1. This presentation was developed by Dr. Steven C
This presentation was developed by Dr. Steven C. Ross for use in MIS 320 classes at Western Washington University. Some of the material contained herein is © 2007, John Wiley & Sons, Inc. and other sources, as noted. All rights reserved.

2. Managerial Support Systems
MIS 320
Managerial Support Systems

3. The Decision Making Process
Intelligence – how do you know there’s a problem? Is it always a problem that requires a decision?
Design – how many possible ways should you have?
Choice – how do you decide?
Implementation – what do you do after you put the solution in place?
* Figure 9.1 from Rainer, et al.

4. Types of Decisions Structured, semistructured, or unstructured
Operational, managerial, or strategic
Structured involves processing a certain kind of information in a specified way
Nonstructured has no right answer, no precise way to get an answer, no rules or criteria to guarantee a good solution
Recurring happens repeatedly, perhaps on a schedule, normally same criteria
Nonrecurring sometimes called ad hoc; may have different criteria from one to the next
* Figure 9.2 from Rainer, et al.

5. Decision Support Systems
“A computer-based information system that combines models and data in an attempt to solve semistructured and some unstructured problems with extensive user involvement.” (Rainer, et al., p. 277)
“The primary objective of a DSS is to improve your effectiveness as a decision maker by providing you with assistance that will complement your insights.” (Haag, et al.)
What are the most important words in the second quote? (your, you, your)

6. DSS Components Three Components Data Sources Model Types
* Graphic from Haag, et al.

7. DSS Components
* Figure 9.3 from Rainer, et al.

8. Executive Information Systems
Designed for needs of top executives
Overlap with “business intelligence” products
Capabilities
Drill-down
Critical success factors (CSF)
Key performance indicators (KPI)
Current status monitoring
Trend analysis
Ad-hoc analysis
Exception reporting

9. Artificial Intelligence or Intelligent Systems
Expert systems
Natural language processing
Natural language generation
Neural networks
Fuzzy logic

10. Expert Systems Reasoning capabilities
Also known as Knowledge-Based Systems
Reasoning capabilities
Diagnostic problems
Prescriptive problems
How is an ES fundamentally different from a DSS?
Expertise
Know facts about the topic
Know questions to ask
Know how to apply to a situation
Diagnostic – what’s wrong?
Prescriptive – what to do about it?

11. Traffic Light Expert System
* Graphic from Haag, et al.

12. Transferring Expertise
Knowledge acquisition (gather from …) 
Knowledge representation (code and organize)
Knowledge inferencing (computer program)
Knowledge transfer (transfer to user)

13. Components of an Expert System
Contains domain area facts and rules
* Graphic from Haag, et al.

14. Developing an Expert System
* Graphic from Haag, et al.

15. Generic ES Categories Interpretation Prediction Diagnosis Design
Planning
Monitoring
Debugging
Repair
Instruction
Control (see Table 9.3 in Rainer et al. for definitions)

16. What an ES Can and Can’t Do
Can do:
Handle massive amounts of info
Reduce errors
Aggregate info from various sources
Improve customer service
Provide consistency
Provide new info
Decrease personnel time
Reduce cost
Potential problems
Domain experts can have difficulty explaining how they solve problems
Process might require too many rules or may be too vague or imprecise
ES can only solve problems for which it is designed – can’t learn from experience

17. Natural Language Processing: Automatic Speech Recognition
* Graphic from Haag, et al.

18. More about ASR Types: Challenges: Discrete or continuous
Speaker-dependent or –independent
Challenges:
Greater storage for expanded vocabulary
Better feature analysis to support continuous speech
More dynamic language models to support speech understanding
More flexible pattern classification to support many people

19. Neural Networks
“A system of programs and data structures that approximates the operation of the human brain.”
Neural networks
Identify
Classify
Predict
… when a vast amount of information is available

20. The Layers of a Neural Network
* Figure 9.7 from Rainer, et al.

21. Fuzzy Logic
“Fuzzy logic deals with uncertainties by simulating the process of human reasoning, allowing the computer to behave less precisely and logically than conventional computers do.” (Rainer et al., p. 294)
Shades of gray – not always black and white
Creative

22. References
Haag, Cummings, and McCubbrey, Management Information Systems for the Information Age (5th Edition), McGraw-Hill Irwin, 2005.
Rainer, Turban, and Potter, Introduction to Information Systems: Supporting and Transforming Business, Wiley, 2007.