1. DECISION MAKING, SYSTEMS, MODELING, AND SUPPORT
Chapter 2
DECISION MAKING, SYSTEMS, MODELING, AND SUPPORT

2. Phases of the decision process
Intelligence
Design
Choice
Implementation
Simon
Huber

3. 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

4. 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.

5. 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)

6. 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

7. 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

8. 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

9. Bounded Rationality (Design phase)
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
Humans like to simplify problems
consider fewer alternatives, criteria, constraints
they are under time pressure, cost
they have limited processing power

10. 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

11. Decision Making: The Implementation Phase
Generic implementation issues include:
Resistance to change
Degree of support of top management
User training

12. 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)

13. How does DSS support Simon’s model of decision-making?
Support for the design phase
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

14. 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 spread-sheeting)
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)

15. 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)