Quantitative Analysis for Management, 13th edition

Published by Pearson (January 5, 2017) © 2018

  • Barry Render Graduate School of Business, Rollins College
  • Ralph M. Stair Florida State University
  • Michael E. Hanna University of Houston - Clear Lake
  • Trevor S. Hale Texas A&M University

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For courses in management science and decision modeling.


Foundational understanding of management science through real-world problems and solutions

Quantitative Analysis for Management helps students to develop a real-world understanding of business analytics, quantitative methods, and management science by emphasizing model building, tangible examples, and computer applications. The authors offer an accessible introduction to mathematical models and then students apply those models using step-by-step, how-to instructions. For more intricate mathematical procedures, the 13th Edition offers a flexible approach, allowing instructors to omit specific sections without interrupting the flow of the material. Supporting computer software enables instructors to focus on the managerial problems and solutions, rather than spending valuable class time on the details of algorithms.

Students see how concepts and techniques are applied in real organizations

  • UPDATED! Information and resources ensure that students have the most current and comprehensive understanding of quantitative analytics and management science.
  • UPDATED! Contemporary problems and review questions reflect the present-day state of quantitative business methods, encouraging students to examine key concepts from a real-world perspective.
  • REVISED! An emphasis on model building and computer applications shows how concepts are used in business.
    • Transportation, assignment, and network models are together in one chapter, focused on modeling with linear programming.
    • Specialized algorithms for the transportation, assignment, and network methods are combined into a single online module for a cohesive learning experience.
  • EXPANDED! QA in Action boxes illustrate how real organizations use quantitative analytics to solve problems.
  • EXPANDED! Modeling in the Real World boxes demonstrate the application of the quantitative analysis approach to every technique discussed in the book.
  • UPDATED! Case Studies provide additional challenging managerial applications.
  • REVISED! A warm introduction to business analytics gives students a preview of the concepts covered in the text.

Students develop and check their understanding of each topic, so that they can study efficiently and effectively

  • UPDATED! Procedure boxes summarize complex quantitative techniques, presenting them as a series of digestible steps.
  • REVISED! Solved Problems, located at the end of each chapter, serve as models for students’ homework problems.
  • UPDATED! Discussion Questions test students’ comprehension of concepts covered in each chapter.
  • EXPANDED! Applications-oriented problems in every chapter evaluate students’ abilities to solve exam-type problems. This edition includes 26 new problems.
  • REVISED! Glossaries at the end of each chapter define key terms covered in the text.
  • EXPANDED! Key Equations are listed at the end of each chapter for review.
  • UPDATED! End-of-chapter bibliographies provide a selection of current, relevant books and articles for further learning.
  • EXPANDED! Internet Homework Problems on the companion website offer additional practice.

By leveraging computer applications like Excel® 2016, students can spend less time on the mechanics and more time on interpretation and comprehension

  • UPDATED! Microsoft® Excel 2016 is incorporated throughout the chapters, providing screenshots to make calculations easily accessible to students.
  • EXPANDED! The Companion Website, located at www.pearsonhighered.com/render, contains a variety of resources to help students master the material in the course. These include the following:
    • EXPANDED! For examples in the text, data files are available in Excel, Excel QM, and POM-QM for Windows. This helps students become familiar with the software, and it helps them understand the input and formulas necessary for working the examples.
    • Internet Homework Problems. In addition to the end-of-chapter problems in the textbook, there are additional problems that instructors may assign.
    • Additional Case Studies are available for most chapters, guiding students through cases such as personnel decisions at Chase Manhattan Bank or curtailment plans for a natural gas company.
    • UPDATED! POM-QM for Windows. Developed by Howard Weiss, this very user-f

Students see how concepts and techniques are applied in real organizations

  • Information and resources ensure that students have the most current and comprehensive understanding of quantitative analytics and management science.
  • Contemporary problems and review questions reflect the present-day state of quantitative business methods, encouraging students to examine key concepts from a real-world perspective.
  • An emphasis on model building and computer applications shows how concepts are used in business.
    • Transportation, assignment, and network models are together in one chapter, focused on modeling with linear programming.
    • Specialized algorithms for the transportation, assignment, and network methods are combined into a single online module for a cohesive learning experience.
  • QA in Action boxes illustrate how real organizations use quantitative analytics to solve problems.
  • Modeling in the Real World boxes demonstrate the application of the quantitative analysis approach to every technique discussed in the book.
  • Case Studies provide additional challenging managerial applications.
  • A warm introduction to business analytics gives students a preview of the concepts covered in the text.

Students develop and check their understanding of each topic, so that they can study efficiently and effective

  • Procedure boxes summarize complex quantitative techniques, presenting them as a series of digestible steps.
  • Solved Problems, located at the end of each chapter, serve as models for students’ homework problems.
  • Discussion Questions test students’ comprehension of concepts covered in each chapter.
  • Applications-oriented problems in every chapter evaluate students’ abilities to solve exam-type problems. This edition includes 26 new problems.
  • Glossaries at the end of each chapter define key terms covered in the text.
  • Key Equations are listed at the end of each chapter for review.
  • End-of-chapter bibliographies provide a selection of current, relevant books and articles for further learning.
  • Internet Homework Problems on the companion website offer additional practice.

By leveraging computer applications like Excel® 2016, students can spend less time on the mechanics and more time on interpretation and comprehension

  • Microsoft® Excel 2016 is incorporated throughout the chapters, providing screenshots to make calculations easily accessible to students.
  • The Companion Website, located at www.pearsonhighered.com/render, contains a variety of resources to help students master the material in the course. These include the following:
    • For examples in the text, data files are available in Excel, Excel QM, and POM-QM for Windows. This helps students become familiar with the software, and it helps them understand the input and formulas necessary for working the examples.
    • POM-QM for Windows. Developed by Howard Weiss, this very user-friendly software can be used to solve most of the homework problems in the text.
    • Excel QM. This Excel add-in will automatically create worksheets for solving problems, which is especially helpful for students with limited Excel experience. Students can learn by examining the formulas that have been created and by seeing the inputs that are automatically generated for using the Solver add-in for linear programming.

Brief Contents

  1. Introduction to Quantitative Analysis
  2. Probability Concepts and Applications
  3. Decision Analysis
  4. Regression Models
  5. Forecasting
  6. Inventory Control Models
  7. Linear Programming Models: Graphical and Computer Methods
  8. Linear Programming Applications
  9. Transportation, Assignment, and Network Models
  10. Integer Programming, Goal Programming, and Nonlinear Programming
  11. Project Management
  12. Waiting Lines and Queuing Theory Models
  13. Simulation Modeling
  14. Markov Analysis
  15. Statistical Quality Control

Online Module 1: Analytic Hierarchy Process

Online Module 2: Dynamic Programming

Online Module 3: Decision Theory and the Normal Distribution

Online Module 4: Game Theory

Online Module 5: Mathematical Tools: Determinants and Matrices

Online Module 6: Calculus-Based Optimization

Online Module 7: Linear Programming: The Simplex Method

Online Module 8: Transportation, Assignment, and Network Algorithms

Barry Render is Professor Emeritus, the Charles Harwood Distinguished Professor of Operations Management, Crummer Graduate School of Business, Rollins College, Winter Park, Florida. He received his BS in Mathematics and Physics at Roosevelt University and his MS in Operations Research and his PhD in Quantitative Analysis at the University of Cincinnati. He previously taught at George Washington University, the University of New Orleans, Boston University, and George Mason University, where he held the Mason Foundation Professorship in Decision Sciences and was Chair of the Decision Science Department. Render has also worked in the aerospace industry for General Electric, McDonnell Douglas, and NASA.

Render has coauthored 10 textbooks published by Pearson, including Managerial Decision Modeling with Spreadsheets, Operations Management, Principles of Operations Management, Service Management, Introduction to Management Science, and Cases and Readings in Management Science. More than 100 articles of Dr. Render on a variety of management topics have appeared in Decision Sciences, Production and Operations Management, Interfaces, Information and Management, Journal of Management Information Systems, Socio-Economic Planning Sciences, IIE Solutions, and Operations Management Review, among others.

Render has been honored as an AACSB Fellow and was named twice as a Senior Fulbright Scholar. He was Vice President of the Decision Science Institute Southeast Region and served as software review editor for Decision Line for six years and as Editor of the New York Times Operations Management special issues for five years. From 1984 to 1993, Render was President of Management Service Associates of Virginia, Inc., whose technology clients included the FBI, the US Navy, Fairfax County, Virginia, and C&P Telephone. He is currently Consulting Editor to Financial Times Press.

Render has taught operations management courses at Rollins College for MBA and Executive MBA programs. He has received that school’s Welsh Award as leading professor and was selected by Roosevelt University as the 1996 recipient of the St. Claire Drake Award for Outstanding Scholarship. In 2005, Render received the Rollins College MBA Student Award for Best Overall Course, and in 2009 was named Professor of the Year by full-time MBA students.

Ralph Stair is Professor Emeritus at Florida State University. He earned a BS in chemical engineering from Purdue University and an MBA from Tulane University. Under the guidance of Ken Ramsing and Alan Eliason, he received a PhD in operations management from the University of Oregon. He has taught at the University of Oregon, the University of Washington, the University of New Orleans, and Florida State University.

He has taught twice in Florida State University’s Study Abroad Program in London. Over the years, his teaching has been concentrated in the areas of information systems, operations research, and operations management.

Stair is a member of several academic organizations, including the Decision Sciences Institute and INFORMS, and he regularly participates in national meetings. He has published numerous articles and books, including Managerial Decision Modeling with Spreadsheets, Introduction to Management Science, Cases and Readings in Management Science, Production and Operations Management: A Self-Correction Approach, Fundamentals of Inform

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