1. Decision Support Systems9
Decision Support Systems
© 2002 McGraw-Hill Companies

2. Chapter Objectives
Identify the changes taking place in the form and use of decision support in e-business enterprises.
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.
© 2002 McGraw-Hill Companies

3. Chapter Objectives
Explain how the following information systems can support the information needs of executives, managers, and business professionals:
A) Executive Information Systems
B) Enterprise Information Portals
C) Enterprise Knowledge Portals
Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business.
Give examples of several ways expert systems can be used in business decision-making situations.
© 2002 McGraw-Hill Companies

4. e-Business Decision Support Applications
Customer
Relationship
Management
Situation: What-if
Scenario Analysis
Supply Chain
Knowledge/Innovation
Enterprise Performance
Monitoring
Employee-
Manager/
Customer/Partner
Using information systems to support business decision making has been one of the primary thrusts of the business use of information technology. However, the e-commerce revolution spawned by the Internet and the World Wide Web is expanding the information and decision support uses and expectations of a company’s employees, managers, customers, suppliers, and other business partners.
The figure highlights several of the major e-business decision support applications that are being customized, personalized, and Web-enabled for use in e-business and e-commerce. These e-business decision support applications are being rapidly made available to employees, managers, customers, suppliers, and other business partners of an internetworked e-business enterprise.
Teaching Tips
This slide corresponds to Figure 9.3 on pp. 295 and relates to the material on pp. 294.
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5. Decisions in the e-Business
Strategic
Management
Tactical
Operational
Decisions
Information
Decision Characteristics
Unstructured
Semi-structured
Structured
To succeed in e-business and e-commerce, companies need information systems that can support the diverse information and decision making needs of business professionals. The type of information required by decision makers in a company is directly related to the level of management and the amount of structure in the decision situation.
Strategic Planning and Control. Top executives develop overall organizational goals, strategies, policies, and objectives through long-range strategic planning. They also monitor the strategic performance of the organization and its overall direction. As a result, they are typically involved in making unstructured decisions; that is decisions where decision procedures to be followed cannot be specified in advance.
Tactical Planning and Control. Middle managers develop short- and medium-range plans and budgets and specify the policies, procedures, and objectives for subunits of the organization. They also acquire and allocate resources and monitor performance of organizational subunits at the department, division, and other workgroup levels. Hence, these managers make more semi-structured decisions in which only some of the decision procedures can be specified in advance.
Operational Planning and Control. Supervisory managers develop short-term planning devices such as production schedules. Supervisors are front-line managers who direct the actions of non-management employees. Their IS needs are often linked to the processing, monitoring, and evaluating of physical products. Thus, their decisions are more structured; that is to say, they can be specified in advance.
Teaching Tips
This slide corresponds to Figure 9.4 on pp. 296 and relates to the material on pp
© 2002 McGraw-Hill Companies

6. Management Information System Reports
Periodic Scheduled
Reports
Exception Reports
Demand Reports
and Responses
Push Reports
Major
Management
Information
Systems Reports
The Management Information System concept, also called information reporting systems, was the original type of management support system. MIS produces information products that support many of the day-to-day decision-making needs of the organization. Three major reporting alternatives include:
Periodic Scheduled Reports. This traditional form of providing information to managers uses a prespecified format designed to provide managers with information on a regular basis. Typical examples include weekly sales analysis reports and monthly financial statements.
Exception Reports. These are generated when a specific set of conditions occur. The IS can be designed to produce exception reports when some process exceeds given parameters and requires management action. Exception reports reduce information overload. They also promote management by exception -- intervening only when decisions need to be made.
Demand Reports and Responses. These provide information whenever a manager demands it. For example, DBMS query languages and report generators allow managers at online workstations to get immediate responses or reports to their requests for information.
Push Reporting. Many companies are using webcasting software to selectively broadcast reports and other information to the networked PCs of managers and specialists over their corporate intranets. In this manner, information is pushed to a manager’s networked workstation.
Teaching Tips
This slide corresponds to the material on pp
© 2002 McGraw-Hill Companies

7. Online Analytical Processing
OLAP
Server
Multi-
dimensional
database
Corporate
Databases
Client PC
Web-enabled OLAP
Software
Data is retrieved from corporate databases
and staged in an OLAP multi-dimensional
Operational DB
Data Marts
Data Warehouse
Online Analytical Processing (OLAP) is a capability of management, decision support, and executive information systems that enables managers and analysts to interactively examine and manipulate large amounts of detailed and consolidated data from many perspectives. Basic analytical operations include:
Consolidation. This involves the aggregation of data. It can be simple roll-ups or complex groupings involving interrelated data. For example, sales offices can be rolled up to districts and districts rolled up to regions.
Drill-Down. OLAP can go in the reverse direction and automatically display detailed data that comprises consolidated data. For example, the sales by individual products or sales reps that make up a region's sales can be accessed easily.
Slicing and Dicing. This refers to the ability to look at the database from different viewpoints. For example, one slice of a database might show all sales of a product within regions. Another slice might show all sales by sales channel. By allowing rapid alternative perspectives, slicing and dicing allows managers to isolate the information of interest for decision making.
Teaching Tips
This slide corresponds to Figure 9.7 pp. 299 and relates to the material on pp
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8. Decision Support Systems
What If-Analysis
Sensitivity Analysis
Goal-Seeking Analysis
Optimization Analysis
Important
Decision
Support
Systems
Analytical Models
Decision support systems (DSS) are computer-based systems that provide managers and business professionals interactive information support for semi-structured and unstructured decisions. Unlike management information systems, DSS rely on model bases.
A model base is a software component that consists of models used in computational and analytical routines that mathematically express relationships between variables.
There are various types of DSS analytical model bases. These include:
What-If Analysis. An end user makes changes to variables, or relationships among variables, and observes the resulting change in the value of other variables.
Sensitivity Analysis. A special type of what-if analysis in which the value of only one variable is changed repeatedly, and the resulting changes on other variables are observed.
Goal-Seeking Analysis. Instead of observing how changes in a variable affect other variables, goal-seeking analysis sets a target value for a variable, and then repeatedly changes other variables until the target value is achieved.
Optimization analysis. A more complex goal-seeking model. Instead of setting a specific target value for a variable, the goal is to find the optimum value for one or more target variables, given certain constraints.
Teaching Tips
This slide corresponds to Figure 9.13 on pp. 304 and relates to the material on pp
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9. Enterprise Information Portals and DSS
Enterprise Information Portal Gateway
Enterprise Information Portal User Interface
Search
Agents
OLAP
Data
Mining
Knowledge
Management
Database Management Functions
Mart
Other
Business
Applications
Operational
Database
Analytical
Base
DSS
What-If Models
Sensitivity Models
Goal-Seeking Models
Optimization Models
Internet
Intranet
Extranet
Cross-platform integration is one of the main objectives of today’s e-business. As shown in the figure, newer DSS packages not only are capable of running under different computer platforms, but can be integrated with corporate data resources, including operational databases, data marts, and data warehouses.
These packages are no longer limited to numeric input and response, but can use data visualization systems to represent complex data using interactive three dimensional graphical forms. This in turns helps users discover patterns and links between decision variables quicker and easier.
As we stated earlier, the objective of today’s e-business is to provide information to anyone that needs it, whenever, and wherever they are. More and more companies are developing Enterprise Information Portals to provide web-enabled access to information. When deployed successfully, this portal provides a universal interface to both corporate knowledge and decision-making tools as well as a wealth of other tools.
Teaching Tips
This slide corresponds to Figure 9.18 on pp. 310 and relates to the material on pp. 309.
© 2002 McGraw-Hill Companies

10. 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
Respond quickly and successfully to new situations.
Recognize the relative importance of elements in a situation
Handle ambiguous, incomplete,or erroneous information
Artificial intelligence (AI) is a field of science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, and engineering.
The goal of AI is to develop computers that can think, as well as see, hear, walk, talk, and feel.
A major thrust of AI is the development of computer functions normally associated with human intelligence, such as reasoning, learning, and problem solving.
Alan Turing in 1950 proposed a test for determining if machines could think. According to the Turing test, a computer could demonstrate intelligence if a human interviewer, conversing with an unseen human and an unseen computer, could not tell which was which. Critics believe that no computer can truly pass the Turing test. They claim that developing intelligence to impart true humanlike capabilities to computers is simply not possible. But progress continues, and only time will tell if the ambitious goals of AI will be achieved.
Teaching Tips
This slide corresponds to Figure 9.21 on pp. 315 and relates to the material on pp. 315.
© 2002 McGraw-Hill Companies

11. Artificial Intelligence Applications
Cognitive
Science
Applications
Artificial
Intelligence
Robotics
Natural
Interface
Expert Systems
Fuzzy Logic
Genetic Algorithms
Neural Networks
Visual Perceptions
Locomotion
Navigation
Tactility
Natural Language
Speech Recognition
Multisensory Interface
Virtual Reality
Artificial Intelligence (AI) is a science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, and engineering. AI works to develop computer functions normally associated with human intelligence. Its goal is to develop computers that can think, see, hear, walk, talk, and even feel. The major application areas of AI can be grouped into three categories:
Cognitive Science. Much of AI development is based upon research in human information processing, which focuses on understanding how the human brain works and how humans think and learn. Major applications in this area include: expert systems, learning systems, fuzzy logic, genetic algorithms, neural networks, and intelligent agents.
Robotics. Robotics is concerned with deploying computers in ways that duplicate the actions (and even the appearance) of humans. Areas of development include visual perception, tactility, dexterity, locomotion, and navigation.
Natural Interface. AI developers hope to make the human-computer interface as natural as possible. Natural language programming, speech recognition, multisensory interfaces, and virtual reality are all areas of development.
Teaching Tips
This slide corresponds to Figure 9.22 on pp. 316 and relates to the material on pp
© 2002 McGraw-Hill Companies

12. AI Application Areas in Business
Neural Networks
Fuzzy Logic Systems
Virtual Reality
Expert Systems
AI Application
Areas in
Business
Intelligent Agents
Genetic Algorithms
There are numerous AI application areas in business. These include:
Neural Networks. Computing systems modeled after the brain’s mesh-like network of interconnected processing elements, called neurons. The interconnected processors in a neural network operate in parallel and interact dynamically. This enables the network to learn to recognize patterns and relationships in the data it processes. For example, a neural network can be used to learn which credit characteristics result in good or bad loans.
Fuzzy Logic. A method of reasoning that allows for approximate values and inferences. This enables fuzzy systems to process incomplete data and quickly provide approximate, but acceptable solutions. Fuzzy systems are used in fuzzy process controller microchips that are incorporated in many Japanese appliances.
Genetic Algorithms. Uses Darwinian randomizing and other mathematical functions to simulate an evolutionary process that yields increasingly better solutions to a problem. They are especially useful for situations in which thousands of solutions are possible and must be evaluated to produce an optimal solution.
Virtual Reality. Is a computer-simulated reality that uses such devices as tracking headsets and data gloves to create virtual worlds that can be experienced through sight, sound, and touch. Current applications of virtual reality include computer-aided design, medical diagnostics, flight simulation, and 3-D video arcade games.
On the next two slides we will focus on two very popular AI business areas.
Teaching Tips
This slide corresponds to Figure 9.22 on pp. 316 and relates to the material on pp
© 2002 McGraw-Hill Companies

13. Intelligent Agents Interface Tutors Search Presentation Agents User
Network
Navigation
Role-
Playing
User
Information
Management
Search
Brokers
Filters
An Intelligent Agent (IA) is a software surrogate that fulfills a stated need or activity. The IA uses built-in and learned knowledge about how an end user behaves or in answer to posed questions, to implement a software solution -- such as the design of a presentation template or spreadsheet -- to solve a specific problem of interest to the end user (e.g. War Games). IAs can be grouped into two categories for business computing:
User Interface Agents.
Interface Tutors. These observe computer operations, correct user mistakes, and provide hints and advice on efficient software use.
Presentation Agents. These show information in a variety of reporting and presentation forms and media based on user preferences.
Network Navigation Agents. These discover paths to information and provide ways to view information that are preferred by a user.
Role-Playing Agents. These play what-if games and other roles to help users understand information and make better decisions.
Information Management Agents.
Search Agents. These help users find files and databases, search for desired information, and suggest and find new types of information products, media, and resources.
Information Brokers. These provide commercial services to discover and develop information resources that fit the business or personal needs of a user.
Information Filters. These receive, find, filter, discard, save, forward, and notify users about products received or desired, including , voice mail, and all other information media.
Teaching Tips
This slide corresponds to Figure 9.28 on pp. 323 and relates to the material on pp
© 2002 McGraw-Hill Companies

14. Components of Expert Systems
The Expert System
Knowledge
Base
User
Workstation
Expert
Advice
Interface
Programs
Inference
Engine
Program
Expert System Development
Engineering
Acquisition
Expert and/or
Knowledge Engineer
An Expert System (ES) is a knowledge-based information system that uses its knowledge about a specific, complex application area to act as an expert consultant to end users. The components of an ES include:
Knowledge Base. A knowledge base contains knowledge needed to implement the task. There are two basic types of knowledge:
Factual knowledge. Facts, or descriptive information, about a specific subject area.
Heuristics. A rule of thumb for applying facts and/or making inferences, usually expressed as rules.
Inference Engine. An inference engine provides the ES with its reasoning capabilities. The inference engine processes the knowledge related to a specific problem. It then makes associations and inferences resulting in recommended courses of action.
User Interface. This is the means for user interactions.
To create an expert system a knowledge engineer acquires the task knowledge from the human expert using knowledge acquisition tools. Using an expert system shell, which contains the user interface and inference engine software modules, the KE then encodes the knowledge into the knowledge base. A reiterative approach is used to test and refine the expert system's knowledge base until it is deemed complete.
Teaching Tips
This slide corresponds to Figure 9.30 on pp. 325 and relates to the material on pp
© 2002 McGraw-Hill Companies

15. Expert System Applications
Decision Management
Diagnostic/Troubleshooting
Maintenance/Scheduling
Design/Configuration
Selection/Classification
Major
Application
Categories
of Expert Systems
Process Monitoring/Control
Expert Systems can be used to accomplish many business tasks:
Decision Management. This includes systems that appraise situations or consider alternatives and make recommendations based on criteria supplied during the discovery process. Examples include loan portfolio analysis, employee evaluation, insurance underwriting, demographic forecasts.
Diagnostic/Troubleshooting. This is the use of systems that infer underlying causes from reported symptoms and history. Examples include equipment calibration, help desk operations, software debugging, medical diagnosis.
Maintenance/Scheduling. This includes systems that prioritize and schedule limited or time-critical resources. Examples include maintenance scheduling, production scheduling, education scheduling, project management.
Design/Configuration. This is the use of systems that help configure equipment components, given existing constraints that must be taken into account. Examples include computer option installation, manufacturability studies, communications networks, optimum assembly plan.
Selection/Classification. These are systems that help users choose products or processes from among large or complex sets of alternatives. Examples include material selection, delinquent account identification, information classification, suspect identification.
Process Monitoring/Control. This includes systems that monitor and control procedures or processes. Examples include machine control (including robotics), inventory control, production monitoring, chemical testing.
Expert systems provide a business with faster, consistent expertise. They also help preserve organizational knowledge. However, they are not without limitations. ES are not suitable for every problem situation. They excel only in solving specific types of problems in a limited domain of knowledge. They fail to solve problems requiring a broad knowledge base. Expert Systems are also difficult and costly to develop and maintain.
Teaching Tips
This slide corresponds to Figure 9.33 on pp. 328 and relates to the material on pp
© 2002 McGraw-Hill Companies

16. Chapter 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. These include the three levels of management activity: strategic, tactical, and operational.
© 2002 McGraw-Hill Companies

17. Chapter Summary (cont)
Online analytical processing is used to analyze complex relationships among large amounts of data stored in multidimensional databases. Data mining analyzes large stores of historical data contained in data warehouses.
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.
© 2002 McGraw-Hill Companies

18. Chapter Summary (cont)
The major application domains in artificial intelligence include a variety of applications in cognitive sciences, robotics, and natural interfaces.
Major AI application areas include:
Neural Networks
Fuzzy Logic
Genetic Algorithms
Virtual Reality
Intelligent Agents
© 2002 McGraw-Hill Companies