Supply Chain Analytics in the Era of Big Data

Supply Chain Analytics in the Era of Big Data

Ching-Chung Kuo (University of North Texas, USA) and Zhen Li (Middle Tennessee State University, USA)
Copyright: © 2014 |Pages: 14
DOI: 10.4018/978-1-4666-5202-6.ch211
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Background

Business Intelligence

Business intelligence (BI) is an umbrella term that refers to a collection of tools and technologies for gathering, storing, accessing, and analyzing data to aid in managerial decision-making. Common functions of BI include data mining, online analytical processing (OLAP), query, and reporting. The history of BI dates back to 1951 when the first commercial computer LEO was used for “meeting business needs through actionable information” by determining the number of cakes and sandwiches to make for the next day based on the previous demand in J. Lyon & Co. tea shops in UK (Elliott, 2011). BI began to receive more attention in the late 1960s when computers were used in decision support systems (DSS).

Over the past half century, recognition of the value of BI has led to the development of many software programs and business solution applications including Minitab, Excel, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, supply chain management (SCM) systems, and others (Kohavi, Rothleder, & Simoudis, 2002). Nowadays, BI is routinely used in virtually all functional areas of a company such as accounting, finance, operations, marketing, human resources, purchasing, and the like (Berry & Linoff, 1999). It also plays a key role in other activities and processes within an organization including fraud detection, supplier relations, and customer attrition to name just a few (Kohavi et al., 2002).

Business Analytics

Traditional software packages such as Excel, ERP, and SCM are valuable for automating operations, but they are incomplete solutions to the enterprise problems today and inadequate for meeting the challenges tomorrow. This is because these systems reflect only what happened instead of what is happening or what will happen. High-performing organizations are currently in search of new approaches that will provide them with real-time information, predictive insights, data visualization, and optimal actions to gain a competitive advantage by mitigating risks and improving profitability. This is the backdrop against which business analytics (BA) emerged as a new area of research.

BA is concerned with the use of computer technologies, statistical techniques, and mathematical models to discover meaningful patterns or relationships in data for the purpose of gaining insights into business operations and making better, fact-based decisions. As a result, it enables organizations to uncover information that may be overlooked in traditional enterprise systems.

According to Evans (2013), BA is the convergence of three important academic disciplines: statistics, business intelligence/information systems, and modeling/optimization. A graphical representation of this concept and the interfaces between the three components is shown in Figure 1, which is adapted from a figure in Evans (2012, p. 6):

Figure 1.

Three academic disciplines involved in BA

Key Terms in this Chapter

Business Analytics: A set of computer technologies, statistical techniques, and mathematical models to discover meaningful patterns or relationships in data for the purpose of gaining insights into business operations and making better, fact-based decisions.

Big Data: A collection of data sets which are too large and complex to process using traditional methods or techniques.

Supply Chain Analytics: A subset of business analytics that focuses on cross-functional activities and processes related to a supply chain.

Business Intelligence: A set of tools and technologies for gathering, storing, accessing, and analyzing data to aid in managerial decision-making.

Descriptive Analytics: The analysis of data to understand what happened in the past and what is happing now.

Prescriptive Analytics: The analysis of data to prescribe the best course of action to take in the future.

Predictive Analytics: The analysis of data to determine how likely events will occur in the future.

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