Decision Support Systems in Business: Changing Landscape and Key Applications

2. Chapter10 Decision Support Systems

3. Learning Objectives • Identify the changes taking place in the form and use of decision support in business • Identify the role and reporting alternatives of management information systems • Describe how online analytical processing can meet key information needs of managers • Explain the decision support system concept and how it differs from traditional management information systems

4. Learning Objectives • Explain how the following information systems can support the information needs of executives, managers, and business professionals • Executive information systems • Enterprise information portals • Knowledge management systems • Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business

5. Learning Objectives • Give examples of several ways expert systems can be used in business decision-making situations

6. 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

7. Case 1: Dashboards for Executives • Web-based “dashboards” • Displays critical information in graphic form • Assembled from data pulled in real time from corporate software and databases • Managers see changes almost instantaneously • Now available to smaller companies • Potential problems • Pressure on employees • Divisions in the office • Tendency to hoard information

8. Case Study Questions • What is the attraction of dashboards to CEOs and other executives? • What real business value do they provide to executives? • The case emphasizes that managers of small businesses and many business professionals now rely on dashboards. • What business benefits do dashboards provide to this business audience?

9. Case Study Questions • What are several reasons for criticism of the use of dashboards by executives? • Do you agree with any of this criticism?

10. Levels of Managerial Decision Making

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• Information products made more valuable by their attributes, characteristics, or qualities • Information that is outdated, inaccurate, or hard to understand has much less value • Information has three dimensions • Time • Content • Form

12. Attributes of Information Quality

13. 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

14. Decision Support Systems

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

16. Business Intelligence Applications

17. 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

18. DSS Components

19. 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

20. 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

21. 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

22. 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

23. Example of Push Reporting • Insert Figure 10.10 here

24. 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

25. 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

26. OLAP Configuration • Insert Figure 10.11

27. 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

28. Data Visualization Systems • DVS • Represents complex data using interactive, three-dimensional graphical forms (charts, graphs, maps) • Helps users interactively sort, subdivide, combine, and organize data while it is in its graphical form

29. DVS Example • Insert Figure 10.14 here

30. 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

31. 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

32. 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

33. Analysis of Customer Demographics

34. 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

35. 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

36. 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

37. Enterprise Information Portals • An EIP is a Web-based interface and integration of MIS, DSS, EIS, and other technologies • Available to all intranet users and select extranet users • Provides access to a variety of internal and external business applications and services • Typically tailored or personalized to the user or groups of users • Often has a digital dashboard • Also called enterprise knowledge portals

38. Dashboard Example

39. Enterprise Information Portal Components

40. Enterprise Knowledge Portal

41. Case 2: Automated Decision Making • Automated decision making has been slow to materialize • Early applications were just solutions looking for problems, contributing little to improved organizational performance • A new generation of AI applications • Easier to create and manage • Decision making triggered without human intervention • Can translate decisions into action quickly, accurately, and efficiently

42. Case 2: Automated Decision Making • AI is best suited for • Decisions that must be made quickly and frequently, using electronic data • Highly structured decision criteria • High-quality data • Common users of AI • Transportation industry • Hotels • Investment firms and lenders

43. Case Study Questions • Why did some previous attempts to use artificial intelligence technologies fail? • What key differences of the new AI-based applications versus the old cause the authors to declare that automated decision making is coming of age? • What types of decisions are best suited for automated decision making?

44. Case Study Questions • What role do humans plan in automated decision-making applications? • What are some of the challenges faced by managers where automated decision-making systems are being used? • What solutions are needed to meet such challenges?

45. 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

46. 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

47. 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

48. Domains of Artificial Intelligence

49. 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

50. 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