1. E. Wainright Martin Carol V. Brown Daniel W. DeHayes Jeffrey A. Hoffer William C. Perkins MANAGINGINFORMATIONTECHNOLOGY FIFTH EDITION CHAPTER 7 M ANAGERIAL S UPPORT S YSTEMS © 2005 Pearson Prenice-Hall Chapter 7

2. © 2005 Pearson Prentice-Hall Chapter 7 - 2 Integrated Business Management

3. © 2005 Pearson Prentice-Hall Chapter 7 - 3 Enterprise Process Flows

4. © 2005 Pearson Prentice-Hall Chapter 7 - 4 D ECISION S UPPORT S YSTEMS Designed to assist decision makers with unstructured problems Usually interactive Incorporates data and models Data often comes from transaction processing systems or data warehouse Page 212

5. © 2005 Pearson Prentice-Hall Chapter 7 - 5 Page 213 Figure 7.1 Decision Support Systems Components D ECISION S UPPORT S YSTEMS Three major components

6. © 2005 Pearson Prentice-Hall Chapter 7 - 6 Forecasting

7. © 2005 Pearson Prentice-Hall Chapter 7 - 7 Page 215 D ATA M INING  Data mining software: Oracle 9i Data Mining and Oracle Data Mining Suite SAS Enterprise Miner IBM Intelligent Miner Modeling Angoss Software’s KnowledgeSEEKER, Knowledge Studio, and KnowledgeExcelerator Datamation’s Data Mining and Business Intelligence Product Data Mining – uses different technologies to search for (mine) “nuggets” of information from data stored in a data warehouse

8. © 2005 Pearson Prentice-Hall Chapter 7 - 8 Page 215  Decision techniques used: Decision trees Linear and logistic regression Clustering for market segmentation Rule induction Nearest neighbor Genetic algorithms Data Mining – uses different technologies to search for (mine) “nuggets” of information from data stored in a data warehouse D ATA M INING

9. © 2005 Pearson Prentice-Hall Chapter 7 - 9 Page 216 see Table 7.1 Uses of Data Mining Uses:  Cross-selling  Customer churn  Customer retention  Direct marketing  Fraud detection  Interactive marketing  Market basket analysis  Market segmentation  Payment or default analysis  Trend analysis D ATA M INING

10. © 2005 Pearson Prentice-Hall Chapter 7 - 10 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 Page 217-218 G ROUP S UPPORT S YSTEMS Middle managers spend 35%, and top managers spend 50-80% of time in meetings!

11. © 2005 Pearson Prentice-Hall Chapter 7 - 11 G ROUP S UPPORT S YSTEMS Figure 7.2 Group Support System Layout Page 217 Traditional “same time, same place” meeting layout

12. © 2005 Pearson Prentice-Hall Chapter 7 - 12 GISs – systems based on manipulation of relationships in space that use geographic data G EOGRAPHIC I NFORMATION S YSTEMS  Early GIS users: Natural resource management Public administration NASA and the military Urban planning Forestry Map makers Page 219

13. © 2005 Pearson Prentice-Hall Chapter 7 - 13 Business Adopts Geographic Technologies G EOGRAPHIC I NFORMATION S YSTEMS  Business uses: Determining site locations Market analysis and planning Logistics and routing Environmental engineering Geographic pattern analysis Page 219

14. © 2005 Pearson Prentice-Hall Chapter 7 - 14 Figure 7.3 Department Store Analysis Page 219 (Reprinted courtesy of Environmental Systems Research Institute, Inc. Copyright © 2003 Environmental Systems Research Institute, Inc. All rights reserved.)

15. © 2005 Pearson Prentice-Hall Chapter 7 - 15 Page 221 Questions geographic analysis can answer:   What is adjacent to this feature?   Which site is the nearest one?   What is contained within this area?   Which features does this element cross?   How many features are within a certain distance of a site? What’s Behind Geographic Technologies G EOGRAPHIC I NFORMATION S YSTEMS

16. © 2005 Pearson Prentice-Hall Chapter 7 - 16 Farm Futures Brownsville Herald Feb 11, 2005

17. © 2005 Pearson Prentice-Hall Chapter 7 - 17 E XECUTIVE I NFORMATION S YSTEMS/ B USINESS I NTELLIGENCE S YSTEMS Page 222-223 Where does EIS data come from?   Filtered and summarized transaction data (internal)   Collected competitive information (internal and external) EISs – a hands-on tool that focuses, filters, and organizes an executive’s information so he or she can make more effective use of it

18. © 2005 Pearson Prentice-Hall Chapter 7 - 18 E XECUTIVE I NFORMATION S YSTEMS/ B USINESS I NTELLIGENCE S YSTEMS Page 222-223 Executive information system (EIS):   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

19. © 2005 Pearson Prentice-Hall Chapter 7 - 19 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 K NOWLEDGE M ANAGEMENT S YSTEMS Page 226

20. © 2005 Pearson Prentice-Hall Chapter 7 - 20 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   Enable people and organizations to enhance learning, improve performance, and produce long- term competitive advantage K NOWLEDGE M ANAGEMENT S YSTEMS Page 226

21. © 2005 Pearson Prentice-Hall Chapter 7 - 21 What’s in people’s heads that contributes to the corporate mission Intellectual capital - Patents - Methodologies - Best practices - Contracts - Reusable software - Formulae - Designs - New product ideas - External information - Your data warehouse -..... Create more knowledge capital with less effort Put it to use more quickly - Make it more accessible Increase speed of circulation Increase the extent of reuse Increase the synergy of bringing people and knowledge capital together Bring more brainpower to bear on an opportunity Increasing Its ValueKnowledge Capital What is Knowledge Capital and what makes it more valuable?

22. © 2005 Pearson Prentice-Hall Chapter 7 - 22 Goals for Knowledge Building Dialogue Learning by Doing Recognize innovation Ease research Analyze information from multiple sources Support remote teams Identify innovators/experts Integrate training development Efficient development of new products Socialization InternalizationCombination Externalization Field Building Linking Explicit Knowledge More ideas contributed More comments Quicker discussion Record the results Build consensus Easy, thorough research Widespread access Comments on results Quick usage of results Wide access to knowledge Frequent reuse Effective learning/change tools Alerts to new and relevant information Environment conducive to innovation

23. © 2005 Pearson Prentice-Hall Chapter 7 - 23 Based on Nonaka, I. and H. Takeuchi, The Knowledge-Creating Company. New York: Oxford Univ. Press, 1995. E-mail, collaboration, search engines, profiling and alerting and security are needed in all quadrants Socialization InternalizationCombination Externalization Threaded discussions Libraries Mail back Search Engines Document management Electronic Publishing Word Processors IPS Brainstorming tools GBL/CBT Markup Tools Workflow Mail enabled apps Data Warehousing Remote Learning Data mining Training Development tools Production system integration KM requires integration of many technologies

24. © 2005 Pearson Prentice-Hall Chapter 7 - 24 MIS grows to Enterprise Level

25. © 2005 Pearson Prentice-Hall Chapter 7 - 25 Page 229 Six areas: Natural languages Robotics Perceptive systems Genetic programming Expert systems Neural networks AI – the study of how to make computers do things that are currently done better by people A RTIFICIAL I NTELLIGENCE

26. © 2005 Pearson Prentice-Hall Chapter 7 - 26 Page 229 A RTIFICIAL I NTELLIGENCE Six areas: Natural languages Robotics Perceptive systems Genetic programming Expert systems Neural networks AI – the study of how to make computers do things that are currently done better by people Most relevant for managerial support

27. © 2005 Pearson Prentice-Hall Chapter 7 - 27 Page 229 Expert systems – attempt to capture the expertise of humans in a computer program E XPERT S YSTEMS Knowledge engineer:   A specially trained systems analyst who works closely with one or more experts in the area of study   Tries to learn about how experts make decisions   Loads information (what learned) into module called knowledge base

28. © 2005 Pearson Prentice-Hall Chapter 7 - 28 Page 229 E XPERT S YSTEMS Figure 7.6 Architecture of an Expert System

29. © 2005 Pearson Prentice-Hall Chapter 7 - 29 Page 230 E XPERT S YSTEMS Approaches:  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) Obtaining an Expert System

30. © 2005 Pearson Prentice-Hall Chapter 7 - 30 Page 230 Standford University’s MYCIN – to diagnose and prescribe treatment for meningitis and blood diseases General Electric’s CATS-1 to diagnose mechanical problems in diesel locomotives AT&T’s ACE to locate faults in telephone cables Market Surveillance software – to detect insider trading FAST software – for credit analysis, used by banking industry Nestle Food’s developed system to provide employees information on pension fund status E XPERT S YSTEMS Examples of Expert Systems

31. © 2005 Pearson Prentice-Hall Chapter 7 - 31 Page 232 Neural networks – attempt to tease out meaningful patterns from vast amounts of data Process: 1. 1. Program given set of data 2. 2. Program analyzed data, works out correlations, selects variables to create patterns 3. 3. Pattern used to predict outcomes, then results compared to known results 4. 4. Program changes pattern by adjusting variable weights or variables themselves 5. 5. Repeats process over and over to adjust pattern 6. 6. When no further adjustment possible, ready to be used make predictions for future cases N EURAL N ETWORKS

32. © 2005 Pearson Prentice-Hall Chapter 7 - 32 Page 232 N EURAL N ETWORKS Table 7.2 Uses of Neural Networks

33. © 2005 Pearson Prentice-Hall Chapter 7 - 33 Page 233 V IRTUAL R EALITY Virtual reality – use of a computer-based system to create an environment that seems real to one or more senses of users Non-entertainment categories:   Training   Design   Marketing

34. © 2005 Pearson Prentice-Hall Chapter 7 - 34 Page 234-235 TrainingU.S. Army to train tank crews Amoco for training its drivers Duracell for training factory workers on using new equipment DesignDesign of automobiles Walk-throughs of air conditioning/ furnace units MarketingInteractive 3-D images of products (used on the Web) Virtual tours used by real estate companies or resort hotels V IRTUAL R EALITY

35. © 2005 Pearson Prentice-Hall Chapter 7 - 35 Other Examples Flight simulator for pilot training in emergency procedures Flight simulator to analyze plane crash Simulators for astronaut training Simulator for training of oil tanker captain Competitive war-gaming for business strategy development and testing