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An Introduction to Management Science: Quantitative Approaches to Decision Making 14th Edition

David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann

  • Published
  • Previous Editions 2012, 2011, 2008
  • 912 Pages
Starting At 135.00 See pricing and ISBN options

Overview

Integrating the latest developments in Microsoft® Office Excel® 2013, Anderson/Sweeney/Williams/Camm/Cochran/Fry/Ohlmann's AN INTRODUCTION TO MANAGEMENT SCIENCE: QUANTITATIVE APPROACHES TO DECISION MAKING, 14E equips students with a sound conceptual understanding of the role that management science plays in the decision-making process. The trusted market leader for more than two decades, the text defines today's management science course. The authors continue to provide unwavering accuracy with the book's emphasis on applications and timely, powerful examples. The book's hallmark problem-scenario approach introduces each quantitative technique within an applications setting. Students must apply the management science model to generate solutions and recommendations for management. For the 14th Edition, all data sets, applications, and screen visuals reflect the details of Excel 2013 to accurately prepare your students to work with the latest spreadsheet tools. In addition, a comprehensive support package offers all the written and online timesaving support you need with trusted solutions written by the text authors. Online content includes online chapters, LINGO software and Excel add-ins.

David R. Anderson, University of Cincinnati

Dr. David R. Anderson is a textbook author and Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He has served as head of the Department of Quantitative Analysis and Operations Management and as Associate Dean of the College of Business Administration. He was also coordinator of the College’s first Executive Program. In addition to introductory statistics for business students, Dr. Anderson has taught graduate-level courses in regression analysis, multivariate analysis, and management science. He also has taught statistical courses at the Department of Labor in Washington, D.C. Professor Anderson has received numerous honors for excellence in teaching and service to student organizations. He is the coauthor of ten textbooks related to decision sciences and actively consults with businesses in the areas of sampling and statistical methods. Born in Grand Forks, North Dakota, he earned his BS, MS, and PhD degrees from Purdue University.

Dennis J. Sweeney, University of Cincinnati

Dr. Dennis J. Sweeney is a leading textbook author, Professor Emeritus of Quantitative Analysis, and founder of the Center for Productivity Improvement at the University of Cincinnati. He also served five years as head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration. In addition, Dr. Sweeney has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. Dr. Sweeney has published more than 30 articles in the area of management science and statistics. The National Science Foundation, IBM, Procter & Gamble, Federated Department Stores, Kroger, and Cincinnati Gas & Electric have funded his research, which has been published in Management Science, Operations Research, Mathematical Programming, Decision Sciences, and other respected journals. Dr. Sweeney is the co-author of ten textbooks in the areas of statistics, management science, linear programming, and production and operations management. Born in Des Moines, Iowa, he earned a B.S. degree from Drake University, graduating summa cum laude. He received his M.B.A. and D.B.A. degrees from Indiana University, where he was an NDEA Fellow.

Thomas A. Williams, Rochester Institute of Technology

Dr. Thomas A. Williams is a well respected textbook author and Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology, where he was the first chairman of the Decision Sciences Department. He taught courses in management science and statistics, as well as graduate courses in regression and decision analysis. Before joining the College of Business at RIT, Dr. Williams served for seven years as a faculty member in the College of Business Administration at the University of Cincinnati, where he developed the undergraduate program in Information Systems and served as its coordinator. The co-author of 11 leading textbooks in the areas of management science, statistics, production and operations management, and mathematics, Dr. Williams has been a consultant for numerous Fortune 500 companies and has worked on projects ranging from the use of data analysis to the development of large-scale regression models. He earned his B.S. degree at Clarkson University and completed his graduate work at Rensselaer Polytechnic Institute, where he received his M.S. and Ph.D. degrees.

Jeffrey D. Camm, Wake Forest University

Jeffrey D. Camm is the Inmar Presidential Chair and Associate Dean of Analytics in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University in Ohio, and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, he served on the faculty of the University of Cincinnati. He has also been a visiting scholar at Stanford University and a visiting professor of business administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published more than 30 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in Science, Management Science, Operations Research, Interfaces, and other professional journals. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati and he was the 2006 recipient of the INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, he has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010 he served as editor-in-chief of Interfaces and has also served on the editorial board of INFORMS Transactions on Education.

James J. Cochran, University of Alabama

James J. Cochran is Professor of Applied Statistics and the Rogers-Spivey Faculty Fellow at the University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S., and M.B.A. degrees from Wright State University and a Ph.D. from the University of Cincinnati. He has been at the University of Alabama since 2014 and has been a visiting scholar at Stanford University, Universidad de Talca, the University of South Africa and Pole Universitaire Leonard de Vinci.

Michael J. Fry, University of Cincinnati

Dr. Michael J. Fry is Associate Professor and Lindner Research Fellow in the Department of Operations, Business Analytics, and Information Systems in the Carl H. Lindner College of Business at the University of Cincinnati, where he also serves as Assistant Director for the Center for Business Analytics. At the University of Cincinnati since 2002, he has been a visiting professor at the Samuel Curtis Johnson Graduate School of Management at Cornell University and the Sauder School of Business at the University of British Columbia. Dr. Fry has published more than twenty research publications in such journals as OPERATIONS RESEARCH, M&SOM, TRANSPORTATION SCIENCE, NAVAL RESEARCH LOGISTICS, IIE TRANSACTIONS, and INTERFACES. His research interests include applying management science methods to the areas of supply chain analytics, sports analytics, and public policy operations. He has worked with many different organizations for his research, Including Dell, Inc., Copeland Corporation, Starbucks Coffee Company, The Cincinnati Fire Department, the State of Ohio Election Commission, the Cincinnati Bengals, and the Cincinnati Zoo and Botanical Gardens. Professor Fry's teaching awards include the 2013 Michael L. Dean Excellence in Graduate Teaching Award and the 2006 Daniel J. Westerbeck Junior Faculty Teaching Award. Born in Killeen, Texas, he earned a B.S. from Texas A&M University, and M.S.E. and Ph.D. degrees from the University of Michigan.

Jeffrey W. Ohlmann, University of Iowa

Jeffrey W. Ohlmann is Associate Professor of Management Sciences in the Tippie College of Business at the University of Iowa, where he has been since 2003. Professor Ohlmann’s research on the modeling and solution of decision-making problems has produced more than a dozen research papers in such journals as MATHEMATICS OF OPERATIONS RESEARCH, INFORMS JOURNAL ON COMPUTING, TRANSPORTATION SCIENCE, and INTERFACES. He has collaborated with companies such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elections and the Cincinnati Bengals. Due to the relevance of his work to industry, he received the George B. Dantzig Dissertation Award and was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice. Born in Valentine, Nebraska, he earned a BS from the University of Nebraska and MS and PhD degrees from the University of Michigan.
  • ADDITIONAL AUTHORS. For the new 14th Edition, the text's renowned author team is joined by three new, outstanding authors--James J. Cochran of the University of Alabama, Michael J. Fry of the University of Cincinnati, and Jeffrey W. Ohlmann of the University of Iowa. All three are strong contributors who bring a thoughtful and fresh view to the subject matter.
  • NEW UTILITY THEORY COVERAGE. Chapter 13 "Decision Analysis" includes an all-new section on Utility Theory to complement the comprehensive material on decision analysis.
  • MICROSOFT® OFFICE EXCEL® 2013 INTEGRATION. Detailing the latest information from Excel 2013, chapter appendices offer step-by-step instructions on how to use Excel Solver and LINGO. Both Excel and LINGO files are available on the text's companion Website for every model illustrated in the text.
  • UPDATED APPENDIX A: BUILDING SPREADSHEET MODELS. This helpful, optional appendix provides useful insights for solving optimization models with Excel Solver. It also discusses the principles of good spreadsheet modeling and offers helping auditing tips as well as practical exercises.
  • UPDATED TRENDS AND SEASONALITY COVERAGE. A fully revised Chapter 15 "Time Series Analysis and Forecasting" includes an updated discussion of trends and seasonality that focuses on the use of regression to estimate linear trends and seasonal effects. In addition, a discussion on using the Excel INEST function to estimate linear trends and seasonal effect has been added to an appendix at the end of this chapter. The revisions represent industry approaches to these important topics.
  • NEW REAL-WORLD CASES AND EXAMPLES. Concise yet insightful "Management Science in Action" summaries describe how each chapter's concepts are actually used in real-world practice. The 14th Edition offers more than 20 engaging new Management Science in Action vignettes and cases as well as homework problems for hands-on practice.
  • NEW CENGAGENOW. CengageNOW is a powerful course management and online homework tool that provides robust instructor control and customization to optimize the learning experience and meet desired outcomes. CengageNOW features author-written homework from the textbook, Integrated e-book, Assessment Options, and Course Management Tools, including a Gradebook.
  • REAL-DATA EXAMPLES. Known for its practical, real-world emphasis, the text provides actual data drawn from real business that emphasizes applications as well as solid management science and quantitative methodology.
  • INTEGRATED SOFTWARE APPLICATIONS. The text integrates coverage of the software applications most commonly used today, helping you equip students with critical skills in LINGO as well as Excel with quantitative add-ins. For your convenience, coverage of LINGO and Excel with add-ins such as TreePlan and Analytic Solver Platform appear in appendixes--enabling you to introduce them when it best fits with your course.
  • ROBUST ONLINE CONTENT. The text's wealth of digital content provides five online chapters, excel templates and add-ins that correspond with text examples and models, and software. Students can also access LINGO trial edition software and Analytic Solver Platform.
  • SELF-TEST EXERCISES. Helpful Self-Test Exercises throughout each chapter with complete solutions allow students to check their understanding of concepts as they progress through the text. The exercises also serve as excellent exam-prep tools. A complete, step-by-step solution to each Self-Test exercise is included in Appendix E.
  • PROVEN AUTHORS. Respected leaders and active consultants in the fields of business and statistics, the Anderson/Sweeney/Williams/Camm/Cochran/Fry/Ohlmann team of authors personally write and confirm all text presentation examples, problems, and Test Bank content to ensure unwavering accuracy and dependability.
  • PROBLEM-SCENARIO APPROACH. A hallmark strength of this text, the authors' proven problem-scenario approach introduces problems using the management science model and introduces each quantitative technique within an application setting. Students must apply the management science technique to each problem to generate a business solution or recommendation.
1. Introduction.
2. An Introduction to Linear Programming.
3. Linear Programming: Sensitivity Analysis and Interpretation of Solution.
4. Linear Programming Applications in Marketing, Finance, and Operations Management.
5. Advanced Linear Programming Applications.
6. Distribution and Network Models.
7. Integer Linear Programming.
8. Nonlinear Optimization Models.
9. Project Scheduling: PERT/CPM.
10. Inventory Models.
11. Waiting Line Models.
12. Simulation.
13. Decision Analysis.
14. Multicriteria Decisions.
15. Time Series Analysis and Forecasting.
16. Markov Processes.
17. Linear Programming: Simplex Method (on Website).
18. Simplex-Based Sensitivity Analysis and Duality (on Website).
19. Solutions Procedures for Transportation and Assignment Problems (on Website).
20. Minimal Spanning Tree (on Website).
21. Dynamic Programming (on Website).
Appendix A: Building Spreadsheet Models.
Appendix B: Areas for the Standard Normal Distribution.
Appendix C: Values of e–λ.
Appendix D: References and Bibliography.
Appendix E: Self-Test Solutions and Answers to Even-Numbered Problems.
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