2. Learning Objectives Discuss how decision making relates to planning
Explain the process of engineering problem solving
Solve problems using three types of decision making tools
Discuss the differences between decision making under
certainty, risk, and uncertainty
Describe the basics of other decision making techniques

3. DECISION MAKING Preview
Decision making and problem solving are used in all management functions, although usually they are considered a part of the planning phase.

4. Relation to Planning
Decision Making: The Process of making a conscious choice between 2 or more alternatives producing most desirable consequences (benefits) relative to unwanted consequences (costs).
If Planning is truly Deciding in advance what to do, how to do it, when to do it and who is to do it, then Decision Making is an essential part of Planning

5. Occasions for Decision
Occasions are in 3 distinct fields:
1. From Authoritative Communications from superiors
2. From Cases Referred for Decision by Subordinates
3. From Cases Originating in the Initiative of the executive concerned

6. Types of Decisions
Routine Decisions (e.g. payroll processing, paying suppliers etc)
Recur frequently
Involve Standard Decision Procedures
Has a Minimum of Uncertainty
Structured Situations
Nonroutine Decisions
Unstructured and Novel Situations
Nonrecurring Nature
High Level of Uncertainty

7. Objective versus Bounded Rationality
A Decision is objectively rational if it is the correct behavior for
maximizing given values in a given situation.
Rationality requires
A complete knowledge and anticipations of consequences
after a choice
Imagination since Consequences lie in future
A choice among all possible alternatives.
We can only talk about bounded rationality

8. Objective versus Bounded Rationality
Objective Rationality looks for the ‘best’ solution whereas Bounded Rationality looks for the ‘good enough’ solution.

9. Management Science Models?
Quantitative techniques used in business for many years in
applications such as return on investment, inventory turnover,
and statistical sampling theory. Quantitative solutions of complex
Problems in operations and management. Today is called management
science. After the World war II, these techniques were applied to long
range military problems and industrial organizations. Management
science characteristics:
1. A System View of Problem: significant interrelated variables
contained in the problem.
2. Team Approach : training work together on specific problems.
3. Emphasis on the Use of Formal Mathematical Models and
Statistical and Quantitative Methods
Models?

10. Models and Their Analysis
Model: Abstraction and Simplification of Reality
(Designed to include Essential Features)
Simplest Model
net income = revenue - expenses - taxes

11. 5 Steps of Modeling Real World Simulated (Model) World
1. Formulate Problem
(Define objectives, variables and constraints)
2. Construct a Model (simple but
realistic representation of system)
3. Test the Model’s Ability
4. Derive a Solution from Model
5. Apply the Model’s Solution to
Real System, Document its Effectiveness

12. Categories of Decision Making
Decision Making Under Certainty: Linear Programming
Decision Making Under Risk: expected value, decision
trees, queuing theory, and simulation
Decision Making Under Uncertainty: Game Theory

13. Payoff (Benefit) Table - Decision Matrix
N N2 ……… Nj ……… Nn
P P2 ……… Pj ……… Pn
A O O12 ……… O1j ……… O1n
A O O22 ……… O2j ……… O2n
… … … ……… … …… …
Ai Oi Oi ……… Oij ……… Oin
Am Om Om ……… Omj …… Omn
Alternative
State of Nature / Probability
Outcome
Sum of n values of pj must be 1

14. A1, A2,…. Am Decision alternatives
N1 , N2 , Nj ……… Nn Decision alternatives, state of nature
P P2 ……… Pj ……… Pn Probability of occurrence
Om Om ……… Omj …… Omn Outcomes

15. Decision Making Under Certainty
Implies that we are certain
of the future state of nature
(or assume we are). Linear programming is a tool for the
decision making under certainty.
This means:-
the probability of pj of future Nj is 1 and all
other futures have zero probability

16. Example for linear programming
A factory is producing two types of products; X and Y. If you can realize 10$ profit per unit of product X and 14$ per unit of product Y. The factory employs five workers: 3 machinists and 2 assemblers and each works only 40 hours a week. Meanwhile, product X requires 3 hours of machining and one hour of assembly per unit. Product Y requires two hours of machining and two hours of assembly per hour. What is the production level of product X and product Y to maximize the profit ?

17. Decision Making Under Certainty
Linear Programming
COMPUTER SOLUTION
In reality, we have more than 2 variables (dimensions) not like machining and assembling only.
Computer solution called Simplex method has been developed to be used with many variables.
For example, an AT&T model of current and future telephone demand has got variables taking 4 to 7 computer hours for single run.
For example, one model of the domestic long-distance network has variables and would take weeks for one solution.
Narendra Karmarkar at AT&T Laboratories has developed a shortcut method * based on “projective geometry” that is 50 to 100 times faster.
* “The Startling Discovery Bell Labs Kept in Shadows”, Business Week, September 21, 1987, pp.69, 72, 76

18. Payoff Table This means:-
Decision Making Under Risk
Payoff Table
This means:-
Each Nj has a known (or assumed) probability of pj and
there may not be one state that results best outcome.

19. Payoff Table This means:-
Decision Making Under Uncertainty
Payoff Table
This means:-
Probabilities pj of future states are unknown.

20. Payoff Table Decision Making Under Risk
There exist a number of possible future states of Nature Nj.
Each Nj has a known (or assumed) probability pj of occurring.
There may not be one future state that results in the best outcome for all alternatives Ai.
Examples of future states and their probabilities
Alternative weather (N1=rain, N2=good weather) will affect the
probability of alternative construction schedule; the probabilities P1 of
rain and p2 of good weather can be estimated from historical data
2. Alternative economic futures (boom and bust) determine the relative
Profitability of high risk investment strategy.

21.  Payoff Table (pjOij) Ei= Decision Making Under Risk n
Expected Values (Ei) : given the future states of nature and their
probabilities, the solution in decision making under risk is the
alternatives Ai that provides the highest expected value Ei, which is
defined as the sum of the products of each outcome Oij
times the probability pj that the associated state of nature Nj occurs n 
j=1
(pjOij)
Ei=
Choose the Alternative Ai giving
the highest expected value

22.  Example of Decision Making Under Risk Alternatives
Calculate Expected Values (Ei)
Example of Decision Making Under Risk
Not Fire
in your house
P1 = P2=0.001
Insure house $ $-200
Do not Insure house $-100,000
Alternatives
Fire
State of Nature
Probabilities n 
j=1
(pjOij)
Ei=
Would you insure your house or not?
E1=$-200
E1=0.999*(-200)+0.001*(-200)
E2=$-100
E2=0.999* *(-100,000)

23. Example: Consider that you own rights to a plot of land under which
there may or may not be oil. You are considering three alternatives:
Doing nothing (don’t drill), drilling your own expense of $
(Drill alone), and farming out, the opportunity to someone who will
drill the well and give you part of the profit if the well is successful.
Drilling your own a small Well will make $ profit and a Big well $.
Farm out alternative with Dry hole will not cost at all, but a small Well
Will make $ and a Big Well will make $ profit.
Constitute the Payoff table and calculate the expected value of each
alternative solve the problem.

24. $720,000 Alternative State of Nature / Probability
Decision Making Under Risk
Calculate Expected Values (Ei)
Well Drilling Example-Decision Making Under Risk
N1:Dry Hole N2 :Small Well N3:Big Well
P1= P2= P3=0.1
A1:Don’t Drill $ $ $0
A2:Drill Alone $-500, $300, $9,300,000
Alternative
State of Nature / Probability
A3:Farm Out $ $125, $1,250,000
Expected
Value
E1=0.6*0+0.3*0+ 0.1*0 $0
E2=0.6*(-500,000)+0.3*(300,000)+ 0.1*(9,300,000)
$720,000
E3=0.6*0+0.3*(125,000)+ 0.1*(1,250,000)
$162,000
$720,000
A2 is the solution if you are willing to risk $500,000

25. Mathematical solution is identical, visual representation is different
Decision Making Under Risk
Calculate Expected Values (Ei)
Decision Trees
Decision
node Ai
Chance
node Nj
Outcome
(Oij)
Probability
(Pj)
Expected Value Ei x =
No Fire:
(-200) x
(0.999) =
(-199.8) +
= $-200
Insure
(-200) x
(0.001) =
(-0.2)
Fire:
No Fire:
(0) x
(0.999) =
(0)
Don’t Insure +
=$-100
Fire:
(-100,000) x
(0.001) =
(-100)
Mathematical solution is identical,
visual representation is different

26. Decision Making Under Uncertainty
We do not know the probabilities pj of future states of nature Nj

27. Decision Making Under Uncertainty
Decision Making Under Risk
Decision Making Under Uncertainty
N1:Dry Hole N2 :Small Well N3:Big Well
A1:Don’t Drill $ $ $0
A2:Drill Alone $-500, $300, $9,300,000
Alternative
State of Nature / Probability
A3:Farm Out $ $125, $1,250,000

28. Decision Making Under Uncertainty
Decision Making Under Uncertainty: Example
Coefficient of optimism
(Maximax) (Maximin)
Hurwicz Equally
Alternative Maximum Minimum (=0.2) Likely
Optimist Pessimist
Maximize [(best outcome)+(1-)(worst outcome)]
$1,450,000*
[0.2(9,300,000)+(1-0.2)(-500,000)]
A2:Drill Alone $9,300,000 * $-500,000
$3,033,333*
E2=-500, ,000+9,300,000 3
[0.2(1,250,000)+(1-0.2)(0)]
$250,000
A3:Farm Out $1,250, $0*
$458,333
E3=0+125,000+1,250,000 3

29. Alternative Alternative State of Nature / Probability
Decision Making Under Risk
Decision Making Under Uncertainty: Maximum Regret
N1:Dry Hole N2 :Small Well N3:Big Well
A1:Don’t Drill $ $ $0
A2:Drill Alone $-500, $300, $9,300,000
Alternative
State of Nature / Probability
A3:Farm Out $ $125, $1,250,000
N1:Dry Hole N2 :Small Well N3:Big Well
We do not know probabilities
A1:Don’t Drill $ $300, $9,300,000
A2:Drill Alone $500,
Alternative
State of Nature / Probability
A3:Farm Out $ $175, $8,050,000
Maximum
Regret
$9,300,000
$500,000
$8,050,000

30. Alternative State of Nature / Probability
Decision Making Under Uncertainty: Maximum Regret
State of Nature / Probability
Alternative
N1:Dry Hole N2 :Small Well N3:Big Well
Maximum
Regret
We do not know probabilities
A1:Don’t Drill $ $300, $9,300,000
$9,300,000
A2:Drill Alone $500,
$500,000
A2 is the solution.
We choose the minimum among maximum regrets.
A3:Farm Out $ $175, $8,050,000
$8,050,000

31. Integrated Data Bases, MIS, DSS and Expert Systems

32. 4 Types of Information Systems
Transaction Processing Systems (TPS)
Management Information Systems (MIS)
Decision Support Systems (DSS)
Expert Systems (ES)

33. Payroll System Personnel Data Projects Data Tax Data Personnel Data
Traditional Approach and Data (User) Oriented Approach
Payroll
System
Project
Management
System
Personnel
Data
Projects
Data
Tax
Data
Personnel
Data
Traditional Approach

34. Payroll System Personnel Data Projects Data Tax Data
Traditional Approach and Data (User) Oriented Approach
Payroll
System
Project
Management
System
Personnel
Data
Projects
Data
Tax
Data
Database Approach

35. 4 Types of Information Systems
Transaction Processing Systems (TPS)
Automate handling of data about business activities (transactions)
Management Information Systems (MIS)
Converts raw data from transaction processing system into meaningful form
Decision Support Systems (DSS)
Designed to help decision makers
Provides interactive environment for decision making

36. 4 Types of Information Systems
Expert Systems (ES)
Replicates decision making process
Knowledge representation describes the way an expert would approach the problem

37. For many companies, information systems
Competition needs very fast decisions and
rapid development of information systems.
Concentrate on what to do rather than how to do.
For many companies, information systems
cost 40 percent of overall costs.

38. Effect of Management Level on Decisions
Management Number of Cost of Making Information
Level Decisions Poor Decisions Needs
Top Least Highest Strategic
Middle Intermediate Intermediate Implementation
First-Line Most Lowest Operational

39. Decisions are useless unless they are put into practice.
Implementation
Decisions are useless unless they are put into
practice.
Courage is the willingness to submerge
oneself in the loneliness, the anxiety, and the
guilt of a decision maker.