1. © Prentice Hall 2002 6.1 CHAPTER 6 Managerial Support Systems

2. © Prentice Hall 2002 6.2 MANAGERIAL SUPPORT SYSTEMS DECISION SUPPORT SYSTEMS DECISION SUPPORT SYSTEMS DATA MINING DATA MINING GROUP SUPPORT SYSTEMS GROUP SUPPORT SYSTEMS GEOGRAPHIC INFO SYSTEMS GEOGRAPHIC INFO SYSTEMS EXECUTIVE INFO SYSTEMS EXECUTIVE INFO SYSTEMS EXPERT SYSTEMS EXPERT SYSTEMS NEURAL NETWORKS NEURAL NETWORKS VIRTUAL REALITY VIRTUAL REALITY*

3. © Prentice Hall 2002 6.3 DECISION SUPPORT SYSTEMS COMPUTER-BASED SYSTEM, USUALLY INTERACTIVE, DESIGNED TO ASSIST MANAGERS IN MAKING DECISIONS COMPUTER-BASED SYSTEM, USUALLY INTERACTIVE, DESIGNED TO ASSIST MANAGERS IN MAKING DECISIONS INCORPORATES BOTH DATA AND MODELS, INTENDED TO ASSIST IN THE SOLUTION OF SEMI- OR UNSTRUCTURED PROBLEMS INCORPORATES BOTH DATA AND MODELS, INTENDED TO ASSIST IN THE SOLUTION OF SEMI- OR UNSTRUCTURED PROBLEMS*

4. © Prentice Hall 2002 6.4 DSS COMPONENTS MODEL MANAGEMENT: Helps user determine appropriate analytic tools MODEL MANAGEMENT: Helps user determine appropriate analytic tools DATA MANAGEMENT: Provides access to select, handle data DATA MANAGEMENT: Provides access to select, handle data USER INTERFACE: Allows user to interact with system USER INTERFACE: Allows user to interact with system*

5. © Prentice Hall 2002 6.5 TYPICAL DSS APPLICATIONS PROFIT & LOSS MODEL PROFIT & LOSS MODEL MACHINE LOADING OF MACHINES IN A JOB SHOP MACHINE LOADING OF MACHINES IN A JOB SHOP COST/BENEFIT ANALYSIS COST/BENEFIT ANALYSIS PRO FORMA FINANCIAL STATEMENT PRO FORMA FINANCIAL STATEMENT “WHAT-IF” ANALYSIS “WHAT-IF” ANALYSIS*

6. © Prentice Hall 2002 6.6 DATA MINING EMPLOYS TECHNIQUES (SUCH AS DECISION TREES OR NEURAL NETWORKS) TO SEARCH OR “MINE” FOR SMALL “NUGGETS” OF INFORMATION FROM VAST QUANTITIES OF DATA STORED IN AN ORGANIZATION’S DATA WAREHOUSE EMPLOYS TECHNIQUES (SUCH AS DECISION TREES OR NEURAL NETWORKS) TO SEARCH OR “MINE” FOR SMALL “NUGGETS” OF INFORMATION FROM VAST QUANTITIES OF DATA STORED IN AN ORGANIZATION’S DATA WAREHOUSE*

7. © Prentice Hall 2002 6.7 DATA MINING TECHNIQUES ONLINE ANALYTICAL PROCESSING: Human-driven analysis querying a database with specific criteria ONLINE ANALYTICAL PROCESSING: Human-driven analysis querying a database with specific criteria DECISION TREES DECISION TREES NEURAL NETWORKS NEURAL NETWORKS MATHEMATICAL PROGRAMMING MATHEMATICAL PROGRAMMING STATISTICAL ANALYSIS STATISTICAL ANALYSIS*

8. © Prentice Hall 2002 6.8 USES OF DATAMINING

9. © Prentice Hall 2002 6.9 USES OF DATAMINING

10. © Prentice Hall 2002 6.10 GROUP SUPPORT SYSTEMS (GPS) SYSTEM DESIGNED TO MAKE GROUP SESSIONS MORE PRODUCTIVE: Brainstorming, issue structuring, voting, conflict resolution SYSTEM DESIGNED TO MAKE GROUP SESSIONS MORE PRODUCTIVE: Brainstorming, issue structuring, voting, conflict resolution A VARIANT OF DSS IN WHICH THE SYSTEM IS DESIGNED TO SUPPORT A GROUP A VARIANT OF DSS IN WHICH THE SYSTEM IS DESIGNED TO SUPPORT A GROUP A SPECIALIZED TYPE OF GROUPWARE A SPECIALIZED TYPE OF GROUPWARE*

11. © Prentice Hall 2002 6.11 GSS CHARACTERISTICS PARALLEL HUMAN PROCESSING PARALLEL HUMAN PROCESSING EQUAL OPPORTUNITY FOR PARTICIPATION EQUAL OPPORTUNITY FOR PARTICIPATION ANONYMITY ANONYMITY COMPLETE RECORD OF MEETING COMPLETE RECORD OF MEETING OUTPUT OF ONE PHASE LEADS TO NEXT OUTPUT OF ONE PHASE LEADS TO NEXT CAN MORE EASILY APPLY STRUCTURE CAN MORE EASILY APPLY STRUCTURE*

12. © Prentice Hall 2002 6.12 GEOGRAPHIC INFORMATION SYSTEMS (GIS) A COMPUTER-BASED SYSTEM DESIGNED TO COLLECT, STORE, RETRIEVE, MANIPULATE, AND DISPLAY SPATIAL DATA A COMPUTER-BASED SYSTEM DESIGNED TO COLLECT, STORE, RETRIEVE, MANIPULATE, AND DISPLAY SPATIAL DATA A SPATIALLY BASED DSS A SPATIALLY BASED DSS TYPICALLY A DIGITIZED MAP WITH OTHER DATA LINKED TO THE MAP COORDINATES TYPICALLY A DIGITIZED MAP WITH OTHER DATA LINKED TO THE MAP COORDINATES*

13. © Prentice Hall 2002 6.13 TWO TYPES OF GIS RASTER RASTER –Grids of equal-sized cells grouped or linked to make lines and shapes –Values of cells vary –Example: Satellite images, pixels on screen VECTOR VECTOR –Points, Lines, and Polygons –Approximates curves, can link into networks –Example: Property boundaries, sales territories*

14. © Prentice Hall 2002 6.14 GIS COVERAGE MODEL WHAT IS ADJACENT TO FEATURE? WHAT IS ADJACENT TO FEATURE? WHICH IS NEAREST SITE? WHICH IS NEAREST SITE? WHAT DOES AREA CONTAIN? WHAT DOES AREA CONTAIN? WHICH FEATURES DOES THIS ELEMENT CROSS? WHICH FEATURES DOES THIS ELEMENT CROSS? HOW MANY FEATURES ARE A CERTAIN DISTANCE FROM SITE? HOW MANY FEATURES ARE A CERTAIN DISTANCE FROM SITE?*

15. © Prentice Hall 2002 6.15 NEW DIRECTIONS FOR GIS 3-D, DYNAMIC SIMULATION 3-D, DYNAMIC SIMULATION MAP-ENABLED INTERNET SITES MAP-ENABLED INTERNET SITES GIS EMBEDDED IN APPLICATIONS GIS EMBEDDED IN APPLICATIONS REAL-TIME TRACKING OF ASSETS- IN-MOTION REAL-TIME TRACKING OF ASSETS- IN-MOTION*

16. © Prentice Hall 2002 6.16 EXECUTIVE INFORMATION SYSTEMS (EIS) COMPUTER APPLICATION USED DIRECTLY BY TOP MANAGERS, WITHOUT THE ASSISTANCE OF INTERMEDIARIES, TO PROVIDE THEM ON-LINE ACCESS TO CURRENT INFORMATION ABOUT STATUS OF ORGANIZATION AND ITS ENVIRONMENT COMPUTER APPLICATION USED DIRECTLY BY TOP MANAGERS, WITHOUT THE ASSISTANCE OF INTERMEDIARIES, TO PROVIDE THEM ON-LINE ACCESS TO CURRENT INFORMATION ABOUT STATUS OF ORGANIZATION AND ITS ENVIRONMENT*

17. © Prentice Hall 2002 6.17 CHARACTERISTICS OF EIS PRIMARILY USED FOR TRACKING AND CONTROL PRIMARILY USED FOR TRACKING AND CONTROL CUSTOMIZED TO THE INDIVIDUAL EXECUTIVE CUSTOMIZED TO THE INDIVIDUAL EXECUTIVE GRAPHICAL GRAPHICAL EASY TO USE EASY TO USE INCORPORATES BOTH HARD AND SOFT DATA INCORPORATES BOTH HARD AND SOFT DATA*

18. © Prentice Hall 2002 6.18 ARTIFICIAL INTELLIGENCE (AI) USING THE COMPUTER TO PERFORM TASKS DONE BY HUMANS IN SELECTED AREAS: NATURAL LANGUAGES NATURAL LANGUAGES ROBOTICS ROBOTICS PERCEPTIVE SYSTEMS PERCEPTIVE SYSTEMS EXPERT SYSTEMS EXPERT SYSTEMS NEURAL NETWORKS NEURAL NETWORKS*

19. © Prentice Hall 2002 6.19 EXPERT SYSTEMS ONE BRANCH OF ARTIFICIAL INTELLIGENCE (AI) ONE BRANCH OF ARTIFICIAL INTELLIGENCE (AI) CONCERNED WITH BUILDING SYSTEMS THAT INCORPORATE DECISION-MAKING LOGIC OF A HUMAN EXPERT IN A SPECIFIC SKILL CONCERNED WITH BUILDING SYSTEMS THAT INCORPORATE DECISION-MAKING LOGIC OF A HUMAN EXPERT IN A SPECIFIC SKILL*

20. © Prentice Hall 2002 6.20 EXPERT SYSTEMS EXPERT SYSTEMS KNOWLEDGE BASE: Model of Human Knowledge KNOWLEDGE BASE: Model of Human Knowledge RULE - BASED EXPERT SYSTEM: AI system based on IF - THEN statements (Bifurcation); Rule Base: Collection of IF - THEN knowledge RULE - BASED EXPERT SYSTEM: AI system based on IF - THEN statements (Bifurcation); Rule Base: Collection of IF - THEN knowledge KNOWLEDGE FRAMES: Knowledge organizes in chunks based on shared relationships KNOWLEDGE FRAMES: Knowledge organizes in chunks based on shared relationships*

21. © Prentice Hall 2002 6.21 EXPERT SYSTEMS EXPERT SYSTEMS AI SHELL: Programming environment of expert system AI SHELL: Programming environment of expert system INFERENCE ENGINE: Search through rule base INFERENCE ENGINE: Search through rule base –FORWARD CHAINING: –FORWARD CHAINING: Uses input, searches rules for answer –BACKWARD CHAINING: –BACKWARD CHAINING: Begins with hypothesis, seeks information until hypothesis accepted or rejected*

22. © Prentice Hall 2002 6.22 EXAMPLES OF EXPERT SYSTEMS MYCIN: Diagnose, treat blood diseases MYCIN: Diagnose, treat blood diseases CATS-1: Diagnose locomotive problems CATS-1: Diagnose locomotive problems MARKET SURVEILLANCE: Detects insider trading on stock market MARKET SURVEILLANCE: Detects insider trading on stock market FINANCIAL ANALYSIS SUPPORT TECHNIQUE: Credit analysis in banks FINANCIAL ANALYSIS SUPPORT TECHNIQUE: Credit analysis in banks INDIVIDUAL DEVELOPMENT PLAN GOAL ADVISOR: Helps set career goals INDIVIDUAL DEVELOPMENT PLAN GOAL ADVISOR: Helps set career goals*

23. © Prentice Hall 2002 6.23 NEURAL NETWORKS BASED ON HOW HUMAN NERVOUS SYSTEM WORKS BASED ON HOW HUMAN NERVOUS SYSTEM WORKS USE STATISTICAL ANALYSIS TO RECOGNIZE PATTERNS FROM VAST AMOUNTS OF DATA BY A PROCESS OF ADAPTIVE LEARNING USE STATISTICAL ANALYSIS TO RECOGNIZE PATTERNS FROM VAST AMOUNTS OF DATA BY A PROCESS OF ADAPTIVE LEARNING CONSIST OF SOFTWARE THAT ATTEMPTS TO EMULATE PROCESSING PATTERNS OF BIOLOGICAL BRAIN CONSIST OF SOFTWARE THAT ATTEMPTS TO EMULATE PROCESSING PATTERNS OF BIOLOGICAL BRAIN*

24. © Prentice Hall 2002 6.24 EXAMPLES OF NEURAL NETWORKS BANKAMERICA: Neural network evaluates commercial loan applications BANKAMERICA: Neural network evaluates commercial loan applications AMERICAN EXPRESS: System reads handwriting on credit card slips AMERICAN EXPRESS: System reads handwriting on credit card slips STATE OF WYOMING: System reads hand-printed numbers on tax forms STATE OF WYOMING: System reads hand-printed numbers on tax forms ARCO AND TEXACO: Neural network helps pinpoint oil and gas deposits ARCO AND TEXACO: Neural network helps pinpoint oil and gas deposits*

25. © Prentice Hall 2002 6.25 EXAMPLES OF NEURAL NETWORKS SPIEGEL: Prune mailing list to eliminate those unlikely to order again SPIEGEL: Prune mailing list to eliminate those unlikely to order again DEERE & COMPANY: Pension fund management DEERE & COMPANY: Pension fund management*

26. © Prentice Hall 2002 6.26 VIRTUAL REALITY (VR) USE OF COMPUTER-BASED SYSTEMS TO CREATE AN ENVIRONMENT THAT SEEMS REAL TO ONE OR MORE SENSE (USUALLY INCLUDING SIGHT) USE OF COMPUTER-BASED SYSTEMS TO CREATE AN ENVIRONMENT THAT SEEMS REAL TO ONE OR MORE SENSE (USUALLY INCLUDING SIGHT) USED IN VIDEO GAMES, TRAINING & EDUCATION, PROVIDING SERVICE AT A DISTANCE, PRODUCT DESIGN, INTERACTIVE WORLD WIDE WEB APPLICATIONS USED IN VIDEO GAMES, TRAINING & EDUCATION, PROVIDING SERVICE AT A DISTANCE, PRODUCT DESIGN, INTERACTIVE WORLD WIDE WEB APPLICATIONS*

27. © Prentice Hall 2002 6.27 CHAPTER 6 Managerial Support Systems