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
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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
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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
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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
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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?
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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
TimeContentForm
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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 supportModeling Information retrievalData warehousingWhat-if scenariosReporting
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Business Intelligence Applications
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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
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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 ExamplesLinear programmingMultiple regression forecastingCapital 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 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
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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
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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 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
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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
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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
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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 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
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
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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 companiesMarket productsPlace merchandise in the storeLay out catalogs and order formsDetermine what new products to offerCustomize solicitation phone calls
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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
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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
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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
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Dashboard Example
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Enterprise Information Portal Components
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Enterprise Knowledge Portal
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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
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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
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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?
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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
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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
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Domains of Artificial Intelligence
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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
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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
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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
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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 RetrievalDistills tidal waves of information into simple
presentationsNatural language technologyDatabase mining
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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
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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 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
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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 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
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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)
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Expert System Application Categories Decision Management
Loan portfolio analysisEmployee performance evaluation Insurance underwriting
Diagnostic/TroubleshootingEquipment calibrationHelp desk operationsMedical diagnosisSoftware debugging
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Expert System Application Categories Design/Configuration
Computer option installationManufacturability studiesCommunications networks
Selection/ClassificationMaterial selectionDelinquent account identification Information classificationSuspect identification
Process Monitoring/Control
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Expert System Application Categories Process Monitoring/Control
Machine control (including robotics) Inventory controlProduction monitoringChemical testing
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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
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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
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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 systemA software package consisting of an expert
system without its knowledge baseHas 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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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
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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
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Example of Fuzzy Logic Rules and Query
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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
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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
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Typical VR Applications Current applications of virtual reality
Computer-aided designMedical diagnostics and treatmentScientific experimentationFlight simulationProduct demonstrationsEmployee trainingEntertainment
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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
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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
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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
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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
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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
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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?
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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
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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
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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?