1. 1 Lecturer: Dr Mohammad Nabil Almunawar E-Business Decision Support

2. 2 Learning Objectives Identify the role and reporting alternatives of management information systems. Explain the decision support system concept and how it differs from traditional management information systems. Explain how executive information systems can support the information needs of executives and managers. Explain the expert systems concept and how it differs from traditional MIS and DSS.

3. 3 E-Business Decision Support Trends  E-commerce is expanding the use of information and decision support  Fast pace of new information technologies like PC hardware and software suites, client/server networks, and networked PC versions of DSS/EIS software, made EIS/DSS access available to lower levels of management, as well as to non-managerial individuals and self-directed teams of business professionals.  Dramatic growth of intranets and extranets that internetwork E- business enterprises and their stakeholders.  E-business decision support applications are being customized, personalized, and web-enabled for use in E-business and E- commerce.

4. 4 Information, Decisions, and Management

5. 5 Management Information System Reports Periodic Scheduled Reports Periodic Scheduled Reports Exception Reports Demand Reports and Responses Demand Reports and Responses Push Reports Major Management Information Systems Reports

6. 6 Online Analytical Processing OLAP basic analytical operations:  Consolidation  Drill-Down  Slicing and Dicing

7. 7 Decision Support Systems (DSS) DSS are computer-based information systems that provide interactive information support to managers and business professionals during the decision-making process. Decision support systems use:  Analytical models  Specialized databases  Decision maker’s own insights and judgments  Interactive, computer-based modeling process to support the making of semistructured and unstructured business decisions

8. 8 DSS What If-Analysis Sensitivity Analysis Goal-Seeking Analysis Optimization Analysis Important Decision Support Systems Analytical Models Important Decision Support Systems Analytical Models

9. 9 Conceptual Model for DSS Data Management Model Management Knowledge manager Dialog management Manager(User) Database: external and internal

10. 10 Data Mining for Decision Support The main purpose of data mining is knowledge discovery, which will lead to decision support. Characteristics of data mining include:  Data mining software analyzes the vast stores of historical business data that have been prepared for analysis in corporate data warehouses.  Data mining attempts to discover patterns, trends, and correlations hidden in the data that can give a company a strategic business advantage.  Data mining software may perform regression, decision-tree, neural network, cluster detection, or market basket analysis for a business.  Data mining can highlight buying patterns, reveal customer tendencies, cut redundant costs, or uncover unseen profitable relationships and opportunities.

11. 11 Executive information systems (EIS) EIS are information systems that combine many of the features of MIS and DSS. The goal of EIS is to provide top executives with immediate and easy access to information about a firm's critical success factors (CSFs). Some features of EIS: Browsing capability. Source: formatted report, briefings, and meetings Multiple presentation formats (text, tubular, graphics) Simple interface (touch screen, possible voice-based for future ESS) Analytical and modeling features (what-if, why) Tailoring and customization to preserve preferences. Access to external data Data from multiple sources

12. 12 Managing Knowledge Artificial Intelligence (AI) AI is a discipline in Computer Science to develop computer-based systems that behave as humans. The systems have the ability to learn natural languages, accomplish coordinated physical tasks (robotics), utilize a perceptual apparatus that inform their behavior and language, and emulate human expertise and decision making (expert systems).

13. 13 Artificial Intelligence Applications Cognitive Science Applications Cognitive Science Applications Artificial Intelligence Artificial Intelligence Robotics Applications Robotics Applications Natural Interface Applications Natural Interface Applications Expert Systems Fuzzy Logic Genetic Algorithms Neural Networks Intelligent Agents Visual Perceptions Locomotion Navigation Tactility Natural Language Speech Recognition Multisensory Interface Virtual Reality

14. 14 Human Intelligence is a way of reasoning (using rules such as if A then B, other rule types) is a way of behaving includes the development and use of metaphors and analogies. Includes the creation and use of concepts.

15. 15 AI refers to an effort to develop machines that can reason, behave, compare, and conceptualize. So far non of AI machines achieving those above dream, however simple and domain specific machines successfully built. Expert systems is one of IA branch that gaining popular in business applications to solve some unstructured problems.

16. 16 Expert Systems An expert system is a knowledge-intensive program that solves a problem by capturing the expertise of a human in a limited domain of knowledge and experience. An expert system can assist decision making by asking relevant questions and explaining the reasons for adopting certain actions. Expert systems are never to be general problem solver (they work for very specific problems)

17. 17 Basic Concept of ES User Knowledge-Base Inference Engine ES Facts Expertise

18. 18 Capability of human expert Recognizing and formulating the problem Solving the problem quickly and properly Explaining the solution Learning from experience Restructuring knowledge breaking rules determining relevance degrading gracefully (awareness of limitation)

19. 19 Some facts about expertise Expertise is usually associated with a high degree of intelligence but is not always connected to the smartest person Expertise is usually associated with quantity of knowledge Experts learn from past successes and mistakes Experts’ knowledge is well-stored, organized, and retrievable quickly Experts can call up patterns from their experience (excellent recall).

20. 20 Objective of ES To transfer expertise from an expert or experts to a computer and then on to others humans (nonexperts). The process involve four activities: knowledge acquisition (from experts or other sources) knowledge representation (in computer) knowledge inferencing knowledge transfer to user.

21. 21 Structure of ES Knowledge acquisition Knowledge base Facts: What is known about domain area Rules: Logical reference e.g., between symptoms & causes Knowledge Engineer Expert Knowledge Refinement Blackboard (Workplace) Plan Agenda Solution Problem Description Inference Engine Draw Conclusions Interpreter Scheduler Consistency Enforcer Recommended Action User Interface User Explanation Facility Facts about The specific incident Consultation EnvironmentDevelopment Environment

22. 22 Some categories of ES Category Problem Addressed Interpretation Inferring situation descriptions from observation PredictionInferring likely consequences of given situations DiagnosisInferring system malfunctions from observation PlanningDeveloping plan to achieve goal(s) MonitoringComparing observations to plans, flagging exceptions DebuggingPrescribing remedies for malfunctions RepairExecuting a plan to administer a prescribed remedy InstructionDiagnosing, debugging, and correcting student performance ControlInterpreting, predicting, repairing and monitoring system behaviors

23. 23 Some Advantages of ES Increase availability Reduced cost Reduced danger Permanence Multiple expertise Increase reliability Explanation Fast response Steady, unemotional, and complete response all the time Intelligent tutor Intelligent database

24. 24 Limits of ES Best used to augment experts' capabilities; ES may not be able to replace the expert. Not truly intelligent; cannot learn new concepts and rules. Not good for problems that lack focus/careful definition. ES demonstrate no common sense. Exhibit limited use in areas where humans are unwilling to let the ES be accountable for actions, e.g., in making final medical diagnosis decisions.

25. 25 Intelligent Agents Interface Tutors Interface Tutors Presentation Agents Presentation Agents Network Navigation Agents Network Navigation Agents Role- Playing Agents Role- Playing Agents User Interface Agents Information Management Agents Search Agents Search Agents Information Brokers Information Brokers Information Filters Information Filters

26. 26 Summary Decision support systems in business are changing. The growth of corporate intranets, extranets, and other web technologies have increased the demand for a variety of personalized, proactive, web-enabled analytical techniques to support DSS. Information systems must support a variety of management decision-making levels and decisions. Decision support systems are interactive computer-based information systems that use DSS software and a model base to provide information to support semi-structured and unstructured decision making. The Objective of ES is to transfer expertise from an expert or experts to a computer and then on to others humans (nonexperts). ES is capbale to support unstructured decision making.