Chapter 2 DECISION MAKING, SYSTEMS, MODELING, AND SUPPORT 12nd semester 2010 Dr. Qusai Abuein 8 th...

Chapter 2

DECISION MAKING, SYSTEMS, MODELING, AND SUPPORT

12nd semester 2010 Dr. Qusai Abuein

8th Edition

Learning Objectives

• Understand the conceptual foundations of decision making

• Understand Simon’s four phases of decision making: intelligence, design, choice, and implementation

• Recognize the concepts of rationality and bounded rationality, and how they relate to decision making


Learning Objectives

• Differentiate between the concepts of making a choice and establishing a principle of choice

• Learn how DSS support for decision making can be provided in practice

• Understand the systems approach


(2.1) Opening Vignette

• Illustrates how does Greenspan (the U.S federal Reserve chairman) decided to raise or lower interest rate.

• He used statistical analysis to data collected from different systems, his 25 years experience, real-time information and knowledge systems.


(2.2) Decision Making: Introduction and Definitions • Knowing:

– How decision making is practiced, – Theories and models of decision making,– Traits of decision makers

• Help in understanding the types of decision support tools that managers can use to make more effective decisions.


(2.2) Decision Making: Introduction and Definitions • Characteristics of decision making

– Groupthink – Decision makers are interested in evaluating what-if

scenarios– Experimentation with the real system may result in

failure– Experimentation with the real system is possible only

for one set of conditions at a time and can be disastrous

– Changes in the decision making environment may occur continuously, leading to invalidating assumptions about the situation


(2.2) Decision Making: Introduction and Definitions • To determine how real decision makers make

decisions, we :1. Must understand the process of decision making

2. Understand appropriate methodologies for assisting decision makers

3. Contributions IS can make

• Only then can we develop DSS to help decision makers.


(2.2) Decision Making: Introduction and Definitions

• A decision is selection the best alternative from two or more solutions. (as defined in ch1)

• Decision making:

The action of selecting among alternatives• Planning involves a series of decisions: What

should be done? When? Where? Why? How? By whom?

• Managers set goals or plan• Planning implies decision making



• Decision making and problem solving• A problem occurs when:

– A system does not meet its goals– Does not yield the predicted results, or– Does not work as planned

• Problem solving may deal with identifying new opportunities.

Q) How to differentiate between problem solving and decision making?

– By examining the phases of decision process.



• Phases of the decision process1. Intelligence 2. Design 3. Choice

• Problem solvingA process in which one starts from an initial state and proceeds to search through a problem space to identify a desired goal. It includes the 4th phase of the decision process

4. Implementation • Some consider the phases (1-4) as problem solving, with the choice

phase as the real decision-making process.• Others, consider phases (1-3) as the formal decision-making, with the

implementation phase as the problem solving102nd semester 2010 Dr. Qusai Abuein


• In this book problem solving is used interchangeably with decision making.



• Decision making disciplines are:– Behavioral– Scientific

• What constitutes a successful decision in practice?– Effectiveness

The degree of goal attainment. Doing the right things– Efficiency

The ratio of output to input. Appropriate use of resources. Doing the things right

Effectiveness is the concern of MSS (Management Support Sys.)

Efficiency is the concern of TPS (Transaction Support System)



• Decision style and decision makers• Decision style

The manner in which a decision maker thinks and reacts to problems. It includes perceptions, cognitive responses, values, and beliefs.

• Decision styles are:– Autocratic– Democratic– Consultative


(2.2) Decision Making: Introduction and Definitions • Decision style and decision makers

– Different decision styles require different types of support• Individual decision makers need access to data

and to experts who can provide advice• Groups need collaboration tools

• In small organizations, decisions are often made by individuals at lower managerial level.

• In medium and large organizations, decisions are made by groups.


(2.3) Models

• The basic idea is to perform the DSS analysis on a model of reality rather than on real system.

• A model is a simplified representation of reality.• Models are classified into:

– Iconic model

A scaled physical replica, such as an airplane model (3D)

– Analog model

An abstract, symbolic model of a system that behaves like the system but looks different, such as charts and figure represents water on mountains. (2D)


(2.3) Models

• Mental model

The mechanisms or images through which a human mind performs sense-making in decision making

• Mathematical (quantitative) model

A system of symbols and expressions that represent a real situation


(2.3) Models

• The benefits of models • MMS uses models for the following reasons:

– Model manipulation is much easier than manipulating a real system

– Models enable the compression of time (simulation)

– The cost of modeling analysis is much lower

– The cost of making mistakes during a trial-and-error experiment is much lower when models are used than with real systems


(2.3) Models

• The benefits of models – With modeling, a manager can estimate the risks

resulting from specific actions within the uncertainty of the business environment

– Mathematical models enable the analysis of a very large number of possible solutions

– Models enhance and reinforce learning and training

– Models and solution methods are readily available on the Web

– Many Java applets are available to readily solve models


(2.4) Phases of the Decision-Making Process


(2.4) Phases of the Decision-Making Process• It advisable to follow a systematic decision-

making process:

1. Intelligence phase

The initial phase of problem definition in decision making

2. Design phase

The second decision-making phase, which involves constructing a model and finding possible alternatives in decision making and assessing their contributions


(2.4) Phases of the Decision-Making Process3. Choice phase

The third phase in decision making, in which an alternative is selected

4. Implementation phase

The fourth decision-making phase, involving actually putting a recommended solution to work


(2.5) Decision Making: The Intelligence Phase • It includes problem (or opportunity) identification.

Some issues that may arise during data collection: – Data are not available – Obtaining data may be expensive – Data may not be accurate or precise enough – Data estimation is often subjective – Data may be insecure – Important data that influence the results may be

qualitative


(2.5) Decision Making: The Intelligence Phase

• Some issues that may arise during data collection – Information overload (too many data)

– Outcomes (or results) may occur over an extended period

– If future data is not consistent with historical data, the nature of the change has to be predicted and included in the analysis


(2.5) Decision Making: The Intelligence Phase

• Problem classification

The conceptualization of a problem in an attempt to place it in a definable category, possibly leading to a standard solution approach

• Problem decomposition

Dividing complex problems into simpler subproblems may help in solving the complex ones

• Problem ownership

Is the process of assigning authority to solve a problem


(2.6) Decision Making: The Design Phase • The design phase involves finding or developing

and analyzing possible courses of action. These include:– Understanding the problem

– Testing solutions for feasibility

– A model of the decision-making problem is constructed, tested, and validated


(2.6) Decision Making: The Design Phase

• Modeling involves conceptualizing a problem and abstracting it to quantitative and/or qualitative form

• Models have:– Decision variables– Principle of choice


(2.6) Decision Making: The Design Phase • Decision variables

A variable in a model that can be changed and manipulated by the decision maker. Decision variables correspond to the decisions to be made, such as quantity to produce, amounts of resources to allocate, and so on

• Principle of choiceThe criterion that describes the acceptability of a solution approach.

Q) What is the difference between Criterion and constraint? (See technology insight 2.1 page 59)

27


(2.6) Decision Making: The Design Phase • Among the many principles of choice,

normative and descriptive are important.• Normative models

Models in which the chosen alternative is demonstrably (proven to be) the best of all possible alternatives – Optimization

The process of examining all the alternatives and proving that the one selected is the best

– Suboptimization An optimization-based procedure that does not consider all the alternatives for or impacts on an organization because a decision made in one area may affect other areas. (details P61,62 are important)


(2.6) Decision Making: The Design Phase • Descriptive model

A model that describes things as they are. These models are typically mathematically based.– Simulation

Is an imitation of reality and the most common descriptive modeling method.

– Narrative is another descriptive decision-making model, it is a story that helps a decision maker uncover the important aspects of the situation and leads to better understanding and framing


(2.6) Decision Making: The Design Phase• Good enough or satisficing

– SatisficingA process by which one seeks a solution that will satisfy a set of constraints. In contrast to optimization, which seeks the best possible solution, satisficing simply seeks a solution that will work well enough up to a predetermined level of performance.

Q) Why accepting satisficing solution?– Due to time pressure (timely decision)– The ability to achieve optimization– Marginal benefit of a better decision does not worth

the marginal cost to obtain it



• Good enough or satisficing – Satisficing is a form of suboptimization.– There may be a best solution (optimum), but it

would be difficult (impossible) to attain: • With a normative model: too much computation may

be involved• With descriptive model: it may not be possible to

evaluate all alternatives

– Q) Since rationality and the use of normative models lead to good decisions, why are so many bad decisions made in practice? H.W.


(2.6) Decision Making: The Design Phase• Developing (generating) alternatives

– In optimization models (linear programming) the alternatives may be generated automatically by the model

– In most MSS situations it is necessary to generate alternatives manually (a lengthy, costly process); issues such as when to stop generating alternatives are very important

– The search for alternatives usually occurs after the criteria for evaluating the alternatives are determined (this way it is easy to find alternatives)

– The outcome of every proposed alternative must be established



• Measuring outcomes – The value of an alternative is evaluated in terms of

goal attainment

• Ex.(1): – Goal Profit maximization– Outcome Profit

• Ex.(2): – Goal Customer Satisfaction maximization– Outcome Customer Satisfaction

Q) What is an AHP? H.W. 332nd semester 2010 Dr. Qusai Abuein

Measured by JD

Measured by # of complaints, level of loyalty to product, or surveys


• Risk – One important task of a decision maker is to

attribute a level of risk to the outcome associated with each potential alternative being considered

– Decisions that lead to unacceptable risks in terms of success are discarded.

– Risk is an important issue when developing BI/DSS



• Scenario

Is a statement of assumptions about the operating environment of a particular system at a given time; that is, it is a narrative description of the decision-situation setting – Scenarios are especially helpful in simulations

and what-if analyses – In simulation and what-if analysis, scenarios are

changes and results are examined



– Scenarios play an important role in MSS because they:

• Help identify opportunities and problem areas• Provide flexibility in planning• Identify the leading edges of changes that

management should monitor• Help validate major modeling assumptions• Allow the decision maker to explore the behavior of a

system through a model• Help to check the sensitivity of proposed solutions to

changes in the environment



– Possible scenarios • The worst possible scenario• The best possible scenario• The most likely scenario• The average scenario

– Scenario determines the context of analysis to be performed.



• Errors in decision making – The model is a critical component in the

decision-making process – A decision maker may make a number of errors

in its development and use– Validating the model before it is used is critical– Gathering the right amount of information, with

the right level of precision and accuracy is also critical


(2.7) Decision Making: The Choice Phase• The choice phase is the one in which the actual decision

is made and involves following appropriate course of action

Q) Why boundary between design and choice phases are not clear? H.W.

• The choice phase includes the search for, evaluation of, and recommendation of an appropriate solution to a model.

• Solving a model is not as solving the problem the model represents.

• Solution to a model is considered a recommended solution to a problem.

• When the recommended solution is successful, then the problem is solved.


(2.7) Decision Making: The Choice Phase• Solving a model involves searching for appropriate

course of action. Searching approaches include:– Analytical techniques (solving a formula)– Algorithms (step-by-step procedures)– Heuristics (rules of thumb)– Blind searches


(2.7) Decision Making: The Choice Phase

• Analytical techniques

Methods that use mathematical formulas to derive an optimal solution directly or to predict a certain result, mainly in solving structured problems

• Algorithm

A step-by-step search in which improvement is made at every step until the best solution is found



• Heuristics

Informal, judgmental knowledge of an application area that constitutes the rules of good judgment in the field. Heuristics also encompasses the knowledge of how to solve problems efficiently and effectively, how to plan steps in solving a complex problem, how to improve performance, and so forth



• Sensitivity analysis

A study of the effect of a change in one or more input variables on a proposed solution

• What-if analysis

A process that involves asking a computer what the effect of changing some of the input data or parameters would be


(2.8) Decision Making: The Implementation Phase

• The implementation to a proposed solution is the introduction of change.

• Change which includes user expectation must be managed.

• Implementation phase involves putting a recommended solution to work.

• Generic implementation issues important in dealing with MSS include: – Resistance to change– Degree of support of top management– User training


(2.9) How Decisions Are Supported

• The decision making process (Figure 2.2 next slide) can be improved with computer support.

• Specific MSS technologies to decision making process include:– Database– Data warehouse



• Support for the intelligence phase – The ability to scan external and internal

information sources for opportunities and problems and to interpret what the scanning discovers

• Web tools and sources are extremely useful for environmental scanning

• Web browsers provide useful front ends for a variety of tools (OLAP, data mining, data warehouses)

• Internal data sources may be accessible via a corporate intranet

• External sources are many and varied



• Support for the design phase – The generation of alternatives for complex

problems requires expertise that can be provided only by a human, brainstorming software, or an ES


(2.9) How Decisions Are Supported • Support for the choice phase

– DSS can support the choice phase through what-if and goal-seeking analyses

– Different scenarios can be tested for the selected option to reinforce the final decision

– KMS helps identify similar past experiences– CRM, ERP, and SCM systems are used to test the

impacts of decisions in establishing their value, leading to an intelligent choice

– An ES can be used to assess the desirability of certain solutions and to recommend an appropriate solution

– A GSS can provide support to lead to consensus in a group



• Support for the implementation phase– DSS can be used in implementation activities

such as decision communication, explanation, and justification.

– DSS benefits are partly due to the vividness and detail of analyses and reports.

– Decision implementation can also be supported by ES.

– ES can be used as an advisory system and provide training that may smooth the implementation.



• New technology support for decision making – Web-based systems have clearly influenced how

decision making is supported– Mobile commerce (m-commerce)– Personal devices

• Personal digital assistants [PDAs]• Cell phones• Tablet computers• laptop computers

– Advanced AI can be utilized in decision making.


End Of Chapter 2

Thank you

2nd semester 2010 Dr. Qusai Abuein 52

Chapter 2 DECISION MAKING, SYSTEMS, MODELING, AND SUPPORT 12nd semester 2010 Dr. Qusai Abuein 8 th...

Documents

Transcript of Chapter 2 DECISION MAKING, SYSTEMS, MODELING, AND SUPPORT 12nd semester 2010 Dr. Qusai Abuein 8 th...