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

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Presentation on theme: "Decision Support Systems Yong Choi School of Business CSU, Bakersfield."— Presentation transcript:

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

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 OR Executive 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 designdrill-down

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


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