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• Title Page
• Copyright
• Pearson’s Commitment to Diversity, Equity, and Inclusion
• Brief Contents
• Contents
• Preface
• About the Authors
• Chapter 1. Introduction to Quantitative Analysis
• 1.1 What Is Quantitative Analysis?
• 1.2 Business Analytics
• 1.3 The Quantitative Analysis Approach
• Defining the Problem
• Developing a Model
• Acquiring Input Data
• Developing a Solution
• Testing the Solution
• Analyzing the Results and Conducting Sensitivity Analysis
• Implementing the Results
• The Quantitative Analysis Approach and Modeling in the Real World
• 1.4 How to Develop a Quantitative Analysis Model
• The Advantages of Mathematical Modeling
• Mathematical Models Categorized by Risk
• 1.5 The Role of Computers and Spreadsheet Models in the Quantitative Analysis Approach
• 1.6 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
• 1.7 Implementation—Not Just the Final Step
• Lack of Commitment and Resistance to Change
• Lack of Commitment by Quantitative Analysts
• 1.8 Developing Skills for Your Career
• Summary
• Glossary
• Key Equations
• Self-Test
• Discussion Questions and Problems
• Case Study: Food and Beverages at Southwestern University Football Games
• Case Study: The Alset Electric Car
• Notes
• Bibliography
• Chapter 2. Probability Concepts and Applications
• 2.1 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
• 2.2 Revising Probabilities with Bayes’ Theorem
• General Form of Bayes’ Theorem
• 2.3 Further Probability Revisions
• 2.4 Random Variables
• 2.5 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
• 2.6 The Binomial Distribution
• Solving Problems with the Binomial Formula
• Solving Problems with Binomial Tables
• 2.7 The Normal Distribution
• Area under the Normal Curve
• Using the Standard Normal Table
• Haynes Construction Company Example
• The Empirical Rule
• 2.8 The F Distribution
• 2.9 The Exponential Distribution
• Arnold’s Muffler Example
• 2.10 The Poisson Distribution
• Summary
• Glossary
• Key Equations
• Solved Problems
• Self-Test
• Discussion Questions and Problems
• Case Study: WTVX
• Case Study: OzDe Distributing
• Notes
• Bibliography
• Appendix 2.1: Derivation of Bayes’ Theorem
• Chapter 3. Decision Analysis
• 3.1 The Six Steps in Decision Making
• 3.2 Types of Decision-Making Environments
• 3.3 Decision Making under Uncertainty
• Optimistic
• Pessimistic
• Criterion of Realism (Hurwicz Criterion)
• Equally Likely (Laplace Criterion)
• Minimax Regret
• 3.4 Decision Making under Risk
• Expected Monetary Value
• Expected Value of Perfect Information
• Expected Opportunity Loss
• Sensitivity Analysis
• A Minimization Example
• 3.5 Using Software for Payoff Table Problems
• QM for Windows
• Excel QM
• 3.6 Decision Trees
• Efficiency of Sample Information 103 Sensitivity Analysis
• 3.7 How Probability Values Are Estimated by Bayesian Analysis
• Calculating Revised Probabilities
• Potential Problem in Using Survey Results
• 3.8 Utility Theory
• Measuring Utility and Constructing a Utility Curve
• Utility as a Decision-Making Criterion
• Summary
• Glossary
• Key Equations
• Solved Problems
• Self-Test
• Discussion Questions and Problems
• Case Study: Starting Right Corporation
• Case Study: Toledo Leather Company
• Case Study: Blake Electronics
• Notes
• Bibliography
• Chapter 4. Regression Models
• 4.1 Scatter Diagrams
• 4.2 Simple Linear Regression
• 4.3 Measuring the Fit of the Regression Model
• Coefficient of Determination
• Correlation Coefficient
• 4.4 Assumptions of the Regression Model
• Estimating the Variance
• 4.5 Testing the Model for Significance
• Triple A Construction Example
• The Analysis of Variance (ANOVA) Table
• Triple A Construction ANOVA Example
• 4.6 Using Computer Software for Regression
• Excel
• Excel QM
• QM for Windows
• 4.7 Multiple Regression Analysis
• Evaluating the Multiple Regression Model
• Jenny Wilson Realty Example
• 4.8 Binary or Dummy Variables
• 4.9 Model Building
• Stepwise Regression
• Multicollinearity
• 4.10 Nonlinear Regression
• 4.11 Cautions and Pitfalls in Regression Analysis
• Summary
• Glossary
• Key Equations
• Solved Problems
• Self-Test
• Discussion Questions and Problems
• Case Study: Brobeson Technologies
• Case Study: North–South Airline
• Case Study: Mostashari Food Distribution LLP
• Notes
• Bibliography
• Appendix 4.1: Formulas for Regression Calculations
• Chapter 5. Forecasting
• 5.1 Types of Forecasting Models
• Qualitative Models
• Causal Models
• Time-Series Models
• 5.2 Components of a Time Series
• 5.3 Measures of Forecast Accuracy
• 5.4 Forecasting Models—Random Variations Only
• Moving Averages
• Weighted Moving Averages
• Exponential Smoothing
• Using Software for Forecasting Time Series
• 5.5 Forecasting Models—Trend and Random Variations
• Exponential Smoothing with Trend
• Trend Projections
• 5.6 Adjusting for Seasonal Variations
• Seasonal Indices
• Calculating Seasonal Indices with No Trend
• Calculating Seasonal Indices with Trend
• 5.7 Forecasting Models—Trend, Seasonal, and Random Variations
• The Decomposition Method
• Software for Decomposition
• Using Regression with Trend and Seasonal Components
• 5.8 Monitoring and Controlling Forecasts
• Adaptive Smoothing
• Summary
• Glossary
• Key Equations
• Solved Problems
• Self-Test
• Discussion Questions and Problems
• Case Study: Forecasting Attendance at SWU Football Games
• Case Study: JVB Electric Company
• Case Study: Forecasting Monthly Sales
• Notes
• Bibliography
• Chapter 6. Inventory Control Models
• 6.1 Importance of Inventory Control
• Decoupling Function
• Storing Resources
• Irregular Supply and Demand
• Quantity Discounts
• Avoiding Stockouts and Shortages
• 6.2 Inventory Decisions
• 6.3 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
• Sensitivity Analysis with the EOQ Model
• 6.4 Reorder Point: Determining When to Order
• 6.5 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
• 6.6 Quantity Discount Models
• Brass Department Store Example
• 6.7 Use of Safety Stock
• 6.8 Single-Period Inventory Models
• Marginal Analysis with Discrete Distributions
• Café du Donut Example
• Marginal Analysis with the Normal Distribution
• Newspaper Example
• 6.9 ABC Analysis
• 6.10 Dependent Demand: The Case for Material Requirements Planning
• Material Structure Tree
• Gross and Net Material Requirements Plans
• Two or More End Products
• 6.11 Just-in-Time Inventory Control
• 6.12 Enterprise Resource Planning
• Summary
• Glossary
• Key Equations
• Solved Problems
• Self-Test
• Discussion Questions and Problems
• Case Study: EOM Enterprises
• Case Study: ADO Cycles
• Case Study: The Pot Company
• Notes
• Bibliography
• Appendix 6.1: Inventory Control with QM for Windows
• Chapter 7. Linear Programming Models: Graphical and Computer Methods
• 7.1 Requirements of a Linear Programming Problem
• 7.2 Formulating LP Problems
• Flair Furniture Company
• 7.3 Graphical Solution to an LP Problem
• Graphical Representation of Constraints
• Isoprofit Line Solution Method
• Corner Point Solution Method
• Slack and Surplus
• 7.4 Solving Flair Furniture’s LP Problem Using QM for Windows, Excel, and Excel QM
• Using QM for Windows
• Using Excel’s Solver Command to Solve LP Problems
• Using Excel QM
• 7.5 Solving Minimization Problems
• Holiday Meal Turkey Ranch
• 7.6 Four Special Cases in LP
• No Feasible Solution
• Unboundedness
• Redundancy
• Alternate Optimal Solutions
• 7.7 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-Side Values
• QM for Windows and Changes in Right-Hand-Side Values
• Excel Solver and Changes in Right-Hand-Side Values
• Summary
• Glossary
• Solved Problems
• Self-Test
• Discussion Questions and Problems
• Case Study: The Traveling Salesman Problem: An Introduction
• Case Study: Mexicana Wire Winding, Inc.
• Notes
• Bibliography
• Chapter 8. Linear Programming Applications
• 8.1 Marketing Applications
• Media Selection
• Marketing Research
• 8.2 Manufacturing Applications
• Production Mix
• Production Scheduling
• 8.3 Employee Scheduling Applications
• Labor Planning
• 8.4 Financial Applications
• Portfolio Selection
• Truck Loading Problem
• 8.5 Ingredient Blending Applications
• Diet Problems
• Ingredient Mix and Blending Problems
• 8.6 Other Linear Programming Applications
• Summary
• Self-Test
• Problems
• Case Study: Fleet Scheduling at Swearingen Trucking
• Case Study: Cable & Moore
• Notes
• Bibliography
• Chapter 9. Transportation, Assignment, and Network Models
• 9.1 The Transportation Problem
• Linear Program for the Transportation Example
• Solving Transportation Problems Using Computer Software
• A General LP Model for Transportation Problems
• Facility Location Analysis
• 9.2 The Assignment Problem
• Linear Program for Assignment Example
• 9.3 The Transshipment Problem
• Linear Program for Transshipment Example
• 9.4 Maximal-Flow Problem
• Example
• 9.5 Shortest-Route Problem
• 9.6 Minimal-Spanning Tree Problem
• Summary
• Glossary
• Solved Problems
• Self-Test
• Discussion Questions and Problems
• Case Study: Revenue Management: Theater Tickets
• Case Study: H&C Hot Sauce
• Case Study: Northeastern Airlines
• Case Study: Southwestern University Traffic Problems
• Notes
• Bibliography
• Appendix 9.1: Using QM for Windows
• Chapter 10. Integer Programming, Goal Programming, and Nonlinear Programming
• 10.1 Integer Programming
• Harrison Electric Company Example of Integer Programming
• Using Software to Solve the Harrison Integer Programming Problem
• Mixed-Integer Programming Problem Example
• 10.2 Modeling with 0–1 (Binary) Variables
• Capital Budgeting Example
• Limiting the Number of Alternatives Selected
• Dependent Selections
• Fixed-Charge Problem Example
• Financial Investment Example
• 10.3 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
• 10.4 Nonlinear Programming
• Nonlinear Objective Function and Linear Constraints
• Both Nonlinear Objective Function and Nonlinear Constraints
• Linear Objective Function with Nonlinear Constraints
• Summary
• Glossary
• Solved Problems
• Self-Test
• Discussion Questions and Problems
• Case Study: Schank Marketing Research
• Case Study: Oakton River Bridge
• Notes
• Bibliography
• Chapter 11. Project Management
• 11.1 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
• 11.2 PERT/Cost
• Planning and Scheduling Project Costs: Budgeting Process
• Monitoring and Controlling Project Costs
• 11.3 Project Crashing
• General Foundry Example
• Project Crashing with Linear Programming
• 11.4 Other Topics in Project Management
• Subprojects
• Milestones
• Resource Leveling
• Software
• Summary
• Glossary
• Key Equations
• Solved Problems
• Self-Test
• Discussion Questions and Problems
• Case Study: Fiore Construction Company
• Case Study: Southwestern University Stadium Construction
• Case Study: Family Planning Research Center of Nigeria
• Notes
• Bibliography
• Appendix 11.1: Project Management with QM for Windows
• Chapter 12. Waiting Lines and Queuing Theory Models
• 12.1 Waiting-Line Costs
• Three Rivers Shipping Company Example
• 12.2 Characteristics of a Queuing System
• Arrival Characteristics
• Waiting-Line Characteristics
• Service Facility Characteristics
• Identifying Models Using Kendall Notation
• 12.3 Single-Channel Queuing Model with Poisson Arrivals and Exponential Service Times (M/M/1)
• Assumptions of the Model
• Queuing Equations
• Arnold’s Muffler Shop Case
• Enhancing the Queuing Environment
• 12.4 Multichannel Queuing Model with Poisson Arrivals and Exponential Service Times (M/M/m)
• Equations for the Multichannel Queuing Model
• Arnold’s Muffler Shop Revisited
• 12.5 Constant Service Time Model (M/D/1)
• Equations for the Constant Service Time Model
• Garcia-Golding Recycling, Inc.
• 12.6 Finite Population Model (M/M/1 with Finite Source)
• Equations for the Finite Population Model
• Department of Commerce Example
• 12.7 Some General Operating Characteristic Relationships
• 12.8 More Complex Queuing Models and the Use of Simulation
• Summary
• Glossary
• Key Equations
• Solved Problems
• Self-Test
• Discussion Questions and Problems
• Case Study: New England Foundry
• Case Study: Winter Park Hotel
• Notes
• Bibliography
• Appendix 12.1: Using QM for Windows
• Chapter 13. Simulation Modeling
• 13.1 Advantages and Disadvantages of Simulation
• 13.2 Monte Carlo Simulation
• Harry’s Auto Tire Example
• Using QM for Windows for Simulation
• Simulation with Excel Spreadsheets
• 13.3 Simulation and Inventory Analysis
• Simkin’s Hardware Store
• Analyzing Simkin’s Inventory Costs
• 13.4 Simulation of a Queuing Problem
• Port of New Orleans
• Using Excel to Simulate the Port of New Orleans Queuing Problem
• 13.5 Simulation Model for a Maintenance Policy
• Three Hills Power Company
• Cost Analysis of the Simulation
• 13.6 Other Simulation Issues
• Two Other Types of Simulation Models
• Verification and Validation
• Role of Computers in Simulation
• Summary
• Glossary
• Solved Problems
• Self-Test
• Discussion Questions and Problems
• Case Study: Alabama Airlines
• Case Study: Statewide Development Corporation
• Case Study: Beltway 8 Toll Road in Houston, Texas
• Case Study: FB Badpoore Aerospace
• Notes
• Bibliography
• Chapter 14. Markov Analysis
• 14.1 States and State Probabilities
• The Vector of State Probabilities for the Grocery Store Example
• 14.2 Matrix of Transition Probabilities
• Transition Probabilities for Grocery Store Example
• 14.3 Predicting Future Market Shares
• 14.4 Markov Analysis of Machine Operations
• 14.5 Equilibrium Conditions
• 14.6 Absorbing States and the Fundamental Matrix: Accounts Receivable Application
• Summary
• Glossary
• Key Equations
• Solved Problems
• Self-Test
• Discussion Questions and Problems
• Case Study: Rentall Trucks
• Notes
• Bibliography
• Appendix 14.1: Markov Analysis with QM for Windows
• Appendix 14.2: Markov Analysis with Excel
• Chapter 15. Statistical Quality Control
• 15.1 Defining Quality and TQM
• 15.2 Statistical Process Control
• Variability in the Process
• 15.3 Control Charts for Variables
• The Central Limit Theorem
• Setting -x-Chart Limits
• Setting Range Chart Limits
• 15.4 Control Charts for Attributes
• p-Charts
• c-Charts
• Summary
• Glossary
• Key Equations
• Solved Problems
• Self-Test
• Discussion Questions and Problems
• Notes
• Bibliography
• 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−λ for Use in the Poisson Distribution 587
• Appendix D: F Distribution Values
• Appendix E: Using QM for Windows
• Appendix F: Using Excel QM and Excel Add-Ins
• Appendix G: Solutions to Selected Problems
• Appendix H: Solutions to Self-Tests
• Index
• A
• B
• C
• D
• E
• F
• G
• H
• I
• J
• K
• L
• M
• N
• O
• P
• Q
• R
• S
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• Y
• Z