1. Decision Making, Systems, Modeling, and Support
CHAPTER 2
Decision Making, Systems, Modeling, and Support

2. Outline Introduction System Modeling How decision Cognition
Decision makers
Summary

3. 1. Introduction
Decision making
Decision making is a process of choosing among alternative courses of action for the purpose of attaining a goal or goals.
Decision making and problem solving
there are four phases in this part. These phases are Intelligence, design, choice and implementation.
Decision making disciplines
Behavioral disciplines include philosophy, psychology…., and scientific disciplines include economics, statistic, decision analysis……

4. 1.1 Some Concepts in Decisions of Enterprise
The decision may be made by a group
Group members may have biases
There are several possibly conflicting objectives
Decision makers are interested in evaluating what-if scenarios
….etc.

5. 2. Systems What is systems? Level (or Hierarchy)
A system is a collection of objects such as people, resources, concepts, and procedures intended to perform an identifiable function or to serve a goal
Level (or Hierarchy)
this concept reflects that all systems are actually subsystems because all are contained within some larger system.

6. 2.1 The structure of a system
Three distinct parts of systems (Figure 2.1)
Inputs
Inputs are elements that enter the system.
Processes
Process are all elements necessary to convert or transform input into outputs.
Outputs
outputs are the finished products or the consequences of being in the system.
Besides these…
Three parts are surrounded by an environment and often include a feedback mechanism.
In addition , a human decision maker is considered part of the system.

7. 2.2 One way to identify the elements of the environment
Two questions： (Churchman, 1975)
1. Does the element matter relative to the
system’s goals? [YES]
2. Is it possible for the decision maker to
significantly manipulate this elements? [NO]

8. 2.3 The Boundary
A system is separated from its environment by a boundary.
The system is inside the boundary , whereas the environment lies outside.

9. 2.4 Closed and Open Systems
Closing the system
Because every system is a subsystem of another, the system analysis process may never end. So, we must confine the system analysis to defined, manageable, boundaries. And this confinement is call “closing the system”
Closed System
A closed system is totally independent from other subsystems or systems.
Open System
An open system is very dependent on its environment. And it accepts inputs from the environment and may deliver outputs to the environment.

10. 2.5 Information System
An information system collects , processes , stores , analyzes , and disseminates information for a specific purpose.
And the information system often located in core section.

11. 3. Models Simplified representation or abstract
The reality is too complex
The classification of models：
1. Iconic models
2. Analog models
3. Mathematical models

12. 3.1 The benefits of Models 1. Compression of time
2. Model manipulation is easier than real system
3 .Lower cost
4. Lower cost in trial-and-error experiment
5. Be used to estimate the risks
6. Analyzing large number of possible solutions
7. Help learning & training

13. 3.2 The Modeling Process Solution Approaches
Trial-and-error
Simulation
Optimization
Heuristics
Decision-Making Process (Simon, 1977)
Intelligence phase
Design phase
Choice phase
Implementation phase
(Figure 2.2)

14. 3.3 The Intelligence Phase
1. Finding the Problem
2. Problem Classification
－Programmed vs Nonprogrammed problems
3.Problem Decomposition
4.Problem Ownership

15. 3.4 The Design Phase
Finding, developing, and analyzing courses of action
Construct, test, validate a model of decision-making
Model－conceptualization of problem
abstraction to
quantitative/qualitative forms

16. Some topics of modeling (relate to quantitative model)
1. The components of the model
2. The structure of the model
3. Selection of a principle of choice
4. Developing alternatives
5. Predicting outcomes
6. Measuring outcomes
7. Scenarios

17. 3.4.1 The Component of Quantitative Model
Uncontrollable variables
Decision
Variables
Mathematical
relationships
Result
variables

18. Variables Area Decision Variables Result Uncontrollable variables
Financial
investment
Investment
amounts
Total
profit
Inflation
rate
Marketing
Where to
advertise
Market
share
Customer’s
income
Intermediate Result Variables

19. 3.4.2 The Structure of Quantitative Models
The Product-Mix Linear Programming Model
MBI Corporation
Decision: How many computers to build next month?
Two types of computers
Labor limit
Materials limit
Marketing lower limits Constraint CC7 CC8 Rel Limit Labor (days) <= 200,000 / mo Materials $ 10,000 15,000 <= 8,000,000/mo Units 1 >= 100 Units 1 >= 200 Profit $ 8,000 12,000 Max Objective: Maximize Total Profit / Month

20. Linear Programming Model
Components Decision variables X1,X2 Result variable Z Uncontrollable variables (constraints)
Solution X1 = X2 = Profit = $5,066,667

21. 3.4.3 Selection of a Principle of Choice
Describe the acceptability of a solution approach
1. Normative models
2. Suboptimization
3. Descriptive models
4. Good enough or satisficing
※Bounded rationality

22. 3.4.4 Developing (Generating) Alternatives
It’s necessary to generate alternatives manually
Searching & creativity－Taking time & costing money
Searching comes after the criteria for evaluating the alternatives

23. 3.4.5 Predicting the Outcome of Each Alternative
Classify the knowledge into three categories
←Increasing knowledge
Complete Risk Ignorance
knowledge
Decreasing knowledge→

24. Decision Making Under Certainty
The decision maker is a perfect predictor of the
future
Decision Making Under Risk
The decision maker have to consider possible
outcomes for each alternative
Calculating and selecting the best expected value
of an alternative → Risk Analysis
Decision Making Under Uncertainty
The decision maker doesn’t know about possible
outcomes

25. 3.4.6 Measuring Outcomes For example： Profit is an outcome
Profit maximization is a goal
※ But units of outcomes and goals are the same

26. 3.4.7 Scenarios
A statement of assumptions about the operating environment of a system
Be helpful in simulation & what-if analysis
In MSS, scenarios play an important role.
(Potential opportunities, problem areas,
flexibility in planning)

27. 3.5 The Choice Phase Search Approaches ─ Analytical techniques
Analytical techniques are used mainly for solving structured problems
─ Algorithms
Analytical techniques may use algorithms to increase the efficiency of the search.
─ Blind and heuristic search approach
Blind: blind research techniques are arbitrary approaches that are not guided
Heuristic search approach: it can reduce the amount of necessary computations.
AHP reference

28. 3.6 Evaluation: Multiple Goals, Sensitivity Analysis, What-If, and Goal Seeking
Today’s management systems are much more complex, and one with a single is few. Instead, managers want to attain simultaneous goals, where some of them conflict.
Conflicts？

29. Sensitivity Analysis: ─ Automatic sensitivity analysis
Sensitivity analysis attempts to assess the impact of a change in the input data or parameters on the proposed solution.
─ Trial and error
It is usually limited to one change at a time, and only for certain variables.

30. ─ What-if analysis (Figure 2.9)
Trial and Error:
─ What-if analysis (Figure 2.9)
If you change one of unit revenue, or unit cost, or initial sales or sales growth rate , you will know the end of the annual net profit.
─ Goal seeking (Figure 2.10)
Goal-seeking analysis calculates the values of inputs necessary to achieve a desired level of an output

31. 3.7 The Implementation Phase
At more than 400 years ago , Machiavelli said: 『
“nothing more difficult carry out , nor more doubtful of success , nor more dangerous to handle , than to initiate a new order of things.” 』

32. 4. How Decisions Are Supported
ANN
MIS
EIS
Intelligence
GDSS
ANN
Management
science
Design
Choice
GDSS
Implementation

33. Intelligence: Design: Choice: Implement:
DSS can support this phase to scan external and internal information sources for opportunities .
Design:
DSS has this capability of generating alternative course of action, discussing the criteria for choice.
Choice:
DSS can support the choice phase through the what-if and goal-seeking analysis.
Implement:
Implementation phase DSS benefits are partly due to the vividness and detail of the analysis and displayed output.

34. 5. Cognitive Cognition theory Cognitive style
Cognition is the set of activities.
Cognitive style
It is the subject process through which people perceive , organize, change information during the decision-making process.
Decision style (table 2.4)
Decision style is the manner in which decision makers think and react to problems.

35. 6. The Decision Makers
Individual
Group

36. 7. Summary
Managerial making is synonymous with the whole process of management
Problem solving is also opportunity evaluation
Systems can be open , interacting with their environment , or closed.
….etc

37. environment
output(s)
input(s)
轉換過程
feedback
boundary