lecture notes by wuyunyi

VIEWS: 1 PAGES: 11

									                                                        Chapter 11
                                    Information Systems for Managerial Decision Support



                                                       LECTURE NOTES

SECTION I: Management Information and Decision Support Systems

11-1   Introduction:

This chapter focuses on the major types of information systems and how they support the diverse information and decision-
making needs of managers.

Analysing Parsons Brinckerhoff

We can learn a lot from this case about how intranet technologies are changing the face of traditional information systems for
managerial information and decision support from the Real World Case of Parsons Brinckerhoff.

Take a few minutes to read it, and we will discuss it (See Parsons Brinckerhoff in section XI)

Information, Decisions, and Management: [Figure 11.2]

The type of information required by managers is directly related to the level of management and the amount of structure in the
decision situation they face. You should realize that 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.

    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.


  Providing information and support for all levels of management decision making is no easy task. Conceptually, several
  major types of information systems are needed:
 Management information systems
 Decision support systems
 Executive information systems

Management Reporting Versus Decision-Making Support:

Management information systems - Focus on providing managers with prespecified information products that report on the
                                      performance of the organization.




                                           - MIS are designed to indirectly support the more structured types of decisions
                                           involved in operational and tactical planning and control.

Objective of MIS                       - Provide information about the performance of basic organizational functions and
                                           processes, such as marketing, manufacturing, and finance.

Decision Support Systems               - Focus on providing information interactively to support specific types of decisions by
                                           individual managers.

                                           - DSS help managers solve typical semistructured and unstructured problems.

Objective of DSS                       - Provide information and decision support techniques needed to solve specific
                                           problems or pursue specific opportunities.


11-2   Management Information Systems:

Management information systems were the original type of management support systems, and are still a major category of
information systems. MIS produce information products that support many of the day-to-day decision-making needs of
management. 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:
    Periodic scheduled reports
    - this traditional form of providing information to managers uses a prespecified format designed to provide managers with
    information on a regular basis.

    Exception Reports
    - reports are produced only when exceptional conditions occur.


    Demand Reports & Responses
    - information is provided whenever a manager demands it.

    Push Reporting
    - information is pushed to a manager’s networked workstation.

Intranet Reporting at CBS

CBS uses enterprise reporting software to create reports that can be stored and updated on an Internet server. The server is
accessible over their nationwide corporate intranet, so that any report could be downloaded as needed, allowing users to get
only the information that they want (and have the clearance to obtain). CBS end users can use browsers on their desktop PCs to
perform virtually any type of ad hoc query on the data in the reports.

Online Analytical Processing: [Figure 11.6]

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.

Online analytical processing involves several basic analytical operations including:

   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 detail data       that comprises consolidated
    data.

   Slicing & 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 analyse       trends and find patterns.


OLAP applications:
1. Access very large amounts of data.
2. Analyse the techniques between many types of business elements.
3. Involves aggregated data.
4. Compare aggregated data over hierarchical time periods.
5. Present data in different perspectives.
6. Involve complex calculations between data elements.
7. 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.


OLAP at MasterCard International

MasterCard International developed OLAP software called Market Advisor, which enables members to query a data warehouse
and drill down into information to analyse transactions and trends online. Market Advisor also provides a 13-month historical
database, extended report graphing, and triggered marketing alerts based on above - or below-average merchant or cardholder
activity.
11-3   Decision Support Systems:

Decision support systems are a major category of management information systems. They are computer-based information
systems that provide interactive information support to managers during the decision-making process. Decision support
systems use:
1. Analytical models
2. Specialized databases
3. Decision makers’ own insights and judgements
4. Interactive, computer-based modelling processes to support the making of semistructured and unstructured decisions by
    individual managers.

DSS are designed to be ad-hoc, quick-response systems that are initiated and controlled by managerial end users. Decision
support systems are thus able to directly support the specific types of decisions and the personal decision-making styles and
needs of individual managers.

DSS Models and Software:

Unlike management information systems, 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.


11-4   Examples of DSS Applications:

Decisions support systems are used for a variety of applications in both business and government.

   Institutional DSS - DSS which are developed to solve large or complex problems that           continually face an organization.

   Ad-hoc DSS - DSS which are quickly developed to solve smaller or less complex                    problems. They are also
    used to solve one-time situations.


   Industry DSS - DSS which are developed to solve problems faced by a specific                  industry.

   Functional DSS - DSS which are developed to solve problems in a specific functional           area.


Examples of DSS applications:
1. DSS at American Airlines
2. DSS at PepsiCo
3. GIS in Business (Geographic information systems)


11-5   Using Decision Support Systems:

Using a decision support system involves an interactive analytical modelling process. Typically, a manager uses a DSS
software package at his workstation to make inquires, 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.
11-6 Executive Information Systems:

Executive information systems (EIS) are information systems that combine many of the features of information reporting
systems and decision support systems. EIS focus on meeting the strategic information needs of top management. The goal of
EIS is to provide top management 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.

Rational for EIS:

Top executives get the information they need from many resources. These include, letters, memos, periodicals, and reports
produced manually or by computer systems. Other major sources of executive information are meetings, telephone calls, and
social activities. Thus, much of a top executive's information comes from noncomputer sources. Computer-generated
information has not played a major role in meeting many top executives' information needs. EIS were developed to meet the
need that MIS was not meeting.


SECTION II: Artificial Intelligence Technologies in Business

11-7   An Overview of Artificial Intelligence:

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.

                                   Question: Is AI possible? Answer: Only time will tell

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 11-17]

AI application 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 a knowledge base and software modules that perform inferences
    on the knowledge, and communicates 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.

    Adaptive Learning Systems - An information system that can modify its behaviour 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 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 development 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 then
    “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 Languages - 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 movement which allows them
to operate.

Speech Recognition - The ability of a computer system to recognizes 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 Aworlds@ as if they actually existed.


11-8   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 programs 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:
1. Military weapons systems
2. Voice recognition
3. Check signature verification
4. Manufacturing quality control
5. Image processing
6. Credit risk assessment
7. Investment forecasting

Neural Nets at Infoseek:

Infoseek has developed a targeted marketing service that more closely targets advertising on its Internet search engine to users’
interests by keeping track of every search that a user makes. The service uses neural network technology to observe all the
searches users run every time they visit the InfoSeek search engine. The neural net software then calculates a single numeric
value, or “vector”, that describes users’ interests. InfoSeek uses that information to match users to the online ads it sells to
advertisers on its Web search pages.

Data Mining a Bank of America:

The Bank of America is using neural net technology in data mining software to develop more accuracy in marketing and pricing
financial products, such as home equity loans.


11-9   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 ambiguous data (fuzzy data) instead of relying only on crisp data, such as binary (yes/no)
choices.

Fuzzy Logic in Business:

An example of the use of fuzzy logic in business is to analyse the credit risk of a business.


11-10 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.




11-11 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:
1.   Computer-aided design (CAD)
2.   Medical diagnostics and treatment
3.   Scientific experimentation in many physical and biological sciences
4.   Flight simulation for training pilots and astronauts
5.   Product demonstrations
6.   Employee training
7.   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


11-12 Intelligent Agents [Figure 11-26]

An intelligent agent (also called intelligent assistants/wizards) is a software surrogate for an end user or a process that fulfills 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 fulfills the intentions of a user. One of the most well-known uses of intelligent
agents are the Wizards found in Microsoft Word, Excel, Access, and Powerpoint.

The use of intelligent agents is expected to grow rapidly as a way to for users to:
1. Simplify software use.
2. Access network resources.
3. Information screening and retrieval.




11-13 Expert Systems

One 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’s 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.

Expert systems can be used for either operational or management information systems, depending on whether they are giving
expert advice to control operational processes or to help managerial end users make decisions.


Components of Expert Systems: [Figure 11-29]

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 system 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


Examples of Expert Systems: [Figure 11-32]

Using an expert system involves an interactive computer-based session, in which:
1. The solution to a problem is explored with the expert-system acting as a consultant.
2. Expert system asks questions of the user, searches its knowledge base for facts and rules or other knowledge.
3. Explains its reasoning process when asked
4. Gives expert advice to the user in the subject area being explored. Examples include: credit management, customer service,
   and productivity management.

Expert System Applications:

Expert systems typically accomplish one or more generic uses. Seven activities include:
1. Decision Management
2. Maintenance/Scheduling
3. Design/configuration
4. Process monitoring/control
5. Diagnostic Troubleshooting
6. Intelligent text/documentation
7. Selection/classification

Advertising Strategy

ADCAD (Advertising Communications Approach Designer) is an expert system that assists advertising agencies in:
1. Setting marketing and communications objectives
2. Selecting creative strategies
3. Identifying effective communications approaches

In particular it is designed to help advertisers of consumer products with the:
1. Development of advertising objectives
2. Ad copy strategies
3. Selection of communications techniques
11-14 Developing Expert Systems

Expert Systems Shells. 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 generations) 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.


11-15 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:
1. Look at examples of the applications of current expert systems, including the generic tasks they accomplish.
2. Identify criteria that make a problem situation suitable for an expert system. Some of this 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.

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.

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.

Benefits of Expert Systems:
1. Captures the expertise of experts. It may outperform a single human expert in many problem situations.
2. Is faster and more consistent than a human expert.
3. Can have the knowledge of several experts.
4. Does not get tired or distracted by too much work or stress.
5. Is available at all times, whereas a human expert may be away, sick, or may have left the company.
6. Can be used to train the novice.
7. Effective use of expert systems can allow a firm to:
a. improve the efficiency of its operations
b. produce new products and services
c. lock in customers and suppliers with new business relationships
d. build knowledge-based strategic information resources.

Limitations of Expert Systems
1. Limited focus (specific problems & specific domains)
2. Inability to learn
3. Difficulties in maintaining expert systems
4. Cost involved in developing them.


11-16 Hybrid AI Systems:

Increasingly, AI developers are constructing products which integrate several AI technologies into a single hybrid AI system.
This frequently includes two popular AI technologies: expert systems and neural nets.

Most integrated AI systems are designed to provide the best features of expert systems, neural nets, or fuzzy logic technologies,
and to offset each other’s strengths and weaknesses.

								
To top