Enhancing Decision-Making with Business Intelligence Systems

2. Chapter10 Decision Support Systems

3. Decision Support in Business • Companies are investing in data-driven decision support application frameworks to help them respond to • Changing market conditions • Customer needs • This is accomplished by several types of • Management information • Decision support • Other information systems

4. Levels of Managerial Decision Making

5. Information Quality • Information products made more valuable by their attributes, characteristics, or qualities • Information that is outdated, inaccurate, or hard to understand has much less value • Information has three dimensions • Time • Content • Form

6. Attributes of Information Quality

7. Decision Structure • Structured (operational) • The procedures to follow when decision is needed can be specified in advance • Unstructured (strategic) • It is not possible to specify in advance most of the decision procedures to follow • Semi-structured (tactical) • Decision procedures can be pre-specified, but not enough to lead to the correct decision

8. Decision Support Trends • The emerging class of applications focuses on • Personalized decision support • Modeling • Information retrieval • Data warehousing • What-if scenarios • Reporting

9. Business Intelligence Applications

10. Decision Support Systems • Decision support systems use the following to support the making of semi-structured business decisions • Analytical models • Specialized databases • A decision-maker’s own insights and judgments • An interactive, computer-based modeling process • DSS systems are designed to be ad hoc, quick-response systems that are initiated and controlled by decision makers

11. DSS Model Base • Model Base • A software component that consists of models used in computational and analytical routines that mathematically express relations among variables • Spreadsheet Examples • Linear programming • Multiple regression forecasting • Capital budgeting present value

12. Applications of Statistics and Modeling • Supply Chain: simulate and optimize supply chain flows, reduce inventory, reduce stock-outs • Pricing: identify the price that maximizes yield or profit • Product and Service Quality: detect quality problems early in order to minimize them • Research and Development: improve quality, efficacy, and safety of products and services

13. Management Information Systems • The original type of information system that supported managerial decision making • Produces information products that support many day-to-day decision-making needs • Produces reports, display, and responses • Satisfies needs of operational and tactical decision makers who face structured decisions

14. Management Reporting Alternatives • Periodic Scheduled Reports • Prespecified format on a regular basis • Exception Reports • Reports about exceptional conditions • May be produced regularly or when an exception occurs • Demand Reports and Responses • Information is available on demand • Push Reporting • Information is pushed to a networked computer

15. Online Analytical Processing • OLAP • Enables managers and analysts to examine and manipulate large amounts of detailed and consolidated data from many perspectives • Done interactively, in real time, with rapid response to queries

16. Online Analytical Operations • Consolidation • Aggregation of data • Example: data about sales offices rolled up to the district level • Drill-Down • Display underlying detail data • Example: sales figures by individual product • Slicing and Dicing • Viewing database from different viewpoints • Often performed along a time axis

17. Geographic Information Systems • GIS • DSS uses geographic databases to construct and display maps and other graphic displays • Supports decisions affecting the geographic distribution of people and other resources • Often used with Global Positioning Systems (GPS) devices

18. Using Decision Support Systems • Using a decision support system involves an interactive analytical modeling process • Decision makers are not demanding pre-specified information • They are exploring possible alternatives • What-If Analysis • Observing how changes to selected variables affect other variables

19. Using Decision Support Systems • Sensitivity Analysis • Observing how repeated changes to a single variable affect other variables • Goal-seeking Analysis • Making repeated changes to selected variables until a chosen variable reaches a target value • Optimization Analysis • Finding an optimum value for selected variables, given certain constraints

20. Data Mining • Provides decision support through knowledge discovery • Analyzes vast stores of historical business data • Looks for patterns, trends, and correlations • Goal is to improve business performance • Types of analysis • Regression • Decision tree • Neural network • Cluster detection • Market basket analysis

21. Analysis of Customer Demographics

22. Market Basket Analysis • One of the most common uses for data mining • Determines what products customers purchase together with other products • Results affect how companies • Market products • Place merchandise in the store • Lay out catalogs and order forms • Determine what new products to offer • Customize solicitation phone calls

23. Executive Information Systems • EIS • Combines many features of MIS and DSS • Provide top executives with immediate and easy access to information • Identify factors that are critical to accomplishing strategic objectives (critical success factors) • So popular that it has been expanded to managers, analysis, and other knowledge workers

24. Features of an EIS • Information presented in forms tailored to the preferences of the executives using the system • Customizable graphical user interfaces • Exception reports • Trend analysis • Drill down capability

25. Dashboard Example

26. Artificial Intelligence (AI) • AI is a field of science and technology based on • Computer science • Biology • Psychology • Linguistics • Mathematics • Engineering • The goal is to develop computers than can simulate the ability to think • And see, hear, walk, talk, and feel as well

27. Attributes of Intelligent Behavior • Some of the attributes of intelligent behavior • Think and reason • Use reason to solve problems • Learn or understand from experience • Acquire and apply knowledge • Exhibit creativity and imagination • Deal with complex or perplexing situations

28. Attributes of Intelligent Behavior • Attributes of intelligent behavior (continued) • Respond quickly and successfully to new situations • Recognize the relative importance of elements in a situation • Handle ambiguous, incomplete, or erroneous information

29. Domains of Artificial Intelligence

30. Cognitive Science • Applications in the cognitive science of AI • Expert systems • Knowledge-based systems • Adaptive learning systems • Fuzzy logic systems • Neural networks • Genetic algorithm software • Intelligent agents • Focuses on how the human brain works and how humans think and learn

31. Robotics • AI, engineering, and physiology are the basic disciplines of robotics • Produces robot machines with computer intelligence and humanlike physical capabilities • This area include applications designed to give robots the powers of • Sight or visual perception • Touch • Dexterity • Locomotion • Navigation

32. Natural Interfaces • Major thrusts in the area of AI and the development of natural interfaces • Natural languages • Speech recognition • Virtual reality • Involves research and development in • Linguistics • Psychology • Computer science • Other disciplines

33. Latest Commercial Applications of AI • Decision Support • Helps capture the why as well as the what of engineered design and decision making • Information Retrieval • Distills tidal waves of information into simple presentations • Natural language technology • Database mining

34. Latest Commercial Applications of AI • Virtual Reality • X-ray-like vision enabled by enhanced-reality visualization helps surgeons • Automated animation and haptic interfaces allow users to interact with virtual objects • Robotics • Machine-vision inspections systems • Cutting-edge robotics systems • From micro robots and hands and legs, to cognitive and trainable modular vision systems

35. Expert Systems • An Expert System (ES) • A knowledge-based information system • Contain knowledge about a specific, complex application area • Acts as an expert consultant to end users

36. Components of an Expert System • Knowledge Base • Facts about a specific subject area • Heuristics that express the reasoning procedures of an expert (rules of thumb) • Software Resources • An inference engine processes the knowledge and recommends a course of action • User interface programs communicate with the end user • Explanation programs explain the reasoning process to the end user

37. Expert System Application Categories • Decision Management • Loan portfolio analysis • Employee performance evaluation • Insurance underwriting • Diagnostic/Troubleshooting • Equipment calibration • Help desk operations • Medical diagnosis • Software debugging

38. Benefits of Expert Systems • Captures the expertise of an expert or group of experts in a computer-based information system • Faster and more consistent than an expert • Can contain knowledge of multiple experts • Does not get tired or distracted • Cannot be overworked or stressed • Helps preserve and reproduce the knowledge of human experts

39. Limitations of Expert Systems • The major limitations of expert systems • Limited focus • Inability to learn • Maintenance problems • Development cost • Can only solve specific types of problems in a limited domain of knowledge

40. Neural Networks • Computing systems modeled after the brain’s mesh-like network of interconnected processing elements (neurons) • Interconnected processors operate in parallel and interact with each other • Allows the network to learn from the data it processes

41. Fuzzy Logic • Fuzzy logic • Resembles human reasoning • Allows for approximate values and inferences and incomplete or ambiguous data • Uses terms such as “very high” instead of precise measures • Used more often in Japan than in the U.S. • Used in fuzzy process controllers used in subway trains, elevators, and cars

42. Example of Fuzzy Logic Rules and Query

43. Virtual Reality (VR) • Virtual reality is a computer-simulated reality • Fast-growing area of artificial intelligence • Originated from efforts to build natural, realistic, multi-sensory human-computer interfaces • Relies on multi-sensory input/output devices • Creates a three-dimensional world through sight, sound, and touch • Also called telepresence

44. Typical VR Applications • Current applications of virtual reality • Computer-aided design • Medical diagnostics and treatment • Scientific experimentation • Flight simulation • Product demonstrations • Employee training • Entertainment