Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

Abstract

Big data analytics has been successfully used for various business functions, such as accounting, marketing, supply chain, and operations. Currently, along with the recent development in machine learning and computing infrastructure, big data analytics in the supply chain are surging in importance. In light of the great interest and evolving nature of big data analytics in supply chains, this study conducts a systematic review of existing studies in big data analytics. This study presents a framework of a systematic literature review from interdisciplinary perspectives. From the organizational perspective, this study examines the theoretical foundations and research models that explain the sustainability and performances achieved through the use of big data analytics. Then, from the technical perspective, this study analyzes types of big data analytics, techniques, algorithms, and features developed for enhanced supply chain functions. Finally, this study identifies the research gap and suggests future research directions.

Big data analytics capabilities and green supply chain performance: investigating the moderated mediation model for green innovation and technological intensity

“…The study's results confirmed that big data analytics capabilities have made a positive contribution to enhancing the green supply chain performance in the Jordanian food and beverages manufacturing firms sector. The study's results are in agreement with much literature (Benzidia et al, 2021;Lee and Mangalaraj, 2022;Mani et al, 2017;Pandey et al, 2021). Big data analytics can provide new and original insights that can be used to improve the response of the green supply chain to various changes (Hsu et al, 2022).…”

“…Studies showed the positive and direct impact of big data analytics capabilities on green innovation and green supply chain performance (Benzidia et al, 2021;Imran et al, 2021;Lee and Mangalaraj, 2022;Mani et al, 2017;Waqas et al, 2021;Yaoteng and Xin, 2022). The causal relationship between green innovation and green supply chain performance has also been established in much previous literature (Bag et al, 2022;Fernando et al, 2019; Rodr ıguez-Gonz alez et al, 2022).…”

“…Big data is the driver of growth in the economy globally (Kannan et al, 2016) and improves competitiveness (Wamba et al, 2017). Big data analytics play a role in supporting the supply chain performance (Li et al, 2015;Sharma et al, 2022) and green supply chains (Dubey et al, 2020) by using different systems, linking and using them to collect clean data and then analyze them to reach new conclusions and knowledge that can be used at the level of supply chains (Arunachalam et al, 2018;Mandal, 2018) leading to improved efficiency of activities and processes (Lee and Mangalaraj, 2022;Nguyen et al, 2018). Big data analytics uses various technological tools, human capabilities and organizational support to process a large set of data, leading to new and previously unknown insights (Gunasekaran et al, 2017) and thus maximizing the benefit at the level of the supply chain in particular and the overall level of the company (Nguyen et al, 2018).…”

“…Several studies have examined the relationship between big data analytics capabilities and green supply chain performance (Benzidia et al, 2021;Lee and Mangalaraj, 2022;Mani et al, 2017;Pandey et al, 2021). These tools related to the collection and analyses of big data provide opportunities to improve sustainable performance through the design and manufacture of environmentally friendly products (Singh et al, 2018).…”

Design and Implementation of Smart Community Big Data Dynamic Analysis Model Based on Logistic Regression Model

“…Its main functions include (1) resource allocation and recovery; (2) resource scheduling and optimization; and (3) system energy consumption and control. Realize resource allocation on demand, dynamic scaling of source pool, resource synchronization control, and automatic adjustment of resource distribution [ 20 ].…”