1. 11.1 © 2010 by Prentice Hall Lecture 7 Managing Knowledge and Collaboration

2. 11.2 © 2010 by Prentice Hall The Knowledge Management Landscape Sales of enterprise content management software for knowledge management expected to grow 15 percent annually through 2012 Information Economy 55% U.S. labor force: knowledge and information workers 60% U.S. GDP from knowledge and information sectors Substantial part of a firm’s stock market value is related to intangible assets: knowledge, brands, reputations, and unique business processes Knowledge-based projects can produce extraordinary ROI Management Information Systems Chapter 11 Managing Knowledge

3. 11.3 © 2010 by Prentice Hall U.S. Enterprise Knowledge Management Software Revenues, 2005-2012 Figure 11-1 Enterprise knowledge management software includes sales of content management and portal licenses, which have been growing at a rate of 15 percent annually, making it among the fastest-growing software applications. Management Information Systems Chapter 11 Managing Knowledge The Knowledge Management Landscape

4. 11.4 © 2010 by Prentice Hall Important dimensions of knowledge Knowledge is a firm asset Intangible Creation of knowledge from data, information, requires organizational resources As it is shared, experiences network effects Knowledge has different forms May be explicit (documented) or tacit (residing in minds) Know-how, craft, skill How to follow procedure Knowing why things happen (causality) Management Information Systems Chapter 11 Managing Knowledge The Knowledge Management Landscape

5. 11.5 © 2010 by Prentice Hall Important dimensions of knowledge (cont.) Knowledge has a location Cognitive event Both social and individual “Sticky” (hard to move), situated (enmeshed in firm’s culture), contextual (works only in certain situations) Knowledge is situational Conditional: Knowing when to apply procedure Contextual: Knowing circumstances to use certain tool Management Information Systems Chapter 11 Managing Knowledge The Knowledge Management Landscape

6. 11.6 © 2010 by Prentice Hall To transform information into knowledge, firm must expend additional resources to discover patterns, rules, and contexts where knowledge works Wisdom: Collective and individual experience of applying knowledge to solve problems Involves where, when, and how to apply knowledge Knowing how to do things effectively and efficiently in ways other organizations cannot duplicate is primary source of profit and competitive advantage that cannot be purchased easily by competitors E.g., Having a unique build-to-order production system Management Information Systems Chapter 11 Managing Knowledge The Knowledge Management Landscape

7. 11.7 © 2010 by Prentice Hall Organizational learning Process in which organizations learn Gain experience through collection of data, measurement, trial and error, and feedback Adjust behavior to reflect experience Create new business processes Change patterns of management decision making Management Information Systems Chapter 11 Managing Knowledge The Knowledge Management Landscape

8. 11.8 © 2010 by Prentice Hall Knowledge management: Set of business processes developed in an organization to create, store, transfer, and apply knowledge Knowledge management value chain: Each stage adds value to raw data and information as they are transformed into usable knowledge Knowledge acquisition Knowledge storage Knowledge dissemination Knowledge application Management Information Systems Chapter 11 Managing Knowledge The Knowledge Management Landscape

9. 11.9 © 2010 by Prentice Hall Knowledge management value chain Knowledge acquisition Documenting tacit and explicit knowledge Storing documents, reports, presentations, best practices Unstructured documents (e.g., e-mails) Developing online expert networks Creating knowledge Tracking data from TPS and external sources Management Information Systems Chapter 11 Managing Knowledge The Knowledge Management Landscape

10. 11.10 © 2010 by Prentice Hall Knowledge management value chain: Knowledge storage Databases Document management systems Role of management: Support development of planned knowledge storage systems Encourage development of corporate-wide schemas for indexing documents Reward employees for taking time to update and store documents properly Management Information Systems Chapter 11 Managing Knowledge The Knowledge Management Landscape

11. 11.11 © 2010 by Prentice Hall Knowledge management value chain: Knowledge dissemination Portals Push e-mail reports Search engines Collaboration tools A deluge of information? Training programs, informal networks, and shared management experience help managers focus attention on important information Management Information Systems Chapter 11 Managing Knowledge The Knowledge Management Landscape

12. 11.12 © 2010 by Prentice Hall Knowledge management value chain: Knowledge application To provide return on investment, organizational knowledge must become systematic part of management decision making and become situated in decision-support systems New business practices New products and services New markets Management Information Systems Chapter 11 Managing Knowledge The Knowledge Management Landscape

13. 11.13 © 2010 by Prentice Hall The Knowledge Management Value Chain Figure 11-2 Knowledge management today involves both information systems activities and a host of enabling management and organizational activities. Management Information Systems Chapter 11 Managing Knowledge The Knowledge Management Landscape

14. 11.14 © 2010 by Prentice Hall New organizational roles and responsibilities Chief knowledge officer executives Dedicated staff / knowledge managers Communities of practice (COPs) Informal social networks of professionals and employees within and outside firm who have similar work-related activities and interests Activities include education, online newsletters, sharing experiences and techniques Facilitate reuse of knowledge, discussion Reduce learning curves of new employees Management Information Systems Chapter 11 Managing Knowledge The Knowledge Management Landscape

15. 11.15 © 2010 by Prentice Hall Three major types of knowledge management systems: Enterprise-wide knowledge management systems General-purpose firm-wide efforts to collect, store, distribute, and apply digital content and knowledge Knowledge work systems (KWS) Specialized systems built for engineers, scientists, other knowledge workers charged with discovering and creating new knowledge Intelligent techniques Diverse group of techniques such as data mining used for various goals: discovering knowledge, distilling knowledge, discovering optimal solutions Management Information Systems Chapter 11 Managing Knowledge The Knowledge Management Landscape

16. 11.16 © 2010 by Prentice Hall Major Types of Knowledge Management Systems Figure 11-3 There are three major categories of knowledge management systems, and each can be broken down further into more specialized types of knowledge management systems. Management Information Systems Chapter 11 Managing Knowledge The Knowledge Management Landscape

17. 11.17 © 2010 by Prentice Hall Three major types of knowledge in enterprise Structured documents Reports, presentations Formal rules Semistructured documents E-mails, videos Unstructured, tacit knowledge 80% of an organization’s business content is semistructured or unstructured Management Information Systems Chapter 11 Managing Knowledge Enterprise-Wide Knowledge Management Systems

18. 11.18 © 2010 by Prentice Hall Enterprise-wide content management systems Help capture, store, retrieve, distribute, preserve Documents, reports, best practices Semistructured knowledge (e-mails) Bring in external sources News feeds, research Tools for communication and collaboration Management Information Systems Chapter 11 Managing Knowledge Enterprise-Wide Knowledge Management Systems

19. 11.19 © 2010 by Prentice Hall An Enterprise Content Management System Figure 11-4 An enterprise content management system has capabilities for classifying, organizing, and managing structured and semistructured knowledge and making it available throughout the enterprise Management Information Systems Chapter 11 Managing Knowledge Enterprise-Wide Knowledge Management Systems

20. 11.20 © 2010 by Prentice Hall Enterprise-wide content management systems Key problem – Developing taxonomy Knowledge objects must be tagged with categories for retrieval Digital asset management systems Specialized content management systems for classifying, storing, managing unstructured digital data Photographs, graphics, video, audio Management Information Systems Chapter 11 Managing Knowledge Enterprise-Wide Knowledge Management Systems

21. 11.21 © 2010 by Prentice Hall Knowledge network systems Provide online directory of corporate experts in well-defined knowledge domains Use communication technologies to make it easy for employees to find appropriate expert in a company May systematize solutions developed by experts and store them in knowledge database Best-practices Frequently asked questions (FAQ) repository Management Information Systems Chapter 11 Managing Knowledge Enterprise-Wide Knowledge Management Systems

22. 11.22 © 2010 by Prentice Hall An Enterprise Knowledge Network System Figure 11-5 A knowledge network maintains a database of firm experts, as well as accepted solutions to known problems, and then facilitates the communication between employees looking for knowledge and experts who have that knowledge. Solutions created in this communication are then added to a database of solutions in the form of FAQs, best practices, or other documents. Management Information Systems Chapter 11 Managing Knowledge Enterprise-Wide Knowledge Management Systems

23. 11.23 © 2010 by Prentice Hall Major knowledge management system vendors include powerful portal and collaboration technologies Portal technologies: Access to external information News feeds, research Access to internal knowledge resources Collaboration tools E-mail Discussion groups Blogs Wikis Social bookmarking Management Information Systems Chapter 11 Managing Knowledge Enterprise-Wide Knowledge Management Systems

24. 11.24 © 2010 by Prentice Hall Learning management systems Provide tools for management, delivery, tracking, and assessment of various types of employee learning and training Support multiple modes of learning CD-ROM, Web-based classes, online forums, live instruction, etc. Automates selection and administration of courses Assembles and delivers learning content Measures learning effectiveness Management Information Systems Chapter 11 Managing Knowledge Enterprise-Wide Knowledge Management Systems

25. 11.25 © 2010 by Prentice Hall Knowledge Work Systems Knowledge work systems Systems for knowledge workers to help create new knowledge and ensure that knowledge is properly integrated into business Knowledge workers Researchers, designers, architects, scientists, and engineers who create knowledge and information for the organization Three key roles: Keeping organization current in knowledge Serving as internal consultants regarding their areas of expertise Acting as change agents, evaluating, initiating, and promoting change projects Management Information Systems Chapter 11 Managing Knowledge

26. 11.26 © 2010 by Prentice Hall Requirements of knowledge work systems Substantial computing power for graphics, complex calculations Powerful graphics, and analytical tools Communications and document management capabilities Access to external databases User-friendly interfaces Optimized for tasks to be performed (design engineering, financial analysis) Management Information Systems Chapter 11 Managing Knowledge Knowledge Work Systems

27. 11.27 © 2010 by Prentice Hall Requirements of Knowledge Work Systems Figure 11-6 Knowledge work systems require strong links to external knowledge bases in addition to specialized hardware and software. Management Information Systems Chapter 11 Managing Knowledge Knowledge Work Systems

28. 11.28 © 2010 by Prentice Hall Examples of knowledge work systems CAD (computer-aided design): Automates creation and revision of engineering or architectural designs, using computers and sophisticated graphics software Virtual reality systems: Software and special hardware to simulate real-life environments E.g. 3-D medical modeling for surgeons VRML: Specifications for interactive, 3D modeling over Internet Investment workstations: Streamline investment process and consolidate internal, external data for brokers, traders, portfolio managers Management Information Systems Chapter 11 Managing Knowledge Knowledge Work Systems

29. 11.29 © 2010 by Prentice Hall Intelligent techniques: Used to capture individual and collective knowledge and to extend knowledge base To capture tacit knowledge: Expert systems, case-based reasoning, fuzzy logic Knowledge discovery: Neural networks and data mining Generating solutions to complex problems: Genetic algorithms Automating tasks: Intelligent agents Artificial intelligence (AI) technology: Computer-based systems that emulate human behavior Intelligent Techniques Management Information Systems Chapter 11 Managing Knowledge

30. 11.30 © 2010 by Prentice Hall Expert systems: Capture tacit knowledge in very specific and limited domain of human expertise Capture knowledge of skilled employees as set of rules in software system that can be used by others in organization Typically perform limited tasks that may take a few minutes or hours, e.g.: Diagnosing malfunctioning machine Determining whether to grant credit for loan Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

31. 11.31 © 2010 by Prentice Hall Rules in an Expert System Figure 11-7 An expert system contains a number of rules to be followed. The rules are interconnected; the number of outcomes is known in advance and is limited; there are multiple paths to the same outcome; and the system can consider multiple rules at a single time. The rules illustrated are for simple credit- granting expert systems. Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

32. 11.32 © 2010 by Prentice Hall How expert systems work Knowledge base: Set of hundreds or thousands of rules Inference engine: Strategy used to search knowledge base Forward chaining: Inference engine begins with information entered by user and searches knowledge base to arrive at conclusion Backward chaining: Begins with hypothesis and asks user questions until hypothesis is confirmed or disproved Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

33. 11.33 © 2010 by Prentice Hall Inference Engines in Expert Systems Figure 11-8 An inference engine works by searching through the rules and “firing” those rules that are triggered by facts gathered and entered by the user. A collection of rules is similar to a series of nested IF statements in a traditional software system; however the magnitude of the statements and degree of nesting are much greater in an expert system Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

34. 11.34 © 2010 by Prentice Hall Successful expert systems Countrywide Funding Corporation in Pasadena, California, uses expert system to improve decisions about granting loans Con-Way Transportation built expert system to automate and optimize planning of overnight shipment routes for nationwide freight-trucking business Most expert systems deal with problems of classification Have relatively few alternative outcomes Possible outcomes are known in advance Many expert systems require large, lengthy, and expensive development and maintenance efforts Hiring or training more experts may be less expensive Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

35. 11.35 © 2010 by Prentice Hall Case-based reasoning (CBR) Descriptions of past experiences of human specialists, represented as cases, stored in knowledge base System searches for stored cases with problem characteristics similar to new one, finds closest fit, and applies solutions of old case to new case Successful and unsuccessful applications are grouped with case Stores organizational intelligence: Knowledge base is continuously expanded and refined by users CBR found in Medical diagnostic systems Customer support Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

36. 11.36 © 2010 by Prentice Hall How Case-Based Reasoning Works Figure 11-9 Case-based reasoning represents knowledge as a database of past cases and their solutions. The system uses a six-step process to generate solutions to new problems encountered by the user. Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

37. 11.37 © 2010 by Prentice Hall Fuzzy logic systems Rule-based technology that represents imprecision used in linguistic categories (e.g., “cold,” “cool”) that represent range of values Describe a particular phenomenon or process linguistically and then represent that description in a small number of flexible rules Provides solutions to problems requiring expertise that is difficult to represent with IF-THEN rules Autofocus in cameras Detecting possible medical fraud Sendai’s subway system use of fuzzy logic controls to accelerate smoothly Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

38. 11.38 © 2010 by Prentice Hall Fuzzy Logic for Temperature Control Figure 11-10 The membership functions for the input called temperature are in the logic of the thermostat to control the room temperature. Membership functions help translate linguistic expressions such as warm into numbers that the computer can manipulate. Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

39. 11.39 © 2010 by Prentice Hall Neural networks Find patterns and relationships in massive amounts of data that are too complicated for human to analyze “Learn” patterns by searching for relationships, building models, and correcting over and over again model’s own mistakes Humans “train” network by feeding it data inputs for which outputs are known, to help neural network learn solution by example Used in medicine, science, and business for problems in pattern classification, prediction, financial analysis, and control and optimization Machine learning: Related AI technology allowing computers to learn by extracting information using computation and statistical methods Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

40. 11.40 © 2010 by Prentice Hall How a Neural Network Works Figure 11-11 A neural network uses rules it “learns” from patterns in data to construct a hidden layer of logic. The hidden layer then processes inputs, classifying them based on the experience of the model. In this example, the neural network has been trained to distinguish between valid and fraudulent credit card purchases. Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

41. 11.41 © 2010 by Prentice Hall Genetic algorithms Useful for finding optimal solution for specific problem by examining very large number of possible solutions for that problem Conceptually based on process of evolution Search among solution variables by changing and reorganizing component parts using processes such as inheritance, mutation, and selection Used in optimization problems (minimization of costs, efficient scheduling, optimal jet engine design) in which hundreds or thousands of variables exist Able to evaluate many solution alternatives quickly Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

42. 11.42 © 2010 by Prentice Hall The Components of a Genetic Algorithm Figure 11-12 This example illustrates an initial population of “chromosomes,” each representing a different solution. The genetic algorithm uses an iterative process to refine the initial solutions so that the better ones, those with the higher fitness, are more likely to emerge as the best solution. Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

43. 11.43 © 2010 by Prentice Hall Hybrid AI systems Genetic algorithms, fuzzy logic, neural networks, and expert systems integrated into single application to take advantage of best features of each E.g., Matsushita “neurofuzzy” washing machine that combines fuzzy logic with neural networks Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

44. 11.44 © 2010 by Prentice Hall Intelligent agents Work in background to carry out specific, repetitive, and predictable tasks for user, process, or software application Use limited built-in or learned knowledge base to accomplish tasks or make decisions on user’s behalf Deleting junk e-mail Finding cheapest airfare Agent-based modeling applications: Systems of autonomous agents Model behavior of consumers, stock markets, and supply chains; used to predict spread of epidemics Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

45. 11.45 © 2010 by Prentice Hall Intelligent Agents in P&G’s Supply Chain Network Figure 11-13 Intelligent agents are helping Procter & Gamble shorten the replenishment cycles for products such as a box of Tide. Management Information Systems Chapter 11 Managing Knowledge Intelligent Techniques

46. 11.46 © 2010 by Prentice Hall Enhancing Decision Making

47. 11.47 © 2010 by Prentice Hall Decision Making and Information Systems Business value of improved decision making Improving hundreds of thousands of “small” decisions adds up to large annual value for the business Types of decisions: Unstructured: Decision maker must provide judgment, evaluation, and insight to solve problem Structured: Repetitive and routine; involve definite procedure for handling so they do not have to be treated each time as new Semistructured: Only part of problem has clear-cut answer provided by accepted procedure Management Information Systems Chapter 12 Enhancing Decision Making

48. 11.48 © 2010 by Prentice Hall Decision Making and Information Systems Senior managers: Make many unstructured decisions E.g., Should we enter a new market? Middle managers: Make more structured decisions but these may include unstructured components E.g., Why is order fulfillment report showing decline in Minneapolis? Operational managers, rank and file employees Make more structured decisions E.g., Does customer meet criteria for credit? Management Information Systems Chapter 12 Enhancing Decision Making

49. 11.49 © 2010 by Prentice Hall Information Requirements of Key Decision-Making Groups in a Firm Figure 12-1 Senior managers, middle managers, operational managers, and employees have different types of decisions and information requirements. Decision Making and Information Systems Management Information Systems Chapter 12 Enhancing Decision Making

50. 11.50 © 2010 by Prentice Hall Decision Making and Information Systems Four stages of decision making 1.Intelligence Discovering, identifying, and understanding the problems occurring in the organization 2.Design Identifying and exploring solutions to the problem 3.Choice Choosing among solution alternatives 4.Implementation Making chosen alternative work and continuing to monitor how well solution is working Management Information Systems Chapter 12 Enhancing Decision Making

51. 11.51 © 2010 by Prentice Hall Stages in Decision Making Figure 12-2 The decision-making process is broken down into four stages. Decision Making and Information Systems Management Information Systems Chapter 12 Enhancing Decision Making

52. 11.52 © 2010 by Prentice Hall Decision Making and Information Systems Information systems can only assist in some of the roles played by managers Classical model of management Five functions of managers Planning, organizing, coordinating, deciding, and controlling More contemporary behavioral models Actual behavior of managers appears to be less systematic, more informal, less reflective, more reactive, and less well organized than in classical model Mintzberg’s behavioral model of managers defines 10 managerial roles falling into 3 categories Management Information Systems Chapter 12 Enhancing Decision Making

53. 11.53 © 2010 by Prentice Hall Decision Making and Information Systems Mintzberg’s 10 managerial roles Interpersonal roles: Figurehead Leader Liaison Informational roles:Nerve center Disseminator Spokesperson Decisional roles:Entrepreneur Disturbance handler Resource allocator Negotiator Management Information Systems Chapter 12 Enhancing Decision Making

54. 11.54 © 2010 by Prentice Hall Decision Making and Information Systems Three main reasons why investments in information technology do not always produce positive results 1.Information quality High-quality decisions require high-quality information 2.Management filters Managers have selective attention and have variety of biases that reject information that does not conform to prior conceptions 3.Organizational culture Strong forces within organizations resist making decisions calling for major change Management Information Systems Chapter 12 Enhancing Decision Making

55. 11.55 © 2010 by Prentice Hall Systems for Decision Support Four kinds of systems for decision support Management information systems (MIS) Decision support systems (DSS) Executive support systems (ESS) Group decision support systems (GDSS) Management Information Systems Chapter 12 Enhancing Decision Making

56. 11.56 © 2010 by Prentice Hall Systems for Decision Support Management information systems (MIS) Help managers monitor and control business by providing information on firm’s performance and address structured problems Typically produce fixed, regularly scheduled reports based on data from TPS E.g., exception reports: Highlighting exceptional conditions, such as sales quotas below anticipated level E.g., California Pizza Kitchen MIS For each restaurant, compares amount of ingredients used per ordered menu item to predefined portion measurements and identifies restaurants with out-of-line portions Management Information Systems Chapter 12 Enhancing Decision Making

57. 11.57 © 2010 by Prentice Hall Systems for Decision Support Decision-support systems (DSS) Support unstructured and semistructured decisions Model-driven DSS Earliest DSS were heavily model-driven E.g., voyage-estimating DSS (Chapter 2) Data-driven DSS Some contemporary DSS are data-driven Use OLAP and data mining to analyze large pools of data E.g., business intelligence applications (Chapter 6) Management Information Systems Chapter 12 Enhancing Decision Making

58. 11.58 © 2010 by Prentice Hall Systems for Decision Support Components of DSS Database Used for query and analysis Current or historical data from number of applications or groups May be small database or large data warehouse User interface Often a Web interface Software system With models, data mining, other analytical tools Management Information Systems Chapter 12 Enhancing Decision Making

59. 11.59 © 2010 by Prentice Hall Overview of a Decision-Support System Figure 12-3 The main components of the DSS are the DSS database, the user interface, and the DSS software system. The DSS database may be a small database residing on a PC or a large data warehouse. Systems for Decision Support Management Information Systems Chapter 12 Enhancing Decision Making

60. 11.60 © 2010 by Prentice Hall Systems for Decision Support Model: Abstract representation that illustrates components or relationships of phenomenon; may be physical, mathematical, or verbal model Statistical models Optimization models Forecasting models Sensitivity analysis models Management Information Systems Chapter 12 Enhancing Decision Making

61. 11.61 © 2010 by Prentice Hall Sensitivity Analysis Figure 12-4 This table displays the results of a sensitivity analysis of the effect of changing the sales price of a necktie and the cost per unit on the product’s break-even point. It answers the question, “What happens to the break-even point if the sales price and the cost to make each unit increase or decrease?” Systems for Decision Support Management Information Systems Chapter 12 Enhancing Decision Making

62. 11.62 © 2010 by Prentice Hall Systems for Decision Support Using spreadsheet pivot tables to support decision making Records of online transactions can be analyzed using Excel Where do most customers come from? Where are average purchases higher? What time of day do people buy? What kinds of ads work best? Pivot table: Categorizes and summarizes data very quickly Displays two or more dimensions of data in a convenient format Management Information Systems Chapter 12 Enhancing Decision Making

63. 11.63 © 2010 by Prentice Hall Sample List of Transactions for Online Management Training Figure 12-5 This list shows a portion of the order transactions for Online Management Training Inc. (OMT Inc.) on October 28, 2008. Systems for Decision Support Management Information Systems Chapter 12 Enhancing Decision Making

64. 11.64 © 2010 by Prentice Hall A Pivot Table that Determines Regional Distribution of Customers Figure 12-6 This PivotTable report was created using Excel 2007 to quickly produce a table showing the relationship between region and number of customers Systems for Decision Support Management Information Systems Chapter 12 Enhancing Decision Making

65. 11.65 © 2010 by Prentice Hall A Pivot Table that Examines Customer Regional Distribution and Advertising Source Figure 12-7 In this pivot table, we are able to examine where customers come from in terms of region and advertising source. It appears nearly 30 percent of the customers respond to e-mail campaigns, and there are some regional variations Systems for Decision Support Management Information Systems Chapter 12 Enhancing Decision Making

66. 11.66 © 2010 by Prentice Hall Systems for Decision Support Data visualization tools: Help users see patterns and relationships in large amounts of data that would be difficult to discern if data were presented as traditional lists of text Geographic information systems (GIS): Category of DSS that use data visualization technology to analyze and display data in form of digitized maps Used for decisions that require knowledge about geographic distribution of people or other resources, e.g.: Helping local governments calculate emergency response times to natural disasters Help retail chains identify profitable new store locations Management Information Systems Chapter 12 Enhancing Decision Making

67. 11.67 © 2010 by Prentice Hall South Carolina used a GIS-based program called HAZUS to estimate and map the regional damage and losses resulting from an earthquake of a given location and intensity. HAZUS estimates the degree and geographic extent of earthquake damage across the state based on inputs of building use, type, and construction materials. The GIS helps the state plan for natural hazards mitigation and response. Systems for Decision Support Management Information Systems Chapter 12 Enhancing Decision Making

68. 11.68 © 2010 by Prentice Hall Systems for Decision Support Web-based customer decision-support systems (CDSS): Support decision-making process of existing or potential customer Use Web information resources and capabilities for interactivity and personalization to help users select products and services E.g., search engines, intelligent agents, online catalogs, Web directories, newsgroup discussions, other tools Automobile companies that use CDSS to allow Web site visitors to configure desired car Financial services companies with Web-based asset- management tools for customers Management Information Systems Chapter 12 Enhancing Decision Making

69. 11.69 © 2010 by Prentice Hall Group decision support systems (GDSS) Interactive system to facilitate solution of unstructured problems by group of decision makers Hardware – computer and networking hardware, overhead projectors, display screens GDSS software collects, documents, ranks, edits and stores participant ideas, responses May require facilitator and staff Enables increasing meeting size and increasing productivity Promotes collaborative atmosphere, guaranteeing anonymity Follow structured methods for organizing and evaluating ideas and preserving meeting results Management Information Systems Chapter 12 Enhancing Decision Making Systems for Decision Support

70. 11.70 © 2010 by Prentice Hall Executive Support Systems (ESS) Executive support systems (ESS) Designed to help executives focus on important performance indications Balanced scorecard method: Measures outcomes on four dimensions: Financial Business process Customer Learning & growth Key performance indicators (KPIs) measure each dimension In developing an ESS, first concern is for senior executives and consultants to develop scorecard and then to automate flow of information for each KPI Management Information Systems Chapter 12 Enhancing Decision Making

71. 11.71 © 2010 by Prentice Hall Executive Support Systems (ESS) Role of ESS in the firm Used by both executives and subordinates Drill-down capability: Ability to move from summary information to finer levels of detail Integrate data from different functional systems for firmwide view Incorporate external data, e.g. stock market news, competitor information, industry trends, legislative action Include tools for modeling and analysis Primarily for status, comparison information about performance Management Information Systems Chapter 12 Enhancing Decision Making

72. 11.72 © 2010 by Prentice Hall Executive Support Systems (ESS) Business value of executive support systems Enables executive to review more data in less time with greater clarity than paper-based systems Needed actions identified and carried out earlier Improves management performance Increases upper management’s span of control Also enables decision making to be decentralized and take place at lower operating levels Increases executives’ ability to monitor activities of lower units reporting to them Management Information Systems Chapter 12 Enhancing Decision Making

73. 11.73 © 2010 by Prentice Hall Executive Support Systems (ESS) National Life Markets life insurance, health insurance, and retirement/investment products executive information system Executive information system: Allows senior managers to access corporate databases through Web interface Shows premium dollars by salesperson Authorized users can drill down into these data to see product, agent, and client for each sale Data can be examined by region, by product, and by broker, and accessed for monthly, quarterly, and annual time periods Management Information Systems Chapter 12 Enhancing Decision Making

74. 11.74 © 2010 by Prentice Hall Executive Support Systems (ESS) Rohm & Haas Chemical and specialty materials firm with 13 business units and over 300 information systems Web-based digital dashboards Display on single screen all of the critical measurements for monitoring company Use KPIs (e.g. gross profit) that managers can drill down on for details (e.g. sales, cost of sales) Customized for multiple levels of management Executive Dashboard – for executives, includes all major KPIs The Pulse – includes three KPIs Reporting and Analysis Toolkit – set of analysis tools Analysis Accelerator – standard sales, gross-profit analyses Management Information Systems Chapter 12 Enhancing Decision Making

75. 11.75 © 2010 by Prentice Hall Executive Support Systems (ESS) Pharmacia Corporation: Global pharmaceutical firm Spends $2 million on research and development annually Balanced scorecard shows: Performance of U.S. or European clinical operations in relation to corporate objectives Attrition rate of new compounds under study Number of patents in clinical trials How funds allocated for research are being spent Management Information Systems Chapter 12 Enhancing Decision Making