Transcript SOM485CH2CLASSSLIDES

Chapter 2
DECISION MAKING,
SYSTEMS,
MODELING, AND
SUPPORT
Phases of the decision
process
1.
2.
3.
4.
Intelligence
Design
Choice
Implementation
Simon
Huber
What is the
relevance
of this
model to
DSS?
What are
some of the
challenges in
each phase
/sub-steps
shown in Fig
2.1?
Herbert Simon
Issues in Intelligence Phase
Example:
• Data collection
– Data is not available or too much data
– Obtaining data is expensive
– Data may not be accurate /precise enough
– Data is qualitative; representing it is difficult
Similarly, even though Simon’s model describes the general steps
humans go through in decision-making, accomplishing each step can
itself be complicated.
Classification & Decomposition
(Intelligence phase)
• Problem classification
- places problem in a definable category
Egs: Product-mix, Capital budgeting, Negotiation
- leads to a standard solution (canned) approach
• Problem decomposition
- divide and conquer
Eg: CSU: Common Management System (SOLAR)
Modeling for Decision Support
(Design phase)
• Modeling involves conceptualizing a real-world
problem and abstracting it to
– a quantitative form or
– qualitative form
• Models capture selected decision variables and their
relationships
Types of Models
• Iconic (Scale): physical replica, Eg. Airplane, building
• Analog: symbolic representation of reality, Eg. Car
dashboard, hierarchy chart
• Quantitative / Mathematical: uses analytic approach,
Eg. LP, EOQ, Regression
• Descriptive / Mental: narrative, uses heuristics (jury
deliberations), cognitive map (banxia.com),
simulation/scenarios
Decision Making:
(The Design Phase )
• Measuring outcomes
– The value of an alternative is evaluated in terms of goal
attainment
• Validate the model
– Done typically through historical data or pilot testing of the model over
a short window of time
Bounded Rationality
(Design phase)
• Rational:
– All alternatives will be evaluated
– Will look for the best (optimum) solution
• Bounded rationality:
– Sub-optimization: failure to look for an overall solution for
the organization
– Satisficing (or good enough) solution
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–
–
–
Humans like to simplify problems
consider fewer alternatives, criteria, constraints
they are under time pressure, cost
they have limited processing power
Decision Making:
(The Choice Phase)
• Objective is to select an alternative/ reach a decision
• Perform Sensitivity analysis
- A study of the effect of a change in one or more input variables on the
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
• Scenario and Risk analysis
Assess level of risk to the outcome associated with each potential
alternative being considered
Decision Making:
The Implementation Phase
• Generic implementation issues include:
– Resistance to change
– Degree of support of top management
– User training
How does DSS support Simon’s model of
decision-making?
• 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
• Internal data sources/warehouses be scanned via a
corporate intranet
• Set up agents/ triggers in software (eg. OS, SQL
Server)
How does DSS support Simon’s model of
decision-making?
• Support for the design phase
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Mostly human intelligence/effort
OLAP, data mining to discover data relationships
Cognitive mapping software
Computational tools/ management science models
Any existing ES/KMS in the decision topic
How does DSS support Simon’s model of
decision-making?
• Support for the choice phase
– DSS can support through comparison of measurable
outcomes of various alternatives; eg. Risk indexes, what-if
(scenarios) and goal-seeking analyses (Excel spreadsheeting)
– KMS help explain heuristics / logic of decision steps
– A GDSS can provide support for group think that lead to
consensus
DSS support to Design & Choice phases overlap (See Fig 2.2)
How does DSS support Simon’s model of
decision-making?
• Support for the implementation phase
– DSS can be used in implementation activities such as
identifying tasks to be completed, critical path analysis,
decision communication among team members, project
management
– DSS can also help with training in the new system
(many DSS software like SPSS come with tutorial modules)