Supply chain analytics Gilvan C. Souza Kelley School of Business, Indiana University, Bloomington, IN 47405, U.S.A. 1. Why analytics in supply chain management? The supply chain for a product is the network of firms and facilities involved in the transformation process from raw materials to a product and in the distribution of that product to customers. In a supply chain, there are physical, financial, and informational flows among different firms. Supply chain analytics focuses on the use of information and analytical tools to make better decisions regard- ing material flows in the supply chain. Put differently, supply chain analytics focuses on analytical approaches to make decisions that better match supply and demand. Well-planned and implemented decisions con- tribute directly to the bottom line by lowering sourcing, transportation, storage, stockout, and disposal costs. As a result, analytics has historically played a significant role in supply chain manage- ment, starting with military operations during and after World War II–—particularly with the develop- ment of the simplex method for solving linear pro- gramming by George Dantzig in the 1940s. Supply chain analytics became more ingrained in decision making with the advent of enterprise resource plan- ning (ERP) systems in the 1990s and more recently with ‘big data’ applications, particularly in descrip- tive and predictive analytics, as I describe with some examples in this article. Business Horizons (2014) 57, 595—605 Available online at www.sciencedirect.com ScienceDirect www.elsevier.com/locate/bushor KEYWORDS Supply chain management; Analytics; Optimization; Forecasting Abstract In this article, I describe the application of advanced analytics techniques to supply chain management. The applications are categorized in terms of descrip- tive, predictive, and prescriptive analytics and along the supply chain operations reference (SCOR) model domains plan, source, make, deliver, and return. Descriptive analytics applications center on the use of data from global positioning systems (GPSs), radio frequency identification (RFID) chips, and data-visualization tools to provide managers with real-time information regarding location and quantities of goods in the supply chain. Predictive analytics centers on demand forecasting at strategic, tactical, and operational levels, all of which drive the planning process in supply chains in terms of network design, capacity planning, production planning, and inventory management. Finally, prescriptive analytics focuses on the use of mathe- matical optimization and simulation techniques to provide decision-support tools built upon descriptive and predictive analytics models. # 2014 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights reserved. E-mail address: gsouza@indiana.edu 0007-6813/$ — see front matter # 2014 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.bushor.2014.06.004 Copyright 2014 by Kelley School of Business, Indiana University. For reprints, call HBS Publishing at ( 800) 545-7685. BH 627 Do Not Copy or Post This document is authorized for educator review use only by Sourabh Bhattacharya, Institute of Management Technology - Hyderabad until Aug 2020. Copying or posting is an infringement of copyright. Permissions@hbsp.harvard.edu or 617.783.7860