Global Competitive-

Global Competitive-

Management Department at Bryant University where he teaches courses in opera- tions management and supply chain management. He has a Ph.D. in Management Science and Information Systems from the University of Rhode Island; and his research interests include quality and com- munication within a supply chain, strategic initiatives through align- ment of supply chain goals, collaborative relationships, and leadership excellence. He has published in a numerous journals, and he was a co-winner of the 2011 Case Studies Award Competition presented by the Decision Sciences Institute.

Mercy Shitemi holds a B.S. in Informatics from Indiana Uni- versity and is currently completing a master’s degree in Information Systems Management at Duquesne University’s John F. Donahue Graduate School of Business. Mercy hails from Eldoret, Kenya.

Robert P. Sroufe is the Murrin Chair of Global Competitive- ness in the John F. Donahue Graduate School of Business and Direc- tor of Applied Sustainability within the Beard Institute at Duquesne University. Dr. Sroufe is an award-winning scholar and teacher. These awards include instructional innovation and best environmen- tal papers from the National Decision Sciences Institute. Within the M.B.A. Sustainability program, he develops and delivers courses on sustainable theories and models including life-cycle analysis, business applications of sustainability tools, and processes for new initiatives; and he oversees action-learning consulting projects every semester with corporate sponsors.

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John K. Visich is a Professor in the Management Department at Bryant University, where he teaches courses in operations manage- ment and supply chain management. He has a Ph.D. in Operations Management from the University of Houston, and his research inter- ests are in supply chain management, radio frequency identification, and corporate social responsibility. He has published in a numerous journals, and he was a co-winner of the 2011 Case Studies Award Competition presented by the Decision Sciences Institute.

Angela M. Wicks is an Associate Professor in the Manage- ment Department at Bryant University, where she teaches courses in operations management and project management. She has a Ph.D. in Operations Management from the University of Houston, and her research interests include hospital performance, patient satisfaction, and health care technology. She has published in numerous journals including the International Journal of Quality Assurance in Health- care , Hospital Topics , and the International Journal of Healthcare Technology and Management .

Charles A. Wood is an Assistant Professor in the Manage- ment Information Systems area at the Palumbo Donahue School of Business at Duquesne University in Pittsburgh, Pennsylvania. After spending over a decade in the “real world” as a systems analyst, team leader, manager, systems architect, and finally as the owner of a suc- cessful consulting company, Chuck returned to academia to complete an M.B.A. and a Ph.D. He has taught at several institutions, including Notre Dame and at the University of Minnesota.

CONTRIBUTOR LIST xv

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Preface

The field of business analytics has been thrust into the global spotlight in recent years. This surge in popularity is largely because of a barrage of books and periodical articles highlighting its potential to help firms create a competitive advantage. Although some techniques contained within the umbrella of business analytics, such as data min- ing, 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- school curricula around the world.

Business analytics can be broadly defined as “the scientific pro- cess 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.

Although 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. Because they are so short, these cases often amount to little more than a slightly expanded homework problem.

1 http://www.informs.org/About-INFORMS/What-is-Analytics

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http://www.informs.org/About-INFORMS/What-is-Analytics
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This collection of cases is designed to supplement core material covering business analysis techniques in courses as varied as statistics, operations management, management science, supply chain mod- eling, and decision analysis. This book fills the gap in the library of business analytics case materials appropriate for undergraduate stu- dents 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 primary analytical technique appropriate for each decision environment. Part 1 , “Fore- casting and Process Analysis,” includes three forecasting cases and one case that focuses on quality control and process improvement. Part 2 , “Optimization and Simulation,” contains cases that utilize the classic management science methods of optimization and simulation. The optimization cases address inventory control and logistics net- work design, and the simulation case addresses the management of process flows. Part 3 , “Decision Analysis,” 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 deci- sion environments vary from facility location to sustainability manage- ment. Part 4 , “Advanced Business Analytics,” 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 tool- box of analytical skills and techniques are well positioned to make thoughtful, reasoned decisions informed by data analysis for their

PREFACE xvii

From the Library of Jikovey McCurdy

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xviii THE APPLIED BUSINESS ANALYTICS CASEBOOK

companies and organizations. These analytical skills are transferrable across companies and industries and can enhance students’ attractive- ness and value to employers throughout their careers.

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