topic 5 managerial-support_systems

MANAGEMENT INFORMATION SYSTEMS

Executive MBA PGSM

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Management Information Systems

MANAGERIAL SUPPORT SYSTEMS

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DECISION SUPPORT SYSTEMS

• Designed to assist decision makers withunstructured problems

• Usually interactive• Incorporates data and models• Data often comes from transaction processing

systems or data warehouse


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INTRA-ORGANIZATIONAL SYSTEMS

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• Three major components:1. Data management: select

and handle appropriatedata

2. Model management:apply the appropriatemodel

3. Dialog management:facilitate user interface tothe DSS


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• Specific DSS – actual DSS applications thatdirectly assist in decision making

• DSS generator – a software package used to builda specific DSS quickly and easily• Example: Microsoft Excel

DSS GeneratorDSS Model 1DSS Model 2DSS Model 3

used to create

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DATA MINING

• Employs different technologies to search for (mine)“nuggets” of information from data stored in a datawarehouse

• Data mining decision techniques:– Decision trees– Linear and logistic regression– Association rules for finding patterns– Clustering for market segmentation– Rule induction– Statistical extraction of if-then rules– Nearest neighbor– Genetic algorithms


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DATA MINING

• Online analytical processing (OLAP)– Essentially querying against a database– Program extracts data from the database and

structures it by individual dimensions, such asregion or dealer

– OLAP described as human-driven, whereas datamining is technique-driven

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DATA MINING

• Data mining software:– Oracle 10g Data Mining

(http://www.oracle.com/technology/products/bi/odm/index.html)

– SAS Enterprise Miner(http://www.sas.com/technologies/analytics/datamining/miner/)

– XLMiner(http://www.xlminer.com/)

– IBM Intelligent Miner Modeling(http://www-306.ibm.com/software/data/iminer/)

– Angoss Software’s KnowledgeSEEKER,KnowledgeSTUDIO, and StrategyBUILDER(http://www.angoss.com/analytics_software/)


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DATA MINING

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DATA MINING


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DATA MINING

• American Honda Motor Co.– Uses SAS Data Mining to analyze warranty claims, call

center data, customer feedback, parts sales, andvehicle sales

– Early warning system to find and forestall problems– Allows analysts to zero in on a single performance

issue– During development, analysts identified issues with

three different vehicle models and were able toresolve the problems quickly

Data Mining example

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GROUP SUPPORT SYSTEMS

• Type of DSS to support a group rather than anindividual

• Specialized type of groupware• Attempt to make group meetings more

productive• Now focus on supporting team in all its

endeavors, including “different time, differentplace” mode – virtual teams

• Example of GSS software: GroupSystems(http://www.groupsystems.com/)


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GROUP SUPPORT SYSTEMS

• Traditional “same-time, same-place” meeting layout

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GEOGRAPHIC INFORMATION SYSTEMS

• Systems based on manipulation of relationshipsin space that use geographic data

• Early GIS users:– Natural resource management– Public administration– NASA and the military– Urban planning– Forestry– Map makers


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• Businesses are increasing their usage ofgeographic technologies

• Business uses:– Determining site locations– Market analysis and planning– Logistics and routing– Environmental engineering– Geographic pattern analysis

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• Approaches to representing spatial data:– Raster-based GISs – rely on dividing space into

small, uniform cells (rasters) in a grid– Vector-based GISs – associate features in the

landscape with a point, line, or polygon– Coverage model – different layers represent

similar types of geographic features in the samearea and are stacked on top of one another

What’s behind geographic technologies

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What’s behind geographic technologies (cont’d)

Questions Answered by Geographic Analysis• What is adjacent to this feature?

• Which site is the nearest one, or how many are within acertain distance?

• What is contained within this area, or how many arecontained within this area?

• Which features does this element cross, or how many pathsare available?

• What could be seen from this location?

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• Thanks to maturity of GIS tools, organizationscan acquire off-the-shelf technologies

• Managing technology options now less of achallenge than managing spatial data– Base maps, zip code maps, street networks, and

advertising media market maps should be bought– Other data are spread throughout the

organization in internal databases

Issues for information systems organizations


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• Environmental Systems Research Institute (ESRI)(http://www.esri.com/)

• MapInfo(http://www.mapinfo.com/)

• Autodesk(http://www.autodesk.com/geospatial)

• Tactician(http://www.tactician.com/)

• Intergraph Corp.(http://www.intergraph.com/)

GIS vendors

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Executive Information Systems/Business Intelligence Systems

• Executive information system (EIS)– Hands-on tool that focuses, filters, and organizes

information so that an executive can make moreeffective use of it

– Data come from:• Filtered and summarized transaction data• Competitive information, assessments and insights


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• Executive information system (EIS) (cont’d)– Delivers online current information about business

conditions in aggregate form– Easily accessible to senior executives and other

managers– Designed to be used without intermediary assistance– Uses state-of-the-art graphics, communications and

data storage methods

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• User base for EISs has expanded to encompass alllevels of management… new label is performancemanagement (PM) software

• Focus on competitive information has also lead tothe term business intelligence system


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• InforPM(http://www.infor.com/solutions/pm/)

• Qualitech Solutions Executive Dashboard(http://www.iexecutivedashboard.com/)

• SAP Strategy Management(http://www.sap.com/solutions/performancemanagement/strategy/)

• SAS/EIS(http://www.sas.com/products/eis/)

• Symphony Metreo SymphonyRPM(http://www.symphony-metreo.com/products/rpm_performance_management.asp)

Commercial EIS software

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• The term “dashboard” is used by many vendors forthis type of layout:


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KNOWLEDGE MANAGEMENT SYSTEMS

• Knowledge management (KM):– Set of practical and action-oriented management

practices– Involves strategies and processes of identifying,

creating, capturing, organizing, transferring, andleveraging knowledge to help compete

– Relies on recognizing knowledge held by individualsand the firm


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• Knowledge management system (KMS):– System for managing organizational knowledge– Technology or vehicle that facilitates the sharing and

transferring of knowledge so that valuable knowledgecan be reused

– Enables people and organizations to enhancelearning, improve performance, and produce long-term competitive advantage

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• Tangible benefits of KMS– Operational improvements

• Faster and better dissemination of knowledge• Efficient processes• Change management processes• Knowledge reuse

– Market improvements• Increased sales• Lower cost of products and services• Customer satisfaction


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• May have little formal management and control– Communities of practice (COP): individuals with similar

interests– COP KMS provides members with vehicle to exchange

ideas, tips, and other knowledge– Members are responsible for validating and structuring

knowledge• May have extensive management and control

– KM team to oversee process of validating knowledge– Team provides structure, organization, and packaging for

how knowledge is presented to users

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• Corporate KMS– KM team formed to develop organization-wide KMS– Coordinators within communities of practice

responsible for overseeing knowledge in thecommunity

– Portal software provides tools, including discussionforums

– Any member of the community can post a question ortip

KMS Initiatives Within a Pharmaceutical Firm


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• Field sales KMS– Another KM team formed to build both content

and structure of KMS for field sales– Taxonomy developed so that knowledge would be

organized separately– KM team formats documents and enters into KMS– Tips and advice required to go through validation

and approval process first

KMS Initiatives Within a Pharmaceutical Firm

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• Supply-side (i.e., knowledge contribution)– Leadership commitment– Manager and peer support for KM initiatives– Knowledge quality control

• Demand-side (i.e., knowledge reuse)– Incentives and reward systems– Relevance of knowledge– Ease of using the KMS– Satisfaction with the use of the KMS

KMS success


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• Social capital– Motivation to participate– Cognitive capability to understand and apply the

knowledge– Strong relationships among individuals

KMS success (cont’d)

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ARTIFICIAL INTELLIGENCE

• The study of how to make computers do thingsthat are currently done better by people

• Six areas of AI research:– Natural languages: systems that translate ordinary

human instructions into a language that computerscan understand and execute

– Robotics: machines that accomplish coordinatedphysical tasks like humans do (see Ch.6)

– Perceptive systems: machines possessing a visualand/or aural perceptual ability that affects theirphysical behavior


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ARTIFICIAL INTELLIGENCE

• Six areas of AI research (cont’d):– Genetic programming: problems are divided into

segments, and solutions to these segments are linkedtogether to breed new solutions

– Expert systems– Neural networks

Most relevant formanagerial support

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EXPERT SYSTEMS

• Attempt to capture the expertise of humans in acomputer program

• Knowledge engineer:– A specially trained systems analyst who works closely with

one or more experts in the area of study– Learns from experts how they make decisions– Loads decision information from experts (“rules”) into

module called knowledge base


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EXPERT SYSTEMS• Major components of an expert system:

– Knowledge base: contains the inference rules that are followed indecision making and the parameters, or facts, relevant to the decision

– Inference engine: a logical framework that automatically executes aline of reasoning when supplied with the inference rules andparameters involved in the decision

– User interface: the module used by the end user

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EXPERT SYSTEMS

• Buy a fully developed system created for aspecific application

• Develop using a purchased expert system shell(basic framework) and user-friendly speciallanguage

• Have knowledge engineers custom build usingspecial-purpose language (such as Prolog orLisp)

Obtaining an expert system


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EXPERT SYSTEMSExamples of Expert Systems

• Stanford University’s MYCIN Diagnoses and prescribes treatment formeningitis and blood diseases

• General Electric’s CATS-1 Diagnoses mechanical problems in diesellocomotives

• AT&T’s ACE Locates faults in telephone cables

• Market Surveillance Detects insider trading

• FAST Used by banking industry for credit analysis

• IDP Goal Advisor Assists in setting short- and long-rangeemployee career goals

• Nestlé Foods Provides employees information on pensionfund status

• USDA’s EXNUT Helps peanut farmers manage irrigated peanutproduction

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NEURAL NETWORKS• Designed to tease out meaningful patterns from vast

amounts of data that humans would find difficult toanalyze without computer support

• Process:1. Program given set of data2. Program analyzed data, works out correlations, selects variables

to create patterns3. Pattern used to predict outcomes, then results compared to

known results4. Program changes pattern by adjusting variable weights or

variables themselves5. Repeats process over and over to adjust pattern6. When no further adjustment possible, ready to be used to

make predictions for future cases


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NEURAL NETWORKS

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VIRTUAL REALITY

• Use of a computer-based system to create anenvironment that seems real to one or more of thehuman senses

• Non-entertainment uses of VR:– Training– Design– Marketing


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VIRTUAL REALITY

Example Uses of VRTraining U.S. Army to train tank crews

Amoco for training its driversDuracell for training factory workers on using newequipment

Design Design of automobilesWalk-throughs of air conditioning/ furnace units

Marketing Interactive 3-D images of products (used on the Web)Virtual tours used by real estate companies or resorthotels

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VIRTUAL REALITY