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

Nenad Stefanovic (), Milos Radenkovic, Zorica Bogdanovic, Jelena Plasic and Andrijana Gaborovic
Additional contact information
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

Sustainability, 2025, vol. 17, issue 1, 1-26

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.

Keywords: big data; sustainable supply chain; business intelligence; analytics; machine learning; model; cloud computing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/1/354/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/1/354/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:1:p:354-:d:1560829

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().