A unique casebook in analytics for supply chain management places the reader in the simulated role of decision-maker, exposes them to the entire decision-making process, and provides opportunities to perform analyses, interpret output, and recommend an optimal course of action.
Contributed by many of today’s leading experts in “big data” for supply chain, operations research, and operation management, this reference’s cases are short, concise, and to the point. Coverage includes:
-
Forecasting and statistical analysis: time series forecasting models, regression models, data visualization, and hypothesis testing
-
Optimization and simulation: linear, integer, and nonlinear programming; Monte Carlo simulation and risk analysis; and stochastic optimization
-
Decision analysis: decision making under uncertainty; expected value of perfect information; decision trees; game theory models; AHP; and multi-criteria decision making
- Advanced business analytics: data warehousing/mining; text mining; neural networks; financial analytics; CRM analytics; and revenue management models
The first complete casebook on business analytics for supply chain and operations management
-
Focused, up-to-date cases put you in the roles of analyst and decision-maker
-
Covers forecasting/statistical analysis, optimization/simulation, decision analysis, and data analysis/mining
-
Ideal for practitioners, graduate students, and undergraduates in SCM, OM, and OR
This unique casebook in analytics for supply chain management places you in the role of decision-maker, exposes you to the entire decision-making process, and provides opportunities to perform analyses, interpret output, and recommend the best course of action.
Contributed by leading experts in “big data” for supply chain and operations management, these cases are short, concise, and to the point. You'll identify and test competing hypotheses; perform risk analyses; optimize decision-making in the face of uncertainty; and improve key processes such as routing and scheduling.
