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Adaptive Cloud-Based Big Data Analytics Model for Sustainable Supply Chain Management

Author

Listed:
  • Nenad Stefanovic

    (Faculty of Technical Sciences Cacak, University of Kragujevac, 32000 Cacak, Serbia)

  • Milos Radenkovic

    (School of Computing, Union University, 11000 Belgrade, Serbia)

  • Zorica Bogdanovic

    (Faculty of Organizational Sciences, University of Belgrade, 11010 Belgrade, Serbia)

  • Jelena Plasic

    (Faculty of Technical Sciences Cacak, University of Kragujevac, 32000 Cacak, Serbia)

  • Andrijana Gaborovic

    (Faculty of Technical Sciences Cacak, University of Kragujevac, 32000 Cacak, Serbia)

Abstract

Due to uncertain business climate, fierce competition, environmental challenges, regulatory requirements, and the need for responsible business operations, organizations are forced to implement sustainable supply chains. This necessitates the use of proper data analytics methods and tools to monitor economic, environmental, and social performance, as well as to manage and optimize supply chain operations. This paper discusses issues, challenges, and the state of the art approaches in supply chain analytics and gives a systematic literature review of big data developments associated with supply chain management (SCM). Even though big data technologies promise many benefits and advantages, the prospective applications of big data technologies in sustainable SCM are still not achieved to a full extent. This necessitates work on several segments like research, the design of new models, architectures, services, and tools for big data analytics. The goal of the paper is to introduce a methodology covering the whole Business Intelligence (BI) lifecycle and a unified model for advanced supply chain big data analytics (BDA). The model is multi-layered, cloud-based, and adaptive in terms of specific big data scenarios. It comprises business process modeling, data ingestion, storage, processing, machine learning, and end-user intelligence and visualization. It enables the creation of next-generation BDA systems that improve supply chain performance and enable sustainable SCM. The proposed supply chain BDA methodology and the model have been successfully applied in practice for the purpose of supplier quality management. The solution based on the real-world dataset and the illustrative supply chain case are presented and discussed. The results demonstrate the effectiveness and applicability of the big data model for intelligent and insight-driven decision making and sustainable supply chain management.

Suggested Citation

  • Nenad Stefanovic & Milos Radenkovic & Zorica Bogdanovic & Jelena Plasic & Andrijana Gaborovic, 2025. "Adaptive Cloud-Based Big Data Analytics Model for Sustainable Supply Chain Management," Sustainability, MDPI, vol. 17(1), pages 1-26, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:1:p:354-:d:1560829
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    References listed on IDEAS

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    1. Guojun Ji & Wen Hong, 2024. "Research on the Manufacturer’s Strategies under Different Supply Interruption Risk Based on Supply Chain Resilience," Sustainability, MDPI, vol. 16(2), pages 1-25, January.
    2. Syed Mithun Ali & Amanat Ur Rahman & Golam Kabir & Sanjoy Kumar Paul, 2024. "Artificial Intelligence Approach to Predict Supply Chain Performance: Implications for Sustainability," Sustainability, MDPI, vol. 16(6), pages 1-31, March.
    3. Zhan, Yuanzhu & Tan, Kim Hua, 2020. "An analytic infrastructure for harvesting big data to enhance supply chain performance," European Journal of Operational Research, Elsevier, vol. 281(3), pages 559-574.
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    1. Dalia Štreimikienė & Ahmad Bathaei & Justas Streimikis, 2025. "Enhancing Sustainable Global Supply Chain Performance: A Multi-Criteria Decision-Making-Based Approach to Industry 4.0 and AI Integration," Sustainability, MDPI, vol. 17(10), pages 1-19, May.

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