The Applied Business Analytics Casebook: Applications In Supply Chain Management, Operations…

Hardcover | October 30, 2013

byMatthew J. Drake

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The first collection of cases on “big data” analytics for supply chain, operations research, and operations management, this reference puts readers in the position of the analytics professional and decision-maker. Perfect for students, practitioners, and certification candidates in SCM, OM, and OR, these short, focused, to-the-point case studies illustrate the entire decision-making process. They provide realistic opportunities to perform analyses, interpret output, and recommend an optimal course of action. Contributed by leading “big data” experts, the cases in The Applied Business Analytics Casebook covers: 

  • 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

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From the Publisher

The first collection of cases on “big data” analytics for supply chain, operations research, and operations management, this reference puts readers in the position of the analytics professional and decision-maker. Perfect for students, practitioners, and certification candidates in SCM, OM, and OR, these short, focused, to-the-point ca...

From the Jacket

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 stu...

Matthew J. Drake, Ph.D., CFPIM, is an Associate Professor of Supply Chain Management and the Director of International Business Programs in the Palumbo-Donahue School of Business at Duquesne University. Dr. Drake primarily teaches analytical courses in the Supply Chain Management program. He holds a B.S. in Business Administration fro...

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Format:HardcoverDimensions:224 pages, 9.25 × 6.25 × 0.5 inPublished:October 30, 2013Publisher:Pearson EducationLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0133407365

ISBN - 13:9780133407365

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Preface The field of business analytics has been thrust into the global limelight in recent years. This surge in popularity is largely due to a barrage of books and periodical articles highlighting its potential to help firms create a competitive advantage. While some techniques contained within the umbrella of business analytics such as data mining, text mining, and neural networks truly represent cutting-edge methodologies that mainly appear in advanced graduate courses, the building block techniques of business analytics such as statistical analysis, optimization, and decision trees are mainstays in business schools around the world. Business analytics can be broadly defined as “the scientific process of transforming data into insight for better decision making.” 1 As a result of this focus on decision making, courses that cover material related to business analytics can benefit greatly from utilizing case studies as a supplement to the core analytical material. Case studies are an effective method for exposing students to the entire decision making process because they put the student in a simulated active role as a decision maker who must perform the analysis and use the output to recommend a course of action. While cases are a mainstay of many graduate business courses, they are used somewhat less frequently in undergraduate courses. One reason for this lack of extensive case adoption in undergraduate courses is the preponderance of long cases published by the major case libraries. Cases appropriate for undergraduates need to be somewhat more focused because the students do not have as much experience as graduate students. Many textbooks include one- or two- page cases at the end of a chapter to illustrate the application of the techniques presented in the chapter. Since they are so short, these cases often amount to little more than a slightly expanded homework problem. This collection of cases is designed to supplement core material covering business analysis techniques in courses as varied as statistics, operations management, management science, and decision analysis. This book fills the gap in the library of business analytics case materials appropriate for undergraduate students with cases of moderate length. The cases are also appropriate for introductory-level graduate courses as instructors can focus the analysis and discussion on more of the complex issues raised in the cases. The cases in the collection are grouped by the analytical technique appropriate for each decision environment. Part I includes three forecasting cases and one that focuses on quality control and process improvement. Part II contains cases that utilize the classic management science methods of optimization and simulation. The optimization cases address inventory control and logistics network design, and the simulation case addresses the management of process flows. Part III includes cases that require the application of a variety of decision analysis tools from decision trees and factor rating to the Analytic Hierarchy Process (AHP), multi-criteria decision analysis, and group decision making. The decision environments vary from facility location to sustainability management. Part IV contains two advanced cases—one that is truly a “big data” case with a large data set and another centered on vehicle routing, a traditionally difficult problem in logistics. It is my hope that the cases in this collection expose students to the power of business analytics and the utility of these techniques in the decision-making process. Students armed with an effective toolbox of analytical skills and techniques are well-positioned to make thoughtful, reasoned decisions informed by data analysis for their companies and organizations. These analytical skills are transferrable across companies and industries and can enhance students’ attractiveness and value to employers throughout their careers. Matthew J. Drake Pittsburgh, Pennsylvania, USA August 2013 1 Source: http://www.informs.org/About-INFORMS/What-is-Analytics" http://www.informs.org/About-INFORMS/What-is-Analytics © Copyright Pearson Education. All rights reserved.

Table of Contents

Part 1: Forecasting and Statistical Analysis

1. Forecasting Sales at Ska Brewing Company

2. Maintaining Financial Success and Expanding into Other Markets at FeedMyPet.com

3. Forecasting Offertory Revenue at St. Elizabeth Seton Catholic Church

4. Pizza Station

Part 2: Optimization and Simulation

5. Inventory Management at Squirrel Hill Cosmetics

6. Safety Stock Planning for a Hong Kong Fashion Retailer

7. Network Design at Commonwealth Pipeline Company

8. Publish or Perish: Scheduling Challenges in the Publishing Industry

Part 3: Decision Analysis

9. Narragansett Brewing Company: Build a Brewery

10. Aluminum vs. Plastic: A Life-Cycle Perspective on the Use of These Materials in Laptop Computers

11. HealthCare’s Corporate Social Responsibility Program

12. Paperbackswap.com: Got Books? 

13. Stranded in the Nyiri Desert: A Group Case Study

Part 4: Advanced Business Analytics

14. Joe’s Coin Shop: Entry into Online Auctions

15.  Vehicle Routing at Otto’s Discount Brigade

 

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The complete, up-to-date casebook for analytics in SCM, OM, and OR: concise, focused cases that make you the analyst and decision-maker.