Decision Support Systems

Chapter 10 Decision Support Systems

James A. O'Brien, and George Marakas. Management Information Systems with MISource 2007, 8th ed. Boston, MA: McGraw-Hill, Inc., 2007. ISBN: 13 9780073323091

Chapter 10 Decision Support Systems 2

Identify the changes taking place in the form and use of decision support in business

Identify the role and reporting alternatives of MIS Describe how online analytical processing can meet key

information needs of managers Explain the decision support system concept and how it differs

from traditional MIS Explain how the following IS can support the information needs

of executives, managers, and business professionals: Executive information systems, Enterprise information portals, and Knowledge management systems

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

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

Learning Objectives


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

support application frameworks to help them respond toChanging market conditionsCustomer needs

This is accomplished by several types ofManagement informationDecision supportOther information systems


Case 1 Dashboards for Executives Web-based “dashboards”

Displays critical information in graphic formAssembled from data pulled in real time from

corporate software and databasesManagers see changes almost instantaneouslyNow available to smaller companies

Potential problemsPressure on employeesDivisions in the officeTendency to hoard information


Case Study Questions What is the attraction of dashboards to CEOs and

other executives?What real business value do they provide

to executives? The case emphasizes that managers of small

businesses and many business professionals now rely on dashboards.What business benefits do dashboards provide

to this business audience? What are several reasons for criticism of

the use of dashboards by executives?Do you agree with any of this criticism?


Levels of Managerial Decision Making


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their attributes, characteristics, or qualities Information that is outdated, inaccurate, or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

TimeContentForm


Attributes of Information Quality


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


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


Decision Support Trends The emerging class of applications focuses on

Personalized decision supportModeling Information retrievalData warehousingWhat-if scenariosReporting


Business Intelligence Applications


Decision Support Systems Decision support systems use the following to

support the making of semi-structured business decisionsAnalytical modelsSpecialized databasesA decision-maker’s own insights and judgmentsAn interactive, computer-based modeling

process DSS systems are designed to be ad hoc,

quick-response systems that are initiated and controlled by decision makers


DSS Components


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 ExamplesLinear programmingMultiple regression forecastingCapital budgeting present value


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


Management Information Systems The original type of information system

that supported managerial decision makingProduces information products that support

many day-to-day decision-making needsProduces reports, display, and responsesSatisfies needs of operational and tactical

decision makers who face structured decisions


Management Reporting Alternatives Periodic Scheduled Reports

Prespecified format on a regular basis Exception Reports

Reports about exceptional conditionsMay 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


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


Online Analytical Operations Consolidation

Aggregation of dataExample: data about sales offices rolled up

to the district level Drill-Down

Display underlying detail dataExample: sales figures by individual product

Slicing and DicingViewing database from different viewpointsOften performed along a time axis


Geographic Information Systems 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


Data Visualization Systems 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


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 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


Data Mining Provides decision support through knowledge

discoveryAnalyzes vast stores of historical business dataLooks for patterns, trends, and correlationsGoal is to improve business performance

Types of analysisRegressionDecision treeNeural networkCluster detectionMarket basket analysis


Analysis of Customer Demographics


Market Basket Analysis One of the most common uses for data mining

Determines what products customers purchase together with other products

Results affect how companiesMarket productsPlace merchandise in the storeLay out catalogs and order formsDetermine what new products to offerCustomize solicitation phone calls


Executive Information Systems 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


Features of an EIS Information presented in forms tailored to the

preferences of the executives using the systemCustomizable graphical user interfacesException reportsTrend analysisDrill down capability


Enterprise Information Portals An EIP is a Web-based interface and integration

of MIS, DSS, EIS, and other technologiesAvailable to all intranet users and select

extranet usersProvides access to a variety of internal and

external business applications and servicesTypically tailored or personalized to the user

or groups of usersOften has a digital dashboardAlso called enterprise knowledge portals


Dashboard Example


Enterprise Information Portal Components


Enterprise Knowledge Portal


Case 2 Automated Decision Making Automated decision making has been slow

to materializeEarly applications were just solutions looking

for problems, contributing little to improved organizational performance

A new generation of AI applicationsEasier to create and manageDecision making triggered without human

interventionCan translate decisions into action quickly,

accurately, and efficiently


Case 2 Automated Decision Making

AI is best suited forDecisions that must be made quickly and

frequently, using electronic dataHighly structured decision criteriaHigh-quality data

Common users of AITransportation industryHotels Investment firms and lenders


Case Study Questions 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 coming of age?

What types of decisions are best suited for automated decision making?

What role do humans plan 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?


Artificial Intelligence (AI) AI is a field of science and technology based on

Computer scienceBiologyPsychologyLinguisticsMathematicsEngineering

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


Attributes of Intelligent Behavior Some of the 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


Domains of Artificial Intelligence


Cognitive Science Applications in the cognitive science of AI

Expert systemsKnowledge-based systemsAdaptive learning systemsFuzzy logic systemsNeural networksGenetic algorithm software Intelligent agents

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


Robotics AI, engineering, and physiology are the basic

disciplines of roboticsProduces robot machines with computer

intelligence and humanlike physical capabilities

This area include applications designed to give robots the powers ofSight or visual perceptionTouchDexterityLocomotionNavigation


Natural Interfaces Major thrusts in the area of AI and the

development of natural interfacesNatural languagesSpeech recognitionVirtual reality

Involves research and development inLinguisticsPsychologyComputer scienceOther disciplines


Latest Commercial Applications of AI Decision Support

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

Information RetrievalDistills tidal waves of information into simple

presentationsNatural language technologyDatabase mining


Latest Commercial Applications of AI 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

RoboticsMachine-vision inspections systemsCutting-edge robotics systems

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


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


Components of an Expert System Knowledge Base

Facts about a specific subject areaHeuristics 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


Components of an Expert System


Methods of Knowledge Representation Case-Based

Knowledge organized in the form of casesCases 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


Methods of Knowledge Representation Object-Based

Knowledge represented as a network of objectsAn object is a data element that includes both

data and the methods or processes that act on those data

Rule-BasedKnowledge represented in the form of rules

and statements of factRules are statements that typically take the

form of a premise and a conclusion (If, Then)


Expert System Application Categories Decision Management

Loan portfolio analysisEmployee performance evaluation Insurance underwriting

Diagnostic/TroubleshootingEquipment calibrationHelp desk operationsMedical diagnosisSoftware debugging


Expert System Application Categories Design/Configuration

Computer option installationManufacturability studiesCommunications networks

Selection/ClassificationMaterial selectionDelinquent account identification Information classificationSuspect identification

Process Monitoring/Control


Expert System Application Categories Process Monitoring/Control

Machine control (including robotics) Inventory controlProduction monitoringChemical testing


Benefits of Expert Systems Captures the expertise of an expert or group of

experts in a computer-based information systemFaster and more consistent than an expertCan contain knowledge of multiple expertsDoes not get tired or distractedCannot be overworked or stressedHelps preserve and reproduce the knowledge

of human experts


Limitations of Expert Systems The major limitations of expert systems

Limited focus Inability to learnMaintenance problemsDevelopment costCan only solve specific types of problems

in a limited domain of knowledge


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


Development Tool Expert System Shell

The easiest way to develop an expert systemA software package consisting of an expert

system without its knowledge baseHas an inference engine and user interface

programs


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


Neural Networks Computing systems modeled after the brain’s

mesh-like network of interconnected processing elements (neurons) Interconnected processors operate in parallel

and interact with each otherAllows the network to learn from the data it

processes


Fuzzy Logic Fuzzy logic

Resembles human reasoningAllows for approximate values and

inferences and incomplete or ambiguous dataUses terms such as “very high” instead of

precise measuresUsed more often in Japan than in the U.S.Used in fuzzy process controllers used in

subway trains, elevators, and cars


Example of Fuzzy Logic Rules and Query


Genetic Algorithms Genetic algorithm software

Uses Darwinian, randomizing, and other mathematical functions

Simulates an evolutionary process, yielding increasingly better solutions to a problem

Being uses to model a variety of scientific, technical, and business processes

Especially useful for situations in which thousands of solutions are possible


Virtual Reality (VR) Virtual reality is a computer-simulated reality

Fast-growing area of artificial intelligenceOriginated from efforts to build natural,

realistic, multi-sensory human-computer interfaces

Relies on multi-sensory input/output devicesCreates a three-dimensional world through

sight, sound, and touchAlso called telepresence


Typical VR Applications Current applications of virtual reality

Computer-aided designMedical diagnostics and treatmentScientific experimentationFlight simulationProduct demonstrationsEmployee trainingEntertainment


Intelligent Agents A software surrogate for an end user or a

process that fulfills a stated need or activityUses built-in and learned knowledge base

to make decisions and accomplish tasks in a way that fulfills the intentions of a user

Also call software robots or bots


User Interface Agents Interface Tutors – observe user computer

operations, correct user mistakes, provide hints/advice on efficient software use

Presentation Agents – show information in a variety of forms/media based on user preferences

Network Navigation Agents – discover paths to information, provide ways to view it based on user preferences

Role-Playing – play what-if games and other roles to help users understand information and make better decisions


Information Management AgentsSearch Agents – help users find files and

databases, search for information, and suggest and find new types of information products, media, resources

Information Brokers – provide commercial services to discover and develop information resources that fit business or personal needs

Information Filters – Receive, find, filter, discard, save, forward, and notify users about products received or desired, including e-mail, voice mail, and other information media


Case 3 Centralized Business Intelligence A reinventing-the-wheel approach to business

intelligence implementations can result inHigh development costsHigh support costs Incompatible business intelligence systems

A more strategic approachStandardize on fewer business intelligence

toolsMake them available throughout the

organization, even before projects are planned


Case 3 Centralized Business Intelligence About 10 percent of the 2,000 largest companies

have a business intelligence competency centerCentralized or virtualPart of the IT department or independent

Cost reduction is often the driving force behind creating competency centers and consolidating business intelligence systemsDespite the potential savings, funding for

creating and running a BI center can be an issue


Case Study Questions What is business intelligence?

Why are business intelligence systems such a popular business application of IT?

What is the business value of the various BI applications discussed in the case?

Is the business intelligence system an MIS or a DSS?


Case 4 Robots, the Common Denominator In early 2004, 22 patients underwent complex

laparoscopic operationsThe operations included colon cancer

procedures and hernia repairsThe primary surgeon was 250 miles awayA three-armed robot was used to perform the

procedures Left arm, right arm, camera arm


Case 4 Robots, the Common Denominator Automakers heavily use robotics

Ford has a completely wireless assembly factory

It also have a completely automated body shop

BMW has two wireless plants in Europe and is setting one up in the U.S.

Vehicle tracking and material replenishment are automated as well


Case Study Questions What is the current and future business value

of robotics? Would you be comfortable with a robot

performing surgery on you? The robotics being used by Ford Motor Co. are

contributing to a streamlining of its supply chainWhat other applications of robots can you

envision to improve supply chain management beyond those described in the case?

Documents

Decision Support Systems