1. Managing Information Technology 6th Edition
CHAPTER 7 MANAGERIAL SUPPORT SYSTEMS

2. DECISION SUPPORT SYSTEMS
Designed to assist decision makers with unstructured problems
Usually interactive
Incorporates data and models
Data often comes from transaction processing systems or data warehouse

3. DECISION SUPPORT SYSTEMS
Three major components:
Data management: select and handle appropriate data
Model management: apply the appropriate model
Dialog management: facilitate user interface to the DSS

4. DECISION SUPPORT SYSTEMS
Specific DSS – actual DSS applications that directly assist in decision making
DSS generator – a software package used to build a specific DSS quickly and easily
Example: Microsoft Excel
used to create
DSS Model 1
DSS Model 2
DSS Model 3
DSS Generator

5. DATA MINING
Employs different technologies to search for (mine) “nuggets” of information from data stored in a data warehouse
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

6. DATA MINING Online analytical processing (OLAP)
Essentially querying against a database
Program extracts data from the database and structures it by individual dimensions, such as region or dealer
OLAP described as human-driven, whereas data mining is technique-driven

7. DATA MINING Data mining software:
Oracle 10g Data Mining (
SAS Enterprise Miner (
XLMiner (
IBM Intelligent Miner Modeling (
Angoss Software’s KnowledgeSEEKER, KnowledgeSTUDIO, and StrategyBUILDER (

8. DATA MINING

9. DATA MINING

10. DATA MINING Data Mining example American Honda Motor Co.
Uses SAS Data Mining to analyze warranty claims, call center data, customer feedback, parts sales, and vehicle 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 to resolve the problems quickly

11. GROUP SUPPORT SYSTEMS
Type of DSS to support a group rather than an individual
Specialized type of groupware
Attempt to make group meetings more productive
Now focus on supporting team in all its endeavors, including “different time, different place” mode – virtual teams
Example of GSS software: GroupSystems (

12. GROUP SUPPORT SYSTEMS
Traditional “same-time, same-place” meeting layout

13. GEOGRAPHIC INFORMATION SYSTEMS
Systems based on manipulation of relationships in space that use geographic data
Early GIS users:
Natural resource management
Public administration
NASA and the military
Urban planning
Forestry
Map makers

14. GEOGRAPHIC INFORMATION SYSTEMS
Businesses are increasing their usage of geographic technologies
Business uses:
Determining site locations
Market analysis and planning
Logistics and routing
Environmental engineering
Geographic pattern analysis

15. GEOGRAPHIC INFORMATION SYSTEMS

16. GEOGRAPHIC INFORMATION SYSTEMS
What’s behind geographic technologies
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 same area and are stacked on top of one another

17. GEOGRAPHIC INFORMATION SYSTEMS

18. GEOGRAPHIC INFORMATION SYSTEMS
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 a certain distance?
What is contained within this area, or how many are contained within this area?
Which features does this element cross, or how many paths are available?
What could be seen from this location?

19. GEOGRAPHIC INFORMATION SYSTEMS
Issues for information systems organizations
Thanks to maturity of GIS tools, organizations can acquire off-the-shelf technologies
Managing technology options now less of a challenge 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

20. GEOGRAPHIC INFORMATION SYSTEMS
GIS vendors
Environmental Systems Research Institute (ESRI) (
MapInfo (
Autodesk (
Tactician (
Intergraph Corp. (

21. 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 more effective use of it
Data come from:
Filtered and summarized transaction data
Competitive information, assessments and insights

22. Executive Information Systems/ Business Intelligence Systems
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

23. Executive Information Systems/ Business Intelligence Systems
User base for EISs has expanded to encompass all levels of management… new label is performance management (PM) software
Focus on competitive information has also lead to the term business intelligence system

24. Executive Information Systems/ Business Intelligence Systems
Commercial EIS software
InforPM (
Qualitech Solutions Executive Dashboard (
SAP Strategy Management (
SAS/EIS (
Symphony Metreo SymphonyRPM (

25. Executive Information Systems/ Business Intelligence Systems
The term “dashboard” is used by many vendors for this type of layout:

26. Executive Information Systems/ Business Intelligence Systems

27. KNOWLEDGE MANAGEMENT SYSTEMS
Knowledge management (KM):
Set of practical and action-oriented management practices
Involves strategies and processes of identifying, creating, capturing, organizing, transferring, and leveraging knowledge to help compete
Relies on recognizing knowledge held by individuals and the firm

28. KNOWLEDGE MANAGEMENT SYSTEMS
Knowledge management system (KMS):
System for managing organizational knowledge
Technology or vehicle that facilitates the sharing and transferring of knowledge so that valuable knowledge can be reused
Enables people and organizations to enhance learning, improve performance, and produce long-term competitive advantage

29. KNOWLEDGE MANAGEMENT SYSTEMS
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

30. KNOWLEDGE MANAGEMENT SYSTEMS
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

31. KNOWLEDGE MANAGEMENT SYSTEMS
KMS Initiatives Within a Pharmaceutical Firm
Corporate KMS
KM team formed to develop organization-wide KMS
Coordinators within communities of practice responsible for overseeing knowledge in the community
Portal software provides tools, including discussion forums
Any member of the community can post a question or tip

32. KNOWLEDGE MANAGEMENT SYSTEMS
KMS Initiatives Within a Pharmaceutical Firm
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

33. KNOWLEDGE MANAGEMENT SYSTEMS
KMS success
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

34. KNOWLEDGE MANAGEMENT SYSTEMS
KMS success (cont’d)
Social capital
Motivation to participate
Cognitive capability to understand and apply the knowledge
Strong relationships among individuals

35. ARTIFICIAL INTELLIGENCE
The study of how to make computers do things that are currently done better by people
Six areas of AI research:
Natural languages: systems that translate ordinary human instructions into a language that computers can understand and execute
Robotics: machines that accomplish coordinated physical tasks like humans do (see Ch.6)
Perceptive systems: machines possessing a visual and/or aural perceptual ability that affects their physical behavior

36. ARTIFICIAL INTELLIGENCE
Six areas of AI research (cont’d):
Genetic programming: problems are divided into segments, and solutions to these segments are linked together to breed new solutions
Expert systems
Neural networks
Most relevant for managerial support

37. EXPERT SYSTEMS
Attempt to capture the expertise of humans in a computer 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

38. EXPERT SYSTEMS Major components of an expert system:
Knowledge base: contains the inference rules that are followed in decision making and the parameters, or facts, relevant to the decision
Inference engine: a logical framework that automatically executes a line of reasoning when supplied with the inference rules and parameters involved in the decision
User interface: the module used by the end user

39. EXPERT SYSTEMS Obtaining an expert system
Buy a fully developed system created for a specific application
Develop using a purchased expert system shell (basic framework) and user-friendly special language
Have knowledge engineers custom build using special-purpose language (such as Prolog or Lisp)

40. Examples of Expert Systems
Stanford University’s MYCIN
Diagnoses and prescribes treatment for meningitis and blood diseases
General Electric’s CATS-1
Diagnoses mechanical problems in diesel locomotives
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-range employee career goals
Nestlé Foods
Provides employees information on pension fund status
USDA’s EXNUT
Helps peanut farmers manage irrigated peanut production

41. NEURAL NETWORKS
Designed to tease out meaningful patterns from vast amounts of data that humans would find difficult to analyze without computer support
Process:
Program given set of data
Program analyzed data, works out correlations, selects variables to create patterns
Pattern used to predict outcomes, then results compared to known results
Program changes pattern by adjusting variable weights or variables themselves
Repeats process over and over to adjust pattern
When no further adjustment possible, ready to be used to make predictions for future cases

42. NEURAL NETWORKS

43. VIRTUAL REALITY
Use of a computer-based system to create an environment that seems real to one or more of the human senses
Non-entertainment uses of VR:
Training
Design
Marketing

44. VIRTUAL REALITY Example Uses of VR Training
U.S. Army to train tank crews
Amoco for training its drivers
Duracell for training factory workers on using new equipment
Design
Design of automobiles
Walk-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 resort hotels

45. VIRTUAL REALITY

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