Quantitative Analysis for Management

Transcription

1 GLOBAL EDITION Quantitative Analysis for Management TWELFTH EDITION Barry Render Ralph M. Stair, Jr. Michael E. Hanna Trevor S. Hale

2 To my wife and sons BR To Lila and Leslie RMS To Zoe and Gigi MEH To Valerie and Lauren TSH Editor in Chief: Donna Battista Head of Learning Asset Acquisition, Global Edition: Laura Dent Acquisitions Editor: Katie Rowland Senior Acquisitions Editor, Global Edition: Steven Jackson Editorial Project Manager: Mary Kate Murray Editorial Assistant: Elissa Senra-Sargent Director of Marketing: Maggie Moylan Senior Marketing Manager: Anne Fahlgren Marketing Assistant: Gianna Sandri Managing Editor: Jeff Holcomb Senior Production Project Manager: Kathryn Dinovo Manufacturing Buyer/Procurement Specialist: Carol Melville Associate Project Editor, Global Edition: Uttaran Das Gupta Senior Manufacturing Controller, Production, Global Edition: Trudy Kimber Art Director, Cover: Blair Brown Cover Designer: PreMediaGlobal Cover Image: Evlakhov Valeriy/Shutterstock Media Project Manager: Lisa Rinaldi Full-Service Project Management: PreMediaGlobal Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook appear on the appropriate page within text. Pearson Education Limited Edinburgh Gate Harlow Essex CM 20 2JE England and Associated Companies throughout the world Visit us on the World Wide Web at: Pearson Education Limited 2015 The rights of Barry Render, Ralph M. Stair, Jr., Michael E. Hanna, and Trevor S. Hale to be identiied as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act Authorized adaptation from the United States edition, entitled Quantitative Analysis for Management, 12th edition, ISBN , by Barry Render, Ralph M. Stair, Jr., Michael E. Hanna, and Trevor S. Hale, published by Pearson Education All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without either the prior written permission of the publisher or a license permitting restricted copying in the United Kingdom issued by the Copyright Licensing Agency Ltd, Saffron House, 6 10 Kirby Street, London EC1N 8TS. All trademarks used herein are the property of their respective owners. The use of any trademark in this text does not vest in the author or publisher any trademark ownership rights in such trademarks, nor does the use of such trademarks imply any afiliation with or endorsement of this book by such owners. Microsoft and/or its respective suppliers make no representations about the suitability of the information contained in the documents and related graphics published as part of the services for any purpose. All such documents and related graphics are provided as is without warranty of any kind. Microsoft and/or its respective suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties and conditions of merchantability, whether express, implied or statutory, itness for a particular purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or proits, whether in an action of contract, negligence or other tortious action, arising out of or in connection with the use or performance of information available from the services. The documents and related graphics contained herein could include technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Microsoft and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s) described herein at any time. Partial screen shots may be viewed in full within the software version speciied. Microsoft and Windows are registered trademarks of the Microsoft Corporation in the U.S.A. and other countries. This book is not sponsored or endorsed by or afiliated with the Microsoft Corporation. ISBN 10: X ISBN 13: British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Typeset in 10/12 Times Roman by PreMediaGlobal. Printed and bound by Courier Kendallville in The United States of America.

3 Quantitative Analysis for Management, Global Edition Table of Contents Cover Title Contents Preface Chapter 1 Introduction to Quantitative Analysis 1.1 Introduction 1.2 What Is Quantitative Analysis? 1.3 Business Analytics 1.4 The Quantitative Analysis Approach Defining the Problem Developing a Model Acquiring Input Data Developing a Solution Testing the Solution Analyzing the Results and Sensitivity Analysis Implementing the Results The Quantitative Analysis Approachand Modeling in the Real World 1.5 How to Develop a Quantitative AnalysisModel The Advantages of Mathematical Modeling Mathematical Models Categorized by Risk 1.6 The Role of Computers and Spreadsheet Models in the Quantitative Analysis Approach 1.7 Possible Problems in the Quantitative Analysis Approach Defining the Problem Developing a Model Acquiring Input Data Developing a Solution Testing the Solution Analyzing the Results 1.8 ImplementationNot Just the Final Step Lack of Commitment and Resistance to Change Lack of Commitment by Quantitative Analysts

4 Case Study: Food and Beverages at Southwestern University Football Games Chapter 2 Probability Concepts and Applications 2.1 Introduction 2.2 Fundamental Concepts Two Basic Rules of Probability Types of Probability Mutually Exclusive and Collectively Exhaustive Events Unions and Intersections of Events Probability Rules for Unions, Intersections, and Conditional Probabilities 2.3 Revising Probabilities with Bayes Theorem General Form of Bayes Theorem 2.4 Further Probability Revisions 2.5 Random Variables 2.6 Probability Distributions Probability Distribution of a Discrete Random Variable Expected Value of a Discrete Probability Distribution Variance of a Discrete Probability Distribution Probability Distribution of a Continuous Random Variable 2.7 The Binomial Distribution Solving Problems with the Binomial Formula Solving Problems with Binomial Tables 2.8 The Normal Distribution Area Under the Normal Curve Using the Standard Normal Table Haynes Construction Company Example The Empirical Rule 2.9 The F Distribution 2.10 The Exponential Distribution Arnolds Muffler Example 2.11 The Poisson Distribution Case Study: WTVX Appendix 2.1: Derivation of Bayes Theorem

5 Chapter 3 Decision Analysis 3.1 Introduction 3.2 The Six Steps in Decision Making 3.3 Types of Decision-Making Environments 3.4 Decision Making Under Uncertainty Table of Contents Optimistic Pessimistic Criterion of Realism (Hurwicz Criterion) Equally Likely (Laplace) Minimax Regret 3.5 Decision Making Under Risk Expected Monetary Value Expected Value of Perfect Information Expected Opportunity Loss Sensitivity Analysis 3.6 A Minimization Example 3.7 Using Software for Payoff Table Problems QM for Windows Excel QM 3.8 Decision Trees Efficiency of Sample Information Sensitivity Analysis 3.9 How Probability Values Are Estimated by Bayesian Analysis Calculating Revised Probabilities Potential Problem in Using Survey Results 3.10 Utility Theory Measuring Utility and Constructing a Utility Curve Utility as a Decision-Making Criterion Case Study: Starting Right Corporation Case Study: Blake Electronics Chapter 4 Regression Models 4.1 Introduction 4.2 Scatter Diagrams

6 4.3 Simple Linear Regression 4.4 Measuring the Fit of the Regression Model Coefficient of Determination Correlation Coefficient 4.5 Assumptions of the Regression Model Estimating the Variance 4.6 Testing the Model for Significance Triple A Construction Example The Analysis of Variance (ANOVA) Table Triple A Construction ANOVA Example 4.7 Using Computer Software for Regression Excel 2013 Excel QM QM for Windows 4.8 Multiple Regression Analysis Evaluating the Multiple Regression Model Jenny Wilson Realty Example 4.9 Binary or Dummy Variables 4.10 Model Building Stepwise Regression Multicollinearity 4.11 Nonlinear Regression 4.12 Cautions and Pitfalls in Regression Analysis Case Study: NorthSouth Airline Appendix 4.1: Formulas for Regression Calculations Chapter 5 Forecasting 5.1 Introduction 5.2 Types of Forecasting Models Qualitative Models Causal Models Time-Series Models 5.3 Components of a Time-Series 5.4 Measures of Forecast Accuracy

7 5.5 Forecasting ModelsRandom Variations Only Moving Averages Weighted Moving Averages Exponential Smoothing Using Software for Forecasting Time Series 5.6 Forecasting ModelsTrend and Random Variations Exponential Smoothing with Trend Trend Projections 5.7 Adjusting for Seasonal Variations Seasonal Indices Calculating Seasonal Indices with No Trend Calculating Seasonal Indices with Trend 5.8 Forecasting ModelsTrend, Seasonal, and Random Variations The Decomposition Method Software for Decomposition Using Regression with Trend and Seasonal Components 5.9 Monitoring and Controlling Forecasts Adaptive Smoothing Case Study: Forecasting Attendance at SWU Football Games Case Study: Forecasting Monthly Sales Chapter 6 Inventory Control Models 6.1 Introduction 6.2 Importance of Inventory Control Decoupling Function Storing Resources Irregular Supply and Demand Quantity Discounts Avoiding Stockouts and Shortages 6.3 Inventory Decisions 6.4 Economic Order Quantity: Determining How Much to Order Inventory Costs in the EOQ Situation Finding the EOQ Sumco Pump Company Example Purchase Cost of Inventory Items

8 Sensitivity Analysis with the EOQ Model 6.5 Reorder Point: Determining When to Order 6.6 EOQ Without the Instantaneous Receipt Assumption Annual Carrying Cost for Production Run Model Annual Setup Cost or Annual Ordering Cost Determining the Optimal Production Quantity Brown Manufacturing Example 6.7 Quantity Discount Models Brass Department Store Example 6.8 Use of Safety Stock 6.9 Single-Period Inventory Models Marginal Analysis with Discrete Distributions Café du Donut Example Marginal Analysis with the Normal Distribution Newspaper Example 6.10 ABC Analysis 6.11 Dependent Demand: The Case for Material Requirements Planning Material Structure Tree Gross and Net Material Requirements Plan Two or More End Products 6.12 Just-In-Time Inventory Control 6.13 Enterprise Resource Planning Case Study: Martin-Pullin Bicycle Corporation Appendix 6.1: Inventory Control with QM for Windows Chapter 7 Linear Programming Models: Graphical and Computer Methods 7.1 Introduction 7.2 Requirements of a Linear Programming Problem 7.3 Formulating LP Problems Flair Furniture Company 7.4 Graphical Solution to an LP Problem Graphical Representation of Constraints Isoprofit Line Solution Method Corner Point Solution Method

9 Slack and Surplus 7.5 Solving Flair Furnitures LP Problem Using QM for Windows, Excel 2013, and Excel QM Using QM for Windows Using Excels Solver Command to Solve LP Problems Using Excel QM 7.6 Solving Minimization Problems Holiday Meal Turkey Ranch 7.7 Four Special Cases in LP No Feasible Solution Unboundedness Redundancy Alternate Optimal Solutions 7.8 Sensitivity Analysis High Note Sound Company Changes in the Objective Function Coefficient QM for Windows and Changes in Objective Function Coefficients Excel Solver and Changes in Objective Function Coefficients Changes in the Technological Coefficients Changes in the Resources or Right-Hand-SideValues QM for Windows and Changes in Right-Hand-Side Values Excel Solver and Changes in Right-Hand-SideValues Case Study: Mexicana Wire Works Chapter 8 Linear Programming Applications 8.1 Introduction 8.2 Marketing Applications Media Selection Marketing Research 8.3 Manufacturing Applications Production Mix Production Scheduling 8.4 Employee Scheduling Applications Labor Planning 8.5 Financial Applications

10 Portfolio Selection Truck Loading Problem 8.6 Ingredient Blending Applications Diet Problems Ingredient Mix and Blending Problems 8.7 Other Linear Programming Applications Problems Case Study: Cable & Moore Chapter 9 Transportation, Assignment, and Network Models 9.1 Introduction 9.2 The Transportation Problem Linear Program for the Transportation Example Solving Transportation Problems Using Computer Software A General LP Model fortransportation Problems Facility Location Analysis 9.3 The Assignment Problem Linear Program for Assignment Example 9.4 The Transshipment Problem Linear Program for Transshipment Example 9.5 Maximal-Flow Problem Example 9.6 Shortest-Route Problem 9.7 Minimal-Spanning Tree Problem Case Study: AndrewCarter, Inc. Case Study: Northeastern Airlines Case Study: Southwestern University Traffic Problems Appendix 9.1: Using QM for Windows Chapter 10 Integer Programming, Goal Programming,and Nonlinear Programming 10.1 Introduction 10.2 Integer Programming Harrison Electric Company Example of Integer Programming

11 Using Software to Solve the Harrison Integer Programming Problem Mixed-Integer Programming Problem Example 10.3 Modeling with 01 (Binary) Variables Capital Budgeting Example Limiting the Number of Alternatives Selected Dependent Selections Fixed-Charge Problem Example Financial Investment Example 10.4 Goal Programming Example of Goal Programming: Harrison Electric Company Revisited Extension to Equally Important Multiple Goals Ranking Goals with Priority Levels Goal Programming with Weighted Goals 10.5 Nonlinear Programming Nonlinear Objective Function and Linear Constraints Both Nonlinear Objective Function and Nonlinear Constraints Linear Objective Function with Nonlinear Constraints Case Study: Schank Marketing Research Case Study: Oakton River Bridge Chapter 11 Project Management 11.1 Introduction 11.2 PERT/CPM General Foundry Example of PERT/CPM Drawing the PERT/CPM Network Activity Times How to Find the Critical Path Probability of Project Completion What PERT Was Able to Provide Using Excel QM for the General Foundry Example Sensitivity Analysis and Project Management 11.3 PERT/Cost Planning and Scheduling Project Costs:Budgeting Process Monitoring and Controlling Project Costs 11.4 Project Crashing General Foundary Example

12 Project Crashing with Linear Programming 11.5 Other Topics in Project Management Subprojects Milestones Resource Leveling Software Case Study: Southwestern University Stadium Construction Case Study: Family Planning Research Center of Nigeria Appendix 11.1: Project Management with QM for Windows Chapter 12 Waiting Lines and Queuing Theory Models 12.1 Introduction 12.2 Waiting Line Costs Three Rivers Shipping Company Example 12.3 Characteristics of a Queuing System Arrival Characteristics Waiting Line Characteristics Service Facility Characteristics Identifying Models Using Kendall Notation 12.4 Single-Channel Queuing Model with PoissonArrivals and Exponential Service Times(M/M/1) Assumptions of the Model Queuing Equations Arnolds Muffler Shop Case Enhancing the Queuing Environment 12.5 Multichannel Queuing Model with Poisson Arrivals and Exponential Service Times(M/M/m) Equations for the Multichannel Queuing Model Arnolds Muffler Shop Revisited 12.6 Constant Service Time Model (M/D/1) Equations for the Constant Service Time Model Garcia-Golding Recycling, Inc Finite Population Model (M/M/1 with Finite Source) Equations for the Finite Population Model Department of Commerce Example

13 12.8 Some General Operating Characteristic Relationships 12.9 More Complex Queuing Models and the Use of Simulation Case Study: New England Foundry Case Study: Winter Park Hotel Appendix 12.1: Using QM for Windows Chapter 13 Simulation Modeling 13.1 Introduction 13.2 Advantages and Disadvantages of Simulation 13.3 Monte Carlo Simulation Harrys Auto Tire Example Using QM for Windows for Simulation Simulation with Excel Spreadsheets 13.4 Simulation and Inventory Analysis Simkins Hardware Store Analyzing Simkins Inventory Costs 13.5 Simulation of a Queuing Problem Port of New Orleans Using Excel to Simulate the Port of New Orleans Queuing Problem 13.6 Simulation Model for a Maintenance Policy Three Hills Power Company Cost Analysis of the Simulation 13.7 Other Simulation Issues Two Other Types of Simulation Models Verification and Validation Role of Computers in Simulation Case Study: Alabama Airlines Case Study: Statewide Development Corporation Case Study: FB Badpoore Aerospace

14 Chapter 14 Markov Analysis 14.1 Introduction 14.2 States and State Probabilities Table of Contents The Vector of State Probabilities for Three Grocery Stores Example 14.3 Matrix of Transition Probabilities Transition Probabilities for the Three Grocery Stores 14.4 Predicting Future Market Shares 14.5 Markov Analysis of Machine Operations 14.6 Equilibrium Conditions 14.7 Absorbing States and the Fundamental Matrix: Accounts Receivable Application Case Study: Rentall Trucks Appendix 14.1: Markov Analysis with QM for Windows Appendix 14.2: Markov Analysis With Excel Chapter 15 Statistical Quality Control 15.1 Introduction 15.2 Defining Quality and TQM 15.3 Statiscal Process Control Variability in the Process 15.4 Control Charts for Variables The Central Limit Theorem Setting x-chart Limits Setting Range Chart Limits 15.5 Control Charts for Attributes p-charts c-charts

15 Appendix 15.1: Using QM for Windows for SPC Appendices Appendix A Areas Under the Standard Normal Curve Appendix B Binomial Probabilities Appendix c Values of e-l for Use in the Poisson Distribution Appendix D F Distribution Values Appendix E Using POM-QM for Windows Appendix F Using Excel QM and Excel Add-Ins Appendix G Solutions to Selected Problems Appendix H Solutions to s Index A B C D E F G H I J K L M N O P Q R S T U V W X Z Online Modules

16 Module 1 Analytic Hierarchy Process M1.1 Introduction M1.2 Multifactor Evaluation Process M1.3 Analytic Hierarchy Process Table of Contents Judy Grims Computer Decision Using Pairwise Comparisons Evaluations for Hardware Determining the Consistency Ratio Evaluations for the Other Factors Determining Factor Weights Overall Ranking Using the Computer to Solve Analytic Hierarchy Process Problems M1.4 Comparison of Multifactor Evaluation and Analytic Hierarchy Processes Appendix M1.1: Using Excel for the Analytic Hierarchy Process Module 2 Dynamic Programming M2.1 Introduction M2.2 Shortest-Route Problem Solved Using Dynamic Programming M2.3 Dynamic Programming Terminology M2.4 Dynamic Programming Notation M2.5 Knapsack Problem Types of Knapsack Problems Rollers Air Transport Service Problem Solved Problem Case Study:United Trucking Internet Case Study Module 3 Decision Theory and the Normal Distribution M3.1 Introduction M3.2 Break-Even Analysis and the Normal Distribution Barclay Brothers Companys New Product Decision Probability Distribution of Demand Using Expected Monetary Value to Make adecision M3.3 Expected Value of Perfect Information and the Normal Distribution

17 Opportunity Loss Function Expected Opportunity Loss Appendix M3.1: Derivation of the Break-Even Point Appendix M3.2: Unit Normal Loss Integral Module 4 Game Theory M4.1 Introduction M4.2 Language of Games M4.3 The Minimax Criterion M4.4 Pure Strategy Games M4.5 Mixed Strategy Games M4.6 Dominance Module 5 Mathematical Tools: Determinants and Matrices M5.1 Introduction M5.2 Matrices and Matrix Operations Matrix Addition and Subtraction Matrix Multiplication Matrix Notation for Systems of Equations Matrix Transpose M5.3 Determinants, Cofactors, and Adjoints Determinants Matrix of Cofactors and Adjoint M5.4 Finding the Inverse of a Matrix Appendix M5.1: Using Excel for Matrix Calculations Module 6 Calculus-Based Optimization

18 M6.1 Introduction M6.2 Slope of a Straight Line M6.3 Slope of a Nonlinear Function M6.4 Some Common Derivatives Second Derivatives M6.5 Maximum and Minimum M6.6 Applications Economic Order Quantity Total Revenue Solved Problem Module 7 Linear Programming: The Simplex Method M7.1 Introduction M7.2 How to Set Up the Initial Simplex Solution Converting the Constraints to Equations Finding an Initial Solution Algebraically The First Simplex Tableau M7.3 Simplex Solution Procedures M7.4 The Second Simplex Tableau Interpreting the Second Tableau M7.5 Developing the Third Tableau M7.6 Review of Procedures for Solving LP Maximization Problems M7.7 Surplus and Artificial Variables Surplus Variables Artificial Variables Surplus and Artificial Variables in the Objective Function M7.8 Solving Minimization Problems The Muddy River Chemical CompanyExample Graphical Analysis Converting the Constraints and Objective Function Rules of the Simplex Method for Minimization Problems First Simplex Tableau for the Muddy RiverChemical Corporation Problem Developing a Second Tableau Developing a Third Tableau Fourth Tableau for the Muddy River Chemical Corporation Problem M7.9 Review of Procedures for Solving LP Minimization Problems M7.10 Special Cases Infeasibility Unbounded Solutions Degeneracy

19 More Than One Optimal Solution M7.11 Sensitivity Analysis with the Simplex Tableau High Note Sound Company Revisited Changes in the Objective Function Coefficients Changes in Resources or RHS Values M7.12 The Dual Dual Formulation Procedures Solving the Dual of the High Note Sound Company Problem M7.13 Karmarkars Algorithm Key Equation Module 8 Transportation, Assignment, and Network Algorithms M8.1 Introduction M8.2 The Transportation Algorithm Developing an Initial Solution: Northwest Corner Rule Stepping-Stone Method: Finding a Least-Cost Solution M8.3 Special Situations with the Transportation Algorithm Unbalanced Transportation Problems Degeneracy in Transportation Problems More Than One Optimal Solution Maximization Transportation Problems Unacceptable or Prohibited Routes Other Transportation Methods M8.4 The Assignment Algorithm The Hungarian Method (FloodsTechnique) Making the Final Assignment M8.5 Special Situations with the AssignmentAlgorithm Unbalanced Assignment Problems Maximization Assignment Problems M8.6 Maximal-Flow Problem Maximal-Flow Technique M8.7 Shortest-Route Problem Shortest-Route Technique Cases