Supply Chain Analytics: Principles and Applications

This reference text resolves problems of physical products, information, and financial transactions by using mathematical and statistical tools to provide robust and validated solutions to real-world problems of the supply chain. It further discusses important concepts such as supply chain segmentation, constrained and unconstrained optimization, time series analysis, double exponential smoothing, and demand probability distributions. This book: Discusses inventory replenishment models including economic order quantity (EOQ), and economic production quantity (EPQ). Explains different mathematical and analytical models applied frequently depending on the supply chain problem. Covers important topics such as linear, integer, and mixed integer programming. Demonstrates how the variation in demand with time follows certain patterns which can be seasonal, cyclical, trend, and random. Highlights forecasting methods, namely, cumulative, naive, moving averages, and exponential smoothing. It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of manufacturing engineering, industrial engineering, supply chain management, mechanical engineering, and production engineering.