DSS Making Systems

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Chapter 2: Decision Making,

Systems,Modeling, and Support

y

Conceptual Foundations of DecisionMaking

y The Systems Approach

y How Support is Provided



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Typical Business Decision Aspects

Decision may be made by a group

Several, possibly contradictory objectives

Hundreds or thousands of alternatives

Results can occur in the future

Attitudes towards risk

³What-if´ scenarios

Trial-and-error experimentation with the real system:may result in a loss

Experimentation with the real system can only bedone once

Changes in the environment can occur continuously

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. AronsonCopyright 1998, Prentice Hall, Upper Saddle River, NJ



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How are decisions made???

What methodologies can be applied?

What is the role of information systems

in supporting decision making?

DSS Decision

Support

Systems




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2.3 Systems

A SYSTEM is a collection of objects such aspeople, resources, concepts, andprocedures intended to perform an

identifiable function or to serve a goal

System Levels (Hierarchy): All systems are

subsystems interconnected throughinterfaces




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The Structure of a System

Three Distinct Parts of Systems

Inputs

Processes

Outputs

Systems

Are surrounded by an environment

Frequently include a feedback mechanism

A human, the decision maker, is usuallyconsidered part of the system




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System

Input(s)

Feedback

Environment

Output(s)

Boundary

Processes



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How to Identify theEnvironment?

Answer Two Questions

1. Does the element matter relative to the system's goals?[YES]

2. Is it possible for the decision maker to significantly

manipulate this element? [NO]

Environmental Elements Can Be

Social

Political

Legal

Physical

Economical

Often Other Systems



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Closed and Open Systems

A Closed System is totally independent of other systems and subsystems

An Open System is very dependent on itsenvironment

a continuum

Defining manageable boundaries is closing thesystem



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TABLE 2.1 A Closed Versus an Open Inventory System

Factors

ManagementScience, EOQ(Closed System)

I nventory DSS(Open System)

Demand Constant Variable, influenced by manyfactors

Unit cost Constant May change daily

Lead time Constant Variable, difficult to predict

Vendors and users Excluded from

analysis

May be included in analysis

Weather and other environmental factors

Ignored M ay influence demand andlead time




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An Information System

Collects, processes, stores, analyzes, anddisseminates information for a specific purpose

Is often at the heart of many organizations

Accepts inputs and processes data to provideinformation to decision makers and helps

decision makers communicate their results




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Performance Measurement

Effectiveness and Efficiency Effectiveness is the degree to which goals are

achievedDoing the right thing!

Efficiency is a measure of the use of inputs (or resources) to achieve outputsDoing the thing right!

Productivity (efficiency/effectiveness)

Measurable?

several non-quantifiable, conflicting goals

MSS: effectiveness focus




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2.4 Models

Major Component of DSS

Use Models instead of experimenting on thereal system

A model is a simplified representation or abstraction of reality.

Reality is generally too complex to copyexactly

Much of the complexity is actually irrelevant

in problem solving




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Degrees of Model Abstraction

(Least to Most)

I conic (Scale) Model : Physical replica of a

system Analog Model behaves like the real system

but does not look like it (symbolicrepresentation)

Mathematical (Quantitative) Models usemathematical relationships to representcomplexityUsed in most DSS analyses




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Benefits of ModelsAn MSS employs models because

1. Time compression2. Easy model manipulation3. Low cost of construction

4. Low cost of execution (especially that of errors)5. Can model risk and uncertainty6. Can model large and extremely complexsystems with possibly infinite solutions

7. Enhance and reinforce learning, andenhance training.

Computer graphics advances: complementmath models using more iconic and analog

models (visual simulation)



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Modeling--Preview

Example: How Much to Order for the Ma-PaGrocery?

The Question: How much bread to stock each

day?

Several Solution Approaches Trial-and-Error

Simulation Optimization

Heuristics




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Decision Making

Decision:a reasoned choice among alternatives

Examples:

± Where to advertise a new product ± What stock to buy ± What movie to see

± Where to go for dinner

Decision Making : a process of choosing among

alternative courses of action for the purpose

of attaining a goal or goals

Decision making vs. problem solving?

ART or SCIENCE?



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The Decision-MakingProcess

Systematic Decision-Making Process (Simon¶sModel)

Intelligence Design

Choice

Implementation

Modeling is Essential to the Process




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Intelligence phase

± Reality is examined

± The problem is identified and defined Design phase

± Representative model is constructed

± The model is validated and evaluation criteriaare set

Choice phase ± Includes a proposed solution to the model

± If reasonable, move on to the

Implementation phase

± Solution to the original problem

Failure: Return to the modeling process

Often Backtrack / Cycle Throughout the Process




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Scan the environment to identify problemsituations or opportunities

Identify organizational goals and objectives

Determine whether they are being met

Explicitly define the problem

Classify the problem

Decompose into sub-problems

Is it my problem (ownership)

Can I solve it

Outcome: Problem statement

2.6 The Intelligence Phase


Problem or Symptom?



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2.7 The Design Phase

Generating, developing, and analyzingpossible courses of action

Includes

Understanding the problem Testing solutions for feasibility

A model is constructed, tested, and validated

Modeling Conceptualization of the problem

Abstraction to quantitative and/or qualitativeforms




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Types of Decisions

Type of structure - Nature of task

Level of decision making - Scope

Structured Unstructured

Strategic

Managerial

Operational



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Nature of Decision

Structured Problems ± Routine and repetitive with standard solution

± Well defined decision making procedure

± Given a well-defined set of input, a well defined set of output is defined

Semi-structured Problems

± Has some structured aspect

± Some of the inputs or outputs or procedures are not welldefined

Unstructured Problems

± All phases of decision making process are unstructured

± Not well defined input, output set and procedures



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Scope of Decision

Operational Planning and Control: ± Focus on efficient and effective execution of specific tasks.

± They affect activities taking place right now

± E.g... What should be today's production level

Management Control and Tactical Planning ± Focus on effective utilization of resources

± more longer range planning horizon

± E.g... What is next years production level Strategic Planning

± Long-range goals and policies for resource allocation

± E.g... What new products should be offered



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Information CharacteristicsCharacteristics Operational Managerial Strategic

Accuracy High ow

Level of detail Detailed Aggregate

Time horizon Present FutureUse Frequent Infrequent

Source Internal External

Scope arrow Wide

Nature Quantitative Qualitative

Age Current Current/old



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3x3 Decision Type GridOperational Tactical Strategic

Structured

Semi-structured

Unstructured



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Assume you own a car dealership. State three decisionsthat you or your employees would make that fit in the gridboxes.

Explain the placement of each decision in the grid.



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Mathematical Model

Identify Variables

Establish Equations describing their

Relationships Simplifications through Assumptions

Balance Model Simplification and theAccurate Representation of Reality

Modeling : An Art and Science




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Decision

VariablesMathematical

Relationships

Result

Variables

Uncontrollable

variables

Quantitative Models

Decision Variables Describe alternative courses of action The decision maker controls them

Result Variables Reflect the level of effectiveness of the system

Dependent variablesResults of Decisions are Determined by the Decision Uncontrollable Factors Relationships among Variables



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TA BLE 2 . 2 Ex amples of t he Components of M odels.

Area

Decision

V ariables

Result

V ariables

UncontrollableV ariables and

ParametersFinancial investment Investment

alternatives andamountsHow long to investW hen to invest

Total profitRate of retur n RO IEarnings per shareLiquidity level

Inflation ratePrim e rateCompetit ion

M arket ing A dvert ising budgetW here to advertise

M arket shareCustomer satisfaction

Custom ers' incomeCom petitors' actions

M anufacturing W hat and how muchto produceInventory levelsCompensationprograms

Total costQ uality levelEmployeesatisfaction

M achine capacityTechnologyM aterials prices

A ccount ing U se of com puters A udit schedule

Data processing costError rate

Computer technology

Tax r atesLegal requirements

Transpor tat ion Shipm ents schedule Total t ranspor t cost Delivery distanceRegulations

Services Staff ing levels Customer sat isfact ion Dem and for services



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Uncontrollable Variables or

Parameters Factors that affect the result variables

N ot under the control of the decision maker

Generally part of the environment Some constrain the decision maker and are

called constraints

Intermediate Result Variables Reflect intermediate outcomes



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The Structure of QuantitativeModels

Mathematical expressions (e.g., equationsor inequalities) connect the components

Simple financial-type modelP = R - C

Present-value model

P = F / (1+i)n




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Example

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 LimitLabor (days) 300 500 <= 200,000 / moMaterials $ 10,000 15,000 <= 8,000,000/mo

Units 1 >= 100Units 1 >= 200Profit $ 8,000 12,000 Max

Objective: Maximize Total Profit / Month




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Example: Linear Programming Model

ComponentsDecision variables

Result variable

Uncontrollable variables (constraints)

Solution

X 1 = 333.33

X 2 = 200Profit = $5,066,667




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The Principle of Choice

What criteria to use?

Best solution?

Good enough solution?

Selection of a Principle of Choice

A decision regarding the acceptabilityof a solution approach

Normative

DescriptiveDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. AronsonCopyright 1998, Prentice Hall, Upper Saddle River, NJ



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Normative Models

The chosen alternative is demonstrably the best(O ptimization)

Normative decision theory is based

on rational decision makers:

Humans are economic beings whose objective is tomaximize the attainment of goals; that is, the decisionmaker is rational

In a given decision situation, all viable alternativecourses of action and their consequences, or at least

the probability and the values of the consequences, areknown

Decision makers have an order or preference thatenables them to rank the desirability of allconsequences of the analysis




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Suboptimization

Narrow the boundaries of a system

Consider a part of a complete system

Leads to (possibly very good, but) non-

optimal solutions

Viable method




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Descriptive Models

Describe things as they are, or as they arebelieved to be

Extremely useful in DSS for evaluating the

consequences of decisions and scenarios No guarantee that a solution is optimal

Often a solution will be "good enough´

Simulation: Well-known descriptive modeling

technique




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Satisficing (Good Enough)

Most human decision makers will settle for agood enough solution

There is a tradeoff between the time and costof searching for an optimum versus the valueof obtaining one

A good enough or satisficing solution may befound if a certain goal level is attained

(Simon [1977])




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Why Satisfice?Bounded Rationality (Simon)

Humans have a limited capacity for rationalthinking

They generally construct and analyze a simplified

model Their behavior with respect to the simplified model

may be rational

But, the rational solution for the simplified modelmay NOT BE rational in the real-world situation

Rationality is bounded not only by limitations onhuman processing capacities, but also byindividual differences

Bounded rationality is why many models are

descriptive, not normativeDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. AronsonCopyright 1998, Prentice Hall, Upper Saddle River, NJ



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Developing (Generating)Alternatives

In Optimization Models: Automatically bythe Model!

Not Always So!

Issue: When to Stop?




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Predicting the Outcome of EachAlternative

Must predict the future outcome of eachproposed alternative

Consider what the decision maker knows (or believes) about the forecasted results

Classify Each Situation as Under ± Certainty

± Risk

± Uncertainty




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Decision Making Under Certainty

Assumes that complete knowledge isavailable (deterministic environment)

Example: U.S. Treasury bill investment

Typically for structured problems with shorttime horizons

Sometimes DSS approach is needed for certainty situations




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Decision Making Under Risk(Risk Analysis)

(Probabilistic or stochastic decision situation)

Decision maker must consider several possibleoutcomes for each alternative, each with agiven probability of occurrence

Long-run probabilities of the occurrences of the given outcomes are assumed known or canbe estimated

Decision maker can assess the degree of riskassociated with each alternative (calculated

risk)




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Risk Analysis

Calculate the expected value of eachalternative

Selecting the alternative with the bestexpected value.

Example: Poker game with some cards faceup (7 card game - 2 down, 4 up, 1 down)




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Decision Making Under Uncertainty

Situations in which several outcomes arepossible for each course of action

BUT the decision maker does not know, or cannot estimate, the probability of occurrence

of the possible outcomes

More difficult - insufficient information

Modeling involves assessing the decision

maker's (and/or the organizational) attitudetoward risk

Example: Poker game with no cards face up (5card stud or draw)




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Measuring Outcomes

Goal attainment

Maximize profit

Minimize cost Customer satisfaction level (Minimize

number of complaints)

Maximize quality or satisfaction ratings(found by surveys)




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Scenarios

Useful in

Simulation

What-if analysis

Many possible scenarios, but «

± Worst possible (Low demand, High costs)

± Best possible (High demand, High Revenue, Low Costs)

± Most likely (Typical or average values)



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2.8 The Choice Phase Search, evaluation, and recommending an

appropriate solution to the model Specific set of values for the decision

variables in a selected alternative

The problem is considered solved after therecommended solution to the model issuccessfully implemented

Search Approaches Analytical Techniques

Algorithms (Optimization)

Blind and Heuristic Search Techniques




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TABLE . Exampl f ri ti

j t r a

machi

the j that r equir e the

least time fir st.

ur chase st cks I f a price-t -ear i sr atiexceeds 0, then do not uy

the stocks.

Tr avel o not use the fr eewaybetween and a.m.

apital investment in hi h-tech pr o jects

Consider only those pr o jectswhose estimated payback

period is lessthan two year s.

ur chase of a house Buy only in a ood

nei hbor hood, but buy onlyin the lower price r ange.




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Evaluation of Alternatives

Evaluation (coupled with the search process)leads to a recommended solution

Multiple Goals, often Conflicting goals

Typical quantitative models have a single goal

Multiple goals: Utility theory, Goalprogramming, Expression of goals asconstraints, etc.

Sensitivity AnalysisChange inputs or parameters, look at modelresults

Automatic, Trial and error



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What-If Analysis

Figure 2.8 - SSpreadsheet example of a what-if query for a cash flow problem

Goal Seeking

Backward solution approach Example: Figure 2.9

Example: What interest rate causes an the netpresent value of an investment to break even?

In a DSS the what-if and the goal-seekingoptions must be easy to perform




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2.10 The I mplementation Phase

There is nothing more difficult to carry out, nor more doubtful of success, nor more dangerousto handle, than to initiate a new order of things(Machiavelli [15 s])

*** The Introduction of a Change ***

Important Issues Resistance to change

Degree of top management support Users¶ roles and involvement in system

development

Users¶ training




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2.11 How Decisions Are

Supported

Specific MSS technologies relationship to thedecision making process (see Figure 2.1 )

Intelligence: DSS, ES, ANN, MIS, Data Mining,OLAP, EIS, GDSS

Design and Choice: DSS, ES, GDSS,Management Science, ANN

Implementation: DSS, ES, GDSS




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2.12 Human Cognition and

Decision Styles

Cognition Theory

Cognition: Activities by which an individual

resolves differences between an internalizedview of the environment and what actuallyexists in that same environment

(Ability to perceive and understand information)

Cognitive models are attempts to explain or understand various human cognitiveprocesses




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Cognitive Style

The subjective process through which individualsperceive, organize, and change information during thedecision-making process

Often determines people's preference for human-machine interface

Impacts on preferences for qualitative versusquantitative analysis and preferences for decision-making aids

Cognitive style research impacts on the design of management information systems

Analytic decision maker

Heuristic decision maker (See Table 2.4)




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TABLE 2 .4 Cognitive-style Decision Approaches.

Problem-solving

Dimension Heuristic Analytic

Approach to learning Learns more by acting than by

analyzing the situation and

places more emphasis on

feedback.

Employs a planned sequential

approach to problem solving;

learns more by analyzing the

situation than by acting and

places less emphasis on

feedback.

Search Uses trial and error and

spontaneous action.

Uses formal rational analysis.

Approach to analysis Uses common sense, intui tion,

and feelings.

Develops explicit, often

quantitative, models of the

situation.

Scope of analysis Views the totality of the

situation as an organic whole

rather than as a structure

constructed from specific parts.

Reduces the problem situation

to a set of underlying causal

functions.

Basis for inferences Looks for highly visible

situational differences that vary

with time.

Locates similarities or

commonalities by comparing

objects.

(Source : G. B. Davis. Management Inform ation System s: Concept ual Foundat ions, Str uct ure, and Developm ent .

New York: McGraw-Hill, 1974 , p. 150. Reproduced with permission of McGraw-Hill, Inc.)



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Decision Styles

The manner in which decision makers Think and react to problems

Perceive

± Their cognitive response

±Their values and beliefs

Varies from individual to individual and from situationto situation

Decision making is a nonlinear process

The manner in which managers make decisions (and

the way they interact with other people) describes their

decision style

There are dozensDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. AronsonCopyright 1998, Prentice Hall, Upper Saddle River, NJ



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Some Decision Styles

Heuristic Analytic

Autocratic

Democratic

Consultative (with individuals or groups)

Combinations and variations

For successful decision making support, an

MSS must fit the ± Decision situation

± Decision style




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The system

± should be flexible and adaptable to different users

± have what-if and goal-seeking ± have graphics

± have process flexibility

An MSS should help decision makers useand develop their own styles, skills, andknowledge

Different decision styles require differenttypes of support

Major factor: individual or group decisionmaker




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Groups

Most major decisions in medium and large organizationsare made by groups

Conflicting objectives are common

Variable size People from different departments

People from different organizations

The group decision making process can be verycomplicated

Consider Group Support Systems (GSS)

Organizational DSS can help in enterprise-wide decisionmaking situations



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Summary

Managerial decision making is synonymouswith the whole process of management

Problem solving also refers to opportunity'sevaluation

A system is a collection of objects such as

people, resources, concepts, and proceduresintended to perform an identifiable function or to serve a goal

DSS deals primarily with open systems

A model is a simplified representation or abstraction of reality

Models enable fast and inexpensiveexperimentation with systems




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Summary (cont.)

Modeling can employ optimization, heuristic, or simulation techniques

Decision making involves four major phases:intelligence, design, choice, andimplementation

What-if and goal seeking are the two mostcommon sensitivity analysis approaches

Computers can support all phases of decisionmaking by automating many of the required

tasks

Human cognitive styles may influence human-machine interaction

Human decision styles need to be recognized

Documents

DSS Making Systems