chap 10 IS 3003

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  1. Decisions made at all levels of the firm
    routine ,common, numerous

    Although value of improving any single decision may be small, improving hundreds of thousands of “small” decisions adds up to large annual value for the business
  2. Types of decisions
    • structured-
    • -Repetitive and routine

    •Involve definite procedure for handling them so do not have to be treated as new

    • •Also called “programmable” decisions – easy to automate
    • semistructured-
    • •Not as well defined by standard procedures

    •Only part of problem has clear-cut answer provided by accepted procedure

    •Also may include a human or subjective element

    • unstructured-.
    • •Novel, unique, important, nonroutine

    •No well-understood or agreed-upon procedure for making them

    •Decision maker must provide judgment, experience, intuition to solve problem
  3. Information Requirments of Key decision making groups in a firm
    senior management-unstructured

    middle management-semi structured

    Operational management-structured.
  4. Simons decision making process
    1. Intelligence-discovering, identifying,understanding the occurring in the orginization. why is there a problem.

    2. Design-identifying and exploring various solutions

    3.Choice-choosing among solution alternatives

    4.Implementation-making chosen alternative work and monitoring how well solution is working.
  5. Quality Dimensions of Decisions
    • 1. Accuracy
    • 2. Comprehensiveness
    • 3. Fairness
    • 4. Speed
    • 5. Coherence
    • 6. Due Process

  6. Systems and Technologies for Supporting Decisions
    •Management information systems (MIS)

    •Decision-support systems (DSS)

    •Executive support systems (ESS)

    •Group-decision support systems (GDSS)

    •Intelligent techniques
  7. Data driven DSS

    Spreadsheet tables can answer multiple dimensions of questions Time of day and average purchase

    •Payment type and average purchase

    •Payment type, region, and source

    •Pivot table

    •Displays two or more dimensions of data in a convenient format
    • Model-driven DSS

    •Spreadsheets can be used to support Sensitivity Analysis

    • •What-if analysis

    •Changing variables in formulas to automatically recalculate and observe the change in outcome

    •Scenario Manager

    • •Goal-seeking analysis

    •Goal Seek

  8. Web-Based Customer Decision-Support Systems (CDSS)
    •Support customers in the decision-making process by providing on-line access to various databases along data analysis SW

    •Include: search engines, intelligent agents, online catalogs, Web directories, newsgroups, e-mail, and so on

    •Many firms have customer Web sites where all the information, models, or other analytical tools for evaluating alternatives are concentrated in one location.

    •E.g., T. Rowe Price online tools, guides for college planning, retirement planning, estate planning, etc
  9. Data Visualization and Geographic Information Systems (GIS)
    •Data visualization tools:

    •Present data in graphical form to help users see patterns and relationships in large quantities of data.

    •Geographic information systems (GIS):

    •Use data visualization technology to analyze and display data in the form of digitized maps.

    •Support decisions that require knowledge about the geographic distribution of people or other resources.
  10. ESS

    executive support systems
    • •General Characteristics/Features
    • –Tailored to individual executives’ needs

    –Easy to use

    –Provide data related to the organization’s “critical success factors”

    –Sophisticated graphics

    –Provide access to both internal and external data


    –Support for unstructured problems

    –Supports consolidation, drill down, and slice and dice capabilities

    • Most ESSs utilize a Digital dashboard

    •Digital dashboard

    –Integrates information from multiple sources and presents it in a unified, understandable format

    –Often includes charts and graphs

    –Presents key performance indicators and critical measurements to support executive decision making

    –Offers up-to-the-minute snapshots of information and assist executive decision-makers in identifying trends and potential problems
  11. systems and technologies for supporting decisions
    • MIS- Management Information Systems
    • Help managers monitor and control a business by providing information on the firm’s performance.

    •Regularly scheduled reports based on data from TPS – prespecified content, format and time period

    •E.g., summary of monthly or annual sales for each of the major sales territories of a company

    •Exception reports – produced when a situation is unusual or requires management action

    •Trigger points – the parameters or criteria for initiating an exception report

    • DSS- Decision support systems
    • –Interactive information system consisting of hardware, software, data, and models (mathematical and statistical) designed to assist decision makers in an organization

    –Support semistructured and unstructured tasks

    –Support decision makers at all organizational levels

    –Incorporate the human element as well as hardware and software

    –Use both internal and external data

    –Include mathematical and statistical models for complex analysis

    • •Earliest DSS were model-driven:

    •A model of the situation/problem is developed, various input factors are changed, and the output changes are measured.

    • •Data-driven DSS:

    •Use OLAP and data mining to analyze large pools of data in major corporate systems

    • •DSS database: collection of current or historical data from a number of applications or groups

    •DSS user interface – permits easy interaction between users of the DSS and the DSS software program and analysis tools

    •DSS software system

    •OLAP tools

    •Data mining tools

    •Mathematical and analytical models

    • Model: a simplified representation or abstraction of reality that illustrates the components or relationships of a phenomenon

    •Statistical models help establish relationships.

    •E.g., relating product sales to differences in age, income, or other factors

    •Optimization models – determine optimal resource allocation to maximize or minimize specified variables

    •Forecasting models used to forecast or predict future performance or behavior

    • Sensitivity analysis models

    •What-if analysis – makes a hypothetical change to the data and observes the impact on the results; “what will happen”

    •Goal seeking analysis – setting a target value or “goal” to achieve, then determining “how to get it”

    • ESS-Executive Support System
    • •Help senior managers address strategic issues and long-term trends

    •Address non-routine decision making (semi-structured or unstructured)

    •Draw summarized information from MIS, DSS, and data from external sources

    •Typically use portal with Web interface to present content

    •Used to monitor organizational performance, track activities of competitors, identify changing market conditions, spot problems, identify opportunities, and forecast trends

    • Most ESSs offering the following capabilities
    • •Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information

    • Drill-down – enables users to get details, and details of details, of information; provide details behind the summary values on a key-indicator or exception report

    • Slice and Dice – looks at information from different perspectives

    • GDSS-Group decision support system
    • •Interactive, computer-based systems that facilitate solving of unstructured problems by set of decision makers.

    •Used in conference rooms with special hardware and software for collecting, ranking, storing ideas and decisions.

    •Promote a collaborative atmosphere by guaranteeing contributors’ anonymity.

    •Support increased meeting sizes with increased productivity.
  12. Expert System Challenges
    •Limited to relatively narrow problems

    •Cannot readily deal with “mixed” knowledge

    •Cannot update its own knowledge/learn on its own

    •Has no “common sense” – doesn’t know when to break the rules

    •Difficult to incorporate human judgment, experience, intuition

    •May have high development & maintenance costs

    •May raise legal and ethical concerns

    •Over time may weaken the human expertise in the organization
  13. expert sytems advantages/benefits
    •Never become distracted, forgetful, or tired

    •Duplicates and preserves the expertise of scarce experts

    •Preserve the expertise of employees who are retiring or leaving an organization

    •Can provide portable expertise – more accessible

    •Creates consistency in decision making

    •Can often outperform a human expert

    •Improves the decision-making skills of nonexperts

    •Can be used to train others to become more knowledgeable in that area of expertise
  14. criteria for NOT using expert systems/ when not to build them
    •very few rules

    •Too many rules

    •Problems are in areas that are too wide and shallow

    •Well-structured numerical problems are involved

    •Disagreement among experts

    •Problems are solved better by human experts
  15. criteria for using expert systems/ when to build them
    •Human expertise is needed but one expert can’t investigate all the dimensions of a problem

    •Scarcity of human experts

    •Knowledge can be represented as rules or heuristics & subject domain is limited enough to capture

    •Decision or task has already been handled successfully by human experts

    •Decision or task requires consistency and standardization

    •High payoff involved
  16. components of expert systems
    •Knowledge acquisition facility – the part of the Expert System SW that provides for acquiring and adding new rules and facts

    •Knowledge base - collection of data, rules, procedures, and relationships that must be followed to achieve value or the proper outcome

    –Specialized knowledge in a specific field

    –Facts about the subject/domain

    –Heuristics describing the reasoning procedures by which an expert reaches a conclusion

    •Knowledge acquisition facility – the part of the Expert System SW that provides for acquiring and adding new rules and facts

    •Knowledge base - collection of data, rules, procedures, and relationships that must be followed to achieve value or the proper outcome

    –Specialized knowledge in a specific field

    –Facts about the subject/domain

    –Heuristics describing the reasoning procedures by which an expert reaches a conclusion

    • Organizing the knowledge in a Knowledge Base:


    Rule – conditional statement that links given conditions to actions or outcomes

    –If-then statements – rules that suggest certain conditions

    –May be combined with probabilities

    •Frames – store conditions or facts about the topic

    •Scripts – describe a sequence of events

    •Knowledge base management system – used to keep the knowledge base updated with changes to facts, figures, and rules; works with the Knowledge Acquisition Facility

    •Explanation facility – performs tasks similar to what a human expert does by explaining to end users how recommendations are derived

    •User Interface – provides user-friendly access to the expert system

    • •Inference engine

    –The part of the Expert system that seeks information and relationships from the user and the knowledge base and then provides answers, predictions and suggestions the way a human expert would

    –Provides the “reasoning” or “thinking”

    –Combines the facts of the situation at hand with the knowledge in the knowledge base to come up with an answer

    –Searches the rules or other forms of knowledge in the knowledge base and “fires” those that are triggered by facts entered by the user

    Reasoning Strategies used by the Inference Engine:

    Forward chaining – process of starting with the facts (data) and working forward to the conclusion

    –Is “data-driven”

    –Starts with information from the user, then searches the knowledge base for rules, relationships that are relevant

    –Series of “If-Then-Else” conditions

    Backward chaining – process of starting with an answer or conclusion and working backward to the supporting facts

    –Is “goal-driven”

    –Attempts to justify the result or conclusion by determining if the facts in the situation would support the conclusion

    –To achieve this goal/outcome, what conditions must be met?
  17. expert systems
    –computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems

    –used to make decisions usually made by more experienced employees or an expert in the field

    –Replicate/mimic human expertise in a field to solve a well-defined problem

    –A knowledge-based information system that uses its knowledge about a specific complex application area to act as an expert consultant to end users

    –Makes human-like inferences (logical conclusion, a conclusion based on reasoning) about knowledge contained in a special knowledge base

    –Useful for dealing with problems of classification in which there are relatively few alternative outcomes and in which these possible outcomes are known in advance
  18. Intelligent techniques for enhancing decision making
    • •Many based on artificial intelligence (AI)

    •Computer-based systems (hardware and software) that attempt to emulate human behavior and thought patterns

    • •Include:

    •Expert systems

    •Case-based reasoning

    •Fuzzy logic

    •Neural networks

    •Genetic algorithms

    •Intelligent agents
  19. Case based reasoning
    knowledge and past experiences of human specialists are represented as cases and stored in data base for later retrieval.

    •Knowledge and past experiences of human specialists are represented as cases (situations, events) and stored in a database for later retrieval.

    •System searches for stored cases with problem characteristics similar to new one, finds closest fit, and applies solutions of old case to new case.

    •System can query the user for clarification or more information in order to find a match

    •Successful and unsuccessful applications are tagged and linked in database.

    •Used in medical diagnostic systems, customer support.
  20. Fuzzy Logic
    •A mathematical method of handling imprecise or subjective information

    •Allows shades of gray - does not require everything to be simple black or white, yes/no, T/F

    •Allows the system to make “educated guesses” based on the likelihood or probability that they are right

    •Allows for the consideration of approximate values, incomplete or ambiguous data

    •Designed to help computers simulate vagueness and uncertainty in common situations

    •More closely resembles human reasoning

    •Provides solutions to problems requiring expertise that is difficult to represent in the form of crisp IF-THEN rules
  21. Neutral Networks
    •Attempt to emulate the way the human brain works

    •Use hardware and software that parallel the processing patterns of a biological brain – modeled after the brain’s mesh-like networks of interconnected neurons

    •Network “learns” patterns from large quantities of data by searching for relationships, building models, and correcting over and over again the model’s own mistakes.

    •Humans “train” the network by feeding it data for which the inputs produce a known set of outputs or conclusions.

    •Useful for solving complex, poorly understood problems for which large amounts of data have been collected
  22. genetic algorithims
    •Attempt to find the optimal solution for a specific problem by examining very large number of alternative solutions for that problem

    •Based on techniques inspired by evolutionary biology: inheritance, mutation, selection, and so on

    •Use adaptive procedures based on the evolutionary natural selection and survival of the fittest processes to generate increasingly better solutions to a problem

    •Used to solve complex problems that are very dynamic and complex, involving hundreds or thousands of variables or formulas
  23. Intelligent Agents
    Programs that work in the background without direct human intervention to carry out specific, repetitive, and predictable tasks for users

    •Use a limited built-in or learned knowledge base to perform rule-based processes

    •Often referred to as “bots”
    -web marketing- collect info about customers, such as items purchased, demographic information and expressed and implied prefernces.

    -virtual catalogs-display product discriptions based on customers previous experiences and prefernces.

    -shopping and information agents-help users navigate through the vast resources available on the web

    -personal agents-agents peform specific tasks for agents. remember filling our info for filling out web forms. completing email addresses.

    -data mining agents- work with a datawarehouse to detect patterns and trends and discover new info and relationships among data that isnt readily apparent.

    -monitoring and surveillance agents-track and report on equipment and network systems to detect problems or potential failures.
  25. Knowledge managment
    The processes developed for creating, storing, transferring, and applying knowledge

    •Increases the ability of organization to learn from environment and to incorporate knowledge into business processes and decision making

    •Knowing how to do things effectively and efficiently in ways that other organizations cannot duplicate is major source of profit and competitive advantage

    •Self-generated business knowledge is a strategic asset and can provide a competitive advantage

    • •Structured: structured text documents (reports, presentations, manuals, books, proposals)

    •Semistructured: e-mail, voice mail, digital pictures, bulletin-board postings, designs

    •Tacit knowledge (unstructured): knowledge residing in heads of employees, rarely written down, personal or informal knowledge

    •Tacit knowledge often represents an organization’s best practices
  26. Enterprise Wide Knowledge management systems-
    •Deal with all three types of knowledge

    •General-purpose, firm-wide systems that collect, store, distribute, and apply digital content and knowledge
    • Exterprise Content Management Systems
    • Digital Asessment Management Systems
    • Knowledge Network Systems
    • Collaboration Tools
    • Social Bookmarking
    • Learning Management Systems

    • •Enterprise content management systems

    •Have capabilities for knowledge capture, storage, retrieval, distribution and preservation

    •Have repositories for documents and best practices

    •Capabilities for collecting and organizing semistructured knowledge such as e-mail

    •Use classification schemes to organize information into meaningful categories so it can be “tagged” for easy retrieval

    • •Digital asset management systems

    •Manage unstructured digital data like photographs, graphic images, video, audio

    • •Knowledge network systems (Expertise location and management systems)

    •Used to organize tacit knowledge

    •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 firm.

    • •Collaboration tools facilitate information sharing and teamwork …

    •Social bookmarking: allows users to save their bookmarks publicly to Web pages and tag with keywords

    •Folksonomies – user-created taxonomies created for shared bookmarks and social tagging

    •Learning management systems (LMS) - provide tools for management, delivery, tracking, and assessment of various types of employee learning and training
  27. KWS
    Knowledge Work Systems
    •Specialized systems for knowledge workers

    •Requirements of knowledge work systems:

    •Specialized tools such as powerful graphics, analytical tools, and communications and document management

    •Computing power to handle sophisticated graphics or complex calculations

    •Access to external databases

    •User-friendly interfaces

    •Knowledge workstations are often designed and optimized for the specific tasks to be performed+

    • •Examples of knowledge work systems

    •Computer-aided design (CAD) systems

    •Investment workstations

    •Virtual reality systems
  28. Virtual Reality
    •Artificial Reality”, computer-simulated reality

    •Goal of virtual reality (VR):

    –Create an environment in which users can interact and participate as they do in the real world

    •VR technology

    –Uses computer-generated, three-dimensional images to create the illusion of interaction in a real-world environment

    –Allows one or more users to move and react/interact in a computer-simulated environment
    • •Visual and aural systems

    –Head Mounted Display with eye screens, position trackers to monitor location and movement

    –Stereo earphones

    • Manual control for navigation

    –Data glove, game controller, data suit with position trackers

    •Walker – input device to capture and track foot movement

    •Central coordinating processor and software system - generates and manipulates high quality graphics in real-time

    –A Haptic Interface relays the sense of touch and other physical sensations by using a glove and position tracker (or other haptic device)
    •Military flight simulations

    •Virtual medicine


    •Education and training


    –Real estate, tourism, product trials

    •Design and testing
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chap 10 IS 3003
chap 10 IS 3003
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