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What is a DSS? “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. DSS Humans: Multiple ways to make decisions. Tools: Computers and IT. VB, VBA, Excel, InterDev, Etc. DSS Data: Facts pertinent to the decision at hand. Algorithms: Math/Flow Chart stuff that helps the tools help the humans make decisions. The Need for Computerized Decision Support and the Supporting Technologies Speedy computations Overcoming cognitive limits in processing and storage Cognitive limits may restrict an individual’s problem solving capability Cost reduction Technical support Quality support Competitive edge: business processes reengineering and empowerment Primary Decision Support Technologies Management Support Systems (MSS) Decision Support Systems (DSS) Group Support Systems (GSS), including Group DSS (GDSS) Executive Information Systems (EIS) Expert Systems (ES) Artificial Neural Networks (ANN) Hybrid Support Systems Cutting Edge Intelligent Systems (Genetic Algorithms, Fuzzy Logic, Intelligent Agents, ...) Let’s look at some leading companies 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. www.datajungle.com – Provides the data for the much of the above. DSS Humans: Multiple ways to make decisions. Tools: Computers and IT. VB, VBA, Excel, InterDev, Etc. DSS Data: Facts pertinent to the decision at hand. Algorithms: Math/Flow Chart stuff that helps the tools help the humans make decisions. DSS Humans: Decision Making Process Tools: Computers and IT. VB, VBA, Excel, InterDev, Etc. DSS Data: Facts pertinent to the decision at hand. Algorithms: Math/Flow Chart stuff that helps the tools help the humans make decisions. DSS Humans: Decision Making Process Data: Facts pertinent to the decision at hand. (2nd Half of Class next two weeks) Tools: Computers and IT. VB, VBA, Excel, InterDev, Etc. DSS Algorithms: Math/Flow Chart stuff that helps the tools help the humans make decisions. Human Cognition and Decision Styles Every person makes decisions differently!!! As a good consultant / analyst, you need to research the decision styles of your system’s target audience / user base. Cognitive Style Two major types: –Analytic decision maker –Heuristic decision maker Some (other) Decision Styles Heuristic Analytic Autocratic Democratic Consultative (with individuals or groups) Combinations and variations For successful decision making support, an MSS must fit the – Decision situation – Decision style 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 Different decision styles require different types of support Major factor: individual or group decision maker The Decision Makers Individuals Groups Individuals May still have conflicting objectives Decisions may be fully automated Groups Most major decisions in medium and large organizations are made by groups Conflicting objectives are common Variable size People from different departments People from different organizations The group decision making process can be very complicated Consider Group Support Systems (GSS) Organizational DSS can help in enterprise-wide decision making situations The Decision-Making Process Systematic Decision-Making Process (Simon [1977]) Intelligence Design Choice Implementation Modeling is Essential to the Process Intelligence phase – Reality is examined – The problem is identified and defined Design phase – Representative model is constructed – The model is validated and evaluation criteria are set Choice phase – Includes a proposed solution to the model – If reasonable, move on to the Implementation phase – Solution to the original problem Failure: Return to the modeling process Often Backtrack / Cycle Throughout the Process 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). Goal Seeking 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? In a DSS the what-if and the goalseeking options must be easy to perform Which systems support which phases? DSS Humans: Multiple ways to make decisions. Many different names! Tools: Computers and IT. VB, VBA, Excel, InterDev, Etc. DSS Data: Facts pertinent to the decision at hand. Algorithms: Math/Flow Chart stuff that helps the tools help the humans make decisions. Acronym Heaven DSS, EIS, ES. DSS, EIS differ in sensitivity of information being offered (EIS is more sensitive). ES: Expert Systems Knowledge Base (facts, rules) Inference Engine (software) User Interface EXPERT SYSTEMS Expert system: Information system that applies reasoning capabilities and stored knowledge to reach a conclusion (low level “AI”). Collect, store, formalize and use large stores of task specific expertise What is expertise? – Knowledge: Structured information – Heuristics: Rules of thumb compiled from experience Simple example: “Diagnosing” whales http://www.aiinc.ca/demos/whale.html EXPERT SYSTEM COMPONENTS The Expert System Expert Advice User User Interface Programs Inference Engine Program Knowledge Base Workstation Expert System Development Knowledge Engineering Knowledge Acquisition Program Workstation Drivers with Pagers Expert and/or Knowledge Engineer Expert Systems Example ITT Commercial Finance Corp., Expert Credit System (ECS) Uses experience and knowledge of senior credit managers. Analyzes credit information, identifies credit proposal strengths and weaknesses, makes recommendations. Available to all decision-making managers (userfriendly, as well). 23 offices, 250 users. $500,000 savings in hard costs, $1 M bad loan write off savings estimated. EXAMPLE: CREDIT CARDS 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? Expert Systems Examples Karl Irwin gets engaged. Diagnosing illnesses. Expert Systems: Leading Companies EXSYS (EXSYS, Inc.) http://www.exsys.com K-Vision (Ginesys Corp.) http://www.ginesys.com KnowledgePro (Knowledge Garden, Inc.) http://www.kgarden.com EXAMPLE: CHOOSING WINES What are relevant facts about wines and meals? What are some example rules of thumb for pairing different kinds of wine with different kinds of meals? WINE EXAMPLE: FACTS Facts about meals (8 facts) Facts about wines (8 facts) Main_course: Meat, Fish, Poultry, Cajun Suggested_color: Red, White Sauce: Tomato, Nontomato Suggested _wine: Chablis, Chardonnay, Burgundy, Beaujolis Flavor: Strong, Delicate Suggested _body: Full, Light WINE EXAMPLE: IF-THEN RULES Intermediate Conclusions If Main_course is Meat or Sauce is Tomato then Suggested_color is Red If Main_course is Fish or Poultry and Sauce is Not_Tomato then Suggested_color is White If Flavor is Strong then Suggested_body is Full If Flavor is Delicate then Suggested_body is Light WINE EXAMPLE: IF-THEN RULES Final Conclusions If Suggested_body is Light and Suggested_color is White then Suggested_wine is Chablis If Suggested_body is Full and Suggested_color is White then Suggested_wine is Chardonnay If Suggested_body is Full and Suggested_color is Red then Suggested_wine is Burgundy If Suggested_body is Light and Suggested_color is Red then Suggested_wine is Beaujolis WHEN IS AN EXPERT SYSTEM APPROPRIATE? Domain: Narrow and well defined Expertise: Requires true expertise in short supply Complexity: Problem is too complex for conventional programming Structure: Solution process must cope with ill-structure, uncertainty, missing data Availability: Have a willing, articulate expert!! DSS vs. Expert System DSS: Expert system – Fixed models and formulas – Mimics human reasoning abilities – Usually user driven; user has expertise, user asks the questions – Usually machine driven; machine has expertise; machine asks the questions – Does not explain answers – Has explanation facility