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Managing Information Technology 6th Edition

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Presentation on theme: "Managing Information Technology 6th Edition"— Presentation transcript:

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 (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/)

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 (http://www.groupsystems.com/)

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) (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/)

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 (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)

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

46 Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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 © 2009 Pearson Education, Inc.   Publishing as Prentice Hall 46


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