1. What is a DSS? n “Decision Support System”: An information system that uses the combination of data, decision models and algorithms, and human intuition, knowledge and judgment to aid a decision maker in reaching a specific decision.

2. Tools: Computers and IT. VB, VBA, Excel, InterDev, Etc. Humans: Multiple ways to make decisions. Algorithms: Math/Flow Chart stuff that helps the tools help the humans make decisions. DSS Data: Facts pertinent to the decision at hand.

3. The Need for Computerized Decision Support and the Supporting Technologies Speedy computations Speedy computations Overcoming cognitive limits in processing and storage Overcoming cognitive limits in processing and storage Cognitive limits may restrict an individual’s problem solving capability Cognitive limits may restrict an individual’s problem solving capability Cost reduction Cost reduction Technical support Technical support Quality support Quality support Competitive edge: business processes reengineering and empowerment Competitive edge: business processes reengineering and empowerment

4. Primary Decision Support Technologies Management Support Systems (MSS) Decision Support Systems (DSS) Decision Support Systems (DSS) Group Support Systems (GSS), including Group DSS (GDSS) Group Support Systems (GSS), including Group DSS (GDSS) Executive Information Systems (EIS) Executive Information Systems (EIS) Expert Systems (ES) Expert Systems (ES) Artificial Neural Networks (ANN) Artificial Neural Networks (ANN) Hybrid Support Systems Hybrid Support Systems Cutting Edge Intelligent Systems (Genetic Algorithms, Fuzzy Logic, Intelligent Agents,...) Cutting Edge Intelligent Systems (Genetic Algorithms, Fuzzy Logic, Intelligent Agents,...)

5. Let’s look at some leading companies n www.cognos.com –“the world's leading vendor of Business Intelligence solutions for e-business”… a DSS company. –Handles chunks of the NASDAQ Web site. n www.datajungle.com –Provides the data for the much of the above.

6. Tools: Computers and IT. VB, VBA, Excel, InterDev, Etc. Humans: Multiple ways to make decisions. Algorithms: Math/Flow Chart stuff that helps the tools help the humans make decisions. DSS Data: Facts pertinent to the decision at hand.

7. Tools: Computers and IT. VB, VBA, Excel, InterDev, Etc. Humans: Decision Making Process Algorithms: Math/Flow Chart stuff that helps the tools help the humans make decisions. DSS Data: Facts pertinent to the decision at hand.

8. Tools: Computers and IT. VB, VBA, Excel, InterDev, Etc. Humans: Decision Making Process Algorithms: Math/Flow Chart stuff that helps the tools help the humans make decisions. DSS Data: Facts pertinent to the decision at hand. (2nd Half of Class - next two weeks)

9. Human Cognition and Decision Styles n Every person makes decisions differently!!! n As a good consultant / analyst, you need to research the decision styles of your system’s target audience / user base.

10. Cognitive Style Two major types: Two major types: –Analytic decision maker –Heuristic decision maker

11. Some (other) Decision Styles Heuristic Heuristic Analytic Analytic Autocratic Autocratic Democratic Democratic Consultative (with individuals or groups) Consultative (with individuals or groups) Combinations and variations Combinations and variations For successful decision making support, an MSS must fit the For successful decision making support, an MSS must fit the –Decision situation –Decision style

12. The system 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 use and develop their own styles, skills, and knowledge An MSS should help decision makers use and develop their own styles, skills, and knowledge Different decision styles require different types of support Different decision styles require different types of support Major factor: individual or group decision maker Major factor: individual or group decision maker

13. The Decision Makers n Individuals n Groups

14. Individuals May still have conflicting objectives May still have conflicting objectives Decisions may be fully automated Decisions may be fully automated

15. Groups Most major decisions in medium and large organizations are made by groups Most major decisions in medium and large organizations are made by groups Conflicting objectives are common Conflicting objectives are common Variable size Variable size People from different departments People from different departments People from different organizations People from different organizations The group decision making process can be very complicated The group decision making process can be very complicated Consider Group Support Systems (GSS) Consider Group Support Systems (GSS) Organizational DSS can help in enterprise-wide decision making situations Organizational DSS can help in enterprise-wide decision making situations

16. The Decision-Making Process Systematic Decision-Making Process (Simon [1977]) Intelligence Intelligence Design Design Choice Choice Implementation Implementation Modeling is Essential to the Process

17. Intelligence phase Intelligence phase –Reality is examined –The problem is identified and defined Design phase Design phase –Representative model is constructed –The model is validated and evaluation criteria are set Choice phase Choice phase –Includes a proposed solution to the model –If reasonable, move on to the Implementation phase Implementation phase –Solution to the original problem Failure: Return to the modeling process Often Backtrack / Cycle Throughout the Process

19. What-If Analysis Golf example from last week -- “What if I changed the price of bags from $105 to $150?” (we’d make more bags). Golf example from last week -- “What if I changed the price of bags from $105 to $150?” (we’d make more bags). Goal Seeking Backward solution approach Backward solution approach Example: If Cisco returns 20% per year, how much would I have to buy now to retire (65) with a million dollars? Example: If Cisco returns 20% per year, how much would I have to buy now to retire (65) with a million dollars? In a DSS the what-if and the goal- seeking options must be easy to perform In a DSS the what-if and the goal- seeking options must be easy to perform

20. Which systems support which phases?

21. Tools: Computers and IT. VB, VBA, Excel, InterDev, Etc. Humans: Multiple ways to make decisions. Algorithms: Math/Flow Chart stuff that helps the tools help the humans make decisions. DSS Data: Facts pertinent to the decision at hand. Many different names!

22. Acronym Heaven n DSS, EIS, ES. n DSS, EIS differ in sensitivity of information being offered (EIS is more sensitive).

23. ES: Expert Systems n Knowledge Base (facts, rules) n Inference Engine (software) n User Interface

24. EXPERT SYSTEMS n Expert system: Information system that applies reasoning capabilities and stored knowledge to reach a conclusion (low level “AI”). n Collect, store, formalize and use large stores of task specific expertise n What is expertise? –Knowledge: Structured information –Heuristics: Rules of thumb compiled from experience n Simple example: “Diagnosing” whales http://www.aiinc.ca/demos/whale.html

25. The Expert System Knowledge Base User Workstation Drivers with Pagers Expert Advice User Interface Programs User Interface Programs Inference Engine Program Inference Engine Program Expert System Development Workstation Knowledge Engineering Knowledge Acquisition Program Knowledge Acquisition Program Expert and/or Knowledge Engineer EXPERT SYSTEM COMPONENTS

26. Expert Systems Example n ITT Commercial Finance Corp., Expert Credit System (ECS) n Uses experience and knowledge of senior credit managers. n Analyzes credit information, identifies credit proposal strengths and weaknesses, makes recommendations. n Available to all decision-making managers (user- friendly, as well). n 23 offices, 250 users. n $500,000 savings in hard costs, $1 M bad loan write off savings estimated.

27. EXAMPLE: CREDIT CARDS n American Express does not have fixed account limits, but instead decides each credit authorization on a case-by-case basis. What might be some rules or heuristics for this decision process?

28. Expert Systems Examples n Karl Irwin gets engaged. n Diagnosing illnesses.

29. Expert Systems: Leading Companies n EXSYS (EXSYS, Inc.) http://www.exsys.com http://www.exsys.com n K-Vision (Ginesys Corp.) http://www.ginesys.com http://www.ginesys.com n KnowledgePro (Knowledge Garden, Inc.) http://www.kgarden.com http://www.kgarden.com

30. EXAMPLE: CHOOSING WINES n What are relevant facts about wines and meals? n What are some example rules of thumb for pairing different kinds of wine with different kinds of meals?

31. WINE EXAMPLE: FACTS

32. WINE EXAMPLE: IF-THEN RULES

34. n Domain: Narrow and well defined n Expertise: Requires true expertise in short supply n Complexity: Problem is too complex for conventional programming n Structure: Solution process must cope with ill-structure, uncertainty, missing data n Availability: Have a willing, articulate expert!! WHEN IS AN EXPERT SYSTEM APPROPRIATE?

35. DSS vs. Expert System DSS: –Fixed models and formulas –Usually user driven; user has expertise, user asks the questions –Does not explain answers Expert system –Mimics human reasoning abilities –Usually machine driven; machine has expertise; machine asks the questions –Has explanation facility