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Decision Support Systems

Decision Support Systems

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Decision Support Systems

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  1. Decision Support Systems Yong Choi School of Business CSU, Bakersfield

  2. Type of Decision-makings • Structured (Programmed) • routine & repetitive, predictable problems • standard solutions exist • Unstructured (Nonprogrammed) • non-routine, unpredictable, “fuzzy” complex problems • no cut-and-dried solutions • Semistructured (Programmed + Nonprogrammed) • non-routine, predictable, • Require a combination of standard solution procedures and individual judgement

  3. Stages of Decision Making • Stage 1: Intelligence • identify the problems/opportunities and then, collect data or information • Stage 2: Design • analyze/develop the possible solutions for the feasibility • GO back to stage 1 if there is insufficient data.

  4. Stages of Decision Making • Stage 3: Choice • Choose one alternative • Go back to stage 1 or 2 if there are no satisfactory solutions. • Stage 4: Implementation • Implement the selected alternative • Failure of implementation  go back to stage 1 or 2 or 3 Ex) Buying a new car

  5. Transaction Processing Systems (TPS) • Developed in the early1960s • Serve the operational management level • Performing and recording daily routine and repetitive transactions • Primary focus: structured decision-makings

  6. Transaction Processing Systems • Lifeblood of an organization • Provide summarized and organized data in the accounting and finance areas • Account receivable and payable • Sales transactions • Payroll

  7. Management Information Systems • Developed in the 1960s • Intended to serve the operational or middle management level • Summary and exception reports • monthly production reports • Quarterly travel expense reports • Difference between expected sales and actual sales of a particular product • Primary focus: fairly structured decision-makings

  8. Decision Support System (DSS) • An interactive computer-based system that helps decision makers in the solution of semi-structured and unstructured problems. • Developed in the early 1970s • Originally, designed to serve the middle management • Primary focus: semi-structured and unstructured decision-makings

  9. Decision Support System • What is a DSS? • A highly flexible and interactive IT system that is designed to support decision making when the problem is not structured. Provide alternative-analysis report • DSS Examples • investment portfolios • Plant expansion • See text book for detail examples

  10. Three Fundamental Components of a DSS Model management component – consists of both the DSS models and the model management system Data management component – stores and maintains the information that you want your DSS to use User interface management component – allows you to communicate with the DSS

  11. Three Fundamental Components

  12. Three Fundamental Components of DSS • Model management component • Data management component • User interface management component • See the online lecture or the textbook for details

  13. Model driven DSS • Primarily stand alone systems • isolated from major organization's information systems • Use models (LP, Simulation) • Sensitivity analysis as a main technique • What-If analysis • Goal Seek Analysis

  14. What-if analysis vs.Goal-seek analysis • Attempt to check the impact of a change in the assumptions (input data) on the proposed solution • What will happen to the market share if the advertising budget increases by 5 % or 10%? • Attempt to find the value of the inputs necessary to achieve a desired level of output • Use “backward” solution approach • A DSS solution yielded a profit of $2M • What will be the necessary sales volume to generate a profit of $2.2M?

  15. Tools for Model Driven DSS • Linear Programming • Lindo • Gindo • Spreadsheet Software • Excel • Lotus 1-2-3 • Quattro Pro

  16. Data Driven DSS • Many current and the newest DSS • Data-driven DSS with On-line Analytical Processing (OLAP) provide the highest level of functionality and decision support that is linked to analysis of large collections of historical data.  • Discover previously unknown patterns by analyzing large pools of data from data warehouse • Data mining as main technique

  17. Data Mining • Help managers to find hidden patterns and relationships in large databases to predict future behavior • “If a house is purchased, then new refrigerator will be purchased within two weeks 65% of the time.”

  18. Model Driven DSS vs.Data Driven DSS A Model Driven DSS uses various models such as statistical model, simulation model or financial modelfor decision makings. So, decisions are based on models. A Data Driven DSS emphasizes access to and manipulation of a time-series of internal company data and sometimes external data to aid decision makings. So, decisions are based on analyzed data.

  19. Web-based DSS for customers • Evaluate and compare real estate prices • Zillow.com: 10402 Loughton Ave. 93311 • Evaluate alternative investment in mortgage portfolios • fidelity.com (on-line investor center) • Evaluate and compare air fares • travelocity.com • Evaluate and compare various automobile prices • aotubytel.com

  20. Executive Information Systems ORExecutive Support Systems • Developed in the late 1980s • Serve the senior management level • Designed mainly to monitor organization’s performance and address decision makings quickly and accurately • Very user-friendly, supported by graphics • Drill-down capability • EIS drill-down interface design

  21. The Need of EIS • Need for more timely and accurate information for better decision makings • Need to access internal/external databases to detect environmental changes • Need to be more proactive due to intensive competition • Gain computer literacy