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Information Systems for Managerial Decision Support

LECTURE NOTES

Management Information and Decision Support Systems

Introduction:

This chapter focuses on the major types of information systems and how they support the diverse

information and decision-making needs of managers.

Information, Decisions, and Management:

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. The decision maker must provide judgement, evaluation

and insights to a novel, important and nonroutine-type decision.

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Strategic Decision Makers - Require more summarized, ad hoc, unscheduled reports, forecasts, and

external intelligence to support their more unstructured planning and policy-making responsibilities.

Tactical Management - Increasingly self-directed teams as well as middle managers develop short-

and medium-range plans, schedules, and budgets and specify the policies, procedures, and business

objectives for their subunits of the organization.

They also allocate resources and monitor the performance of their organizational subunits, including

departments, divisions, process teams, and other workgroups.

Semistructured Decisions - Some decision procedures can be prespecified, but not enough to lead to a

definite recommended decision. Only part of the decision has a clear-cut answer provided by an

accepted procedure.

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. It involves a repetitive and routine-type decision where there is a definite

procedure to follow.

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.

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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.

Management Information Systems:

This is the IS at the management level of an organisation that serves the function of planning, controlling

and decision making by providing a routine summary and exception reports. 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-

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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

Online Analytical Processing:

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:

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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.

Decision Support Systems:

Decision support systems are a major category of management information systems. It is the IS of the

organisation that provides support at the management level and combines data and sophisticated

analytical tools or data analysis tools to help solve nonroutine decision making. 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

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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.

5. Data mining – analysis of large pools of data to find patterns and rules that can be used to guide

decision making and predict future behaviour.

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.

Examples of DSS Applications:

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

Institutional DSS - DSS that 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 that are developed to solve problems faced by a specific industry.

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

Using Decision Support Systems:

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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.

Optimisation 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.

Executive Information Systems:

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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.

ESS – executive support systems – strategic level – address unstructured decision making through

advanced graphics and communications. It is not meant to solve specific problems.

Artificial Intelligence Technologies in Business

An Overview of Artificial Intelligence:

Artificial intelligence is an effort to develop computer-based systems of hardware and software that

behave like humans; they are able to learn natural languages, accomplish co-ordinated physical tasks

(robotics), use a perceptual apparatus that informs their physical behaviour and language (visual and oral

perception systems), and emulate human expertise and decision making (expert systems). They exhibit

logic, reasoning, intuition, and common sense. Business and other organizations are significantly

increasing their attempts to assist the human intelligence and productivity of their knowledge workers

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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:

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. It is a knowledge-intensive computer program

that captures the expertise of a human in limited domains of knowledge. 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 that adds a knowledge base and some reasoning

capability to the database and other components found in other types of computer-based information

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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. It is rule-based AI that tolerates imprecision by using

non-specific terms to solve problems.

Neural Network - software can learn by processing sample problems and their solutions. It is hardware

and software that attempts to emulate the processing patterns of the biological brain. 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), randomising, and other

mathematical functions to simulate evolutionary processes that can generate increasingly better solutions

to problems. It is a problem solving method that promotes the evolution of solutions to specified

problems using the model of living organisms adapting to their environment.

Intelligent Agents - Use expert system and other AI technologies to serve as software surrogates for a

variety of end user applications. It is software that uses built-in or a learned knowledge base to carry out

specific, repetitive, and predictable takes for an individual user, business process or software application.

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)

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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 worlds as if they actually existed.

Neural networks:

Neural networks are computing systems modelled on the human brain's mesh-like network of

interconnected processing elements, called neurons. Of course, neural networks are much simpler than

the human brain (estimated to have more than 100 billion neuron brain cells). Like the brain, however,

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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

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.

Genetic Algorithms:

The use of genetic algorithms is a growing application of artificial intelligence. Genetic algorithm

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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.

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:

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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

An intelligent agent (also called intelligent assistants/wizards) is a software surrogate for an end user or a

process that fulfils a stated need or activity. An intelligent agent uses a built-in and learned knowledge

base about a person or process to make decisions and accomplish tasks in a way that fulfils the intentions

of a user. One of the most well-known uses of intelligent agents 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.

Expert Systems

One of the most practical and widely implemented applications of artificial intelligence in business is the

development of expert systems and other knowledge-based information systems.

Knowledge-based information system - adds a knowledge base to the major components found in other

types of computer-based information systems.

Expert System - A computer-based information system that uses its knowledge about a specific complex

application area to act as an expert consultant to users. ES’s provide answers to questions in a very

specific problem area by making humanlike inferences about knowledge contained in a specialized

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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:

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.

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People resources: - People resources include:

1. Knowledge engineers

2. End users

Examples of Expert Systems:

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

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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

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 specialty requires good people skills, as well as a background in artificial

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intelligence and information systems.

The Value of Expert Systems

Expert systems are not the answer to every problem facing an organization. The question becomes “what

types of problems are most suitable to expert system solutions?” Ways to answer this question include:

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:

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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.

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

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fuzzy logic technologies, and to offset each other’s strengths and weaknesses.

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