Decision Support Systems

Decision Support in Business• Companies are investing in data-driven

decision support application frameworks to help them respond to

– Changing market conditions

– Customer needs

• This is accomplished by several types of

– Management information

– Decision support

– Other information systems

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Levels of Managerial Decision Making

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Information Quality• Information products made more valuable

by their attributes, characteristics, or qualities

– Information that is outdated, inaccurate, or hard to understand has much less value

• Information has three dimensions– Time

– Content

– Form

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Attributes of Information Quality

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Decision Structure

• Structured (operational)– The procedures to follow when decision

is needed can be specified in advance

• Unstructured (strategic)– It is not possible to specify in advance

most of the decision procedures to follow

• Semi-structured (tactical)– Decision procedures can be pre-specified,

but not enough to lead to the correct decision

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Decision Support Systems

Management Information Systems

Decision Support Systems

Decision support provided

Provide information about the performance of the

organization

Provide information and techniques to analyze

specific problems

Information form and frequency

Periodic, exception, demand, and push reports

and responses

Interactive inquiries and responses

Information format

Prespecified, fixed format Ad hoc, flexible, and adaptable format

Information processing methodology

Information produced by extraction and manipulation

of business data

Information produced by analytical modeling of

business data

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Decision Support Trends

• The emerging class of applications focuses on

– Personalized decision support

– Modeling

– Information retrieval

– Data warehousing

– What-if scenarios

– Reporting10-8

Business Intelligence Applications

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Decision Support Systems

• Decision support systems use the following to support the making of semi-structured business decisions– Analytical models– Specialized databases– A decision-maker’s own insights and

judgments– An interactive, computer-based modeling

process

• DSS systems are designed to be ad hoc, quick-response systems that are initiated and controlled by decision makers

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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

• Spreadsheet Examples

– Linear programming

– Multiple regression forecasting

– Capital budgeting present value

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Applications of Statistics and Modeling

– Supply Chain: simulate and optimize supply chain flows, reduce inventory, reduce stock-outs

– Pricing: identify the price that maximizes yield or profit

– Product and Service Quality: detect quality problems early in order to minimize them

– Research and Development: improve quality, efficacy, and safety of products and services

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Management Information Systems

• The original type of information system that supported managerial decision making

– Produces information products that support many day-to-day decision-making needs

– Produces reports, display, and responses

– Satisfies needs of operational and tactical decision makers who face structured decisions

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Management Reporting Alternatives

• Periodic Scheduled Reports– Prespecified format on a regular basis

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

exception occurs

• Demand Reports and Responses– Information is available on demand

• Push Reporting– Information is pushed to a networked

computer

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Online Analytical Processing (OLAP)

• Enables managers and analysts to examine and manipulate large amounts of detailed and consolidated data from many perspectives

• Done interactively, in real time, with rapid response to queries

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Online Analytical Operations

• Consolidation– Aggregation of data– Example: data about sales offices rolled up

to the district level

• Drill-Down– Display underlying detail data– Example: sales figures by individual product

• Slicing and Dicing– Viewing database from different viewpoints– Often performed along a time axis

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Geographic Information Systems (GIS)

• DSS uses geographic databases to construct and display maps and other graphic displays

• Supports decisions affecting the geographic distribution of people and other resources

• Often used with Global Positioning Systems (GPS) devices

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Data Visualization Systems (DVS)

• Represents complex data using interactive, three-dimensional graphical forms (charts, graphs, maps)

• Helps users interactively sort, subdivide, combine, and organize data while it is in its graphical form

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Using Decision Support Systems

• Using a decision support system involves an interactive analytical modeling process– Decision makers are not demanding

pre-specified information

– They are exploring possible alternatives

• What-If Analysis– Observing how changes to selected variables

affect other variables

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Using Decision Support Systems

• Sensitivity Analysis– Observing how repeated changes to a single

variable affect other variables

• Goal-seeking Analysis– Making repeated changes to selected variables

until a chosen variable reaches a target value

• Optimization Analysis– Finding an optimum value for selected

variables, given certain constraints

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Data Mining

• Provides decision support through knowledge discovery– Analyzes vast stores of historical business data– Looks for patterns, trends, and correlations– Goal is to improve business performance

• Types of analysis– Regression– Decision tree– Neural network– Cluster detection– Market basket analysis

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Analysis of Customer Demographics

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Market Basket Analysis

• One of the most common uses for data mining– Determines what products customers purchase

together with other products

• Results affect how companies– Market products

– Place merchandise in the store

– Lay out catalogs and order forms

– Determine what new products to offer

– Customize solicitation phone calls

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Executive Information Systems (EIS)

– Combines many features of MIS and DSS

– Provide top executives with immediate and easy access to information

– Identify factors that are critical to accomplishing strategic objectives (critical success factors)

– So popular that it has been expanded to managers, analysis, and other knowledge workers

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Features of an EIS

• Information presented in forms tailored to the preferences of the executives using the system

– Customizable graphical user interfaces

– Exception reports

– Trend analysis

– Drill down capability

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Enterprise Information Portals

• An EIP is a Web-based interface and integration of MIS, DSS, EIS, and other technologies– Available to all intranet users and select

extranet users

– Provides access to a variety of internal and external business applications and services

– Typically tailored or personalized to the user or groups of users

– Often has a digital dashboard

– Also called enterprise knowledge portals

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Enterprise Information Portal Components

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Artificial Intelligence (AI)

• AI is a field of science and technology based on– Computer science– Biology– Psychology– Linguistics– Mathematics– Engineering

• The goal is to develop computers than can simulate the ability to think– And see, hear, walk, talk, and feel as well

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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 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

• Applications in the cognitive science of AI– Expert systems– Knowledge-based systems– Adaptive learning systems– Fuzzy logic systems– Neural networks– Genetic algorithm software– Intelligent agents

• Focuses on how the human brain works and how humans think and learn

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Latest Commercial Applications of AI• Decision Support

– Helps capture the why as well as the what of engineered design and decision making

• Information Retrieval– Distills tidal waves of information into simple presentations

– Natural language technology

– Database mining• Virtual Reality

– X-ray-like vision enabled by enhanced-reality visualization helps surgeons

– Automated animation and haptic interfaces allow users to interact with virtual objects

• Robotics– Machine-vision inspections systems– Cutting-edge robotics systems

• From micro robots and hands and legs, to cognitive and trainable modular vision systems

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Expert Systems

• An Expert System (ES)

– A knowledge-based information system

– Contain knowledge about a specific, complex application area

– Acts as an expert consultant to end users

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Components of an Expert System

• Knowledge Base– Facts about a specific subject area– Heuristics that express the reasoning

procedures of an expert (rules of thumb)

• Software Resources– An inference engine processes the

knowledge and recommends a course of action

– User interface programs communicate with

the end user– Explanation programs explain the

reasoning process to the end user

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Components of an Expert System

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Methods of Knowledge Representation

• Case-Based – Knowledge organized in the form of cases

– Cases are examples of past performance, occurrences, and experiences

• Frame-Based– Knowledge organized in a hierarchy or

network of frames

– A frame is a collection of knowledge about an entity, consisting of a complex package of data values describing its attributes

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Methods of Knowledge Representation

• Object-Based– Knowledge represented as a network of objects

– An object is a data element that includes both data and the methods or processes that act on those data

• Rule-Based– Knowledge represented in the form of rules

and statements of fact

– Rules are statements that typically take the form of a premise and a conclusion (If, Then)

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Expert System Application Categories• Decision Management

– Loan portfolio analysis– Employee performance evaluation– Insurance underwriting

• Diagnostic/Troubleshooting– Equipment calibration– Help desk operations– Medical diagnosis– Software debugging

• Design/Configuration– Computer option installation– Manufacturability studies– Communications networks

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Expert System Application Categories (cont’d)

• Selection/Classification– Material selection– Delinquent account identification– Information classification– Suspect identification

• Process Monitoring/Control– Machine control (including robotics)– Inventory control– Production monitoring– Chemical testing

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Benefits of Expert Systems

• Captures the expertise of an expert or group of experts in a computer-based information system– Faster and more consistent than an expert

– Can contain knowledge of multiple experts

– Does not get tired or distracted

– Cannot be overworked or stressed

– Helps preserve and reproduce the knowledge of human experts

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Limitations of Expert Systems

• Limited focus

• Inability to learn

• Maintenance problems

• Development cost

• Can only solve specific types of problems in a limited domain of knowledge

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Developing Expert Systems• Suitability Criteria for Expert Systems

– Domain: the domain or subject area of the problem is small and well-defined

– Expertise: a body of knowledge, techniques, and intuition is needed that only a few people possess

– Complexity: solving the problem is a complex task that requires logical inference processing

– Structure: the solution process must be able to cope with ill-structured, uncertain, missing, and conflicting data and a changing problem situation

– Availability: an expert exists who is articulate, cooperative, and supported by the management and end users involved in the development process

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Development Tool

• Expert System Shell

– The easiest way to develop an expert system

– A software package consisting of an expert system without its knowledge base

– Has an inference engine and user interface programs

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Knowledge Engineering

• A knowledge engineer

– Works with experts to capture the knowledge (facts and rules of thumb) they possess

– Builds the knowledge base, and if necessary,

the rest of the expert system

– Performs a role similar to that of systems analysts in conventional information systems development

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