Supply Chain Analytics: Challenges and Opportunities

Supply Chain Analytics: Challenges and Opportunities

Xiuli He (University of North Carolina at Charlotte,USA), Satyajit Saravane (University of North Carolina at Charlotte, USA) and Qiannong Gu (Ball State University, USA)
Copyright: © 2014 |Pages: 12
DOI: 10.4018/978-1-4666-5202-6.ch212

Chapter Preview


Evolution Of Business Intelligence In Scm

Information technology has played a dominant role in the evolution of SCM. In the 1960s, manufacturers managed their inventories focusing on the process of movement and the accountability of inventory. In the 1970s, manufacturers shifted to Material Requirement Planning (MRP) transactions. Later in the 1980s, Manufacturing Resources Planning (MRP II) came. Characteristic modules in a typical MRP II system include master production schedule, item master data, Bill of Materials (BOM), production resource data, inventory and orders, purchasing management, material requirements planning, and cost reporting/management. In 1990, Gartner Group employed the acronym ERP as an extension of MRP, later MRP II, and computer-integrated manufacturing. ERP systems experienced rapid growth in the 1990s.

ERP systems initially focused on back office operations that did not interact directly with customers. ERP systems are usually designed to record business transactions data, make changes to existing data, reconcile data, keep track of business transactions, run predefined business reports, and manage business transactions (Chou et al., 2005). Thus, traditional ERP systems are not advanced enough for current economic conditions (SAS, 2010). Firms have invested heavily on ERP, SCM, and CRM. These systems collect huge amounts of transactional data to generate reports which drive managerial decisions. The supply chain analytical solutions focusing on specific needs like point of sales (POS) reporting, inventory management, supply chain visibility, merchandise planning, business intelligence, and store size optimization can enhance the value of previous transactional data by providing forward-looking analytical insights.

Key Terms in this Chapter

Business Intelligence and Analytics: The extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.

Supply Chain Analytics: The extensive use of data and its analysis through statistical and quantitative models to derive predictive insights for future, improve decision making and to achieve operational efficiency at every level in the supply chain.

Big Data: Datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.

Data Analytics: The business intelligence and analytics technologies that are grounded mostly in data mining and statistical analysis.

Data Science: A set of quantitative and qualitative methods that support and guide the extraction of information and knowledge from data to solve relevant problems and predict outcomes.

Data Mining: The actual extraction of knowledge from data via technologies that incorporate various quantitative and qualitative methods.

Supply Chain Management: A set of approaches utilized to efficiently integrate suppliers, manufacturers, warehouse and stores, so that merchandise is produced and distributed at the right quantities, to the right locations, and at the right time, in order to minimize system-wide costs while satisfying service level requirements.

Complete Chapter List

Search this Book: