Management Science and Decision Technology / Edition 2

Management Science and Decision Technology / Edition 2

by Jeff D. Camm, James R. Evans
     
 

ISBN-10: 0324007159

ISBN-13: 9780324007152

Pub. Date: 10/28/1999

Publisher: Cengage Learning

The focus of this book is on using data and spreadsheet models effectively for the analysis of business problems and decision making. Included are discussions of building good spreadsheet models; data collection, visualization, and statistical analysis; forecasting; optimization using Excel Solver; decision and risk analysis; and simulation using Crystal Ball

Overview

The focus of this book is on using data and spreadsheet models effectively for the analysis of business problems and decision making. Included are discussions of building good spreadsheet models; data collection, visualization, and statistical analysis; forecasting; optimization using Excel Solver; decision and risk analysis; and simulation using Crystal Ball add-in for Excel and Arena BE. The principal focus is on gaining insight and intuition for better decisions, with applications in operations planning, finance, and marketing.

Product Details

ISBN-13:
9780324007152
Publisher:
Cengage Learning
Publication date:
10/28/1999
Edition description:
New Edition
Pages:
408
Product dimensions:
8.20(w) x 10.10(h) x 0.90(d)

Table of Contents

Preface ix
Part 1 Models, Data Management, and Analysis
Modeling for Decisions and Insight
1(48)
Applications and Benefits of Management Science
3(1)
Strategic Business Decisions
3(1)
Scheduling Airline Crews
3(1)
Poultry Production
3(1)
Search for Sunken Treasure
4(1)
Financial Strategies
4(1)
Decision Technology
4(4)
Spreadsheets
5(2)
Spreadsheet Add-Ins
7(1)
Stand-Alone Software
7(1)
Developing Models
8(8)
Descriptive Models
8(1)
Management Science Practice: Labor Scheduling at Taco Bell
9(4)
Optimization Models
13(3)
Models Involving Uncertainty
16(1)
The Importance of Good Data
16(1)
Analyzing and Solving Models
16(10)
Model Analysis
17(1)
Management Science Practice: Model Analysis of HIV Transmission through Needle Sharing
17(5)
Tornado and Spider Charts
22(2)
Solving Optimization Models
24(2)
Interpreting and Using Model Results
26(1)
A Case Study of Modeling and Analysis: Inventory Management
27(11)
Managing Inventory Systems
27(2)
Model Development
29(3)
Model Analysis and Solution
32(3)
Interpreting Results
35(2)
Applying Inventory Theory to Cash Management
37(1)
Problems
38(5)
Notes
43(1)
Bibliography
43(1)
Appendix: Basic Spreadsheet Skills
44(5)
Functions
44(2)
Auditing Your Spreadsheets
46(1)
Creating Tornado and Spider Charts
46(3)
Data Management and Analysis
49(47)
Applications of Data Management and Analysis
50(1)
Law Enforcement
50(1)
Health Care
50(1)
Retail Operations and Strategy
51(1)
Hotel Location
51(1)
Data Storage and Retrieval
51(9)
Management Science Practice: Allders, International
52(3)
Sorting and Filtering Data
55(4)
Using Auto Filter to Check Data Accuracy
59(1)
Using Data Retrieval Methods in Optimization Modeling
59(1)
Data Visualization
60(6)
Charts
61(2)
Geographic Data Maps
63(3)
Data Analysis
66(12)
Descriptive Statistics
67(1)
Pivot Tables
68(3)
Probability Distributions and Data Fitting with Crystal Ball
71(3)
Covariance and Correlation
74(3)
Applying Covariance in Financial Portfolio Optimization Models
77(1)
Regression Analysis
78(13)
Simple Linear Regression
79(3)
Statistical Issues in Regression
82(4)
Multiple Linear Regression
86(4)
Building Regression Models
90(1)
Using Regression in Optimization Models
90(1)
Technology for Statistical Analysis
91(1)
Problems
92(2)
Notes
94(1)
Bibliography
95(1)
Forecasting
96(44)
Applications of Forecasting
97(1)
Plant Closure at Allied-Signal
97(1)
Call-Center Demand at L. L. Bean
98(1)
Inventory Management at IBM
98(1)
Time Series Components
98(4)
Models for Time Series with Only an Irregular Component
102(5)
Single Moving Average
102(1)
Measuring Forecast Accuracy
102(3)
Single Exponential Smoothing
105(2)
Models for Time Series with a Trend Component
107(4)
Double Moving Average
107(2)
Double Exponential Smoothing
109(2)
Models for Time Series with a Seasonal Component
111(4)
Additive Seasonality
111(2)
Multiplicative Seasonality
113(2)
Models for Time Series with Trend and Seasonal Components
115(2)
Holt-Winters Additive Model
115(1)
Holt-Winters Multiplicative Model
116(1)
Forecasting with Regression Models
117(8)
Management Science Practice: A Forcasting Model for Supermarket Checkout Services
118(3)
Linear Regression for Time Series Forecasting
121(2)
Using Indicator Variables in Regression Models
123(2)
Forecasting Technology
125(1)
Choosing the Best Forecasting Method
126(1)
Forecasting with CB Predictor
126(7)
Interpreting CB Predictor Outputs
133(1)
Applications of Forecasting to Model Building
133(3)
Problems
136(3)
Note
139(1)
Bibliography
139(1)
Part 2 Optimization
Linear and Multiobjective Optimization Models
140(59)
The Structure of Linear and Multiobjective Optimization Models
141(2)
Applications of Linear and Multiobjective Programming Models
143(2)
Land Management at the National Forest Service
143(1)
Credit Collection
144(1)
Research and Development Funding
144(1)
Site Selection
144(1)
Modeling Optimization Problems in Excel
145(1)
Building Linear Programming Models
146(18)
Dimensionality Checks
148(1)
LP Modeling Examples
148(6)
Standard Form
154(8)
Management Science Practice: Using Transportation Models for Procter & Gamble's Product Sourcing Decisions
162(2)
Solving Linear Programming Models
164(5)
Intuition and LP Solutions
164(1)
When Intuition Can Fail
165(1)
Using Excel Solver
166(3)
Interpreting Solver Reports and Sensitivity Analysis
169(8)
Answer and Limit Reports
170(1)
Sensitivity Report
171(3)
Scenario Analysis
174(3)
Solving Multiobjective Models
177(7)
Weighed Objective Approach
177(3)
Absolute Priorities Approach
180(1)
Goal-Programming Approach
181(2)
Management Science Practice: Goal Programming for Site Location at Truck Transport Corporation
183(1)
Technology for Linear Optimization
184(1)
Problems
185(11)
Notes
196(1)
Bibliography
196(1)
Appendix: Using Premium Solver to Solve Linear Programs
197(2)
Integer and Nonlinear Optimization Models
199(47)
Applications of Integer and Nonlinear Models
200(1)
Project Selection at the National Cancer institute
200(1)
Crew Scheduling at American Airlines
200(1)
Mortgage Valuation Models at Prudential Securities
201(1)
Paper Production
201(1)
Building Integer-Programming Models
201(11)
Management Science Practice: Sales Staffing at Qantas
204(8)
Solving Integer-Programming Models
212(6)
Using Solver for Integer Programs
212(2)
Sensitivity Analysis
214(4)
Building Nonlinear Optimization Models
218(8)
Management Science Practice: Kanban Sizing at Whirlpool Corporation
225(1)
Solving Nonlinear Programming Problems
226(7)
Using Excel Solver
226(2)
Sensitivity Analysis
228(1)
The Problems of Nonlinearity
229(2)
Using Premium Solver's Evolutionary Algorithm for Difficult Problems
231(2)
Technology for Integer and Nonlinear Optimization
233(1)
Problems
234(7)
Notes
241(1)
Bibliography
241(1)
Appendix: Using Premium Solver to Solve Integer and Nonlinear Programs
242(4)
Solving Linear Integer Programs
242(1)
Solving Nonlinear Programs
243(3)
Part 3 Decision, Risk, and Simulation
Decision and Risk Analysis
246(39)
Applications of Decision and Risk Analysis
248(1)
Environmental Impact Assessment
248(1)
Assessment of Catastrophic Risk
248(1)
Sports Strategies
249(1)
Risk Assessment for the Space Shuttle
249(1)
Structuring Decision Problems
249(5)
Generating Alternatives
249(1)
Defining Outcomes
250(1)
Decision Criteria
250(1)
Decision Trees
251(1)
Management Science Practice: Collegiate Athletic Drug Testing
252(2)
Decision Strategies
254(1)
Understanding Risk in Making Decisions
254(6)
Average Payoff Strategy
255(1)
Aggressive Strategy
255(1)
Conservative Strategy
256(1)
Opportunity Loss Strategy
256(1)
Quantifying Risk---Insights from Finance
257(1)
An Application of Decision and Risk Analysis: Evaluating Put and Call Options
258(2)
Expected Value Decision Making
260(6)
Management Science Practice: The Overbooking Problem at American Airlines
261(2)
An Application of Expected Value Analysis: The "Newsvendor" Problem
263(2)
Expected Value of Perfect Information
265(1)
Optimal Expected Value Decision Strategies
266(2)
Sensitivity Analysis of Decision Strategies
268(1)
Technology for Decision Analysis
268(1)
Risk Trade-Offs and Multiobjective Decisions
269(3)
Utility and Decision Making
272(5)
Exponential Utility Functions
275(2)
Problems
277(6)
Notes
283(1)
Bibliography
284(1)
Monte Carlo Simulation
285(42)
Applications of Monte Carlo Simulation
286(2)
New Venture Planning
286(1)
Pharmaceutical Research
287(1)
Project Management
287(1)
Building and Implementing Monte Carlo Simulation Models
288(4)
Sampling From Probability Distributions
292(2)
Building Simulation Models with Crystal Ball
294(8)
Interpreting Crystal Ball Output
298(4)
Statistical Issues in Monte Carlo Simulation
302(1)
Monte Carlo Simulation Examples
303(7)
Newsvendor Problem
304(2)
Management Science Practice: Simulating a CD Portfolio
306(2)
Pricing Stock Options
308(2)
Crystal Ball Tornado Chart Extender
310(2)
Optimization and Simulation
312(4)
Problems
316(5)
Notes
321(1)
Bibliography
321(1)
Appendix: Additional Crystal Ball Features
321(6)
Correlated Assumptions
321(2)
Freezing Assumptions
323(1)
Overlay Charts
323(1)
Trend Charts
323(1)
Sensitivity Charts
324(3)
Systems Modeling and Simulation
327(52)
Applications of Dynamic System Models
328(2)
Toll Booth Improvement
328(1)
Designing Security Checkpoints
329(1)
Technology Evaluation
329(1)
Dental Practice Management
329(1)
Forest Fire Management
330(1)
Modeling and Simulating Dynamic Systems
330(5)
Queueing Systems
335(4)
Customer Characteristics
335(1)
Service Characteristics
336(1)
Queue Characteristics
337(1)
System Configuration
337(1)
Management Science Practice: One Line or More?
338(1)
Modeling and Simulating Queueing Systems
339(3)
The Dynamics of Waiting Lines
342(4)
Analytical Queueing Models
346(8)
The M/M/1 Queueing Model
347(1)
Other Queueing Models
348(1)
Little's Law
349(1)
Analytical Models vs. Simulation
350(1)
Sensitivity Analysis
351(2)
Management Science Practice: Queueing Models for Telemarketing at L.L. Bean
353(1)
Modeling and Simulating Dynamic Inventory Systems
354(5)
Using Simulation to Optimize Inventory Systems
357(2)
Systems Simulation Technology
359(11)
Arena Business Edition
359(1)
Management Science Practice: Designing an Air Force Repair Center Using Simulation
360(10)
Using Simulation Successfully
370(2)
Verification and Validation
371(1)
Statistical Issues
372(1)
Problems
372(6)
Notes
378
Bibliography
374(5)
Appendix A Normal Distribution Table 379(2)
Index 381

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