Big Data Analytics in Supply Chain
Posted on: 01 October 2021 by David Warner
Big Data Analytics on a collection of such copious data sets can cultivate a proactive decision-making approach for predicting key opportunities and risks in Supply Chain.
With the world moving towards the Industry 4.0 Standard, the number of machines, processes, and services generating and collecting large quantities of data will increase greatly in the future. This will give rise to Big Data, which is enormous amounts of data that cannot be processed with conventional computation techniques. To uncover patterns in the huge amounts of data and gain valuable insights on it, Big Data Analytics is devised. Supply Chain is a significant contributor to Big Data wherein the diversity of information is large. The data accumulated by Supply Chain contains information from the key entities such as manufacturing, logistics, and retail. The use of Big Data Analytics on a collection of such copious data sets can cultivate a proactive decision-making approach for predicting key opportunities and risks in Supply Chain. This paper discusses the various applications, benefits and the challenges of Big Data and Big Data Analytics in the Supply Chain.
“High-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”
Supply Chain can be considered as a combination of four independent yet interlinked entities such as Marketing, Procurement, Warehouse Management and Transportation. Supply Chain Management is responsible for creating and maintaining the links of different entities in a business which are responsible for procurement of raw materials to ultimate end user delivery of the product (Halo, 2018). This paper focuses on the sources of generated data in Supply Chain, opportunities in Supply Chain from the analysis of collected data and the challenges in utilization of that data. In this review paper we will be discussing about the importance, potential opportunities and challenges of Big Data applications in Supply Chain and logistics.
Big Data Analytics involves the use of advanced analytics techniques to extract valuable knowledge from vast amounts of data, facilitating data-driven decision-making. Big Data Analytics consists of three different levels of analytics. Each level of analytics has a different role and desired outcome. For this literature review, we consider the three levels of Big Data Analytics to be Prescriptive Analytics, Predictive Analytics and Descriptive Analytics. Currently, the level of consideration received by Prescriptive Analytics, followed by Predictive analytics with Descriptive Analytics receiving the least amount of consideration.
Prescriptive Analytics finds application data from processes such as Manufacturing, Logistics, Transportation and Warehousing along with newly introduced processes such as Cyber Physical Systems in the Industry 4.0 trend. Predictive analytics finds strong applications in Procurement, Risk assessment, Risk Management, Forecasting. Descriptive Analytics has the widest scope in terms of the number of processes in a system. Descriptive analytics finds application in development of effective and summarizing reports on raw data that is easy for human interpretation.