IS for Managerial Decision Support - OttoDocument Transcript
Information Systems for Managerial Decision Support
Management Information and Decision Support Systems
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
• 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.
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
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
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-
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:
• 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.
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
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
• 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:
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
• 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:
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
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
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
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
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
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
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 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.
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.
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
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).
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)
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
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.
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
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
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
• Hardware resources: - These include:
1. Stand alone microcomputer systems
2. Microcomputer workstations and terminals connected to minicomputers or mainframes in a
3. Special-purpose computers.
• 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
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
4. Process monitoring/control
5. Diagnostic Troubleshooting
6. Intelligent text/documentation
ADCAD (Advertising Communications Approach Designer) is an expert system that assists advertising
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.
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
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
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
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
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
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.