Big Data Analytics in Supply Chain Management

Theory and Applications

Iman Rahimi, Amir H. Gandomi, Simon James Fong, M. Ali Ülkü, Iman Rahimi, Amir H. Gandomi, Simon James Fong, M. Ali Ülkü

• Editorial: CRC Press
• Año: 2020
• ISBN:
  9781000326932
• Edición: 1
• Categoría: Ciencia de la computación
• Categoría: Bases de datos

In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations.

From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research.

Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain problems.

1 Big Data Analytics in Supply Chain Management

A Scientometric Analysis

Iman Rahimi

Universiti Putra Malaysia

Amir H. Gandomi

University of Technology Sydney

M. Ali Ülkü

Dalhousie University

Simon James Fong

University of Macau

Contents

1.1 Introduction

1.2 Analysis

1.2.1 Data Collection

1.3 Scientometric Analysis

1.3.1 An Analysis on Keywords

1.3.2 A Short Analysis on Countries and Affiliations

1.3.3 Co-author Analysis

1.3.4 An Analysis on Sources

1.3.5 Co-citation Analysis

1.3 Discussion and Conclusion

References

1.1 Introduction

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 Öztürk 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 (Ülkü 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 growth 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 scientometrics and charts.

1.2 Analysis

1.2.1 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 shows the distribution of papers from 2000 onward.

Most of the analysis in this chapter was done with VOSviewer, which is known 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).

1.3 Scientometric Analysis

1.3.1 An Analysis on Keywords

Figure 1.2 presents a cognitive map on which the node size is comparable with 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 10 keywords and the number of occurrences found in the analysis are shown in Table 1.1.

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

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

1.3.2 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

No....