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ISSN Approved Journal || eISSN: 2582-8185 || CODEN: IJSRO2 || Impact Factor 8.2 || Google Scholar and CrossRef Indexed

Peer Reviewed and Referred Journal || Free Certificate of Publication

Research and review articles are invited for publication in March 2026 (Volume 18, Issue 3) Submit manuscript

Harnessing big data for Sustainable Supply Chain Management (SSCM): Strategies to reduce carbon footprint

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  • Harnessing big data for Sustainable Supply Chain Management (SSCM): Strategies to reduce carbon footprint

Uchechukwu Christopher Anozie 1, *, Oyinlola Esther Obafunsho 2, Rebecca Olubunmi Toromade 3 and Gbenga Adewumi 4

1 College of Business, Auburn University, USA.
2 School of professional studies, Saint Louis University, USA.
3 Faculty of Engineering and Informatics, Computer Science, University of Bradford, UK.
4 Whitman school of Management, Syracuse University, USA.

Review Article
 
International Journal of Science and Research Archive, 2024, 12(02), 1099–1104.
Article DOI: 10.30574/ijsra.2024.12.2.1344
DOI url: https://doi.org/10.30574/ijsra.2024.12.2.1344

Received on 12 June 2024; revised on 20 July 2024; accepted on 23 July 2024

In today's global economy, managing supply chains sustainably is crucial for businesses wanting to reduce their environmental impact while still making a profit. This article reviews how big data analytics can help achieve these sustainability goals. By using big data, companies can improve their supply chain practices, reduce carbon emissions, and implement more sustainable business strategies. Big data provides detailed insights and better levels of control over various supply chain activities, making it a vital tool for driving sustainability. Big data isn't just about internal operations; it also uncovers supplier practices, letting businesses assess their supply chains' environmental impact. Predictive maintenance, driven by big data, plays a powerful role here. It keeps operations running smooth by monitoring equipment health and foreseeing issues before they cause downtime. This proactive approach not only ensures machinery runs efficiently but also lowers energy consumption and emissions associated with breakdowns. Big data also plays a crucial role in optimizing transportation, where it analyzes traffic flow, weather data, and fuel efficiency to design smarter delivery routes. This approach cuts down on fuel consumption and emissions, making logistics more eco-friendly. Energy efficiency is also a priority; big data tracks energy usage across facilities, uncovering areas where consumption can be reduced. This not only lowers energy bills but also decreases greenhouse gas emissions. Big data goes beyond just making things greener; it also helps businesses save money! Here's how: by using big data to predict exactly how much of a product people will buy, companies can avoid making more than they need. This means less waste and less sitting around in warehouses, which is good for the environment and good for the company's bottom line. In short, big data is a win-win for both the planet and your wallet. Case studies from top industry players like Walmart, Nestlé and Maersk illustrate how big data improves sustainable supply chain management (SSCM) with tangible benefits. Yet, integrating big data has its own challenge: ensuring data accuracy, addressing privacy issues, and recruiting skilled personnel are key hurdles. Looking ahead, trends in SSCM—such as AI, machine learning, blockchain, and IoT advancements—hold promise for enhanced insights and predictive capabilities, shaping the future of sustainable supply chains.

Big data analytics; Energy efficiency; Sustainable supply chain management; Carbon emissions reduction; Transportation Optimization; Predictive maintenance

https://ijsra.net/sites/default/files/fulltext_pdf/IJSRA-2024-1344.pdf

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Uchechukwu Christopher Anozie, Oyinlola Esther Obafunsho, Rebecca Olubunmi Toromade and Gbenga Adewumi. Harnessing big data for Sustainable Supply Chain Management (SSCM): Strategies to reduce carbon footprint. International Journal of Science and Research Archive, 2024, 12(02), 1099–1104. Article DOI: https://doi.org/10.30574/ijsra.2024.12.2.1344

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


All statements, opinions, and data contained in this publication are solely those of the individual author(s) and contributor(s). The journal, editors, reviewers, and publisher disclaim any responsibility or liability for the content, including accuracy, completeness, or any consequences arising from its use.

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