1. Management Science Chapter 1
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Chapter Topics
The Management Science Approach to Problem Solving
Model Building: Break-Even Analysis
Computer Solution
Management Science Modeling Techniques
Business Usage of Management Science Techniques
Management Science Models in Decision Support Systems
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3. The Management Science Approach
Management science uses a scientific approach to solving management problems.
It is used in a variety of organizations to solve many different types of problems.
It encompasses a logical mathematical approach to problem solving.
Management science, also known as operations research, quantitative methods, etc., involves a philosophy of problem solving in a logical manner.
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4. The Management Science Process
Figure 1.1
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Steps in the Management Science Process
Observation - Identification of a problem that exists (or may occur soon) in a system or organization.
Definition of the Problem - problem must be clearly and consistently defined, showing its boundaries and interactions with the objectives of the organization.
Model Construction - Development of the functional mathematical relationships that describe the decision variables, objective function and constraints of the problem.
Model Solution - Models solved using management science techniques.
Model Implementation - Actual use of the model or its solution.
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6. Example of Model Construction (1 of 3)
Information and Data:
Business firm makes and sells a steel product
Product costs $5 to produce
Product sells for $20
Product requires 4 pounds of steel to make
Firm has 100 pounds of steel
Business Problem:
Determine the number of units to produce to make the most profit, given the limited amount of steel available.
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7. Example of Model Construction (2 of 3)
Variables: X = # units to produce (decision variable)
Z = total profit (in $)
Model: Z = $20X - $5X (objective function)
4X = 100 lb of steel (resource constraint)
Parameters: $20, $5, 4 lbs, 100 lbs (known values)
Formal Specification of Model:
maximize Z = $20X - $5X
subject to 4X = 100
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8. Example of Model Construction (3 of 3)
Model Solution:
Solve the constraint equation:
4x = 100
(4x)/4 = (100)/4
x = 25 units
Substitute this value into the profit function:
Z = $20x - $5x
= (20)(25) – (5)(25)
= $375
(Produce 25 units, to yield a profit of $375)
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Model Building:
Break-Even Analysis (1 of 9)
Used to determine the number of units of a product to sell or produce that will equate total revenue with total cost.
The volume at which total revenue equals total cost is called the break-even point.
Profit at break-even point is zero.
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Model Building: Break-Even Analysis (2 of 9)
Model Components
Fixed Cost (cf) - costs that remain constant regardless of number of units produced.
Variable Cost (cv) - unit production cost of product.
Volume (v) – the number of units produced or sold
Total variable cost (vcv) - function of volume (v) and unit variable cost.
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Model Building: Break-Even Analysis (3 of 9)
Model Components
Total Cost (TC) - total fixed cost plus total variable cost.
Profit (Z) - difference between total revenue vp (p = unit price) and total cost, i.e.
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12. Model Building: Break-Even Analysis (4 of 9)
Computing the Break-Even Point
The break-even point is that volume at which total revenue equals total cost and profit is zero:
The break-even point
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13. Break-Even Analysis (5 of 9)
Model Building:
Break-Even Analysis (5 of 9)
Example: Western Clothing Company
Fixed Costs: cf = $10000
Variable Costs: cv = $8 per pair
Price : p = $23 per pair
The Break-Even Point is:
v = (10,000)/(23 -8)
= pairs
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14. Break-Even Analysis (6 of 9)
Model Building:
Break-Even Analysis (6 of 9)
Figure 1.2
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15. Break-Even Analysis (7 of 9)
Model Building:
Break-Even Analysis (7 of 9)
Figure 1.3
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16. Break-Even Analysis (8 of 9)
Model Building:
Break-Even Analysis (8 of 9)
Figure 1.4
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17. Break-Even Analysis (9 of 9)
Model Building:
Break-Even Analysis (9 of 9)
Figure 1.5
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18. Break-Even Analysis: Excel Solution (1 of 5)
Exhibit 1.1
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19. Break-Even Analysis: Excel QM Solution (2 of 5)
Exhibit 1.2
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20. Break-Even Analysis: Excel QM Solution (3 of 5)
Exhibit 1.3
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21. Break-Even Analysis: QM Solution (4 of 5)
Exhibit 1.4
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22. Break-Even Analysis: QM Solution (5 of 5)
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Exhibit 1.5

23. Figure 1.6 Modeling Techniques
Classification of Management Science Techniques
Figure Modeling Techniques
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Characteristics of Modeling Techniques
Linear Mathematical Programming - clear objective; restrictions on resources and requirements; parameters known with certainty. (Chap 2-6, 9)
Probabilistic Techniques - results contain uncertainty. (Chap 11-13)
Network Techniques - model often formulated as diagram; deterministic or probabilistic. (Chap 7-8)
Other Techniques - variety of deterministic and probabilistic methods for specific types of problems including forecasting, inventory, simulation, multicriteria, etc. (Chap 10, 14-16)
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Business Use of Management Science
Some application areas:
- Project Planning
- Capital Budgeting
- Inventory Analysis
- Production Planning
- Scheduling
Interfaces - Applications journal published by Institute for Operations Research and Management Sciences (INFORMS)
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Decision Support Systems (DSS)
A decision support system is a computer-based system that helps decision makers address complex problems that cut across different parts of an organization and operations.
Features of Decision Support Systems
Interactive
Use databases & management science models
Address “what if” questions
Perform sensitivity analysis
Examples include:
ERP – Enterprise Resource Planning
OLAP – Online Analytical Processing
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27. Management Science Models Decision Support Systems (2 of 2)
Figure 1.7 A Decision Support System
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