Quantitative analysis for management

Cover
Title Page
Copyright Page
About the Authors
Brief Contents
Contents
Preface
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 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
	Summary
	Glossary
	Key Equations
	Self-Test
	Discussion Questions and Problems
	Case Study: Food and Beverages at Southwestern University Football Games
	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
	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)
		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
		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
	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 2016
		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: North–South Airline
	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: Forecasting Monthly Sales
	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: Martin-Pullin Bicycle Corporation
	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 2016, 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: Mexicana Wire Winding, Inc.
	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: Cable & Moore
	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: Andrew–Carter, Inc.
	Case Study: Northeastern Airlines
	Case Study: Southwestern University Traffic Problems
	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
	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: Southwestern University Stadium Construction
	Case Study: Family Planning Research Center of Nigeria
	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
	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: FB Badpoore Aerospace
	Bibliography
Chapter 14: Markov Analysis
	14.1. States and State Probabilities
		The Vector of State Probabilities for 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
	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
	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 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 Self-Tests
Index
Module 1: Analytic Hierarchy Process
	M1.1. Multifactor Evaluation Process
	M1.2. Analytic Hierarchy Process
		Judy Grim’s 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
	M1.3. Comparison of Multifactor Evaluation and Analytic Hierarchy Processes
	Summary
	Glossary
	Key Equations
	Solved Problems
	Self-Test
	Discussion Questions and Problems
	Bibliography
	Appendix M1.1: Using Excel for the Analytic Hierarchy Process
Module 2: Dynamic Programming
	M2.1. Shortest-Route Problem Solved Using Dynamic Programming
	M2.2. Dynamic Programming Terminology
	M2.3. Dynamic Programming Notation
	M2.4. Knapsack Problem
		Types of Knapsack Problems
		Roller’s Air Transport Service Problem
	Summary
	Glossary
	Key Equations
	Solved Problem
	Self-Test
	Discussion Questions and Problems
	Case Study: United Trucking
	Bibliography
Module 3: Decision Theory and the Normal Distribution
	M3.1. Break-Even Analysis and the Normal Distribution
		Barclay Brothers Company’s New Product Decision
		Probability Distribution of Demand
		Using Expected Monetary Value to Make a Decision
	M3.2. Expected Value of Perfect Information and the Normal Distribution
		Opportunity Loss Function
		Expected Opportunity Loss
	Summary
	Glossary
	Key Equations
	Solved Problems
	Self-Test
	Discussion Questions and Problems
	Bibliography
	Appendix M3.1: Derivation of the Break-Even Point
	Appendix M3.2: Unit Normal Loss Integral
Module 4: Game Theory
	M4.1. Language of Games
	M4.2. The Minimax Criterion
	M4.3. Pure Strategy Games
	M4.4. Mixed Strategy Games
	M4.5. Dominance
	Summary
	Glossary
	Solved Problems
	Self-Test
	Discussion Questions and Problems
	Bibliography
Module 5: Mathematical Tools: Determinants and Matrices
	M5.1. Matrices and Matrix Operations
		Matrix Addition and Subtraction
		Matrix Multiplication
		Matrix Notation for Systems of Equations
		Matrix Transpose
	M5.2. Determinants, Cofactors, and Adjoints
		Determinants
		Matrix of Cofactors and Adjoint
	M5.3. Finding the Inverse of a Matrix
	Summary
	Glossary
	Key Equations
	Self-Test
	Discussion Questions and Problems
	Bibliography
	Appendix M5.1: Using Excel for Matrix Calculations
Module 6: Calculus-Based Optimization
	M6.1. Slope of a Straight Line
	M6.2. Slope of a Nonlinear Function
	M6.3. Some Common Derivatives
		Second Derivatives
	M6.4. Maximum and Minimum
	M6.5. Applications
		Economic Order Quantity
		Total Revenue
	Summary
	Glossary
	Key Equations
	Solved Problems
	Self-Test
	Discussion Questions and Problems
	Bibliography
Module 7: Linear Programming: The Simplex Method
	M7.1. How to Set Up the Initial Simplex Solution
		Converting the Constraints to Equations
		Finding an Initial Solution Algebraically
		The First Simplex Tableau
	M7.2. Simplex Solution Procedures
		The Second Simplex Tableau
		Interpreting the Second Tableau
		The Third Simplex Tableau
		Review of Procedures for Solving LP Maximization Problems
	M7.3. Surplus and Artificial Variables
		Surplus Variables
		Artificial Variables
		Surplus and Artificial Variables in the Objective Function
	M7.4. Solving Minimization Problems
		The Muddy River Chemical Corporation Example
		Graphical Analysis
		Converting the Constraints and Objective Function
		Rules of the Simplex Method for Minimization Problems
		First Simplex Tableau for the Muddy River Chemical Corporation Problem
		Developing a Second Tableau
		Developing a Third Tableau
		Fourth Tableau for the Muddy River Chemical Corporation Problem
		Review of Procedures for Solving LP Minimization Problems
	M7.5. Special Cases
		Infeasibility
		Unbounded Solutions
		Degeneracy
		More Than One Optimal Solution
	M7.6. Sensitivity Analysis with the Simplex Tableau
		High Note Sound Company Revisited
		Changes in the Objective Function Coefficients
		Changes in Resources or RHS Values
	M7.7. The Dual
		Dual Formulation Procedures
		Solving the Dual of the High Note Sound Company Problem
	M7.8. Karmarkar’s Algorithm
	Summary
	Glossary
	Key Equations
	Solved Problems
	Self-Test
	Discussion Questions and Problems
	Bibliography
Module 8: Transportation, Assignment, and Network Algorithms
	M8.1. The Transportation Algorithm
		Developing an Initial Solution: Northwest Corner Rule
		Stepping-Stone Method: Finding a Least-Cost Solution
		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
	M8.2. The Assignment Algorithm
		The Hungarian Method (Flood’s Technique)
		Making the Final Assignment
		Special Situations with the Assignment Algorithm
		Unbalanced Assignment Problems
		Maximization Assignment Problems
	M8.3. Maximal-Flow Problem
		Maximal-Flow Technique
	M8.4. Shortest-Route Problem
		Shortest-Route Technique
	Summary
	Glossary
	Solved Problems
	Self-Test
	Discussion Questions and Problems
	Bibliography