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

Outline Introduction System Modeling How decision Cognition Decision makers Summary

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……

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.

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.

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.

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]

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.

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.

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.

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

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

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)

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

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

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

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

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

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) 300 500 <= 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

Linear Programming Model Components Decision variables X1,X2 Result variable Z Uncontrollable variables (constraints) Solution X1 = 333.33 X2 = 200 Profit = $5,066,667

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

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

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

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

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

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)

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

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？

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.

─ 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

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.” 』

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

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.

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.

6. The Decision Makers Individual Group

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

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