MANAGING INFORMATION TECHNOLOGY 7 th EDITION

Transcription

1 MANAGING INFORMATION TECHNOLOGY 7 th EDITION CHAPTER 6 MANAGERIAL SUPPORT SYSTEMS Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-1

2 PART II - APPLICATION AREAS Intra-organizational systems: Enterprise systems: (Ch. 5) support all or most of the organization Managerial Support systems (Ch. 6) support a specific manager or group of managers Inter-organizational systems: e-business applications (Ch. 7) - B2C link businesses with end consumers - B2B link businesses with other businesses - Intermediaries Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-2

3 MANAGERIAL SUPPORT SYSTEMS Decision Support Systems Data Mining Group Support Systems Geographic Information Systems Executive Information Systems Business Intelligence Systems Knowledge Management Systems Expert Systems Neural Networks Virtual Reality Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-3

4 DECISION SUPPORT SYSTEMS Interactive decision support for complete or poorly structured problems Data often comes from transaction processing systems or data warehouse Incorporates data and models Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-4

5 DECISION SUPPORT SYSTEMS Three major components: 1. Data management: select and handle appropriate data 2. Model management: apply the appropriate model 3. Dialog management: facilitate user interface to the DSS Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-5

6 DECISION SUPPORT SYSTEMS Specific DSS actual DSS applications that directly assist in decision making DSS generator a software package (ex. Spreadsheet) used to build a specific DSS quickly and easily DSS Generator used to create DSS Model 1 DSS Model 2 DSS Model 3 Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-6

7 DATA MINING Employs different technologies to search for (mine) nuggets of information from data stored in a data warehouse 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 Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-7

8 ONLINE ANALYTICAL PROCESSING (OLAP) Human- driven analysis: - Querying against a database - Program extracts data from the database and structures it by individual dimensions, such as region or dealer Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-8

9 USES OF DATA MINING Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-9

10 DATA MINING PRODUCT EXAMPLES Xerox installed Rapid Insight Analytics software to mine customer order, sales prospects and supply chain data to develop monthly and quarterly forecasts. Farmers Insurance Group uses IBM s DecisionEdge software to mine data. Vermont County store (VCS) a catalog retailer uses SAS s Enterprise miner software to segment its customers to create appropriate direct marketing lists. Data Mining software: - Oracle 10g Data Mining - SAS Enterprise Miner - IBM Intelligent Miner Modeling - Angoss Software s Knowledge SEEKER, Knowledge STUDIO, and Strategy BUILDER - Rapid Miner SAS Enterprise Miner XL Miner Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-10

11 DATA MINING More Data Mining examples Data mining urban legend - beer and diapers Can data mining catch terrorists Data mining gamers Mayo builds toward customized medicines Data mining to locate Venusian Volcanoes Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-11

12 GROUP SUPPORT SYSTEMS (GSS) Decision support for group meetings Goal: more productive meetings Includes different time, different place mode = virtual teams Product example: Group Systems (Purchased by IBM) Group Systems Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-12

13 GROUP SUPPORT SYSTEMS Traditional setup for same-time, same-place GSS Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-13

14 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 Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-14

15 GEOGRAPHIC INFORMATION SYSTEMS Current business uses: - Determining site locations - Market analysis and planning - Logistics and routing - Environmental engineering - Geographic pattern analysis Applications for mobile users: ; - Logistics (fastest route) - Location intelligence Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-15

16 GEOGRAPHIC INFORMATION SYSTEMS Representation of 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 data model different layers represent similar types of geographic features in the same area and are stacked on top of one another Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-16

17 GEOGRAPHIC INFORMATION SYSTEMS Coverage data model Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-17

18 GEOGRAPHIC INFORMATION SYSTEMS Organizations can buy off-the-shelf technologies and spatial data: - Base maps, zip code maps, street networks, and advertising media market maps Other data sources may be spread throughout the organization in different internal databases Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-18

19 GEOGRAPHIC INFORMATION SYSTEMS GIS Vendors Environmental Research Institute (ESRI) Pitney Bowes ( with its MapInfo products) Autodesk Tactician Corp. Intergraph Corp. ESRI MapInfo Tactician Intergraph Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-19

20 Executive Information Systems (EIS)/ Business Intelligence Systems Hands-on tool that focuses, filters, and organizes information so that an executive can make more effective use of it User base for EISs has expanded to encompass all levels of management Today also called performance management software Focus on competitive information today referred to as business intelligence systems Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-20

21 Executive Information Systems/ Business Intelligence Systems - Delivers online current information about business conditions in aggregate form - Filtered and summarized transaction data - Competitive information, assessments and insights - 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 Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-21

22 Executive Information Systems/ Business Intelligence Systems Commercial EIS software Executive Dashboard from Qualitech Solutions Oracle Enterprise performance Management Systems SAP Business Objects Strategy Management SAS/EIS Symphony RPM from Symphony Metreo IBM Cognos Business Intelligence MicroStrategy Intelligence Server Oracle Business Intelligence Suite SAP Business Objects BI solutions SAS Business Intelligence Infor PM Executive Dashboard SAP Business Objects SAS/EIS Symphony Metreo Infor PM Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-22

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26 Executive Information Systems/ Business Intelligence Systems Dashboard layout for data representation: Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-26

27 KNOWLEDGE MANAGEMENT SYSTEMS What is Knowledge management (KM)? Practices to manage Organizational knowledge Strategies and processes for identifying, creating, capturing, organizing, transferring, and leveraging knowledge held by individuals and the firm Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-27

28 KNOWLEDGE MANAGEMENT SYSTEMS What is a Knowledge management system (KMS)? System to help manage organizational knowledge Technologies that facilitate the sharing and transferring of knowledge so that it can be reused Enables people and organizations to learn from others to improve performance of individuals, groups and the organization as a whole Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-28

29 KNOWLEDGE MANAGEMENT SYSTEMS Potential benefits of a corporate 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 Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-29

30 KNOWLEDGE MANAGEMENT SYSTEMS Example: Corporate KMS in a Pharmaceutical Firm - KM team formed to develop organization-wide KMS - Coordinators within communities of practice (COP) 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 Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-30

31 KNOWLEDGE MANAGEMENT SYSTEMS Example continued: Corporate KMS Field sales KMS - 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 Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-31

32 KNOWLEDGE MANAGEMENT SYSTEMS KMS Success Factors: Knowledge Contribution (Supply Side) - Leadership commitment - Manager and peer support for KM initiatives - Knowledge quality control Knowledge Reuse (Demand Side) - Incentives and reward systems - Relevance of knowledge - Ease of using the KMS - Satisfaction with the use of the KMS Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-32

33 ARTIFICIAL INTELLIGENCE The study of how to make computers do things that are currently done better by people Natural languages: systems that translate ordinary human instructions into a language that computers can understand and execute Perceptive systems: machines possessing a visual and/or aural perceptual ability that affects their physical behavior Genetic programming/ evolutionary design: problems are divided into segments, and solutions to these segments are linked together breeding new solutions Expert systems Neural networks Most relevant for Managerial Support Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-33

34 Expert Systems EXPERT SYSTEMS Captures the expertise of humans for a particular domain 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 Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-34

35 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 Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-35

36 EXPERT SYSTEMS Options for obtaining an Expert System: Buy a fully developed system created for a specific application Develop a system using a purchased expert system shell (basic framework) and user-friendly special language Custom build system by knowledge engineers using a specialpurpose language (such as Prolog or Lisp) Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-36

37 EXPERT SYSTEMS Stanford University s MYCIN General Electric s CATS-1 AT&T s ACE Market Surveillance FAST IDP Goal Advisor Nestlé Foods USDA s EXNUT Examples of Expert Systems Diagnoses and prescribes treatment for meningitis and blood diseases Diagnoses mechanical problems in diesel locomotives Locates faults in telephone cables Detects insider trading Used by banking industry for credit analysis Assists in setting short- and longrange employee career goals Provides employees information on pension fund status Helps peanut farmers manage irrigated peanut production Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-37

38 NEURAL NETWORKS Neural Networks Systems designed to tease out meaningful patterns from vast amounts of data that humans would find difficult to analyze without computer support How it works: 1. Program given set of data 2. Program analyzes data, works out correlations, selects variables to create patterns 3. Pattern used to predict outcomes, then results compared to known results 4. Program changes pattern by adjusting variable weights or variables themselves 5. Repeats process over and over to adjust pattern 6. When no further adjustment identified, ready to be used to make predictions for future cases Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-38

39 NEURAL NETWORKS Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-39

40 VIRTUAL REALITY (VR) 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 - Meetings - Social Collaborations Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-40

41 VIRTUAL REALITY (VR) Training Design Marketing Example Uses of VR U.S. Army to train tank crews Amoco for training its drivers Duracell for training factory workers on using new equipment Design of automobiles Walk-throughs of air conditioning/ furnace units Interactive 3-D images of products (used on the Web) Virtual tours used by real estate companies or resort hotels Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-41

42 VIRTUAL REALITY (VR) Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-42

43 COPYRIGHT All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Copyright 2012 Pearson Education, Inc. Publishing as Prentice Hall Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall 6-43