Chapter-5 Decision Support System

PROF. MURUGAVEL. KN

Department of Computer Science and Engineering

Decision Support System

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D E P A R T M E N T O F C O M P U T E R S C I E N C E A N D E N G I N E E R I N G


Birla Institute of Technology – International center CORE International Institute of Higher Education FZE

PO.No. 41222 Al Dhaith South • Academy Zone # 3 Ras Al Khaimah, UAE

e-Mail: [email protected] • Mob : +971 50 6800720

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Table of Contents

I Lecture Overview ..................................................................... 1 Foundatioin Concepts of DSS .......................................................... 1

II Learning Objectivesl ............................................................... 3 III Lecture Notes ........................................................................ 4

Section I : Decision Support in Business ......................................... 4 Business and Decision Support ............................................ 4 Decision Support Trends ....................................................... 8 Management Information Systems ........................................ 8 Online Anallytical Processing ............................................... 9 Decision Support System .................................................... 10 Using Decision Support Systems ........................................ 11 Executive Information Systems ........................................... 12 Enterprise Information Portals and Decision Support ......... 13 Knowledge Management Systems ...................................... 14

Section II : Artificial Intelligence Technologies in Business ............ 15 Business and AI .................................................................. 15 An Overview of Artificial Intelligence ................................... 15 Neural Networks .................................................................. 18 Fuzzy Logic Systems ......................................................... 18 Genetic Algorithms .............................................................. 19 Virtual Reality (VR) .............................................................. 20 Intelligence Agency ............................................................. 20 Expert Systems .................................................................. 21 Developing Expert System .................................................. 24 The Value of Expert Systems .............................................. 24 Benefits of Expert Systems ................................................ 25 Limitations of Expert Systems ............................................. 25

IV Key Terms and Concepts - Defined .................................... 26

D E C I S I O N S U P P O R T S Y S T E M

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oundation Concepts: Decision Support Systems shows how management information systems, decision support systems, executive information systems, expert systems, and artificial intelligence

technologies can be applied to decision-‐making situations faced by business managers and professionals in today’s dynamic business environment. Information, Decisions, and Management – Information systems can support a variety of management decision making levels and decisions. These include the three levels of management activity (strategic, tactical, and operational decision making) and three types of decision structures (structured, semistructured, and unstructured). Information systems provide a wide range of information products to support these types of decisions at all levels of the organization. Decision Support Trends – Major changes are taking place in traditional MIS, DSS, and EIS tools for providing the information and modeling managers need to support their decision-‐making. Decision support in business is changing, driven by rapid developments in end user computing and networking; Internet, Web browser, and related technologies; and the explosion of an e-‐commerce activity. The growth of corporate intranets, extranets, as well as the Web, has accelerated the development of “executive class” interfaces like enterprise information portals, enterprise knowledge portals, and Web-‐enabled decision support software tools, and their use by lower levels of management and by individuals and teams of business professionals. In addition, the dramatic expansion of ecommerce has opened the door to the use of enterprise portals and DSS tools by the suppliers, customers, and other business stakeholders of a company for customer relationship and supply chain management and other e-‐business applications.

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Management Information Systems – Management information systems provide prespecified reports and responses to managers on a periodic, exception, demand, or push reporting basis, to meet their need for information to support decision-‐making. OLAP and Data Mining – Online analytical processing interactively analyzes complex relationships among large amounts of data stored in multidimensional databases. Data mining analyzes the vast amounts of historical data that have been prepared for anlaysis in data warehouses. Both technologies discover patterns, trends, and exception conditions in a company’s data that support their business analysis and decision-‐making. Decision Support Systems – Decision support systems are interactive, computer-‐based information systems that use DSS software and a model base and database to provide information tailored to support semistructured and unstructured decisions faced by individual managers. They are designed to use decision maker’s own insights and judgments in an ad hoc, interactive, analytical modeling process leading to a specific decision. Executive Information Systems – Executive information systems are information systems originally designed to support the strategic information needs of top management. However, their use is spreading to lower levels of management and business professionals. EIS are easy to use and enable executives to retrieve information tailored to their needs and preferences. Thus, EIS can provide information about a company’s critical success factors to executives to support their planning and control responsibilities. Enterprise Information and Knowledge Portals – Enterprise information portals provide a customized and personalized Web-‐based interface for corporate intranets to give their users easy access to a variety of internal and external business applications, databases, and information services that are tailored to their individual preferences and information needs. Thus, an EIP can supply personalized Web-‐enabled information, knowledge, and decision support to executives, managers, and business professionals, as well as customers, suppliers, and other business partners. An enterprise knowledge portal is a corporate intranet portal that extends the use of an EIP to include knowledge management functions and knowledge base resources to that it becomes a major form of knowledge management system for a company. Artificial Intelligence – The major application domains of artificial intelligence (AI) include a variety of applications in cognitive science, robotics, and natural interfaces. The goal of AI is the development of computer functions normally associated with human physical and mental capabilities, such as robots that

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see, hear, talk, feel, and move, and software capable of reasoning, learning, and problem solving. Thus, AI is being applied to many applications in business operations and managerial decision making, as well as in many other fields. AI Technologies – The many applications areas of AI are summarized in Figure 5.23, including neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents. Neural nets are hardware or software systems based on simple models of the brain’s neuron structure that can learn to recognize patterns in data. Fuzzy logic systems use rules of approximate reasoning to solve problems where data are incomplete or ambiguous. Genetic algorithms use selection, randomizing, and other mathematics functions to simulate an evolutionary process that can yield increasingly better solutions to problems. Virtual reality systems are multisensory systems that enable human users to experience computer-‐simulated environments as if they actually existed. Intelligent agents are knowledge-‐based software surrogates for a user or process in the accomplishment of selected tasks. Expert Systems – Expert systems are knowledge-‐based information systems that use software and a knowledge base about a specific, complex application area to act as expert consultants to users in many business and technical applications. Software includes an inference engine program that makes inferences based on the facts and rules are stored in the knowledge base. A knowledge base consists of facts about a specific subject area and heuristics (rules of thumb) that express the reasoning procedures of an expert. The benefits of expert systems (such as preservation and replication of expertise) must be balanced with their limited applicability in many problem situations.

II. LEARNING 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. • Explain how executive information systems can support the

information needs of executives, managers, and business professionals.

o Executive information systems o Enterprise information portals o Enterprise knowledge portals

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• 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.

III. LECTURE NOTES

Section I: Decision Support in Business BUSINESS AND DECISION SUPPORT To succeed in e-‐business and e-‐commerce, companies need information systems that can support the diverse information and decision-‐making needs of their managers and business professionals. This chapter focuses on the major types of management information systems, decision support, and executive information systems. The chapter concentrates on how the Internet, intranets, and other web-‐enabled information technologies have significantly strengthened the role of information systems play in supporting the decision-‐making activities of every manager and knowledge worker in the internetworked e-‐business enterprise. Analyzing Siemens AG We can learn a lot from this case about how Internet and intranet technologies are changing the face traditional information systems for managerial information and decision support. Take a few minutes to read the case, and we will discuss it (See Siemens AG in section IX). Information, Decisions, and Management: [Figure 5.2]

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The type of information required by decision-‐makers in a company is directly related to the level of management decision-‐making and the amount of structure in the decision situations they face. The framework of the classic managerial pyramid applies even in today’s downsized organizations and flattened or non-‐hierarchical organizational structures. Levels of management decision making still exist, but their size, shape, and participants continue to change as today’s fluid organizational structures evolve. Thus, the levels of managerial decision making that must be supported by information technology in a successful organization are: • Strategic Management: -‐ Typically, a board of directors and an executive

committee of the CEO and top executives develop overall organizational goals, strategies, policies, and objectives as part of a strategic planning process. They monitor the strategic performance of the organization and its overall direction in the political, economic, and competitive business environment. Unstructured Decisions -‐ Involve decision situations where it is not possible to specify in advance most of the decision procedures to follow.

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Strategic Decision Makers -‐ Require more summarized, ad hoc, unscheduled reports, forecasts, and external intelligence to support their more unstructured planning and policy-‐making responsibilities.

• Tactical Management -‐ Increasingly self-‐directed teams as well as middle managers develop short-‐ and medium-‐range plans, schedules, and budgets and specify the policies, procedures, and business objectives for their subunits of the organization. They also allocate resources and monitor the performance of their organizational subunits, including departments, divisions, process teams, and other workgroups. Semistructured Decisions -‐ Some decision procedures can be prespecified, but not enough to lead to a definite recommended decision. Tactical Decision-‐Makers -‐ Require information from both the operational level and the strategic level to support their semistructured decision making responsibilities.

• Operational Management -‐ The members of self-‐directed teams or

supervisory managers develop short-‐range plans such as weekly production schedules. They direct the use of resources and the performance of tasks according to procedures and within budgets and schedules they establish for the teams and other workgroups of the organization. Structured Decisions -‐ Involve situations where the procedures to follow when a decision is needed can be specified in advance. Operational Decision Makers -‐ Require more prespecified internal reports emphasizing detailed current and historical data comparisons that support their more structured responsibilities in day-‐to-‐day operations.

Information Quality: [Figure 5.3]

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What characteristics would make information valuable and useful to you?

• Examine the characteristics or attributes of information quality. Information that is outdated, inaccurate, or hard to understand would not be very meaningful, useful, or valuable to you or other end users.

• People want information of high quality, that is, information products whose characteristics, attributes, or qualities help make it valuable to them.

• Three dimensions of information are time, content, and form.

Decision Structure: Providing information and support for all levels of management decision-‐making is no easy task. Therefore, information systems must be designed to

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produce a variety of information products to meet the changing needs of decision-‐makers throughout an organization. DECISION SUPPORT TRENDS Information systems are increasingly being used to support business decision-‐making. A number of trends have occurred in this area:

• e-‐commerce is expanding the information and decision support uses and expectations of a company’s employees, managers, customers, suppliers, and other business partners.

• 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 nonmanagerial individuals and self-‐directed teams of business professionals.

• The Internet and the World Wide Web have also contributed greatly to the concept of e-‐commerce.

• 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.

MANAGEMENT INFORMATION SYSTEMS Management information systems were the original type of information systems developed to support managerial decision-‐making. A management information system produces information products that support many of the day-‐to-‐day decision-‐making needs of managers and business professionals. Reports, displays, and responses produced by information systems provide information that managers have specified in advance as adequately meeting their information needs. Such predefined information products satisfy the information needs of managers at the operational and tactical levels of the organization who are faced with more structured types of decision situations. Management Reporting Alternatives: MIS provide a variety of information products to managers. Three major reporting alternatives are provided by such systems as:

• Periodic scheduled reports -‐ o Traditional form of providing information to managers. Uses a

prespecified format designed to provide managers with information on a regular basis.

• Exception Reports

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o Reports that are produced only when exceptional conditions occur.

• Demand Reports and Responses o Information is provided whenever a manager demands it.

• Push Reporting o Information is pushed to a manager’s networked workstation.

ONLINE ANALYTICAL PROCESSING: [Figure 5.8]

Online analytical processing 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 (analytical databases, data marts, data warehouses, data mining techniques, and multidimensional database structures, specialized servers and web-‐enabled software products). Online analytical processing involves several basic analytical operations: • Consolidation -‐ Involves the aggregation of data. This can involve simple

roll-‐ups or complex groupings involving interrelated data. • Drill-‐Down -‐ OLAP can go in the reverse direction and automatically

display detailed data that comprises consolidated data. • Slicing and Dicing -‐ Refers to the ability to look at the database from

different viewpoints. Slicing and dicing is often performed along a time axis in order to analyze trends and find patterns.

OLAP applications: • Access very large amounts of data to discover patterns, trends, and

exception conditions • Analyze the techniques between many types of business elements. • Involve aggregated data. • Compare aggregated data over hierarchical time periods. • Present data in different perspectives.

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• Involve complex calculations between data elements. • Are able to respond quickly to user requests so that managers or analysts

can pursue an analytical or decision thought process without being hindered by the system.

DECISION SUPPORT SYSTEMS Decision support systems 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. DSS Models and Software: Decision support systems rely on model bases as well as databases as vital system resources. A DSS model base is a software component that consists of models used in computational and analytical routines that mathematically express relationships among variables. Examples include:

• Spreadsheet models • Linear programming models • Multiple regression forecasting models • Capital budgeting present value models

Geographic Information and Data Visualization Systems Geographic information systems (GIS) and data visualization systems (DVS) are special categories of DSS that integrate computer graphics with other DSS features.

• Geographic Information System – is a DSS that uses geographic databases to construct and display maps and other graphics displays that support decisions affecting the geographic distribution of people and other resources.

• Data Visualization Systems – DVS systems represent complex data using interactive three-‐dimensional graphical forms such as charts, graphs, and maps. DVS tools help users to interactively sort, subdivide, combine, and organize data while it is in its graphical form.

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USING DECISION SUPPORT SYSTEMS: [Figure 5.14]

Using a decision support system involves an interactive analytical modelling process. Typically, a manager uses a DSS software package at his workstation to make inquiries, responses and to issue commands. This differs from the demand responses of information reporting systems, since managers are not demanding prespecified information. Rather, they are exploring possible alternatives. They do not have to specify their information needs in advance. Instead they use the DSS to find the information they need to help them make a decision. Using a DSS involves four basic types of analytical modelling activities: • What-‐If Analysis: -‐ In what-‐if analysis, an end user makes changes to

variables, or relationships among variables, and observes the resulting changes in the values of other variables.

• Sensitivity Analysis: -‐ Is a special case of what-‐if analysis. Typically, the value of only one variable is changed repeatedly, and the resulting changes on other variables are observed. So sensitivity analysis is really a case of what-‐if analysis involving repeated changes to only one variable at a time. Typically, sensitivity analysis is used when decision-‐makers are uncertain about the assumptions made in estimating the value of certain key variables.

• Goal-‐Seeking Analysis: -‐ Reverses the direction of the analysis done in what-‐if and sensitivity analysis. Instead of observing how changes in a variable affect other variables, goal-‐seeking analysis sets a target value for

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a variable and then repeatedly changes other variables until the target value is achieved.

• Optimization Analysis: -‐ Is a more complex extension of goal-‐seeking analysis. 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. Then one or more other variables are changed repeatedly, subject to the specified constraints, until the best values for the target variables are discovered.

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.

EXECUTIVE INFORMATION SYSTEMS Executive information systems (EIS) are information systems that combine many of the features of management information systems and decision support systems. EIS focus on meeting the strategic information needs of top management. The goal of EIS is to provide top executives with immediate and easy access to information about a firm's critical success factors (CSFs), that is, key factors that are critical to accomplishing the organization’s strategic objectives. Features of an EIS: • More features such as web browsing, electronic mail, groupware tools,

and DSS and expert system capabilities are being added. • Information is presented in forms tailored to the preferences of the

executives using the system. Heavy use of graphical user interface and graphics displays.

• Information presentation methods used by an EIS include exception reporting and trend analysis. The ability to drill down allows executives to quickly retrieve displays of related information at lower levels of detail.

• Internet and intranet technologies have added capabilities to EIS systems.

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• EIS’s have spread into the ranks of middle management and business professionals as they have recognized their feasibility and benefits, and as less-‐expensive systems for client/server and corporate intranets become available.

ENTERPRISE INFORMATION PORTALS AND DECISION SUPPORT Major changes and expansion are taking place in traditional MIS, DSS, and EIS tools for providing the information and modeling that managers need to support their decision making. Some of these changes include: • Decision support in business is changing, driven by rapid developments in

end user computing and networking; Internet, web browser, and related technologies, and the explosion of e-‐commerce activity.

• Growth of corporate intranets, extranets, as well as the Web, has accelerated the development and use of “executive class” information delivery and decision support software tools by lower levels of management and by individuals and teams of business professionals.

• Dramatic expansion of e-‐commerce has opened the door to the use of such e-‐business DSS tools by the suppliers, customers, and other business stakeholders of a company for customer relationship management, supply chain management, and other e-‐business applications.

Enterprise Information Portals: • Enterprise information portals are being developed by companies as a

way to provide web-‐enabled information, knowledge, and decision support to executives, managers, employees, suppliers, customers, and other business partners.

• Enterprise information portals are described as a customized and personalized web-‐based interface for corporate intranets that give users easy access to a variety of internal and external business applications, databases, and services.

• Enterprise information portal is the entry to corporate intranets that serve as the primary knowledge management systems for many companies. They are often called enterprise knowledge portals by some vendors. Knowledge management systems are defined as the use of information technology to help gather, organize, and share business knowledge within an organization.

• Enterprise information portals can play a major role in helping a company use its intranets as knowledge management systems to share and disseminate knowledge in support of its business decision-‐making.

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KNOWLEDGE MANAGEMENT SYSTEMS [Figure 5.20]

Knowledge management has become one of the major strategic uses of information technology. Many companies are building knowledge management systems (KMS) to manage organizational learning and business know-‐how. The goal of KMS is to help knowledge workers create, organize, and make available important business knowledge, wherever and whenever it’s needed in an organization. This includes processes, procedures, patterns, reference works, formulas, “best practices,” forecasts, and fixes. Internet and Intranet web sites, groupware, data mining, knowledge bases, discussion forums, and videoconferencing are some of the key information technologies for gathering, storing, and distributing this knowledge. Characteristics of KMS: ·∙ KMS are information systems that facilitate organizational learning and knowledge creation. ·∙ KMS use a variety of information technologies to collect and edit information, assess its value, disseminate it within the organization, and apply it as knowledge to the processes of a business.

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·∙ KMS are sometimes called adaptive learning systems. That’s because they create cycles of organizational learning called learning loops, where the creation, dissemination, and application of knowledge produces an adaptive learning process within a company. ·∙ KMS can provide rapid feedback to knowledge workers, encourage behavior changes by employees, and significantly improve business performance. ·∙ As an organizational learning process continues and its knowledge base expands, the knowledge-‐creating company integrates its knowledge into its business processes, products, and services. This makes it a highly innovative and agile provider of high quality products and customer services and a formidable competitor in the marketplace. Section II: Artificial Intelligence Technologies in Business BUSINESS AND AI Business and other organizations are significantly increasing their attempts to assist the human intelligence and productivity of their knowledge workers with artificial intelligence tools and techniques. AI includes natural languages, industrial robots, expert systems, and intelligent agents. Analyzing BAE Systems We can learn a lot about the business value of using the Internet and artificial intelligence technologies from this case. Take a few minutes to read it, and we will discuss it (BAE Systems in Section IX). AN OVERVIEW OF ARTIFICIAL INTELLIGENCE Artificial intelligence (AI) is a 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.

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The Domains of Artificial Intelligence: [Figure 5.23]

AI applications can be grouped into three major areas: • Cognitive Science -‐ This area of artificial intelligence is based on research

in biology, neurology, psychology, mathematics, and many allied disciplines. It focuses on researching how the human brain works and how humans think and learn. The results of such research in human information processing are the basis for the development of a variety of computer-‐based applications in artificial intelligence.

Applications in the cognitive science area of AI include:

Expert Systems -‐ A computer-‐based information system that uses its knowledge about a specific complex application area to act as an expert consultant to users. The system consists of knowledge base and software modules that perform inferences on the knowledge, and communicate answers to a user’s questions.

Knowledge-‐Based Systems -‐ An information system, which adds a knowledge base and some, reasoning capability to the database and other components, found in other types of computer-‐based information systems.

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Adaptive Learning Systems -‐ An information system that can modify its behavior based on information acquired as it operates.

Fuzzy Logic Systems -‐ Computer-‐based systems that can process data that are incomplete or only partially correct. Such systems can solve unstructured problems with incomplete knowledge by developing approximate inferences and answers.

Neural Network -‐ software can learn by processing sample problems and their solutions. As neural nets start to recognize patterns, they can begin to program themselves to solve such problems on their own.

Genetic Algorithm -‐ software uses Darwinian (survival of the fittest), randomizing, and other mathematical functions to simulate evolutionary processes that can generate increasingly better solutions to problems.

Intelligent Agents -‐ Use expert system and other AI technologies to serve as software surrogates for a variety of end user applications.

• Robotics: -‐ AI, engineering, and physiology are the basic disciplines of robotics. This technology produces robot machines with computer intelligence and computer-‐controlled, humanlike physical capabilities.

Robotics applications include: 1. Visual perception (sight) 2. Tactility (touch) 3. Dexterity (skill in handling and manipulation) 4. Locomotion (ability to move over any terrain) 5. Navigation (properly find one’s way to a destination)

• Natural Interface: -‐ The development of natural interfaces is considered a

major area of AI applications and is essential to the natural use of computers by humans. For example, the developments of natural languages and speech recognition are major thrusts of this area. Being able to talk to computers and robots in conversational human languages and have them “understand” us is the goal of AI researchers. This application area involves research and development in linguistics, psychology, computer science, and other disciplines. Efforts in this area include:

Natural Language -‐ A programming language that is very close to human language. Also, called very highlevel language.

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Multisensory Interfaces -‐ The ability of computer systems to recognize a variety of human body movement, which allows them to operate.

Speech Recognition -‐ The ability of a computer system to recognize speech patterns, and to operate using these patterns.

Virtual Reality -‐ The use of multisensory human/computer interfaces that enables human users to experience computer-‐simulated objects, entities, spaces, and “worlds” as if they actually existed.

NEURAL NETWORKS Neural networks are computing systems modelled on the human brain's mesh-‐like network of interconnected processing elements, called neurons. Of course, neural networks are much simpler than the human brain (estimated to have more than 100 billion neuron brain cells). Like the brain, however, such networks can process many pieces of information simultaneously and can learn to recognize patterns and program themselves to solve related problems on their own. Neural networks can be implemented on microcomputers and other computer systems via software packages, which simulate the activities of a neural network of many processing elements. Specialized neural network coprocessor circuit boards are also available. Special-‐purpose neural net microprocessor chips are used in some application areas. Uses include: o Military weapons systems o Voice recognition o Check signature verification o Manufacturing quality control o Image processing o Credit risk assessment o Investment forecasting o Data mining FUZZY LOGIC SYSTEMS Fuzzy Logic is a method of reasoning that resembles human reasoning since it allows for approximate values and inferences (fuzzy logic) and incomplete or

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ambiguous data (fuzzy data) instead of relying only on crisp data, such as binary (yes/no) choices. Fuzzy Logic in Business: Examples of applications of fuzzy logic are numerous in Japan, but rate in the United States. The United States has tended to prefer using AI solutions like expert systems or neural networks. Japan has implemented many fuzzy logic applications, especially the use of special-‐purpose fuzzy logic microprocessors chips, called fuzzy process controllers. Examples of fuzzy logic applications in Japan include:

• Riding in subway trains and elevators • Riding in cars that are guided or supported by fuzzy process controllers • Trading shares on the Tokyo Stock Exchange using a stock-‐trading

program based on fuzzy logic • Japanese-‐made products t that use fuzzy logic microprocessors include

auto-‐focus cameras, auto-‐stabilizing, camcorders, energy-‐efficient air conditioners, self-‐adjusting washing machines, and automatic transmissions.

GENETIC ALGORITHMS The use of genetic algorithms is a growing application of artificial intelligence. Genetic algorithm software uses Darwinian (survival of the fittest); randomizing, and other mathematical functions to simulate an evolutionary process that can yield increasingly better solutions to a problem. Genetic algorithms were first used to simulate millions of years in biological, geological, and ecosystem evolution in just a few minutes on a computer. Now genetic algorithm software is being used to model a variety of scientific, technical, and business processes. Genetic algorithms are especially useful for situations in which thousands of solutions are possible and must be evaluated to produce an optimal solution. Genetic algorithm software uses sets of mathematical process rules (algorithms) that specify how combinations of process components or steps are to be formed. This may involve:

• Trying random process combinations (mutation) • Combining parts of several good processes (crossover) • Selecting good sets of processes and discarding poor ones (selection)

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VIRTUAL REALITY (VR) Virtual reality (VR) is computer-‐simulated reality. VR is the use of multisensory human/computer interfaces that enable human users to experience computer-‐simulated objects, entities, spaces, and "worlds" as if they actually existed (also called cyberspace and artificial reality). VR Applications:

• Computer-‐aided design (CAD) • Medical diagnostics and treatment • Scientific experimentation in many physical and biological sciences • Flight simulation for training pilots and astronauts • Product demonstrations • Employee training • Entertainment (3-‐D video games)

VR Limitations: The use of virtual reality seems limited only by the performance and cost of its technology. For example, some VR users develop:

• Cybersickness -‐ eye strain, motion sickness, performance problems • Cost of VR is quite expensive

INTELLIGENT AGENTS [Figure 5.29]

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An intelligent agent (also called intelligent assistants/wizards) is a software surrogate for an end user or a process that fulfils a stated need or activity. An intelligent agent uses a built-‐in and learned knowledge base about a person or process to make decisions and accomplish tasks in a way that fulfils the intentions of a user. One of the most well known uses of intelligent agents is the wizards found in Microsoft Office and other software suites. The use of intelligent agents is expected to grow rapidly as a way for users to:

• Simplify software use. • Search websites on the Internet and corporate intranets • Help customers do comparison-‐shopping among the many e-‐

commerce sites on the Web.

EXPERT SYSTEMS One of the most practical and widely implemented applications of artificial intelligence in business is the development of expert systems and other knowledge-‐based information systems. • Knowledge-‐based information system -‐ adds a knowledge base to the

major components found in other types of computer-‐based information systems.

• Expert System -‐ A computer-‐based information system that uses its knowledge about a specific complex application area to act as an expert consultant to users. ES provide answers to questions in a very specific problem area by making humanlike inferences about knowledge contained in a specialized knowledge base. They must also be able to explain their reasoning process and conclusions to a user.

Components of Expert Systems: [Figure 5.31]

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The components of an expert system include a knowledge base and software modules that perform inferences on the knowledge and communicate answers to a user’s question. The interrelated components of an expert system include:

• Knowledge base: -‐ the knowledge base of an ES contains: 1. Facts about a specific subject area 2. Heuristics (rule of thumb) that express the reasoning procedures of

an expert on the subject.

• Software resources: -‐ An ES software package contains: 1. Inference engine that processes the knowledge related to a specific

problem. 2. User interface program that communicates with end users. 3. Explanation program to explain the reasoning process to the user. 4. Software tools for developing expert systems include knowledge

acquisition programs and expert system shells.

• Hardware resources: -‐ These include: 1. Stand alone microcomputer systems 2. Microcomputer workstations and terminals connected to

minicomputers or mainframes in a telecommunications network. 3. Special-‐purpose computers.

• People resources: -‐ People resources include:

1. Knowledge engineers 2. End-‐users

Expert System Applications: [Figure 5.34]

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Using an expert system involves an interactive computer-‐based session, in which:

• The solution to a problem is explored with the expert system acting as a consultant.

• Expert system asks questions of the user, searches its knowledge base for facts and rules or other knowledge.

• Explains its reasoning process when asked. • Gives expert advice to the user in the subject area being explored.

Examples include: credit management, customer service, and productivity management.

Expert systems typically accomplish one or more generic uses. Six activities include:

• Decision Management • Diagnostic/troubleshooting • Maintenance Scheduling

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• Design/configuration • Selection/classification • Process monitoring/control

DEVELOPING EXPERT SYSTEMS The easiest way to develop an expert system is to use an expert system shell as a developmental tool. An expert system shell is a software package consisting of an expert system without a kernel, that is, its knowledge base. This leaves a shell of software (the inference engine and user interface programs) with generic inferencing and user interface capabilities. Other development tools (such as rule editors and user interface generators) are added in making the shell a powerful expert system development tool. Knowledge Engineering A knowledge engineer is a professional who works with experts to capture the knowledge (facts and rules of thumb) they possess. The knowledge engineer then builds the knowledge base using an interactive, prototyping process until the expert system is acceptable. Thus, knowledge engineers perform a role similar to that of systems analysts in conventional information systems development. Obviously, knowledge engineers must be able to understand and work with experts in many subject areas. Therefore, this information systems speciality requires good people skills, as well as a background in artificial intelligence and information systems. THE VALUE OF EXPERT SYSTEMS Expert systems are not the answer to every problem facing an organization. The question becomes “what types of problems are most suitable to expert system solutions?” Ways to answer this question include:

• Look at examples of the applications of current expert systems, including the generic tasks they accomplish.

• Identify criteria that make a problem situation suitable for an expert system. Some of these important criteria include: Domain, expertise, complexity, structure, and availability.

Domain: The domain, or subject area, of the problem is relatively small and limited to a well-‐defined problem area.

Expertise: Solutions to the problem require the efforts of an expert. That

is, a body of knowledge, techniques, and intuition is needed that only a few people possess.

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Complexity: Solution of the problem is a complex task that requires logical inference processing, which would not be handled as well by conventional information processing.

Structure: The solution process must be able to cope with ill-‐structured,

uncertain, missing, and conflicting data, and a problem situation that changes with the passage of time.

Availability: An expert exists who is articulate and cooperative, and who has

the support of the management and end users involved in the development of the proposed system.

Benefits of Expert Systems: Before deciding to acquire or develop an expert system, it is important that managerial end users evaluate its benefits and limitations. In particular, they must decide whether the benefits of a proposed expert system will exceed its costs.

• Captures the expertise of expert or group of experts in a computer-‐based information system.

• May outperform a single human expert in many problem situations. • Faster and more consistent than a human expert. • Can have the knowledge of several experts. • Does not get tired or distracted by too much work or stress. • Available at all times, whereas a human expert may be away, sick, or

may have left the company. • Helps preserve and reproduce the knowledge of experts • Can be used to train the novice. • Effective use of expert systems can allow a firm to have a competitive

advantage by: a. Improving the efficiency of its operations. b. Producing new products and services. c. Locking in customers and suppliers with new business

relationships. d. Building knowledge-‐based strategic information resources.

Limitations of Expert Systems:

• Limited focus (specific problems and specific domains). • Inability to learn. • Difficulties in maintaining expert systems. • Cost involved in developing them. • Excel only in solving specific types of problems in a limited domain of

knowledge.

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IV. KEY TERMS AND CONCEPTS -‐ DEFINED

Analytical Modeling: Interactive use of computer-‐based mathematical models to explore decision alternatives using what-‐if analysis, sensitivity analysis, goal-‐seeking analysis, and optimization analysis. Analytical Modeling – Goal-‐Seeking Analysis: Making repeated changes to selected variables until a chosen variable reaches a target value. Analytical Modeling -‐ Optimization Analysis: Finding an optimum value for selected variables in a mathematical model, given certain constraints. Analytical Modeling -‐ Sensitivity Analysis: Observing how repeated changes to a single variable affect other variables in a mathematical model. Analytical Modeling -‐ What-‐if Analysis: Observing how changes to selected variables affect other variables in a mathematical model. Artificial Intelligence: A science and technology, whose goal is to develop computers that can think, as well as see, hear, walk, talk, and feel. Artificial Intelligence -‐ Application Areas: Major areas of AI research and development include cognitive science, computer science, robotics, and natural interface applications. Artificial Intelligence -‐ Domains: The major domains of AI intelligence are grouped under three major areas: Cognitive science applications, robotics applications, and natural interface applications. Data Mining: Using special-‐purpose software to analyze data from a data warehouse to find hidden patterns and trends.

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Data Visualization Systems: DVS systems represent complex data using interactive three-‐dimensional graphical forms such as charts, graphs, and maps. DVS tools help users to interactively sort, subdivide, combine, and organize data while it is in its graphical form. Decision Structure: Information systems can support a variety of management levels and decisions. These include the three levels of management activity (strategic, tactical, and operational), and three types of decision structures (structured, semistructured, and unstructured). Decision Support versus Management Reporting: Information reporting systems focus on providing managers with prespecified information products that report on the performance of the organization. Decision support systems focus on providing information interactively to support specific types of decisions by individual managers. Decision Support System: An information system that utilizes decision models, a database, and a decision maker’s own insights in an ad hoc, interactive analytical modelling process to reach a specific decision by a specific decision maker. Decision Support Trends: Major changes are taking place in traditional MIS, DSS, and EIS tools for providing the information and modeling managers need to support their decision-‐making. DSS Software resources include software packages such as DSS generators and spreadsheet packages that support database management, model database management, and dialog generation and management. Enterprise Information Portal: Enterprise information portals are being developed by companies as a way to provide web-‐enabled information, knowledge, and decision support to executives, managers, employees, suppliers, customers, and other business partners.

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Enterprise Knowledge Portal: Enterprise information portals are the entry to corporate intranets that serve as their knowledge management systems. These portals are often called enterprise knowledge portals by their vendors. Executive Information System: An information system that provides strategic information tailored to the needs of top management. Expert System: A computer-‐based information system that uses its knowledge about a specific complex application area to act as an expert consultant to users. Expert System -‐ Applications: Includes applications such as diagnosis, design, prediction, interpretation, and repair. Expert System -‐ Benefits and Limitations: Benefits include the preservation and replication of expertise. They have limited applicability in many problem situations. Expert System -‐ Components: The system consists of a knowledge base and software modules that perform inferences on the knowledge, and communicate answers to a user’s questions. Expert System -‐ Development: Expert systems can be purchased or developed if a problem situation exists that is suitable for solution by expert systems rather than by conventional experts and information processing. Expert System Shell: An expert system without its knowledge base. Fuzzy Logic: A computer-‐based system that can process data that are incomplete or only partially correct, i.e., fuzzy data. Such systems can solve unstructured problems with incomplete knowledge as humans do. Genetic Algorithms: Genetic algorithms use sets of mathematical process rules (algorithms) that specify how combinations of process components or steps are to be formed.

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Geographic Information System: A GIS is a DSS that constructs and displays maps and other graphics displays that support decisions affecting the geographic distribution of people and other resources. Inference Engineering: The software component of an expert system, which processes the rules and facts, related to a specific problem and makes associations and inferences resulting in recommended sources of action. Intelligent Agent: A knowledge base software surrogate for a user or process in the accomplishment of selected tasks. Knowledge Base: A computer-‐accessible collection of knowledge about a subject in a variety of forms, such as facts and rules of inference, frames, and objects. Knowledge Engineer: A specialist who works with experts to capture the knowledge they possess in order to develop a knowledge base for expert systems and other knowledge-‐based systems. Knowledge Management System: Knowledge management systems are defined as the use of information technology to help gather, organize, and share business knowledge within an organization. Level of Management Decision Making: Information systems can support a variety of management levels and decisions. These include the three levels of management activity (strategic, tactical, and operational), and three types of decision structures (structured, semistructured, and unstructured). Management Information System: A management support system that produces prespecified reports, displays, and responses on a periodic, exception, or demand basis. Model Base: An organized collection of conceptual, mathematical, and logical models that express business relationships, computational routines, or analytical techniques. Such models are stored in the form of programs and program subroutines, command files, and spreadsheets.

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Neural Network: Massively parallel neurocomputer systems whose architecture is based on the human brain’s mesh-‐like neuron structure. Such networks can process many pieces of information simultaneously and can learn to recognize patterns and programs themselves to solve related problems on their own. Online Analytical Processing: Management, decision support, and executive information systems can be enhanced with an online analytical processing capability. Through OLAP, managers are able to analyze complex relationships in order to discover patterns, trends, and exception conditions in an online, realtime process that supports their business analysis and decision-‐making. Reporting Alternatives: Three major reporting alternatives include periodic scheduled reports, exception reports, and demand reports and responses. Robotics: The technology of building machines (robots) with computer intelligence and human like physical capabilities. Virtual Reality: The use of multisensory human/computer interfaces that enable human users to experience computer-‐simulated objects, entities, spaces, and “worlds” as if they actually existed.

V. DISCUSSION QUESTIONS ? Is the form and use of information and decision support in e-‐business

changing and expanding?

? Has the growth of self-‐directed teams to manage work in organizations changed the need for strategic, tactical, and operational decision making in business?

? What is the difference between the ability of a manager to retrieve

information instantly on demand using an MIS and the capabilities provided by a DSS?

? In what ways does using an electronic spreadsheet package provide

you with the capabilities of a decision support system?

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? Are enterprise information portals making executive information systems unnecessary?

? Can computers think? Will they EVER be able to?

? What are some of the most important applications of AI in business?

? What are some of the limitations or dangers you see in the use of AI technologies such as expert systems, virtual reality, and intelligent agents? What could be done to minimize such effects?

Documents

Chapter-5 Decision Support System