This article provides the descriptions on the application of developed techniques of analytics for supply chain management. The applications are sorted regarding predictive, descriptive, and prescriptive analytics and along the Supply Chain Operations Reference (SCOR) model domains plan, source, make, deliver, and return. Predictive analytics focuses on the demand forecasting at strategic, tactical, and operational levels, these all are the elements of the planning process in supply chains, including network design, capacity planning, production planning, and inventory management.
Descriptive analytics focuses the utilization of data from Radio Frequency Identification (RFID) chips, Global Positioning Systems (GPSs), and data-visualization tools to enhance the real-time information of managers about the amount and location of goods in the supply chain. Lastly, Prescriptive analytics centers on the application of mathematical optimization and simulation techniques to equip with decision-support tools built upon the models of descriptive and predictive analytics.
Related Case Solutions & Analyses:
Bunge: Poised for Growth
General Motors: Building a Digital Loyalty Network Through Demand and Supply Chain Integration
Bullwhip Effect in Supply Chains
Delta & Pine Land: Measuring the Value of Transgenic Cotton
Kamakura Ironworks Co. Ltd.
Cisco Systems IncSupply chain risk management system
Cirque Du Soleil
Zappos.com: Developing a Supply Chain to Deliver Wow
7-Eleven In Taiwan: Adaption Of Convenience Stores To New Market Environments
Wal-Mart
