2. Decision Support, Knowledge Management and Expert Systems Brian Mennecke

3. How can IT be used to support decision makers? By supporting various individual and team activities and roles: –Communication and team interaction –The assimilation and filtering of data –Assist with problem recognition –Assist with problem solving –Putting together the results into a cohesive package

4. Data is turned into information, but the decision maker also needs Knowledge to make decisions Types of knowledge: –Descriptive Knowledge –Procedural Knowledge –Reasoning Knowledge Forms of Knowledge –Tacit Knowledge –Explicit Knowledge

7. Examples of technologies that can support or enhance the transformation of knowledge (IBM Systems Journal) Tacit to TacitTacit to Explicit E-meetingsAnswering questions Synchronous collaboration (chat)Annotation Explicit to TacitExplicit to Explicit VisualizationText search Browsable video/audio of presentations Document categorization

8. Knowledge Management Tools Text and Forms management Database and Reporting management Spreadsheet, Solvers and Charts management Programming management. Rules management

9. Decision Support Systems (DSS) DSS can be classified as –data-oriented provide tools for the manipulation and analysis of data –model-based generally have some kind of mathematical model of the decision being supported

10. A model of a DSS

11. A model of a Spatial DSS

12. So, how does a DSS benefit decision makers Supplements the decision maker Allows improved intelligence, decision, and choice activities Facilitates problem solving Provides assistance with non-structures decisions Assists with knowledge management

13. Information Requirements by Management Level Strategic Management Tactical Management Operational Management Decisions Information

14. Structured vs. Semi-Structured For each decision you make, the decision will fall into one of the following categories: –Structured Decisions –Unstructured –Semi-Structured

15. Structured Decisions Often called “programmed decisions” because they are routine and there are usually specific policies, procedures, or actions that can be identified to help make the decision –“This is how we usually solve this type of problem”

16. Unstructured Decisions Decision scenarios that often involve new or unique problems and the individual has little or no programmatic or routine procedure for addressing the problem or making a decision

17. Semi-structured Decisions Decision scenarios that have some structured components and some unstructured components.

18. DSS Examples American Airlines Yield Management –maximizes the revenue or yield from each flight through overbooking, discount seats, and traffic management –resulted in total quantifiable benefits of more than $1.4 billion for AA Pfizer distribution system –supports decisions about the US distribution network for distributing finished goods, including warehousing, transportation and ultimate delivery to the customer Merrill Lynch’s Integrated Choice Account Structure –helped design appropriate account structures and pricing for the company Integrated Choice program –analysis considered the total revenue at risk, estimated what accounts customers would choose, and the impact of their choice on revenues –Helped the company increase assets and customers

19. The Role of the Decision Maker Decision makers can be –Individuals –Teams –Groups –Organizations All of these types of decision makers will differ in their knowledge and experience; therefore, there will be differences in how they will react to a given problem scenario

20. The Decision Making Process Regardless of the type of decision maker, all decisions involve the following steps –Intelligence –Design –Choice –Decision –Implementation

21. Strategies for Making Decisions Optimization Satisficing Elimination by Aspects Incrementalism Mixed Scanning Analytic Hierarchy Process

22. Types of Models Deterministic: linear programming and production planning Stochastic: queuing theory and regression analysis Simulation: transportation analysis and production modeling Domain-specific: meteorological models, geologic models, economic models

23. Conceptual Models Formal approaches are not always feasible Most all problem is always completely new Decision makers can therefore recall and combine a variety of past experiences to create a model of the current situation The Garbage can approach to decision making

24. Spatial DSS: A Geographic Information System A geographic information system (GIS) is a computer-based information system that provides tools to collect, integrate, manage, analyze, model, and display data that is referenced to an accurate cartographic representation of objects in space. (Mennecke, Dangermond, Santoro, Darling, & Crossland, 1995).

25. Location Based Services Location-based services incorporate information about the user's location into the provision of products or services. These include… –Locator services (e.g., where’s the closest ATM?) –Navigation systems (e.g., in the car or on your PC) –M-commerce applications (e.g., proximity alerts, closest service, mobile advertizing)

26. GIS Examples Online: –www.MapQuest.comwww.MapQuest.com –Maps.google.comMaps.google.com Desktop –ArcGIS by ESRIESRI –MS MapPointMS MapPoint

27. Expert Systems An expert system acts or behaves like a human expert in a field or area.

28. Expert Systems Advisory programs that attempt to imitate the reasoning process of human experts Reasons to build Expert Systems –to make the expertise of an individual available to others in the field –to capture knowledge from an expert who is likely to be unavailable in the future –to provide consistency in decision making

29. Characteristics of Human Experts Recognize and Formulate the problem Solve the problem relatively quickly Explain the solution and rationale Learn from experience Restructure knowledge Break the rules when necessary Determine relevance

30. Components of an Expert System An expert system consists of a collection of integrated and related components, including –Knowledge Base –An Inference Engine –Explanation Facility –Knowledge Acquisition Subsystem –A User Interface.

31. The Knowledge Base The knowledge base stores all relevant information, data, rules, cases, and relationships used by the expert system. A knowledge base must assemble the knowledge of multiple human experts.

32. Fuzzy logic - entails dealing with ambiguous criteria or probabilities and events that are not mutually exclusive. A semantic network is a collection of items or nodes linked together to show the relationship between items in the knowledge base. The Knowledge Base

33. A rule is a conditional statement that links given conditions to actions or outcomes. A frame is another approach used to capture and store knowledge in a knowledge base. It relates an object or item to various facts or values. An expert system can use cases in developing a solution to the current problem or situation. The Knowledge Base

34. The Inference Engine The purpose of the inference engine is to seek information and relationships from the knowledge base and to provide answers, predictions, and suggestions the way a human expert would. The inference engine must find the right facts, interpretations, and rules and assemble them correctly.

35. Forward chaining starts with the facts and works forward to the conclusions. Backward chaining is the process of starting with conclusions and working backward to the supporting facts. The Inference Engine

37. The Explanation Facility The explanation facility allows a user or decision maker to understand how the expert system arrived at certain conclusions or results.

38. The Knowledge Acquisition Facility The overall purpose of the knowledge acquisition facility is to provide a convenient and efficient means for capturing and storing all components of the knowledge base.

39. The User Interface Specialized user interface software is used for designing, creating, updating, and using expert systems. The overall purpose of the user interface is to make the development and use of an expert system easier for users and decision makers.

40. Characteristics of Expert Systems Expert systems have the ability to: –Explain their reasoning or suggested decisions. –Display “intelligent” behavior. –Manipulate symbolic information and draw conclusions. –Draw conclusions from complex relationships. –Provide portable knowledge. –Can deal with uncertainty.

41. Capabilities of Expert Systems Expert systems offer a number of powerful capabilities and benefits. Some capabilities of expert systems include: –Superior problem solving. –Ability to save and apply knowledge and experience to problems.

42. –Reduced response time for complex problems. –The ability to look at problems from a variety of perspectives. Expert systems can be used to solve problems in every field or discipline, and can assist in all stages of problem-solving. Capabilities of Expert Systems

43. Benefits of Expert Systems Increased Output and Productivity Increased Quality Reduced Downtime Captures Scarce Expertise Flexibility Equipment Operation Knowledge Transfer to Remote Locations Reliability Response Time Integration of Several Expert Opinions Operation in Hazardous Environments Incomplete Information Educational Benefits

44. –Possibility of error. –Cannot refine own knowledge base. –Difficult to maintain. –May have high development costs. –Raise legal and ethical concerns. –Expertise is hard to extract –Expert Vocabulary and Jargon –Requires a Knowledge Engineer –Experts do not perform well under pressure Limiting Characteristics of Expert Systems

45. Uses of Expert Systems Strategic goal setting Planning Design Scheduling Monitoring Diagnosis Debugging Repair Instruction Control Prediction Interpretation

46. When to Use Expert Systems Factors that make expert systems worth the high cost: –A high potential payoff or significantly reduced downside risk. –The ability to capture and preserve irreplaceable human experience. –The ability to develop a system more consistent than human experts.

47. –Expertise needed at a number of locations at the same time. –Expertise needed in a hostile environment that is dangerous to human health. –The expert system solution can be developed faster than the solution from human experts. –Expertise needed for training and development so as to share the wisdom and experience of human experts with many people. When to Use Expert Systems

48. Expert Systems Development Steps in the expert systems development process include: –Determining requirements. –Identifying experts. –Constructing expert system components. –Implementing results. –Maintenance and review.

49. Participants in Developing Expert Systems. The domain expert - the individual or group that has the expertise or knowledge one is trying to capture in the expert system. The knowledge engineer - an individual who has training and/or experience in the design, development, implementation, and maintenance of an expert system.

50. The knowledge user is the individual or group who uses and benefits from the expert system. Knowledge users do not need any previous training in computers or expert systems. Participants in Developing Expert Systems.

51. Difficulties of Knowledge Acquisition Process Transfer to a Machine, i.e., more detailed Number of participants, i.e., Expert, KE, system designer, user and the computer Knowledge expression difficulties Structuring the knowledge Not always cognitive in nature, feelings, memory sensations, etc. Lack of time from experts Complexity of testing and refining knowledge

52. Knowledge Elicitation Methods Interview Analysis –Protocol Analysis –Discussion of a Prototype –Directed Interviews –Informal Interviews Observations of Experts Questionnaires and Experts’ Reports Analysis of Documented Knowledge

53. Limitations of Questionnaires & Expert Reports Require experts to act as KE Report bias: Reflect ‘how it should be done’ instead of ‘how it is really done.’ Experts often include untested ideas Time consuming and experts lose interest Experts must be proficient in process documenting techniques such as flowcharting

54. Functional Applications of Expert Systems Accounting-related systems. Capital resource planning. Loan application analysis. Financial management. Manufacturing. Strategic marketing applications.

55. Examples of Expert Systems The Port of Singapore Authority Expert Systems –planning and managing all operations of the port E.g., allocating berths to ships, planning the stowage of containers, the allocation of resources in general, and reading container numbers and operating trucking gates –managing shipping traffic and the activities of the port E.g., assigning ships to anchorages, scheduling the movement of vessels through channels to terminals, deploying pilots to tugs and launches, routing launches, and deploying tugboats

56. Sample Expert Systems What’s wrong with your car? http://www.expertise2go.com/webesie/car/ http://www.expertise2go.com/webesie/car/ Buying the right PDA http://www.expertise2go.com/shop/pda.htm http://www.expertise2go.com/shop/pda.htm Choosing a Desktop PC http://www.expertise2go.com/shop/desktop.htm http://www.expertise2go.com/shop/desktop.htm