1. ENGM 742: Engineering Management and Labor Relations
Decision Making

2. Categories of Decision Making Tools
Routine vs. Nonroutine
Objective vs. Bounded Rationality
Level of Certainty

3. Decision Making Under Certainty
Linear Program: Used to determine optimal allocation of an organization’s limited resources
State the problem
Decision Variables
Objective function
Constraints
OR Web:

4. Sec. 3.1Alt. Prototype Problem
Determine the appropriate number of tractors and shakers to make so as to maximize total profit.
Constraints
Do not exceed demand for either product
Do not exceed capacity of assembly area

5. Sec. 3.1Alt. Model Variables Profit
X1 = number of tractors to manufacture
X2 = number of shakers to manufacture
Profit
Z = 3X1 + 5X2 (in $1,000’s)

6. Sec. 3.1Alt. Model Constraints Demand for tractors X1 < 8,000
Demand for shakers
X2 < 6,000
Capacity of Assembly
3X1 + 4X2 < 36,000
Non-negativity
X1 > 0 , X2 > 0

7. Sec. 3.1Alt. Model Max Z = 3X1 + 5X2 s.t. X1 < 8,000 X2 < 6,000

8. Sec. 3.2 Some Terminology Corner Point Feasible Sol. (CPF) (0,6) (4,6) 8 6 4 2
Corner Point
Feasible Sol.
(CPF)
(0,6)
(4,6)
(8,3)
(8,0)
(0,0)

9. Decision Making Under Risk
Expected Value
Sum: (probability of future * value of future)
Decision Trees
Graphical method for finding the expected value
Queuing
Simulation

10. K-Corp 125 -90 40 60 80 20 -50 No Competition (.8) New Design
Re-engineer
New Design
do nothing
No Competition (.8)
Competition (.2)
Competition (.5) No
Comp.
(.5)
New
do no
No Comp. (.3)
Comp. (.7)
125
-90 40 60 80 20
-50

11. K-Corp (60) (38) 125 -90 40 60 80 20 -50 No Competition (.8)
Re-engineer
New Design
do nothing
No Competition (.8)
Competition (.2)
Competition (.5) No
Comp.
(.5)
New
do no
No Comp. (.3)
Comp. (.7)
125
-90 40 60 80 20
-50
(60)
(38)

12. K-Corp (60) [60] (38) 125 -90 40 60 80 20 -50 No Competition (.8)
Re-engineer
New Design
do nothing
No Competition (.8)
Competition (.2)
Competition (.5) No
Comp.
(.5)
New
do no
No Comp. (.3)
Comp. (.7)
125
-90 40 60 80 20
-50
(60)
[60]
(38)

13. K-Corp (82) (50) (60) [60] (38) (-50) 125 -90 40 60 80 20 -50
Re-engineer
New Design
do nothing
No Competition (.8)
Competition (.2)
Competition (.5) No
Comp.
(.5)
New
do no
No Comp. (.3)
Comp. (.7)
125
-90 40 60 80 20
-50
(82)
(50)
(60)
[60]
(38)
(-50)

14. K-Corp (82) [82] (50) (60) [60] (38) (-50) 125 -90 40 60 80 20 -50
Re-engineer
New Design
do nothing
No Competition (.8)
Competition (.2)
Competition (.5) No
Comp.
(.5)
New
do no
No Comp. (.3)
Comp. (.7)
125
-90 40 60 80 20
-50
(82)
[82]
(50)
(60)
[60]
(38)
(-50)

15. K-Corp (38) (60) [60] (82) (50) (-50) [82] 125 -90 40 60 80 20 -50
Re-engineer
New Design
do nothing
No Competition (.8)
Competition (.2)
Competition (.5) No
Comp.
(.5)
New
do no
No Comp. (.3)
Comp. (.7)
125
-90 40 60 80 20
-50
(38)
(60)
[60]
(82)
(50)
(-50)
[82]

16. K-Corp (38) (60) [60] (82) (50) (-50) [82] 125 -90 40 60 80 20 -50
Re-engineer
New Design
do nothing
No Competition (.8)
Competition (.2)
Competition (.5) No
Comp.
(.5)
New
do no
No Comp. (.3)
Comp. (.7)
125
-90 40 60 80 20
-50
(38)
(60)
[60]
(82)
(50)
(-50)
[82]

17. Decision Making Under Uncertainty
Maximax
Maximin
Equally likely
Aspiration-Level

18. Aspiration-Level Aspiration: max probability that payoff > 60,000
P{PA1 > 60,000} = 0.8
P{PA2 > 60,000} = 0.3
P{PA3 > 60,000} = 0.3
Choose A2 or A3

19. Maximin Select Aj: maxjminkV(jk)
e.g., Find the min payoff for each alternative.
Find the maximum of minimums
Sell Land
Choose best alternative when comparing worst
possible outcomes for each alternative.

20. Bayes’ Decision Rule
E[A1] > E[A3] > E[A2]
choose A1

21. Forecasting Essential preliminary to effective planning
Engineering manager must be concerned with both future markets and future technology

22. Why Forecasting? New facility planning Production planning
Work force scheduling
Sensitivity analysis

23. Long Range Forecasts Design new products
Determine capacity for new product
Long range supply of materials

24. Short Range Forecasts Amount of inventory for next month
Amount of product to produce next week
How much raw material delivered next week
Workers schedule next week

25. Sales Forecast

26. Sales Forecast

27. Sales Forecast

28. Sales Forecast

29. Forecasting Quantitative Methods
Time Series Methods Moving Average Weighted Moving Average Exponential Smoothing
Association or Causal Method Multiple Regression

30. Forecasting Qualitative Methods
Judgment Methods Expert Opinion Delphi Historical
Counting Methods Market Testing Market Survey

31. Delphi Method Eliminates effects of interactions between members
Experts do not need to know who other experts are
Delphi coordinator asks for opinions, forecasts on subject

32. Which Method? Textbook authors suggest: Select a few methods
Make forecasts
Take simple average

33. Forecasting New Products
First use judgmental
Expert opinions
Consumer intentions

34. Management Science Characteristics
Systems view of the problem
Team approach
Emphasis on use of formal mathematical models and statistical and quantitative techniques

35. Management Science Process
Formulate the Problem
Construct a Mathematical Model
Test the Model
Derive a Solution from the Model
Apply the Model’s Solution to the Real System

37. For Review
Compare and contrast mission and vision statements. Provide an example of how each is used by middle managers.
Which of the forces in Porter’s five forces model is the most important in making decisions? Why?
What role do goals and objectives play in the strategic planning process?
Provide one example of how a forecast can be used to help management decision making and one example of how a forecast can mislead a management decision.