Chapter 9

Decision Support Systems

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Learning 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.

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Learning Objectives

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

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Learning Objectives

5. Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business.

6. Give examples of several ways expert systems can be used in business decision-making situations.

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Information required at different management levels

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Levels of Management Decision Making

• Strategic management– Executives develop organizational goals, strategies,

policies, and objectives

– As part of a strategic planning process

• Tactical management– Managers and business professionals in self-directed

teams

– Develop short- and medium-range plans, schedules and budgets

– Specify the policies, procedures and business objectives for their subunits

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Levels of Management Decision Making

• Operational management– Managers or members of self-directed teams – Develop short-range plans such as weekly production

schedules

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Decision Structure

• Structured – situations where the procedures to follow when a decision is needed can be specified in advance

• Unstructured – decision situations where it is not possible to specify in advance most of the decision procedures to follow

• Semistructured - decision procedures that can be prespecified, but not enough to lead to a definite recommended decision

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Information Quality

• Information products whose characteristics, attributes, or qualities make the information more value

• Information has 3 dimensions:– Time– Content– Form

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Business Intelligence Applications

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Decision Support Systems

• DSS• Provide interactive information support to

managers and business professionals during the decision-making process

• Use:– Analytical models

– Specialized databases

– A decision maker’s own insights and judgments

– Interactive computer-based modeling

• To support semistructured business decisions

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DSS components

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DSS Model base

• Model base– A software component that consists of models used in

computational and analytical routines that mathematically express relations among variables

• Examples:– Linear programming models,– Multiple regression forecasting models– Capital budgeting present value models

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Management Information Systems

• MIS• Produces information products that support

many of the day-to-day decision-making needs of managers and business professionals

• Prespecified reports, displays and responses• Support more structured decisions

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MIS Reporting Alternatives

• Periodic Scheduled Reports– Prespecified format on a regular basis

• Exception Reports– Reports about exceptional conditions– May be produced regularly or when exception occurs

• Demand Reports and Responses– Information available when demanded

• Push Reporting– Information pushed to manager

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Online Analytical Processing

• OLAP– Enables mangers and analysts to examine and

manipulate large amounts of detailed and consolidated data from many perspectives

– Consolidation • Aggregation of data

– Drill-down • Display detail data that comprise consolidated data

– Slicing and Dicing• Ability to look at the database from different viewpoints

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OLAP Technology

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Geographic Information Systems

• GIS– 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– Often used with Global Position Systems (GPS)

devices

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Data Visualization Systems

• DVS – DSS that represents complex data using interactive

three-dimensional graphical forms such as charts, graphs, and maps

– DVS tools help users to interactively sort, subdivide, combine, and organize data while it is in its graphical form.

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Using DSS

• What-if Analysis – End user makes changes to variables, or relationships

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

• Sensitivity Analysis – Value of only one variable is changed repeatedly and

the resulting changes in other variables are observed

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Using DSS

• Goal-Seeking– Set a target value for a variable and then repeatedly

change other variables until the target value is achieved– How can analysis

• Optimization – Goal is to find the optimum value for one or more target

variables given certain constraints – One or more other variables are changed repeatedly

until the best values for the target variables are discovered

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Data Mining

• Main purpose is to provide decision support to managers and business professionals through knowledge discovery

• Analyzes vast store of historical business data• Tries to discover patterns, trends, and

correlations hidden in the data that can help a company improve its business performance

• Use regression, decision tree, neural network, cluster analysis, or market basket analysis

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Market Basket Analysis

• One of most common data mining for marketing• The purpose is to determine what products

customers purchase together with other products

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Executive Information Systems

• EIS– Combine many features of MIS and DSS– Provide top executives with immediate and easy access

to information– About the factors that are critical to accomplishing an

organization’s strategic objectives (Critical success factors)

– So popular, expanded to managers, analysts and other knowledge workers

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Knowledge Management Systems

• The use of information technology to help gather, organize, and share business knowledge within an organization

• Enterprise Knowledge Portals– EIPs that are the entry to corporate intranets that serve

as knowledge management systems

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Case 2: Harrah’s Entertainment,LendingTree, DeepGreen Financial, and Cisco Systems:

• The promise of AI of automating decision making has been very slow to materialize.

• The new generation AI applications are easier to create and manage, do not require anyone to identify the problems or to initiate the analysis, decision-making capabilities are embedded into the normal flow of work, and are triggered without human intervention.

• They sense online data or conditions, apply codified knowledge or logic and make decisions with minimal human intervention.

• But they rely on experts and managers to create and maintain rules and monitor the results.

• Also, managers in charge of automated decision systems must develop processes for managing exceptions.

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Case Study Questions

1. Why did some previous attempts to use artificial intelligence technologies fail? What key differences of the new AI-based applications versus the old cause the authors to declare that automated decision making is finally coming of age?

2. What types of decisions are best suited for automated decision making? Provide several examples of successful applications from the companies in this case to illustrate your answer.

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Case Study Questions

3. What role do humans play in automated decision making applications? What are some of the challenges faced by managers where automated decision-making systems are being used? What solutions are needed to meet such challenges?

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Artificial Intelligence (AI)

• A field of science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, and engineering

• Goal is to develop computers that can simulate the ability to think, as well as see, hear, walk, talk, and feel

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Attributes of Intelligent Behavior

• Think and reason• Use reason to solve problems• Learn or understand from experience• Acquire and apply knowledge• Exhibit creativity and imagination• Deal with complex or perplexing situations• Respond quickly and successfully to new situations• Recognize the relative importance of elements in a

situation• Handle ambiguous, incomplete, or erroneous

information

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Domains of Artificial Intelligence

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Cognitive Science

• Based in biology, neurology, psychology, etc.• Focuses on researching how the human brain

works and how humans think and learn

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Robotics

• Based in AI, engineering and physiology• Robot machines with computer intelligence and

computer controlled, humanlike physical capabilities

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Natural Interfaces

• Based in linguistics, psychology, computer science, etc.

• Includes natural language and speech recognition

• Development of multisensory devices that use a variety of body movements to operate computers

• Virtual reality– Using multisensory human-computer interfaces that

enable human users to experience computer-simulated objects, spaces and “worlds” as if they actually exist

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Expert Systems

• ES• A knowledge-based information system (KBIS)

that uses its knowledge about a specific, complex application to act as an expert consultant to end users

• KBIS is a system that adds a knowledge base to the other components on an IS

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Expert System Components

• Knowledge Base– Facts about specific subject area– Heuristics that express the reasoning procedures of an

expert (rules of thumb)

• Software Resources – Inference engine processes the knowledge and makes

inferences to make recommend course of action– User interface programs to communicate with end user– Explanation programs to explain the reasoning process

to end user

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Look at www.20q.net, Eliza or http://www.zabaware.com/webhal/hampy.html for examples of artificial intelligence

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Expert System Components

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Expert System Benefits

• Faster and more consistent than an expert• Can have the knowledge of several experts• Does not get tired or distracted by overwork or

stress• Helps preserve and reproduce the knowledge of

experts

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Expert System Limitations

• Limited focus• Inability to learn• Maintenance problems• Developmental costs• Can only solve specific types of problems in a

limited domain of knowledge

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Neural Networks

• Computing systems modeled after the brain’s mesh-like network of interconnected processing elements, called neurons

• Interconnected processors operate in parallel and interact with each other

• Allows network to learn from data it processes

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Fuzzy Logic

• Method of reasoning that resembles human reasoning

• Allows for approximate values and inferences and incomplete or ambiguous data instead of relying only on crisp data

• Uses terms such as “very high” rather than precise measures

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Genetic Algorithms

• Software that 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

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Virtual Reality (VR)

• Computer-simulated reality • Relies on multisensory input/output devices such

as– a tracking headset with

video goggles and stereo earphones,

– a data glove or jumpsuit with fiber-optic sensors that track your body movements, and

– a walker that monitors the movement of your feet

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Intelligent Agents

• A software surrogate for an end user or a process that fulfills a stated need or activity

• Uses its built-in and learned knowledge base • To make decisions and accomplish tasks in a

way that fulfills the intentions of a user

• Also called software robots or bots