Big Data Analytics in Supply Chain Management: A Scientometric Analysis

Iman Rahimi

Universiti Putra Malaysia

Amir H. Gandomi

University of Technology Sydney

M. Ali Ulku

Dalhousie University

Simon James Fong

University of Macau


The study of big data is constantly expanding, and the main characteristics of big data are now subdivided into the “5V” concept, consisting of Volume, Velocity, Variety, Veracity, and Value (Emrouznejad 2016, Kitchin and McArdle 2016, Onay and Oztiirk 2018). As big data have experienced a transition from being an emerging topic to a growing research field, it has become essential to classify the different types of research and examine the general trends of this research area. Continuous efforts to create more sophisticated technology to gather data at different steps of the supply chain have led to a new era of supply chain analytics (Ulkii and Engau 2020). Using big data, developers such as Amazon, United Parcel Service (UPS), and Wal-Mart, are gaining unprecedented mastery over their supply chains. They are achieving greater oversight into inventory levels, order fulfillment rates, and material and product delivery using predictive data analytics to adjust supply with demand, leveraging new' planning strengths to optimize their sales channel strategies, optimizing supply chain strategy and competitive priorities, and even launching powerful new' ventures. The concurrence of events, such as grow'th in approval of supply chain technologies, data inundation, and a shift in management focus from heuristics to data-driven decision-making, has collectively resulted in the advent of the big data era. In spite of these opportunities, many supply chain operations are restricted or obtain no value from big data. Using these methods, we can overcome widespread difficulties by making the most of big data in the supply chain and increasing cost efficiencies from the already produced data. This process should allow recognition of potential research fields for future research.

In this chapter, a short analysis on big data analytics in supply chain management was done. This chapter presents a general analysis of the current developments in the growing field of big data analytics in supply chain management by using scientomet- rics and charts.


Data Collection

For this scientific analysis, a scientometric mapping technique was used to discover the most common keywords used among published articles. First, we searched for the topics “big data” and “supply chain management” in the SCOPUS database between 2000 and present. More than 700 research articles were found (as of June 14, 2020). Figure 1.1 show's the distribution of papers from 2000 omvard.

Most of the analysis in this chapter was done with VOSviewer, which is know'n as a powerful software for scientometric analysis (Van Eck and Waltman 2010, 2011, 2013), and some researchers have used VOSviewer for their analysis (Emrouznejad and Marra 2016, Rahimi et al. 2017, Gandomi et al. 2020).

Scientometric Analysis

An Analysis on Keywords

Figure 1.2 presents a cognitive map on which the node size is comparable w'ith a number of documents in the indicated scientific discipline, for example, the keywords “big data,” “Internet of things,” and “data analytics” possess large nodes.

The top Ю keywords and the number of occurrences found in the analysis are shown in Table 1.1.

Number of documents on “Big Data Analytics in Supply Chain Management.”

FIGURE 1.1 Number of documents on “Big Data Analytics in Supply Chain Management.”

Cognitive map (keywords analysis considering co-occurrences)

FIGURE 1.2 Cognitive map (keywords analysis considering co-occurrences).

A Short Analysis on Countries and Affiliations

Figure 1.3 shows top organizations that contribute to rankings in the field. Hong Kong Polytechnic University has the first rank (13%), University of Kent possesses the second rank (9%), and California State University and Montpellier Business Schools are in third place (8%).

Figure 1.4 presents the countries ranked by the number of published articles. As is shown, the United States possesses the first rank followed by China, India, the United Kingdom, Germany, France, Australia, Hong Kong, Italy, and Malaysia.

TABLE 1.1 Top 10 Keywords





Big data



Supply chain management



Supply chains



Information management






Supply chain



Big data analytics



Data analytics



Internet of things



Sustainable development





Top organizations ranking by number of documents

FIGURE 1.3 Top organizations ranking by number of documents.

Co-author Analysis

Figure l .5 depicts the analysis of coauthors that networks use to present the robust and fruitful connections among collaborating researchers. The links through the networks’ present channels of knowledge and networks that highlight the scientific communities engaged in research on the big data analytics in supply chain management are shown.

An Analysis on Sources

Figure l .6 illustrates the density map of title of sources. There are many sources, including Lecture Notes in Computer Science, Journal of Business Logistics, Computers and Industrial Engineering, International Journal of Production Research, International

Top countries ranking by number of documents

FIGURE 1.4 Top countries ranking by number of documents.

The scientific community (coauthor) working on “Big data Analytics in Supply Chain Management.”

FIGURE 1.5 The scientific community (coauthor) working on “Big data Analytics in Supply Chain Management.”

Journal of Production Economics, Production and Operations Management Journal contributing in the field. The density of the nodes (journal title) is shown by color, and the high density belongs to some reputed sources such as Lecture Notes in Computer Science while IEEE International Conference possesses the low density.

Co-citation Analysis

Co-citation analysis is another metric that has been presented in this chapter. The co-citation analysis of cited authors has been demonstrated in Figure 1.7. The

Density map (Title)

FIGURE 1.6 Density map (Title).

Co-citation analysis (Cited authors)

FIGURE 1.7 Co-citation analysis (Cited authors).

parameter settings have a minimum of one citation for each author, resulting in 38,602 authors with strength co-citation links. The top-cited authors in the field are A. Gunasekaran, R. Dubey, S.J. Childe, and T. Papadopoulos.

Discussion and Conclusion

In this chapter, scientometric analysis of “Big Data Analytics in Supply Chain Management” for the time period between 2000 and 2020 was explored. Keywords analysis and citation analysis with VOSviewer software were used, and the most commonly used keywords were identified. Keyword, organization, country coauthor, and co-citation analyses were investigated in this chapter. The analysis of keywords indicates that information management, Internet of Things, sustainable development, and competition are among well-described topics in the field. The United States, China, India, the United Kingdom, Germany, France, Australia, Hong Kong, Italy, and Malaysia are most active countries in the field of big data analytics in supply chain management. A short analysis on the sources indicates that Lecture Notes in Computer Science, Journal of Business Logistics, International Journal of Production Research, and Production and Operations Management Journal are well-known journals that have the most contributions to big data analytics in supply chain management. A comprehensive and systematic review as a future direction is highly recommended. Applicability of evolutionary computations and interdisciplinary works in the case of big data are important matters for practical problems.


Emrouznejad, A. (2016). Big Data Optimization: Recent Developments and Challenges. Springer, Berlin.

Emrouznejad, A. and M. Marra (2016). Big data: Who, what and where? Social, cognitive and journals map of big data publications with focus on optimization. In: Emrouznejad, A. (ed.) Big Data Optimization: Recent Developments and Challenges, pp. 1-16. Springer, Cham.

Gandomi, A. H., Emrouznejad, A., Rahimi, I. (2020). Evolutionary computation in scheduling: A scientometric analysis. In: Gandomi, A.H., Emrouznejad, A., Jamshidi, M.M., Deb, K., Rahimi, I., (eds.) Evolutionary Computation in Scheduling, pp. 1-10. Wiley, Hoboken, NJ.

Kitchin, R. and G. McArdle (2016). What makes big data, big data? Exploring the ontological characteristics of 26 datasets. Big Data & Society. 3(1). doi: 10.1177/2053951716631130.

Onay, C. and E. Oztiirk (2018). A review of credit scoring research in the age of big data. Journal of Financial Regulation and Compliance, 26(3), 382-405.

Rahimi, I.. Ahmadi, A, Zobaa, A. F.. Emrouznejad, A., Abdel Aleem. S. H. E. (2017). Big Data Optimization in Electric Power Systems: A Review, CRC Press, Boca Raton, FL.

Ulkii, M. A. and A. Engau (2020). Sustainable supply chain analytics. In: W. Leal Filho Encyclopedia of the UN Sustainable Development Coals. Springer (forthcoming).

Van Eck, N. and L. Waltman (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.

Van Eck, N. J. and L. Waltman (2011). Text mining and visualization using VOSviewer. arXiv preprint arXiv:l 109.2058.

Van Eck, N. J. and L. Waltman (2013). VOSviewer manual. Leiden: Univeristeit Leiden 1(1): 1-53.

Supply Chain Analytics Technology for Big Data

Sivagnanam Rajamanickam Mani Sekhar, Swathi Chandrashekar; and Siddesh Gaddadevara Matt

Ramaiah Institute of Technology, Bangalore, India


Big data more often than not incorporates data and information with sizes over and above the potential of software tools that are regularly used to apprehend, select, organise, and process information enclosed with the bounds of an adequate elapsed clock [1]. Although it emphasises on unstructured data, its philosophy embraces structured and semi-structured data too. Structured data embodies data - often numeric in nature, that is already organised by the institution through data sets and spread sheets. Unstructured data are cluttered and do not belong to any preordained model. It involves data obtained from social media sources, which assist institutions to muster information on customer requirements. Nevertheless, it is not the amount of data that is vital. The main concern, however, is how that data are utilised in the organisations [2]. It is possible to examine the big data for perceptions that make way

Big data process

FIGURE 2.1 Big data process.

for enhanced judgements and tactical organisations advancements [3]. Figure 2.1 portrays the all-inclusive technique involved in deducing information from big data. Big data process can be further divided into management and analytics. The methodologies involved in acquiring and storing data and information are associated with data management. It is also involved in preparing and retrieving data for further analysis. Data analytics represents the approaches required to perform analysis and gather information intelligence [4].

Henceforward, big data define the large volume of data, necessitates a group of methodologies, procedures, and automation along with innovative structures of integration to expose perceptions obtained from data and information which are known to be varied, intricate, and are of enormous in measure [5].

The definition of big data can be summarised as the three V’s [6] mentioned below:

  • • Volume: It is the quantum of produced and stocked data. The magnitude of information gathered decides the usefulness, significance, and prospect insight. Organisations gather information from a humongous mixture of sources, together with IoT devices, business transactions, equipment from industries, audios, videos, social media, and many more [6]. The pressure on the storage load has been relieved in present scenarios by using platforms like data lakes and Hadoop.
  • • Velocity: Data gush into businesses at a strikingly exceptional momentum due to the broadening of IoT and require to be grasped in an appropriate method. Popular RFID labels, automated meters, detectors and sensors, propel prerequisites to deal with the inundations of such data and information instantaneously.
  • • Variety: Information is known to be in a heterogeneous format. Structured data - obtained from traditional databases in numeric form to unstructured data - word presentations and documents, emails, visuals, sounds [7].

Supply Chain: Big data analytics models to unveil intricate details in the voluminous data and acquire valuable insights from it. And, supply chain is an exceptionally powerful contributor to big data [8]. Supply chain is a complex web of all the people, agencies, assets, activities, and technologies demanded in the development to the purchase of a product, including all the functions that emerge with receiving an order to fulfilling the end user’s request, through developing a product, operations and marketing, distribution networks, business and finance, and end-user or customer service [9].

The entities connected in the supply chain include manufacturers, vendors, repositories or warehouses, travel agencies for transportation, distribution centres, and retail agents. Activities of the supply chain comprise converting raw products and apparatuses into a fully furnished and completed product that is produced to the users [10].

The inputs gathered by Supply Chain contain data from important entities like manufacturing and production, development, logistics, and retail [11]. The application of Big Data Analytics on a stream of such abundant data in the supply chain can nurture a dynamic phenomenon of making decisions to predict prospects and perils.

Introduction to Supply Chain Analytics Technology

It exhibits the potentiality to take information-based judgements, with the grounds of the outline and overview obtained of the vast information, with the help of visual tools such as charts, diagrams, graphs, tables, and more. Supply chains produce enormous quantities of information. It provides assistance by discovering patterns and providing insights. Supply Chain Analytics enhances the process of making decisions for all tasks by making use of the data and quantitative and analytical methods [12].

Supply Chain Analytics lays down the foundation for businesses to accomplish the desired challenging growth, improve their profits, and increase their market shares by exploiting the derived insights from the gathered data.

Necessity for Supply Chain Analytics for Big Data

Businesses, Organisations, Companies dealing with enormous amounts of data require Supply Chain Analytics to assist them in making faster, smarter, and more efficient decisions [13]. With Supply Chain Analytics, organisations can improve their forecasting, identify their drawbacks and inefficiencies, react better to user’s requirements and needs, drive innovation, and follow innovative ideas.

Supply Chain Analytics is required to:

  • • execute the precise solutions to specifically analyse, predict, and interpret data big
  • • identify the risk indicators
  • • responding in a punctual manner to the insights obtained

It is also known to be the fundamental foundation for involving cognitive technologies like artificial intelligence (AI). Cognitive technologies like humans perform understanding, reasoning, learning, and interacting but at aggressive speed and capacity. Such advancement with the supply chain has turned over a new' leaf for the optimisation of big data [14].

< Prev   CONTENTS   Source   Next >