Big Data Analytics in Supply Chain Management

1.3. Methodology and research questions

There have been many developments in big data collection and analysis methods in recent years. The author of this paper used the Thomson Reuters Web of Science search tool to track the number of "big data" publications over the last decade. Since several studies were performed on the topic of big data, we narrowed our search to the articles that have "big data" in their title. We also tried different combinations of title and topic searches in the Web of Science and came up with the solution that the best combination was to keep "big data" in the title search and the other keywords in the topic. For example, a search for articles with both "big data" and "supply chain" as a topic for the year 2019 gave us 173 articles; several of them were not related to the focus of our study at all. On the other hand, a search for articles with both "big data" and "supply chain" in the title for the year 2019 gave us only 15 articles, with many valuable articles not included. However, a search for articles with "big data" in the title and "supply chain" as a topic for the same year resulted in 54 articles that covered topics related to our study. Our methodology here was the same as that in many other review papers to collect the literature, provide the descriptive analysis (in this section), develop categories of interest, and evaluate the papers (sections 2, 3, and 4).

Figure 2 illustrates the number of published articles from 2010 to 2019 with the word "big data" in their title and "manufacturing", "supply chain", and "business" in the topic. As can be seen in Figure 2, there is an increase in the number of publications in the field in recent years. It is worth nothing that not much research was published on big data before 2010. About 15% of the big data publications have the word "business" in their topic, and many of publications were dedicated to other fields of study, such as engineering and science. Among the business-related studies, about 46% of publications focused on "supply chain" and "logistics" in their topic, and about 12% have manufacturing as their topic.

Figure 2 Number of publications with "Big Data" in title. 

Different academic journals cover different subject areas; therefore, journals that publish most of the papers in an area of research can be a useful guide for those who are looking for the available literature or submitting their own contribution. The Thomson Reuters Web of Science was also used to develop a bibliometric of big data publications containing the terms "supply chain", "logistics", and "manufacturing" when looking at the journal title and impact factor, as can be seen in Figure 3.

Figure 3 Publishing journal (impact factor in parentheses) with a topic of a) Manufacturing or b) Supply Chain and logistics. 

Figure 3 shows that "Annals of Operations Research", "Sustainability", "Technological Forecasting and Social Change" and "The International Journal of Logistics Management" are the journals which published the most papers that applied big data analytics in supply chain management. The greatest impact factor among these journals is for the "Journal of Cleaner Production," with an impact factor of 6.395.

Information technology developments encouraged many researchers and practitioners to use big data analytics in manufacturing systems, logistics processes, and other functions of a supply chain. Moreover, since the benefits of big data analytics have attracted more researchers into the field, several more papers have been written that review the literature and define possible future directions. According to Figure 2, most research in this area has been performed in recent years; therefore, a current literature review can give more insight for researchers who want to focus their work on big data applications in supply chain management. Moreover, our study categorizes the literature into three key areas: manufacturing systems, logistics processes, and supply chain functions. In each category, papers are grouped based on their main topic, and quality papers are summarized in tables to give more information about each paper. The current study is trying to answer the following questions:

• What are the different categories of big data analytics that are used in supply chain management?
• What are the factors that affect the attractiveness of using big data analytics in supply chain management studies?
• What supply chain management research topics are studied more often by big data analytics?
• What are the hurdles and advantages of using big data analytics in supply chain management research? What must be done in the future?

The remainder of this article is dedicated to reviewing published research in the specialized fields. Section 2 surveys published research that looks at applying big data analytics to manufacturing systems. Sections 3 and 4 review the published research with a focus on using big data analytics in supply chain management and logistics processes. In each subsection the subjects most often studied are introduced, and some of the quality papers are summarized in relevant tables. Section 5 concludes this study by discussing the hurdles and advantages of using big data analytics. Moreover, some directions for the future studies are hypothesized in Section 5.