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MIS-410: Decision Support System (DSS)
Ashis Talukder
Lecturer, MISDhaka University
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Book:DECISION SUPPORT SYSTEMS ANDBUSINESS INTELLIGENCE
Efraim Turban
Jay E Aronson
Teng-Peng LiangReamesh Sharda
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Chapter 2:DECISION MAKING, SYSTEMS,MODELING, AND SUPPORT
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Learning Objectives Understand the conceptual foundations of
decision making
Understand Simons four phases of
decision making: intelligence, design,
choice, and implementation
Recognize the concepts of rationality and
bounded rationality, and how they relateto decision making
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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
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Decision Making:Introduction and Definitions Characteristics of decision making
Groupthink
Decision makers are interested in evaluating what-ifscenarios
Experimentation with the real system may result infailure
Experimentation with the real system is possible onlyfor one set of conditions at a time and can be
disastrous Changes in the decision making environment may
occur continuously, leading to invalidatingassumptions about the situation
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Decision Making:Introduction and Definitions Characteristics of decision making
Changes in the decision making environment may
affect decision quality by imposing time pressure on
the decision maker
Collecting information and analyzing a problem
takes time and can be expensive. It is difficult to
determine when to stop and make a decision
There may not be sufficient information to make an
intelligent decision
Information overload: Too much information
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Decision Making:Introduction and Definitions Decision making
The action of selecting among alternative courses ofactions to attain a goal or goals.
Important managerial function: planning
Planning involves series of decisions: what should be done? When?
Where?
Why?
How?
By whom?
Manger sets goals (or plans), planning implies decisionmaking.
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Decision Making:Introduction and Definitions Problem Occurs
When a system doesnt meet its
established goals
Doesnt yield the predicted result
Doesnt work as planned
Problem solving may also deal with
identifying new opportunities.
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Decision Making:Introduction and Definitions Phases of the decision process
1. Intelligence
2. Design
3. Choice
Problem solving
A process in which one starts from an initialstate and proceeds to search through a
problem space to identify a desired goal. Itincludes the 4th phase of the decision process4. Implementation
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Decision Making:Introduction and Definitions Decision making disciplines
Behavioral:
anthropology, law, philosophy, political science,
psychology, social psychology, sociology Scientific
Computer Science, decision analysis,
economics, engineering, hard sciences (biology,
chemistry, physics), ManagementSciences/Operation Researches, Mathematics
and Statistics.
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Decision Making:Introduction and Definitions Each discipline has its own sets of assumptions about
reality and methods
Each also contributes a unique, valid view of howpeople make decisions.
Finally there is a lot of variations in what constitutes asuccessful decision in practice.
Example: Crainer (2002) discussed 75 greatestmanagement decisions ever made
Successful decision 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
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Decision Making:Introduction and Definitions Decision style and decision makers
Decision style
The manner in which a decision maker thinks andreacts to problems. It includes perceptions, cognitive
responses, values, and beliefs changes fromindividual to individual and situation to situation.
Heuristic and Analytic
Autocratic
Democratic
Consultative Hybrid (Consultative & heuristic, analytic & autocratic)
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Decision Making:Introduction and Definitions Decision style and decision makers
Different decision styles require different
types of support
Individual decision makers need access to dataand to experts who can provide advice
Groups need collaboration tools
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Models A model is a simplified representation or
abstraction of the reality.
Simplified: since reality may be too complex
Classification based on degree ofabstraction:
Iconic model
A scaled physical replica,
may be 3D: model of airplane, a car, a bridge
2D iconic model: photographs
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Models Analog model
An abstract, symbolic model of a system thatbehaves like the system but looks different
More abstract that iconic and symbolicrepresentation of reality
Examples: 2D charts and diagrams Organization charts: depicts structure, authority,
responsibility relationship
Maps: different color represents different objects, such asbodies of water or mountains.
Stock Market Charts: represents the price movements ofstocks
Blueprint of a machine or house
Animations, videos, and movies.
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Models Mental model
The mechanisms or images through which a human mindperforms sense-making in decision making
Used in time-pressure situation (airplane pilot considerwhether to fly)
Consider the utility & risks of each alternative
When mostly qualitative factors are in decision making
Mathematical (quantitative) model
A system of symbols and expressions that represent a realsituation
When relationship cant be represented by icons oranalogically
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Models The benefits of models
Model manipulation is much easier than
manipulating a real system
Models enable the compression of time
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
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Models The benefits of models
With modeling, a manager can estimate the risksresulting from specific actions within theuncertainty of the business environment
Mathematical models enable the analysis of a verylarge number of possible solutions
Models enhance and reinforce learning andtraining
Models and solution methods are readily availableon the Web
Many Java applets are available to readily solvemodels
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Phases of theDecision-Making Process Simon (77): systematic decision making
process has 3 major phases, he later
added a 4th phase :
Intelligence
Design
Choice
Implementation
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Phases of theDecision-Making Process
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Phases of theDecision-Making Process There is continuous flow of activity from
intelligence to design to choice
At any phase, there may be a return to a
previous phase (feedback)
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Phases of the
Decision-Making Process
Intelligence phase
The initial phase of problem definition in decision
making
Design phase
The second decision-making phase, which involves
finding possible alternatives in decision making and
assessing their contributions A model that represents the system is constructed
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Phases of the
Decision-Making Process
Choice phase
The third phase in decision making, in which
an alternative is selected
Implementation phase The fourth decision-making phase, involving
actually putting a recommended solution to
work
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Decision Making:The Intelligence Phase (Phase 1) A decision maker attempts to
determine whether a problem exists (monitoring/analyzingthe productivity level),
identity its symptoms
Determine its magnitude Explicitly define the problem.
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
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Decision Making:The Intelligence Phase (Phase 1) Problem (or opportunity) identification:some
issues that may arise during data collection
Data estimation is often subjective
Data may be insecure
Important data that influence the results may be
qualitative
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Decision Making:The Intelligence Phase (Phase 1) Problem (or opportunity) identification:some
issues that may arise during data collection
Information overload
Outcomes (or results) may occur over an extendedperiod
If future data is not consistent with historical data,
the nature of the change has to be predicted andincluded in the analysis
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Decision Making:The Intelligence Phase (Phase 1) Problem classification
The conceptualization of a problem in an attempt to place it in adefinable category, possibly leading to a standard solution approach
Problem decomposition Dividing complex problems into simpler sub-problems may help in
solving the complex problem
Poor structured problem may have high structured sub-problems(consider: semi-structured problem)
Problem ownership The jurisdiction (authority) to solve a problem
Done in INTELLIGENCE phase
Assignment of authority to solve a problem is called problemownership
Example: high-interest rate, what is the managerial role?
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Decision Making:The Design Phase (Phase 2) The design phase involves finding or
developing and analyzing possible courses of
action
This includes:
Understanding the problem
Testing solutions for feasibility
A model of the decision-making problem isconstructed, tested, and validated
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Decision Making:The Design Phase (Phase 2) Modeling involves conceptualizing a problem and abstracting
it to quantitative and/or qualitative form
(Mathematical) Models have: Decision variables and relationships among them
Principle of choice Simplification of relations (expression) are made, if
necessary, through assumption. (linear, even if some nonlinear effect)
Proper balance between simplification and presentation of
reality needed for cost-benefit trade-off Simpler model leads: lower development cost, easier
manipulation, faster solution but less representative to thereal problem, and can produce inaccurate results.
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Decision Making:The Design Phase (Phase 2) Decision variables
A variable in a model that can be changedand manipulated by the decision maker.
Decision variables correspond to thedecisions to be made, such as quantity toproduce, amounts of resources to allocate,and so on
Principle of choice
The criterion for making a choice amongalternatives
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Decision Making:The Design Phase (Phase 2) Normative models
Models in which the chosen alternative is
demonstrably the best of all possible alternatives
Optimization The process of examining all the alternatives and proving
that the one selected is the best
Three ways to achieve optimization:
Get the highest level of goal attainment from a given set ofresources
Find the alternatives with the highest ratio of goal attainment to
cost
Find the alternative with minimum cost that will meet the goal.
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Decision Making:The Design Phase (Phase 2) Normative models
Suboptimization
An optimization-based procedure that does notconsider all the alternatives for or impacts on an
organization
Example:marketing department introduces E-marketing by web site, may affect the production
department.
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Decision Making:The Design Phase (Phase 2) Descriptive model
A model that describes things as they are or
believed to be.
Models are Mathematically based
Simulation
An imitation of reality
Narrative is a story that helps a decision makeruncover the important aspects of the situation and
leads to better understanding and framing
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Decision Making:The Design Phase (Phase 2) Classes of Descriptive model include
Complex Inventory Decision
Environmental-impact analysis
Financial Planning Information Flow
Markov Analysis (Prediction)
Scenario Analysis
Simulation (Alternative types)
Technological Forecasting
Waiting-line (Queuing) managment
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Decision Making:The Design Phase (Phase 2) GOOD-ENOUGH or SATISFICING
Satisficing
A 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 asolution that will work well enough
something less than the best
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Decision Making:The Design Phase (Phase 2) Good enough or satisficing
Reasons for satisficing:
Time pressures (decision may loose value over time)
Ability to achieve optimization Recognition that the marginal benefit of a better
solution is not worth the marginal cost to obtain it
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Decision Making:The Design Phase (Phase 2) Developing (generating) alternatives
In optimization models 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
The outcome of every proposed alternative must be
established
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Decision Making:The Design Phase (Phase 2) Measuring outcomes
The value of an alternative is evaluated in terms
of goal attainment
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
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Decision Making:The Design Phase (Phase 2) Scenario
A statement of assumptions about the
operating environment of a particular system
at a given time; a narrative description of thedecision-situation setting
Scenarios are especially helpful in simulations
and what-if analyses
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Decision Making:The Design Phase (Phase 2) 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 asystem through a model
Help to check the sensitivity of proposed solutions to
changes in the environment
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Decision Making:The Design Phase (Phase 2) Possible scenarios
The worst possible scenario
The best possible scenario
The most likely scenario The average scenario
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Decision Making:The Design Phase (Phase 2) Errors in decision making
The model is a critical component in the decision-making process
A decision maker may make a number of errors inits 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 alsocritical
Reference: Sawyer(99)s seven deadly sins ofdecision making
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Decision Making:The Choice Phase (Phase 3) Solving a decision-making model involves
searching for an appropriate course of action:
Search for
Evaluation of Recommendation of an appropriate solution to a
model
Searching Approaches include:
Analytical techniques (solving a formula) Algorithms (step-by-step procedures)
Heuristics (rules of thumb)
Blind searches
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Decision Making:The Choice Phase (Phase 3) Analytical techniques
Methods that use mathematical formulas to
derive an optimal solution directly or to predict
a certain result, mainly in solving structuredproblems
Algorithm
A step-by-step search in which improvementis made at every step until the best solution is
found
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Decision Making:The Choice Phase (Phase 3) 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
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Decision Making:The Implementation Phase (Phase 4)
Complex: Long, involved process with vagueboundaries.
Generic implementation issues important in
dealing with MSS include: Resistance to change
Degree of support of top management
User training
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How Decisions Are Supported
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How Decisions Are Supported Support for the intelligence phase
The ability to scan external and internalinformation sources for opportunities andproblems and to interpret what the scanningdiscovers 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
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How Decisions Are Supported 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 anES
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How Decisions Are Supported Support for the choice phase
DSS can support the choice phase through what-if andgoal-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 theimpacts of decisions in establishing their value, leading toan intelligent choice
An ES can be used to assess the desirability of certainsolutions and to recommend an appropriate solution
A GSS can provide support to lead to consensus in agroup
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How Decisions Are Supported 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
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How Decisions Are Supported New technology support for decision making
Mobile commerce (m-commerce)
Personal devices
Personal digital assistants [PDAs] Cell phones
Tablet computers
:aptop computers