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E-raamat: Managerial Decision Modeling: Business Analytics with Spreadsheets, Fourth Edition

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  • Formaat: 828 pages
  • Ilmumisaeg: 07-Aug-2017
  • Kirjastus: De Gruyter
  • Keel: eng
  • ISBN-13: 9781501506314
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Managerial Decision Modeling: Business Analytics with Spreadsheets, Fourth EditionSuurem pilt
  • Formaat: 828 pages
  • Ilmumisaeg: 07-Aug-2017
  • Kirjastus: De Gruyter
  • Keel: eng
  • ISBN-13: 9781501506314
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This book fills a void for a balanced approach to spreadsheet-based decision modeling. In addition to using spreadsheets as a tool to quickly set up and solve decision models, the authors show how and why the methods work and combine the user's power to logically model and analyze diverse decision-making scenarios with software-based solutions. The book discusses the fundamental concepts, assumptions and limitations behind each decision modeling technique, shows how each decision model works, and illustrates the real-world usefulness of each technique with many applications from both profit and nonprofit organizations.

The authors provide an introduction to managerial decision modeling, linear programming models, modeling applications and sensitivity analysis, transportation, assignment and network models, integer, goal, and nonlinear programming models, project management, decision theory, queuing models, simulation modeling, forecasting models and inventory control models.

The additional material files











Chapter 12









Excel files for each chapter









Excel modules for Windows









Excel modules for Mac









4th edition errata







can be found under Supplementary Materials
Chapter 1 Introduction to Managerial Decision Modeling 1(32)
1.1 What is Decision Modeling?
2(1)
1.2 Types of Decision Models
3(3)
Deterministic Models
3(1)
Probabilistic Models
4(1)
Quantitative versus Qualitative Data
5(1)
Using Spreadsheets in Decision Modeling
5(1)
1.3 Steps Involved in Decision Modeling
6(5)
Step 1: Formulation
7(2)
Step 2: Solution
9(1)
Step 3: Interpretation and Sensitivity Analysis
10(1)
1.4 Spreadsheet Example of a Decision Model: Tax Computation
11(5)
1.5 Spreadsheet Example of a Decision Model: Break-Even Analysis
16(5)
Using Goal Seek to Find the Break-Even Point
18(3)
1.6 Possible Problems in Developing Decision Models
21(3)
Defining the Problem
21(1)
Developing a Model
22(1)
Acquiring Input Data
22(1)
Developing a Solution
23(1)
Testing the Solution
23(1)
Analyzing the Results
23(1)
1.7 Implementation-Not lust the Final Step
24(1)
1.8 Summary
24(3)
1.9 Exercises
27(6)
Chapter 2 Linear Programming Models: Graphical and Computer Methods 33(68)
2.1 Introduction
34(1)
2.2 Developing a Linear Programming Model
35(3)
Formulation
35(1)
Solution
35(1)
Interpretation and Sensitivity Analysis
36(1)
Properties of a Linear Programming Model
36(1)
Basic Assumptions of a Linear Programming Model
37(1)
2.3 Formulating a Linear Programming Problem
38(5)
Linear Programming Example: Flair Furniture Company
38(1)
Decision Variables
39(1)
The Objective Function
39(1)
Constraints
40(1)
Nonnegativity Constraints and Integer Values
41(1)
Guidelines for Developing a Correct LP Model
41(2)
2.4 Graphical Solution of a Linear Programming Problem with Two Variables
43(11)
Graphical Representation of Constraints
43(3)
Painting Time Constraint
46(1)
Feasible Region
47(1)
Identifying an Optimal Solution by Using Level Lines
48(3)
Identifying an Optimal Solution by Using All Corner Points
51(1)
Comments on Flair Furniture's Optimal Solution
52(1)
Extension to Flair Furniture's LP Model
52(2)
2.5 A Minimization Linear Programming Problem
54(4)
Holiday Meal Turkey Ranch
55(1)
Graphical Solution of the Holiday Meal Turkey Ranch Problem
56(2)
2.6 Special Situations in Solving Linear Programming Problems
58(4)
Redundant Constraints
58(1)
Infeasibility
59(1)
Alternate Optimal Solutions
60(1)
Unbounded Solution
61(1)
2.7 Setting Up and Solving Linear Programming Problems Using Excel's Solver
62(17)
Using Solver to Solve the Flair Furniture Problem
63(2)
The Objective Cell
65(2)
Creating Cells for Constraint RHS Values
67(1)
Entering Information in Solver
68(8)
Using Solver to Solve Flair Furniture Company's Modified Problem
76(1)
Using Solver to Solve the Holiday Meal Turkey Ranch Problem
77(2)
2.8 Algorithmic Solution Procedures for Linear Programming Problems
79(1)
2.9 Summary
80(5)
2.10 Exercises
85(16)
Chapter 3 Linear Programming Modeling Applications with Computer Analyses in Excel 101(80)
3.1 Using-Linear Programming to Solve Real-World Problems
102(1)
3.2 Manufacturing Applications
103(9)
Product Mix Problem
103(5)
Make-Buy Decision Problem
108(4)
3.3 Marketing Applications
112(6)
Media Selection Problem
112(1)
Marketing Research Problem
113(5)
3.4 Finance Applications
118(5)
Portfolio Selection Problem
118(3)
Alternate Formulations of the Portfolio Selection Problem
121(2)
3.5 Employee Staffing Applications
123(4)
Labor Planning Problem
123(4)
Extensions to the Labor Planning Problem
127(1)
Assignment Problem
127(1)
3.6 Transportation Applications
127(7)
Vehicle Loading Problem
127(5)
Expanded Vehicle Loading Problem-Allocation Problem
132(1)
Transportation Problem
133(1)
3.7 Blending Applications
134(7)
Diet Problem
134(2)
Blending Problem
136(5)
3.8 Multiperiod Applications
141(10)
Production Scheduling Problem
141(6)
Sinking Fund Problem
147(4)
3.9 Summary
151(2)
3.10 Exercises
153(28)
Chapter 4 Linear Programming Sensitivity Analysis 181(58)
4.1 Importance of Sensitivity Analysis
182(1)
Why Do We Need Sensitivity Analysis?
182(1)
4.2 Sensitivity Analysis Using Graphs
183(10)
Types of Sensitivity Analysis
185(1)
Impact of Changes in an Objective Function Coefficient
185(2)
Impact of Changes in a Constraint's Right-Hand-Side Value
187(6)
4.3 Sensitivity Analysis Using Solver Reports
193(7)
Solver Reports
194(1)
Sensitivity Report
195(1)
Impact of Changes in a Constraint's RHS Value
196(2)
Impact of Changes in an Objective Function Coefficient
198(2)
4.4 Sensitivity Analysis for a Larger Maximization Example
200(6)
Anderson Home Electronics Example
200(3)
Some Questions We Want Answered
203(2)
Alternate Optimal Solutions
205(1)
4.5 Analyzing Simultaneous Changes by Using the 100% Rule
206(1)
Simultaneous Changes in Constraint RHS Values
206(1)
Simultaneous Changes in OFC Values
207(1)
4.6 Pricing Out New Variables
207(4)
Anderson's Proposed New Product
207(4)
4.7 Sensitivity Analysis for a Minimization Example
211(5)
Burn-Off Diet Drink Example
211(1)
Burn-Off's Excel Solution
212(1)
Answering Sensitivity Analysis Questions for Burn-Off
213(3)
4.8 Summary
216(2)
4.9 Exercises
218(21)
Chapter 5 Transportation, Assignment, and Network Models 239(64)
5.1 Types of Network Models
239(3)
Transportation Model
240(1)
Transshipment Model
240(1)
Assignment Model
240(1)
Maximal-Flow Model
241(1)
Shortest-Path Model
241(1)
Minimal-Spanning Tree Model
241(1)
Implementation Issues
241(1)
5.2 Characteristics of Network Models
242(2)
Types of Arcs
242(1)
Types of Nodes
243(1)
Common Characteristics
243(1)
5.3 Transportation Model
244(8)
LP Formulation for Executive Furniture's Transportation Model
246(1)
Solving the Transportation Model Using Excel
247(2)
Unbalanced Transportation Models
249(2)
Alternate Optimal Solutions
251(1)
An Application of the Transportation Model: Facility Location
251(1)
5.4 Transportation Models with Max-Min and Min-Max Objectives
252(4)
5.5 Transshipment Model
256(6)
Executive Furniture Company Example-Revisited
256(1)
LP Formulation for Executive Furniture's Transshipment Model
256(2)
Lopez Custom Outfits-A Larger Transshipment Example
258(1)
LP Formulation for Lopez Custom Outfits Transshipment Model
259(3)
5.6 Assignment Model
262(6)
Fix-It Shop Example
263(1)
Solving Assignment Models
264(2)
LP Formulation for Fix-It Shop's Assignment Model
266(2)
5.7 Maximal-Flow Model
268(4)
Road System in Waukesha, Wisconsin
268(1)
LP Formulation for Waukesha Road System's Maximal-Flow Model
269(3)
5.8 Shortest-Path Model
272(4)
Ray Design Inc. Example
273(1)
LP Formulation for Ray Design Inc.'s Shortest-Path Model
274(2)
5.9 Minimal-Spanning Tree Model
276(3)
Lauderdale Construction Company Example
276(3)
5.10 Summary
279(3)
5.11 Exercises
282(21)
Chapter 6 Integer, Goal, and Nonlinear Programming Models 303(80)
6.1 Models That Relax Linear Programming Conditions
304(1)
Integer Programming Models
304(1)
Goal Programming Models
305(1)
Nonlinear Programming Models
305(1)
6.2 Models with General Integer Variables
305(12)
Harrison Electric Company
306(3)
Using Solver to Solve Models with General Integer Variables
309(4)
Solver Options
313(2)
Should We Include Integer Requirements in a Model?
315(2)
6.3 Models with Binary Variables
317(8)
Portfolio Selection at Simkin and Steinberg
317(5)
Set-Covering Problem at Sussex County
322(3)
6.4 Mixed Integer Models: Fixed-Charge Problems
325(6)
Locating a New Factory for Hardgrave Machine Company
326(5)
6.5 Goal Programming Models
331(13)
Goal Programming Example: Wilson Doors Company
331(4)
Solving Goal Programming Models with Weighted Goals
335(3)
Solving Goal Programming Models with Ranked Goals
338(6)
Comparing the Two Approaches for Solving GP Models
344(1)
6.6 Nonlinear Programming Models
344(10)
Why Are NLP Models Difficult to Solve?
345(2)
Solving Nonlinear Programming Models Using Solver
347(7)
Computational Procedures for Nonlinear Programming Problems
354(1)
6.7 Summary
354(3)
6.8 Exercises
357(26)
Chapter 7 Project Management 383(66)
7.1 Planning, Scheduling, and Controlling Projects
384(3)
Phases in Project Management
384(3)
Use of Software Packages in Project Management
387(1)
7.2 Project Networks
387(7)
Identifying Activities
388(1)
Identifying Activity Times and Other Resources
389(1)
Project Management Techniques: PERT and CPM
389(2)
Project Management Example: General Foundry, Inc.
391(1)
Drawing the Project Network
392(2)
7.3 Determining the Project Schedule
394(8)
Forward Pass
396(2)
Backward Pass
398(1)
Calculating Slack Time and Identifying the Critical Path(s)
399(2)
Total Slack Time versus Free Slack Time
401(1)
7.4 Variability in Activity Times
402(8)
PERT Analysis
403(3)
Probability of Project Completion
406(2)
Determining Project Completion Time for a Given Probability
408(1)
Variability in Completion Time of Noncritical Paths
409(1)
7.5 Managing Project Costs and Other Resources
410(7)
Planning and Scheduling Project Costs: Budgeting Process
410(3)
Monitoring and Controlling Project Costs
413(2)
Managing Other Resources
415(2)
7.6 Project Crashing
417(8)
Crashing General Foundry's Project (Hand Calculations)
418(3)
Crashing General Foundry's Project Using Linear Programming
421(3)
Using Linear Programming to Determine Earliest and Latest Starting Times
424(1)
7.7 Summary
425(4)
7.8 Exercises
429(20)
Chapter 8 Decision Analysis 449(60)
8.1 What is Decision Analysis?
450(1)
8.2 The Five Steps in Decision Analysis
450(3)
Thompson Lumber Company Example
451(2)
8.3 Types of Decision-Making Environments
453(1)
8.4 Decision Making Under Uncertainty
454(7)
Maximax Criterion
455(1)
Maximin Criterion
455(1)
Criterion of Realism (Hurwicz)
456(1)
Equally Likely (Laplace) Criterion
457(1)
Minimax Regret Criterion
457(1)
Using Excel to Solve Decision-Making Problems under Uncertainty
458(3)
8.5 Decision Making Under Risk
461(5)
Expected Monetary Value
461(1)
Expected Opportunity Loss
462(1)
Expected Value of Perfect Information
463(1)
Using Excel to Solve Decision-Making Problems under Risk
464(2)
8.6 Decision Trees
466(3)
Folding Back a Decision Tree
467(2)
8.7 Decision Trees for Multistage Decision-Making Problems
469(6)
A Multistage Decision-Making Problem for Thompson Lumber
469(1)
Expanded Decision Tree for Thompson Lumber
470(2)
Folding Back the Expanded Decision Tree for Thompson Lumber
472(2)
Expected Value of Sample Information
474(1)
8.8 Estimating Probability Values Using Bayesian Analysis
475(3)
Calculating Revised Probabilities
476(2)
Potential Problems in Using Survey Results
478(1)
8.9 Utility Theory
478(7)
Measuring Utility and Constructing a Utility Curve
479(4)
Utility as a Decision-Making Criterion
483(2)
8.10 Summary
485(3)
8.11 Exercises
488(21)
Chapter 9 Queuing Models 509(56)
9.1 The Importance of Queuing Theory
510(1)
Approaches for Analyzing Queues
510(1)
9.2 Queuing System Costs
511(2)
9.3 Characteristics of a Queuing System
513(8)
Arrival Characteristics
513(3)
Queue Characteristics
516(1)
Service Facility Characteristics
516(3)
Measuring the Queue's Performance
519(1)
Kendall's Notation for Queuing Systems
520(1)
Variety of Queuing Models Studied Here
520(1)
9.4 M/M/1 Queuing System
521(8)
Assumptions of the M/M/1 Queuing Model
521(1)
Operating Characteristic Equations for an M/M/1 Queuing System
522(1)
Arnold's Muffler Shop Example
523(1)
Using ExcelModules for Queuing Model Computations
524(3)
Cost Analysis of the Queuing System
527(1)
Increasing the Service Rate
528(1)
9.5 M/M/s Queuing System
529(4)
Operating Characteristic Equations for an M/M/s Queuing System
530(1)
Arnold's Muffler Shop Revisited
531(2)
Cost Analysis of the Queuing System
533(1)
9.6 M/D/1 Queuing System
533(3)
Operating Characteristic Equations for an M/D/1 Queuing System
534(1)
Garcia-Golding Recycling, Inc.
535(1)
Cost Analysis of the Queuing System
536(1)
9.7 M/G/1 Queuing System
536(4)
Operating Characteristic Equations for an M/G/1 Queuing System
537(1)
Meetings with Professor Crino
537(2)
Using Excel's Goal Seek to Identify Required Model Parameters
539(1)
9.8 M/M/S/infinity/N Queuing System
540(6)
Operating Characteristic Equations for the Finite Population Queuing System
542(1)
Department of Commerce Example
543(1)
Cost Analysis of the Queuing System
544(2)
9.9 More Complex Queuing Systems
546(1)
9.10 Summary
547(4)
9.11 Exercises
551(14)
Chapter 10 Simulation Modeling 565(82)
10.1 Why Create a Simulation?
566(3)
Simulation Basics
566(2)
Advantages and Disadvantages of Simulation
568(1)
10.2 Monte Carlo Simulation
569(5)
Step 1: Establish a Probability Distribution for Each Variable
570(1)
Step 2: Simulate Values from the Probability Distributions
571(2)
Step 3: Repeat the Process for a Series of Replications
573(1)
10.3 Role of Computers in Simulation
574(8)
Types of Simulation Software Packages
575(1)
Random Generation from Some Common Probability Distributions Using Excel
575(7)
10.4 Simulation Model to Compute Expected Profit
582(9)
Setting Up the Model
583(2)
Replication by Copying the Model
585(1)
Replication Using Data Table
586(1)
Analyzing the Results
587(4)
10.5 Simulation Model of an Inventory Problem
591(10)
Simkin's Hardware Store
591(2)
Setting Up the Model
593(3)
Computation of Costs
596(1)
Replication Using Data Table
596(1)
Analyzing the Results
597(1)
Using Scenario Manager to Include Decisions in a Simulation Model
598(3)
Analyzing the Results
601(1)
10.6 Simulation Model of a Queuing Problem
601(4)
Denton Savings Bank
601(1)
Setting Up the Model
602(2)
Replication Using Data Table
604(1)
Analyzing the Results
604(1)
10.7 Simulation Model of a Revenue Management Problem
605(5)
Judith's Airport Limousine Service
605(1)
Setting Up the Model
606(2)
Replicating the Model Using Data Table and Scenario Manager
608(1)
Analyzing the Results
609(1)
10.8 Other Types of Simulation Models
610(1)
Operational Gaming
610(1)
Systems Simulation
610(1)
10.9 Summary
611(4)
10.10 Exercises
615(32)
Chapter 11 Forecasting Models 647(84)
11.1 What is Forecasting?
648(1)
11.2 Types of Forecasts
649(1)
Qualitative Models
650(1)
Time-Series Models
650(1)
Causal Models
650(1)
11.3 Qualitative Forecasting Models
650(1)
11.4 Measuring Forecast Error
651(1)
11.5 Basic Time-Series Forecasting Models
652(16)
Components of a Time Series
653(1)
Stationary and Nonstationary Time-Series Data
654(1)
Moving Averages
654(1)
Using ExcelModules for Forecasting Model Computations
655(4)
Weighted Moving Averages
659(5)
Exponential Smoothing
664(4)
11.6 Trend and Seasonality in Time-Series Data
668(10)
Linear Trend Analysis
668(1)
Scatter Chart
669(3)
Least-Squares Procedure for Developing a Linear Trend Line
672(4)
Seasonality Analysis
676(2)
11.7 Decomposition of a Time Series
678(6)
Multiplicative Decomposition Example: Sawyer Piano House
678(1)
Using ExcelModules for Multiplicative Decomposition
679(5)
11.8 Causal Forecasting Models: Simple and Multiple Regression
684(21)
Causal Simple Regression Model
684(2)
Causal Simple Regression Using ExcelModules
686(6)
Causal Simple Regression Using Excel's Analysis ToolPak (Data Analysis)
692(4)
Causal Multiple Regression Model
696(1)
Causal Multiple Regression Using ExcelModules
696(4)
Causal Multiple Regression Using Excel's Analysis ToolPak (Data Analysis)
700(5)
11.9 Summary
705(5)
11.10 Exercises
710(21)
Appendix A: Probability Concepts and Applications 731(36)
A.1 Fundamental Concepts
731(2)
Types of Probability
732(1)
A.2 Mutually Exclusive and Collectively Exhaustive Events
733(3)
Adding Mutually Exclusive Events
734(1)
Law of Addition for Events that Are Not Mutually Exclusive
735(1)
A.3 Statistically Independent Events
736(1)
A.4 Statistically Dependent Events
737(3)
A.5 Revising Probabilities with Bayes' Theorem
740(2)
General Form of Bayes' Theorem
741(1)
A.6 Further Probability Revisions
742(1)
A.7 Random Variables
743(2)
A.8 Probability Distributions
745(5)
Probability Distribution of a Discrete Random Variable
745(2)
Expected Value of a Discrete Probability Distribution
747(1)
Variance of a Discrete Probability Distribution
747(1)
Probability Distribution of a Continuous Random Variable
748(2)
A.9 The Normal Distribution
750(6)
Area under the Normal Curve
751(1)
Using the Standard Normal Table
752(1)
Haynes Construction Company Example
753(3)
A.10 The Exponential Distribution
756(1)
A.11 The Poisson Distribution
757(1)
A.12 Summary
758(2)
A.13 Exercises
760(7)
Appendix B: Useful Excel 2016 Commands and Procedures for Installing ExcelModules 767(20)
B.1 Introduction
767(1)
B.2 Getting Started
767(2)
Organization of a Worksheet
768(1)
Navigating through a Worksheet
769(1)
B.3 The Ribbon, Toolbars, and Tabs
769(6)
Excel Help
774(1)
B.4 Working with Worksheets
775(1)
B.5 Using Formulas and Functions
775(5)
Copying Formulas
779(1)
Errors in Using Formulas and Functions
779(1)
B.6 Printing Worksheets
780(1)
B.7 Excel Options and Add-Ins
781(3)
B.8 ExcelModules
784(3)
Installing ExcelModules
784(1)
Running ExcelModules
784(2)
ExcelModules Help and Options
786(1)
Appendix C: Areas Under The Standard Normal Curve 787(2)
Appendix D: Brief Solutions to All Odd-Numbered End-Of-Chapter Problems 789(6)
Index 795
Nagraj Balakrishnan, University of Michigan, USA, Barry Render, Ralph M. Stair, Jr., Charles L. Munson, Washington State University
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