DSS CHAP 10

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Decision Support SystemsI. CHAPTER OVERVIEW

This chapter 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 todays dynamic business environment. Section I: Section II: Decision Support in Business Artificial Intelligence Technologies in Business

II. LEARNING OBJECTIVESLearning Objectives 1. Identify the changes taking place in the form and use of decision support in business. 2. Identify the role and reporting alternatives of management information systems. 3. Describe how online analytical processing can meet key information needs of managers. 4. Explain the decision support system concept and how it differs from traditional management information systems. 5. 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. Knowledge management systems 6. Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business. 7. Give examples of several ways expert systems can be used in business decision-making situations.

OBrien, Management Information Systems, 7/e IM - Chapter 10 pg. 1

III. TEACHING SUGGESTIONSInstructors can use Figure 10.2 to discuss the different levels of management and the structure of decision situations they face. It can also be used to discuss the different types of information that is required. The instructor should discuss the three information-reporting alternatives as outlined in the text (periodic, exception, and demand). While using this figure be sure to stress that unstructured, semistructured, and structured decisions are information products that are produced by the three levels of management (operational, tactical, and strategic). Figure 10.11 illustrates the concept of online analytical processing. OLAP may involve the use of specialized servers and multidimensional databases. It also provides fast answers to complex queries posed by managers and analysts using management, decision support, and executive information systems. Figure 10.15 illustrates four basic types of analytical modelling activities (1) what-if analysis, (2) sensitivity analysis, (3) goal-seeking analysis, and (4) optimisation analysis. Figure 10.20 outlines the components of an enterprise information portal. It can be used to identify how e-business decision support systems can be personalized for executives, managers, employees, suppliers, customers, and other business partners. Figure 10.23 illustrates some of the attributes of intelligent behavior. AI is attempting to duplicate these capabilities in the design of computer-based systems. The major application areas of artificial intelligence can be explained using Figure 10.24. This figure groups AI applications into four major areas - cognitive science, computer science, robotics, and natural interfaces. Figure 10.25 summarizes a few of the many types of intelligent agents that are in use or currently being developed. Figure 10.26 outlines some of the major categories and examples of typical expert systems. Figure 10.29 gives an excellent example of many major application categories and examples of typical expert systems. Figure 10.36 details the components of an expert system. This figure emphasizes that the software modules perform inferences on a knowledge base built by an expert and/or knowledge engineer. This model provides expert answers to an end users questions in an interactive process.


IV. LECTURE NOTES Section I: Decision Support in BusinessIntroductionTo succeed in business today, companies need information systems that can support the diverse information and decision-making needs of their managers and business professionals. This is accomplished by several types of management information, decision support, and other information systems. The Internet, intranets, and other Web-enabled information technologies have significantly strengthened the role that information systems play in supporting the decision-making activities of every manager and knowledge worker in business. Analyzing Allstate Insurance, Aviva Canada, and Others: We can learn a lot from this case about the value of business intelligence. Take a few minutes to read the case, and we will discuss it (See Allstate Insurance, Aviva Canada, and Others: Centralized Business Intelligence at Work in section IX). Information, Decisions, and Management: [Figure 10.2] 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 todays 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 todays 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. 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.

Decision Structure: Providing information and support for all levels of management decision-making is no easy task. Therefore, information systems must be designed to produce a variety of information products to meet the changing needs of decision-makers throughout an organization.

Decision Support TrendsUsing information systems to support business decision making has been on of the primary thrusts of the business use of information technology. The fast pace of new information technologies like PC hardware and software suites, client/server networks, and networked PC versions of DSS/EIS software made decision support available to lower levels of management, as well as to non-managerial individuals and self-directed team of business professionals. This trend has accelerated with the dramatic growth of the Internet and intranets and extranets that internetwork companies and their stakeholder. The e-commerce initiatives that are being implemented by many companies are also expanding the information and decision support uses and expectations of a company and its business partners. Todays businesses are responding to with a variety of personalized and proactive Web-based analytical techniques to support the decision-making requirements of all of their constituents. The dramatic expansion of DSS growth has opened the door to the user of business intelligence (BI) tools by the suppliers, customers, and other business stakeholders of a company for customer relationship management, supply chain management, and other e-business applications.

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 makers own insights and judgments Interactive, computer-based modeling process to support the making of semistructured and unstructured business decisions DSS Components: 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.OBrien, Management Information Systems, 7/e IM - Chapter 10 pg. 4

Examples include: Spreadsheet models Linear programming models Multiple regression forecasting models Capital budgeting present value models

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 - Traditional form of providing information to managers. Uses a prespecified format designed to provide managers with information on a regular basis. Exception Reports - Reports that are produced only when exceptional conditions occur. Demand Reports and Responses - Information is provided whenever a manager demands it. Push Reporting - Information is pushed to a managers networked workstation.

Online Analytical Processing: [Figure 10.10]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 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

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.

Using Decision Support Systems: [Figure 10.15]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 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.OBrien, Management Information Systems, 7/e IM - Chapter 10 pg. 6

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 organizations strategic objectives. Capabilities of EIS include: 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. EISs 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 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: [Figure 10.20] Enterprise information portals (EIP) 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, andOBrien, Management Information Systems, 7/e IM - Chapter 10 pg. 7

services.

Enterprise Knowledge Portals: 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.

Knowledge Management SystemsIn many organizations, hypermedia databases at corporate intranet websites have become the knowledge bases for storage and dissemination of business knowledge. This knowledge frequently takes the form of best practices, policies, and business solutions at the project, team, business unit, and enterprise levels of the company. For many companies, enterprise information portals are the entry to corporate intranets that serve as their knowledge management systems. Enterprise information portals play an essential role in helping companies use their intranets as knowledge management systems to share and disseminate knowledge in support of business decision making by managers and business professionals.


IV. LECTURE NOTES (cont) Section II: Artificial Intelligence Technologies in BusinessBusiness and AIBusiness 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 Intelligent agents

Analyzing Wal-Mart, BankFinancial, and HP We can learn a lot about the business vale of artificial intelligence technologies from this case. Take a few minutes to read it, and we will discuss it (See Wal-Mart, BankFinancial, and HP: The Business Value of AI in Section IX).

An Overview of Artificial Intelligence [Figure 10.23]: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. The Domains of Artificial Intelligence: [Figure 10.24 & Figure 10.25] 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 users 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. 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.OBrien, Management Information Systems, 7/e IM - Chapter 10 pg. 9

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 ones 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 high-level language. Multisensory Interfaces - The ability of computer systems to recognize a variety of human body movements, 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.

Expert SystemsOne of the most practical and widely implemented application 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 10.26]OBrien, Management Information Systems, 7/e IM - Chapter 10 pg. 10

The components of an expert system include a knowledge base and software modules that perform inferences on the knowledge and communicate answers to a users 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 10.29] 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 System Applications: [Figure 9.34] Expert systems typically accomplish one or more generic uses. Six activities include: Decision management Diagnostic/troubleshooting Maintenance scheduling Design/configuration Selection/classification Process monitoring/control

Developing Expert SystemsThe 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.

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: Military weapons systems Voice recognition Check signature verification Manufacturing quality control Image processing Credit risk assessment Investment forecasting Data mining

Fuzzy Logic SystemsFuzzy Logic is a method of reasoning that resembles human reasoning since it allows for approximate values and inferences (fuzzy logic) and incomplete or 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:OBrien, Management Information Systems, 7/e IM - Chapter 10 pg. 12

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 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)

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 10.36]: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 wellOBrien, Management Information Systems, 7/e IM - Chapter 10 pg. 13

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.


IV. LECTURE NOTES (cont)Summary Information, Decisions, and Management. Information systems can support a variety of management decision-making levels and decision. These include the three levels of management activity (strategic, tactical, and operational decision making) and three types of decision structures (structures, 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 modelling managers need to support their decision making. Decision support in business is changing, driven by rapid developments in end user computing and networking; Internet and Web technologies; and Web-enables business applications. The growth of corporate intranets, extranets, as well as the Web, has accelerated the development of executive class interfaces like enterprise information portals and Web-enabled business professionals. In addition, the growth of e-commerce and e-business applications has expanded the use of enterprise portals and DSS tools by the suppliers, customers, and other business stakeholders of a company. Management Information Systems. Management information systems provide pre-specified reports and responses to managers of 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 analysis in data warehouses. Both technologies discover patterns, trends, and exceptional conditions in a companys data that support their business analysis and decision making. Decision Support Systems. Decision support systems are interactive, computer-bases information systems that use DSS software and a model base and database to provide information tailored to support semistructured and unstructured decision faced by individual managers. They are designed to use a decision makers own insights and judgements in an ad hoc, interactive, analytical modelling 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 companys 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. As enterprise knowledge portal is a corporate intranet portal that extends the use of an EIP to include knowledge management functions and knowledge base resources so 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 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 application areas of AI are summarized in Figure 10.24, 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 brains 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 simulated 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-bases software surrogates for a user of a 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 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 balance with their limited applicability in many problem situations.


V. KEY TERMS AND CONCEPTS - DEFINEDAnalytical Modeling (333): 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 (335): Making repeated changes to selected variables until a chosen variable reaches a target value. Analytical Modeling - Optimization Analysis (335): Finding an optimum value for selected variables in a mathematical model, given certain constraints. Analytical Modeling - Sensitivity Analysis (334): Observing how repeated changes to a single variable affects other variables in a mathematical model. Analytical Modeling - What-if Analysis (333): Observing how changes to selected variables affect other variables in a mathematical model. Artificial Intelligence (343): 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 (345): Major areas of AI research and development include cognitive science, computer science, robotics, and natural interface applications. Artificial Intelligence Domains (345): The major domains of AI intelligence are grouped under three major areas: Cognitive science applications, robotics applications, and natural interface applications. Business Intelligence (325): A term primarily used in industry that incorporates a range of analytical and decision support applications in business including data mining, decision support systems, knowledge management systems, and online analytical processing. Data Mining (336): Using special-purpose software to analyze data from a data warehouse to find hidden patterns and trends. Data Visualization Systems (331): 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 (323): 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 System (326): An information system that utilizes decision models, a database, and a decision makers own insights in an ad hoc, interactive analytical modelling process to reach a specific decision by a specific decision maker.

Decision Support Trends (324): Major changes are taking place in traditional MIS, DSS, and EIS tools for providing the information and modelingOBrien, Management Information Systems, 7/e IM - Chapter 10 pg. 17

managers need to support their decision-making. DSS Components (326): Decision support systems rely on model bases as well as databases as vital system resources. Enterprise Information Portal (339): 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 Knowledge Portal (341): An enterprise information portal that serves as a knowledge management system by providing users with access to enterprise knowledge bases. Executive Information System (338): An information system that provides strategic information tailored to the needs of top management. Expert System (348): 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 (349): Includes applications such as diagnosis, design, prediction, interpretation, and repair. Expert System - Benefits and Limitations (350): Benefits include the preservation and replication of expertise. They have limited applicability in many problem situations. Expert System Components (348): The system consists of a knowledge base and software modules that perform inferences on the knowledge, and communicate answers to a users questions. Expert System System Development (352): 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 (353): An expert system without its knowledge base. Fuzzy Logic (355): 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 (356): Genetic algorithms use sets of mathematical process rules (algorithms) that specify how combinations of process components or steps are to be formed. Geographic Information System (331): 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 Engine (348): 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 (359): A knowledge base software surrogate for a user or process in the accomplishment of selected tasks. Knowledge Base (348): 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 (353): 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 (341): 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 (320): 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 (328): A management support system that produces prespecified reports, displays, and responses on a periodic, exception, or demand basis. Model Base (326): 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. Neural Network (354): Massively parallel neurocomputer systems whose architecture is based on the human brains 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 (329): 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 (328): Three major reporting alternatives include periodic scheduled reports, exception reports, and demand reports and responses. Robotics (347): The technology of building machines (robots) with computer intelligence and human like physical capabilities. Virtual Reality (356): 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.


VI. REVIEW QUIZ - Match one of the key terms and concepts1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 8 9 23 6 12 24 28 7 3 25 1 1d 1c 1a 1b 27 4 5 10 Decision support trends DSS components Level of management decision making Decision structure Executive information system Management information system Reporting alternatives Decision support system Business intelligence Model base Analytical modelling What-if analysis Sensitivity analysis Goal-seeking analysis Optimization analysis Online analytical processing Data mining Data visualization system Enterprise information portal 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 22 11 2 2a 29 30 17 13 13a 13b 20 18 14 13d 21 26 15 19 16 Knowledge management system Enterprise knowledge portal Artificial intelligence AI Application areas Robotics Virtual reality Geographic information system Expert system Expert system Applications Expert system Benefits & limitations Knowledge base Inference engine Expert system shell Expert system System development Knowledge engineer Neural network Fuzzy logic Intelligent agent Genetic algorithms


VII. ANSWERS TO DISCUSSION QUESTIONS1. Is the form and use of information and decision support systems for managers and business professionals changing and expanding? Why or why not?

Yes, the form and use of information and decision support in e-business is changing and expanding. Certainly changes are taking place in traditional MIS, DSS, and EIS tools, and these changes are being driven by the rapid developments in end user computing and networking. Internet, web browser, and related technologies, and the explosion of e-commerce activities are also causing rapid change. The growth of corporate intranets, extranets, as well as the Web, has accelerated the development of executive class interfaces like enterprise information portals, and Web enabled decision support software tools and their use by lower of management and by individuals and teams of business professionals. The expansion of e-commerce has increased the use of enterprise portals and DSS tools by the suppliers, customers, and other business stakeholders of a company. 2. Has the growth of self-directed teams to manage work in organizations changed the need for strategic, tactical, and operational decision making in business?

Although there has been tremendous growth in the use of self-directed teams in organizations in order to manage the work, the basics for decision making have not changed that much. Strategic, tactical, and operational decision making continue to be carried out in organizations regardless of how the work is completed. What has changed is the way in which the work is being completed. Through technology, self-directed teams now have new and creative ways of completing their duties. 3. 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?

Managers have traditionally relied on the capabilities of MIS to obtain the data that they required. However, the information for these requests had traditionally been structured in advance, and was of the structured type of request. In a DSS support system, the capabilities are much broader. Now managers can query the information in a number of ways, and these systems can handle the ad hoc queries that come about. DSS provide the capabilities for a manager to participate in interactive analytical modeling in order to make more informed decision. DSS software is capable of supporting semistructured and unstructured decisions faced by individual managers. They are designed to use decision makers own insights and judgments in an ad hoc, interactive, analytical modeling process which will lead them to a specific decision. 4. Refer to the Real World Case on Allstate Insurance, Aviva Canada, and others in the chapter. Companies appear to believe that business intelligence is a business issue and not a technology issue. If this is the case, why does it appear that companies are placing more and more responsibility for BI in the hands of the IT department? In what ways does using an electronic spreadsheet package provide you with the capabilities of a decision support system?

5.

An electronic spreadsheet package can be thought of as one of the earlier forms of decision support systems. Spreadsheets allow users to complete what-if, sensitivity, goal seeking, and optimization analysis. They also provide some features of database management and dialog management support. 6. Are enterprise information portals making executive information systems unnecessary? Explain your reasoning.

First of all, in answering the question students should explain what an EIP system is versus an EIS system. As such,


EIPs are 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. EISs on the other hand, are designed to provide strategic information that are tailored to the needs of top management. Whether or not EIPs will eventually make EIS systems unnecessary is a matter of debate. Students may agree that as more and more enriched features are added to EIP systems that their importance will be heightened. On the other hand, EIS systems are also being developed with enriched features such as Web browsing, electronic mail, groupware tools, and DSS and expert systems capabilities to make them even more useful to managers and business professionals. 7. Refer to the Real World Case on Wal-Mart, BankFinancial, and HP in the chapter. Why are neural network and expert system technologies used in many data-mining applications? Reasons could include: Neural networks can learn from the data it processes, thereby learning to recognize patterns and relationships in the data it processes. Thus neural networks can change the strengths of the interconnections between the elements in response to changing patterns in the data it receives and the results that occur. The neural network technology can be used to evaluate or make decisions on its own. An example is that of BankFinancial using neural networks to more accurately target promotions to customers and prospects. Expert system technologies act as a consultant to end users in very specific problem areas by making humanlike inferences about knowledge contained in a specialized knowledge base. Expert systems must be able to explain their reasoning process and conclusions to a user. An example would be the If-Ten analysis used by Wal-Mart in managing its inventory.

8.

Can computers think? Will they ever be able to? Explain why or why not.

Computers will probably never be able to reason in the same way that humans do. However, computers are likely to be able to perform more and more tasks that up until now could only be performed by humans. Experimentation continues to develop in the field of artificial intelligence, and improvements are ongoing. Will a computer ever pass the Turing test is questionable. 9. What are some of the most important applications of AI in business? Defend your choices.

In business, expert systems are probably the most important application of artificial intelligence, though the use of such systems is still quite limited. In other areas, robotics is widely used in manufacturing, and natural interface applications are becoming more and more a part of information systems for many different applications. Major areas of AI research and development include cognitive science, computer science, robotics, and natural interface applications. 10. 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?

Students will suggest a number of answers to this question. However, one possible solution could deal with the ethical issues of these systems. Are they being used for the good of society or is the potential for their misuse growing increasingly with the more complex developments taking place. The design of these systems is both complex and powerful. We must begin to ask ourselves what is the harmful potential of these systems, and how far will we be willing to go to use them to supplement the human reasoning process that we are born with.


VIII. ANSWERS TO ANALYSIS EXERCISES1. BizRate.com: eCommerce Website Reviews a. Use BizRate.com to check out retailers for a product you want to buy. How thorough, valid, and valuable were the reviews to you? Explain. Many of the sites had received high ratings. Ratings in the 4 4.5 category are common, and gives the indication that they are relatively good sites to shop from. b. How could other businesses use a similar web-enabled review system? Give an example. Similar web-enabled reporting systems could be used in a large number of business situations. This could include reporting systems on automotive dealerships, hotels, restaurants, airlines, amusement parks, and car rental agencies. c. How is BizRate.com similar to a web enabled decision support system (DSS)? Decision support systems provide summaries of critical information, real-time monitoring, and exception reporting to decision makers. They also allow decision makers to drill down into the information in order to receive more detailed information on specific topics. BizRate.com is similar to a DSS in those regards. One could easily imagine a similarly designed system that provided information about authorized vendors, products, bids, availability, performance, order tracking, and account status to an organization's purchasing agents.


2.

Enterprise Application Integration a. Using Figure 10.22, indicate whether or not each of the attributes of artificially intelligent behavior applies to Amazon.com's case based reasoning system. Students' answers will vary, however the results should differ meaningfully from the portal's default settings. b. For those attributes that apply as indicated by your answers above, explain how Amazon.com's system creates that behavior. For example, Amazon.com handles ambiguous, incomplete, or erroneous information by linking its recommendations to a specific book rather than to the user's search terms. In short, Amazon.com's system works to reduce ambiguity by forcing a user to select a specific book first. Students' answers will vary depending on the product and the review. One business EAI provider, www.sunopsis.com, allows not only access to disparate systems but also allows analysis between data elements across these systems. For example, an executive may choose to see if a relationship exists between overdue accounts (accounts receivable) and shipping delays (shipping).


3.

Case Based System Sells Books on Amazon a. Using Figure 10.22, indicate whether or not each of the attributes of artificially intelligent behavior applies to Amazon.com's case based reasoning system. Attribute Applies to Amazon Think and reason no Use reason to solve problems no or very rudimentary Learn from experience yes Acquire and apply knowledge yes Exhibit creativity nothing outside the bounds of its rules Deal with complex issues no Respond quickly to new situations no (but does OK for almost new) Recognize relative importance no (simple tallies) Handle ambiguous information yes (sort of) b. For those attributes that apply as indicated by your answers above, explain how Amazon.com's system creates that behavior. For example, Amazon.com handles ambiguous, incomplete, or erroneous information by linking its recommendations to a specific book rather than to the user's search terms. In short, Amazon.com's system works to reduce ambiguity by forcing a user to select a specific book first. Attribute Applies to Amazon Think and reason Does not apply. Case-based systems typically use very simple rules for evaluating cases with modestly sophisticated systems for interpolating between near misses when no case matches exactly. Use reason to solve Does not apply. At best, a case-base system might do a bit of problems interpolation. Learn from experience Yes! Case-based systems learn from experience by storing examples of past behavior and then matching these examples to the current situation. The more examples, the better the results. Yes! Amazon.com acquires knowledge through sales and applies this Acquire and apply knowledge through its case based reasoning engine. knowledge Exhibit creativity Does not apply. The system is no more creative than the evaluation rules provided by the programmer. However, if customers have shown creativity in the past (purchasing paper making books along with origami books), then the systems results will also reflect this case driven bit of creativity. Respond quickly to Does not apply. The first time Amazon.com offers a title for sale, the new situations case-based system has no prior cases to use and so produces no results. However, as soon as Amazon.com completes a few sales, the system can begin offering recommendations. Does not apply. Amazon's system uses simple tallies to determine relative Recognize relative importance. importance Handle ambiguous Rather than relying on a natural language search, Amazon simply information provides results for a very specific book title. The system does not permit ambiguous information. However, if the user has selected the wrong book, Amazon's system will provide results appropriate to the incorrect selection. That is, Amazon's results probably won't help much.

4.

Palm City Police Department


a. b.

Build a spreadsheet to perform the analysis described above and print it out. Currently, no funds are available to hire additional officers. Based on the citywide ratios, the department has decided to develop a plan to shift resources as needed in order to ensure that no precinct has more than 1,100 residents per police officer and no precinct has more than seven violent crimes per police officer. The department will transfer officers from precincts that easily meet these goals to precincts that violate one or both of these ratios. Use "goal seeking" on your spreadsheet to move police officers between precincts until the goals are met. You can use the goal seek function to see how many officers would be required to bring each precinct into compliance and then judgmentally reduce officers in precincts that are substantially within the criteria. Print out a set of results that allow the departments to comply with these ratios and a memorandum to your instructor summarizing your results and the process you used to develop them.

[See Data/Solutions File Ch 10 Exercise 4.xls]


IX. ANSWERS TO REAL WORLD CASESRWC 1: KeyCorp, Allstate Insurance, Aviva Canada, and Others: Centralized Business Intelligence At Work 1. What is business intelligence? Why are business intelligence systems such a popular business application of IT? Business intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. One reason for its popularity is the high visibility of the data it makes available to business units to be used in decision making. Data from BI centers supports sales forecasting, financial projections, CRM solutions, new product development, etc. 2. What is the business value of the various BI applications discussed in the case? Examples in the case include: Information integration across business units or applications Support of new initiatives and customer relationship management Support for integration in an M&A environment Reuse data-mappings (links between data and its source) Provide a comprehensive picture of the competitive environment, with information from both company and competitors 3. Is a business intelligence system an MIS or a DSS? To the extent that Decision Support Systems provide one or mode logical models embedded within them to support decision making, business intelligence is an MIS. BI applications support the processes of organizing, categorizing and accessing data; however, all decision making capabilities rest within the user.


RWC 2: Wal-Mart, BankFinancial, and HP: The Business Value of AI 1. What is the business value of AI technologies in business today? Use several examples from the case to illustrate your answer. Business values of AI technologies would be illustrated by these examples: 2. AI software helps engineers create better jet engines. AI technology boosts productivity by monitoring equipment and signaling when preventive maintenance is needed. It is used to gain new insights into the tremendous amount of data on the human genome. Use of neural networks for detecting credit-card fraud. Used to qualify for debit card insurance. Shifts through a deluge of data to uncover patterns and relationships that would elude an army of human searches. Predicting customer behavior for companies such as banks.

What are some of the benefits and limitations of data mining for business intelligence? Use BankFinancials experience to illustrate your answer. Benefits would include: Potential for mining cost-savings and revenue-boosting ideas. More accurately target promotions to customers and prospects. Helping users set up predictive models. Reduce the time it takes the bank to develop a model by 50% to 70%. Developing applications such as a model to predict customer churn, the rate at which customers come and go.

3.

Why have banks and other financial institutions been leading users of AI technologies like neural networks? What are the benefits and limitations of this technology? Why banks and other financial institutions have used AI (benefits) would include: Detecting credit-card fraud. Use of predictive models to understand customer behavior. Revenue enhancing. Cost reduction.

Limitations would include: Biggest stumbling block is getting the data. Accessing the correct data needed for predictive models (limited to only account data prepared weekly and monthly when daily customer activity data is needed). Dealing with disparate data sources. Systems integration and interface work is needed.


RWC 3: Proctor & Gamble and Others: Using Agent-Based Modeling for Supply Chain Management 1. Do you agree with Proctor & Gamble that a supply chain should be called a supply network? Why or why not? Discussion points that students should develop would include: 2. A supply network is more complex than what was intended when supply chain was coined to describe the activities of a company with its customers, suppliers, and other business partners. Agent-based modeling is more sophisticated and involves more advanced applications of IT that chain no longer adequately describes the supply management for a company such as P&G. Computer modeling adds an additional dimension to a traditional supply chain management system.

What is the business value of agent-based modeling? Use P&G and other companies in this case as examples. Discussion points would include: P&G performs what-if simulations to test the impact of new logistics rules on three key metrics: inventory levels, transportation costs and in-store stock-outs. The model convinced P&G to relax rigid rules in order to improve overall performance of the supply network. The model convinced P&G that cultural changes, such as convincing freight managers that its sometimes OK to let a truck go half full, is good. There is a need for more flexibility in the manufacturing operations to reduce stock-outs and keep customers happier. There is a need for more flexibility in distribution. Southwest Airlines used agent-based modeling to optimize cargo routing. Air Liquide America LP reduced production and distribution costs with agent-based modeling. Merck & Co. used it to help find more efficient ways to distribute anti-HIV drugs in Zimbabwe. Ford Motor Co. used it to simulate buyer preferences to optimize the trade-offs between production costs and customer demands. Edison Chouest Offshore LLC optimized its deployment of service and supply vessels in the Gulf of Mexico.


3.

Visit the website of NuTech Solutions. How does NuTech use AI techniques to help companies gain adaptive business intelligence? Give several examples from the website case studies. Examples from site could include: Branch Banking and Trust ARROW. ChevronTexaco scheduling optimization.. DaimlerChrysler Aerospace engineering design optimization. Dutch Ministry of Traffic scheduling optimization. F. E. Bording supply network optimization. General Motors vehicle distribution system. U. S. Internal Revenue Service expert system development. Kraftwerksunion (KWU) scheduling optimization. Major Automaker marketing diffusion. Major Producer resource allocation optimization. Major Telecom Company engineering design optimization. Major U. S. Automaker data mining. Major U. S. Bank vehicle distribution system. NASA modeling and simulation. Nasdaq modeling and simulation.


RWC 4: Boehringer Ingelheim: Using Web-Based Tools for Financial Analysis and Reporting 1. What are the business benefits and limitations of Boehringers Web-based financial analysis and reporting systems? Discussion points would include: Rapidly consolidate and present key financial data on a daily, weekly or monthly basis which allows it to drill down and draw conclusions based on the latest available financial and operational data. Boehringer is now able to close its books for most of its divisions just two hours after the close of business at the end of each month vs. a three day requirement in the past. The accounting department can spot product sales trends and track expenses quickly. Boehringer can create multidimensional views of profit and loss data. 2. Which of Boehringers financial analysis and reporting systems are MIS tools? DSS tools? Why? Students should present discussion that would include consideration of: Decision support provided o MIS provide information about the performance of Boehringer. o DSS provide information and decision support techniques to analyze specific problems or opportunities. Information form and frequency o MIS periodic, exception, demand, and push reports and responses. o DSS interactive inquiries and responses. Information format o MIS - Prespecified, fixed format o DSS ad hoc, flexible, and adaptable format Information processing methodology o MIS information produced by extraction and manipulation of business data. o DSS information produced by analytical modeling of business data. 3. How could the Cognos tools used by Boehringer be used for marketing and other business analysis and reporting applications? Visit the Cognos website to help you answer.

Discussion points would include: Use of the Cognos tools by the marketing staff to increase Boehringers competitive position. Use of the Cognos tools by the marketing staff to improve their customer relationship management system. Training of the marketing staff to use the Cognos tools. Ability of Boehringers IT staff to implement the system in all divisions.


DSS CHAP 10

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