Abstract
Abstract Paper aims This study reviews the available literature regarding big data analytics applications in supply chain management and provides insight on topics that received a good deal of attention and topics that still require investigation. This review considers the expansion of big data analytics in supply chain management from 2010 to 2019. Originality Beyond displaying the increasing frequency of using big data analytics in supply chain management, the authors also aim to develop a useful categorization of applying business analytics in supply chain management and define opportunities for future research in the field. Research method This paper briefly discusses big data applications in supply chain management. Four common steps in review papers are performed: collecting articles (Thomson Reuters Web of Science), descriptive analysis, defining categories, and evaluating the material. Main findings According to both information technology development trends and the availability of data, more companies are using big data analytics in their supply chains. About 60% of the research on big data applications in supply chain management were published after 2017. These publications have increasingly focused on big data applications in predictive analysis, rather than in the other three types of data analysis: descriptive analysis, diagnostic analysis, and prescriptive analysis. Implications for theory and practice This review shows that the collected data by many companies can be analyzed using big data analytics methods to develop the business growth plan, market direction forecast, manufacturing process simulation, delivery optimization, inventory management, and marketing and sales processes, among many other activities in a supply chain. The number of articles using case studies in the literature is greater than the number of theoretical publications. This shows that big data analytics has now been properly developed for practical applications, rather than just being a theoretical concept.
Highlights
This review considers the expansion of big data analytics in supply chain management from 2010 to 2019
Implications for theory and practice: This review shows that the collected data by many companies can be analyzed using big data analytics methods to develop the business growth plan, market direction forecast, manufacturing process simulation, delivery optimization, inventory management, and marketing and sales processes, among many other activities in a supply chain
A number of studies show that big data analytics can improve the entire operational performance in manufacturing systems. (Yadegaridehkordi et al, 2018) developed a hybrid approach to study the effect of the adoption of big data analytics on manufacturing companies’ performance. (Popovič et al, 2018) showed that big data analytics’ capability, along with organizational readiness and certain design factors, could enhance a business’s performance
Summary
Big data “I have just bought a house! I have bought a big house!” When people talk about big objects, generally there is a common sense of the word “BIG”. Big data refers to a high volume of data with a high velocity and a high variety; these. The first time that Big Data was defined by the 3V model (Volume, Velocity, and Variety) was in a study by Laney (2001b). A big data system can be separated into four consecutive phases: data generation, data acquisition, data storage, and data analytics (Hu et al, 2014)
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