Decision Support Systems

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 • Accounts receivable, order entry, payroll

3. Type of Decision-makings • Unstructured (Nonprogrammed) • non-routine, unpredictable, “fuzzy” complex problems • no cut-and-dried solutions • Negotiation, Lobbying

4. Type of Decision-makings • Semistructured (Programmed + Nonprogrammed) • non-routine, predictable, • Require a combination of standard solution procedures and individual judgement • Production Scheduling, design lay-out of factory

5. 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.

6. 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

7. 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

8. 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

9. 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

10. Management Information Systems • Difference between expected sales and actual sales of a particular product • Primary focus: fairly structured decision-makings

11. Decision-Support Systems • Developed in the early 1970s • Serve the middle management • Provide alternative-analysis report • investment portfolios • Plant expansion • Primary focus: semistructured and unstructured decision-makings • See text book for detail examples • Type of DSS • Model driven vs. Data driven

12. DSS Components • Three Major Components • Data management module • Model management module • Dialog management module

13. DSS Components

14. DSS Components • The Data Management Module • Gives user access to databases • Usually linked to external databases

15. DSS Components • The Model Management Module • Selects appropriate model to analyze data • Linear regression model

16. A linear regression model for predicting sales volume as a function of dollars spent on advertising DSS Components

17. DSS Components • The Dialog Module • Interface between user and other modules • Prompts user to select a model • Allows database access and data selection • Lets user enter/change parameters • Displays analysis results • Textual, tabular, and graphical displays

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

19. What-if 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%?

20. Goal-seek analysis • 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?

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

22. Data Driven DSS • Many current and the newest DSS • Extract and analyze complex information by analyzing large pools of data • Support decision makings for the future by discovering previously unknown patterns • Data mining as main technique

23. 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.”

24. 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

25. 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

26. 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