The document discusses various types of decision support systems that can help managers at different levels of an organization. It describes executive support systems that help senior managers address strategic issues through integrated business information and data visualization. It also discusses expert systems that capture human expertise in specific domains through knowledge bases and inference engines. Finally, it summarizes decision support systems as interactive tools that combine data and models to support semi-structured and unstructured decision-making.

1. Example –Voyage Estimating Decision Support System

2. Executive Support System It provides a generalized computing and communications environment that help senior managers address strategic issues and identify long-term trends in the firm and its environment. ESS address non-routine decisions requiring judgment, evaluation, and insight because there is no agreed-on procedure for arriving at a solution. ESS present graphs and data from many internal and external sources through an interface that is easy for senior managers to use. Often the information is delivered to senior executives through a portal , which uses a Web interface to present integrated personalized business content.

3. Model of an Executive Support System

4. Interrelationship Among Systems

5. Management Information Systems MBA (Family Business and Entrepreneurship) Term – II, Batch 2010-12 Session – V December 29, 2010 -NM

6. Artificial Intelligence and Experts Systems AI is the effort to develop computer based systems that can behave like humans, with the ability to learn languages, accomplish physical tasks, use a perceptual apparatus, and emulate human experience and decision making. Features of AI It is man made. Can not fully think the way human beings do. AI is a kind of computer program that enables the computer to think or behave the way programmer wants. Unlike other IS it has potential to extend a manager’s problem solving ability beyond his normal capabilities.

7. Areas of AI Learning Natural language Expert systems Robotics Perspective System (vision, hearing) Neural Networks All hardware Artificial Intelligence

8. Expert Systems An expert system is a knowledge – intensive program that solves a problem by capturing the expertise of a human in limited domains of knowledge and experience. The traditional definition of a computer program is usually: algorithm + data structures = program In an expert system, the definition changes to: inference engine + knowledge = expert system

9. Components of ES User Interface Knowledge Base Inference Engine Development Engine

10. Components of ES (contd..) User User interface Inference engine Knowledge programmer Development engine Knowledge base Problem domain Instructions/ information Solution/ explanation Knowledge

11. APPLICATIONS OF EXPERT SYSTEM Diagnosis and Troubleshooting of Devices and Systems of All Kinds Planning and Scheduling Configuration of Manufactured Objects from Subassemblies Financial Decision Making Knowledge Publishing Process Monitoring and Control Design and Manufacturing Customer Relationship Management so on

12. IS in Multiunit Organization Shipping Logistics Finance Marketing Accounts HR Sales Inventory control Purchasing Production Control Order Control Production

13. Network Communication Source: [email_address] Internet Vs Intranet Vs Extranet

15. Intranet Concepts Intranet- An infrastructure based on Internet standards and technologies that supports sharing of content within a single organization. Intranet functions much like the Internet except the content is not available to the public. Information such as company memos, employee handbooks and company databases may be made available on an intranet to those who have password access.

18. Extranet Concepts An extranet is a set of content shared by a well-defined group, but one that crosses enterprise boundaries. Extranets are similar to intranet except that the information is proprietary to a specific outside audience. A company’s dealers might have access to technical or pricing information which is not available to the general public .

21. Business Value of Improved Decision Making Example Decision Decision Maker Number of Annual Decision Estimated value to firm of a single improved decision Annual Value Allocate support to most suitable customer Accounts Manager 12 $100,000 $1,200,000 Predict Call centre daily demand Call centre Management 4 150,000 600,000 Decide parts inventory levels daily Inventory manager 365 5,000 1,825,000 Identify competitive bids from major supplier Senior Manager 1 2,000,000 2,000,000 Schedule production to fill orders Manufacturing Manager 150 10,000 1,500,00 Allocate labor to complete job Production floor manager 100 4,000 400,000

22. Decision Making –Types of Decision Decisions are made at all levels of the firm. Some decisions are very common and routine but exceptionally valuable. Although the value of improving any single one of these decisions may be small, improving hundreds of thousands of these small decisions adds up to a large annual value. Decisions are classified according to type: Unstructured decisions are those in which the decision maker must provide judgment, evaluation, and insights into the problem definition. Structured decisions , by contrast, are repetitive and routine, and decision makers can follow a definite procedure for handling them to be efficient. Semistructured decisions are those in which only part of the problem has a clear-cut answer provided by an accepted procedure. In general, structured decisions are more prevalent at lower organizational levels, and unstructured decision making is more common at higher levels.

23. There are different types of decision-making at different levels: Senior executives face many unstructured decision situations, such as establishing the firm's five or ten-year goals Middle management faces more structured decision scenarios but their decisions may include unstructured components. Operational management and rank-and-file employees tend to make more structured decisions

25. Decision Making Process There are four different stages in decision making: Intelligence : Consists of identifying and understanding a problem Design : Involves exploring various solutions Choice : Consists of choosing among available solutions Implementation : Involves making the chosen alternative work and monitoring how the solution is working.

26. Simon’s four stages of Decision Making

27. Managers and decision making in the real world The classical model of management describes 5 functions of managers: Planning, organizing, coordinating, deciding, and controlling (Henri Fayol). Contemporary behavioral models of management state that the actual behavior of managers appears to be less systematic, more informal, and less well organized than the classical model envisions.

28. Managerial roles fall into three categories Interpersonal roles : Managers act as figureheads, leaders, liaisons. Informational roles : Managers act as a nerve center, information disseminators, and spokespersons. Decisional roles : Managers act as entrepreneurs, disturbance handlers, resource allocators, and negotiators.

29. In some of these roles, information systems are not helpful for improving decisions, such as for the roles of figurehead, leader, entrepreneur, or disturbance handler. Additionally, IT investments for supporting decision making may not produce positive results for three main reasons: Information quality: High-quality decisions require high-quality information regardless of information systems. There are seven dimensions of information quality when designing decision-support systems: Accuracy, integrity, consistency, completeness, validity, timeliness, and accessibility. Even with timely, accurate information, some managers make bad decisions. Management filters: Managers filter by turning off information they do not want to hear because it does not conform to their prior conceptions. Biases. Organizational inertia and politics: Organizations are bureaucracies with limited capabilities and competencies for acting decisively. When environments change and new business models should be followed, strong forces within organizations resist making decisions calling for major change.

30. Systems for Decision Support Systems for Decision Support Whereas MIS primarily address structured problems. DSS support semistructured and unstructured problem analysis. An MIS provides managers with reports based on routine flows of data and assists in the general control of the business, whereas a DSS emphasizes change, flexibility, and a rapid response. With a DSS there is less of an effort to link users to structured information flows and a correspondingly greater emphasis on models, assumptions, ad hoc queries, and display graphics .

31. DSS A decision support system (DSS) is an interactive, user-friendly management-level computer system that combines data and sophisticated analytical models and tools to support semi-structured and unstructured decision making. A DSS does not make decisions; rather it is a powerful tool that is used to support decision-making.

32. Types of DSS There are two basic types of DSS: Data-driven DSS Model-driven DSS. The earliest DSS were model-driven DSS , which were primarily standalone systems that used some type of model to perform "what-if" and other kinds of analyses.

33. Data-driven DSS Data-driven DSS support decision making by allowing users to extract data from large databases, which are often in corporate data warehouses. Online analytical processing (OLAP) and data mining are then be used to analyze the data