA in these respective areas of GSCM. The study helps to portray the extent of usage of BDA tools in distinct GSCM fields. The literature review also sheds light on certain gaps in the research work. It caters to the directions for future work in the dedicated field.

Еще

Big data analytics, reverse logistics, green procurement, green supply chain management, green product innovation

Короткий адрес: https://sciup.org/148324610

IDR: 148324610   |   DOI: 10.18137/cardiometry.2022.22.306312

Список литературы Scope of Big Data analytics in green supply chain management: a review

  • Accenture. The role of big data and analytics in the developing world, (2013).
  • T. Arunachalam, Y. Palanichamy, Does the soft aspects of TQM influence job satisfaction and commitment? The TQM Journal (2017)..
  • C. Bai, J. Sarkis, Integrating sustainability into supplier selection with grey system and rough set methodologies. Int J Prod Eco, 252-264, (2010).
  • G. Cajaiba-Santana, Social innovation: moving the field forward. A conceptual framework. Technological Forecasting and Social Change, 42-51, (2014).
  • Y. Chen, S. Lai, C. Wen, The influence of green innovation performance on corporate advantage in Taiwan. Bus. Ethics, 331-339, (2006).
  • F. Ciliberti, P. Pontrandolfo, B. Scozzi, Logistics social responsibility: standard adoption and practicesin Italian companies. Int J Prod Eco, 88-106, (2008).
  • Forrester. The Forrester Wave™: Big Data Predictive Analytics Solutions. Forrester, (2013).
  • A. Mark, L. Douglas, The Importance of ’ Big Data’: A Definition. Gartner, (2012).
  • K. Mathiyazhagan, K. Govindan, A. NoorulHaq, Y. Geng, An ISM approach for the barrier analysis in implementing green supply chain management. Journal of Cleaner Production, (2013).
  • H. Min, G. Zhou, Supply chain modeling: past, present, and future. Computers & Industrial Engineering, 231-249, (2002).
  • A. Presley, L. Meade, J. Sarkis, A strategic sustainability justification methodology for organizational decisions: a reverse logistics illustration. Int J Prod Res, (2017).
  • P. Rao, Greening the supply chain: a new initiative in South East Asia. International Journal of Operations & Production Management (2002).
  • P. Rao, Green Supply Chain Management: A Study Based on SMEs in India. Journal of Supply Chain Management Systems, 15-24, (2019).
  • A. Venu, A Review on Green Supply Chain Management. Mukt Shabd Journal, 30-36, (2020).
  • M. White, Digital workplaces: vision and reality. . Business Information Review, 205-214, (2012).
  • S. Zailani, M. Iranmanesh, M. Shaharudin, K. Govindan, Y. Chong, Green innovation adoption in the automotive supply chain: the Malaysian case. Clean. Prod., 1115-1122, (2015).
Еще
Статья научная