B. Information Technology (IS ) CISB434: Decision Support Systems

1. B. Information Technology (IS)CISB434: Decision Support Systems Chapter 1: Introduction to Decision Support Systems

2. Learning outcomes • Identify information systems for aiding decision making • MIS and DSS • Types of Decision-Support Systems • Components of DSS • DSS Applications • Web-based Customer DSS

3. IntroductionDefinition • A Decision Support System (DSS) • assists management decision-making by • combining data; • sophisticated analytical models and tools; • and user friendly software • a single powerful system that can support • semi-structured and unstructured decision making

4. Introduction to Decision Support Systems MIS and DSS

5. Management Information Sys.MIS • Earliest applications for supporting management decision • Provide information on firm’s performance • help managers monitor and control the business • Produce fixed, scheduled reports • data extracted and analyzed from Transaction Processing System (TPS)

6. Management Information Sys. Typical MIS Report • Summarise monthly sales • Highlight exceptional conditions • e.g. drop of sales quotas below a set level • employees have exceeded spending limit in health care • Latest MISs offer online access • On-demand • Intranet and Web-based

7. Decision-Support SystemsDSS • Provide nonroutine decisions and user control • Emphasize change, flexibility and rapid response • Easier access to structured information flows • Greater emphasis on models, assump-tions, ad hoc queries and display

8. Decision-Support SystemsStructures of Problems

9. Example of a Structured and Semistructured Problem • Structured problem: How much will I earn after two years if I invest $100,000 in municipal bonds that pay 4 percent per annum tax free? • Semistructured problem: If I invest $100,000 in stock XYZ and sell the stock in two years, how much money will I make? • How are these problems different?

10. Examples of Structured and Semistructured Problems

11. Introduction to Decision Support Systems Types of Decision Support System

12. Two Types of DSSModel-Driven • Stand-alone system • Uses models to perform what-if analysis • Usually developed in isolation for a particular group • Utilizes strong theory or model • Good user interface • Easy to use

13. Two Types of DSSModel-Driven: Example

14. Two Types of DSSData-Driven • Analyzes large pools of data from firm’s information systems • Allows users to extract useful information • Data from Transaction Processing Sys-tems (TPS) are collected in a Data Warehouse • Online analytical processing (OLAP) and data mining are used to analyze the data

15. Data-Driven DSSOLAP • Traditional database queries provide one-dimensional data analysis • OLAP supports • multidimensional data analysis, and • complex request for information

16. Data-Driven DSS Data Mining • Data mining offers • insights into corporate data by finding hid-den patterns and relationships • inferring rules to predict future behaviour • Use the patterns and rules to • guide decision making • forecast the effect of the decisions

17. Data-Driven DSS Data Mining Information • Associations • occurrences linked to a single event • e.g. sales of drinks and crisps increases by 80% when there is a football match • Sequences • linking of events over time • e.g. when a new house is bought, orders for kitchen cabinet happens 65% after two weeks

18. Data-Driven DSS Data Mining Information • Classification • describe a group to which an item belongs by examining existing items and inferring a set of rules • e.g. identify characteristics of customers who are likely to leave, who they are, so as to devise special campaign

19. Data-Driven DSS Data Mining Information • Clustering • discover different groupings within data • e.g. finding affinity groups for bank cards • Forecasting • Use a series of values to forecast what other values will be • e.g forecasting sales figures from prior sales

20. Data-Driven DSS Data Mining Tools • Data mining uses • statistical analysis tools • neural networks • fuzzy logic • genetic algorithms • rule-based systems

21. Data-Driven DSS Knowledge Discovery • Data mining offers knowledge disco-very • the process of identifying novel and valuable pattern • in large volumes of data • through selection, preparation and evalua-tion of contents of large databases

22. Introduction to Decision Support Systems Components of DSS

23. Components of DSSDSS Database • Collection of current or historical data • e.g a small database • a Data Warehouse • Extracts or copies of production data-base • avoids interfering with operational sys-tems

24. Components of DSSDSS Software System & UI • Software tools for data analysis • OLAP tools • data mining tools • mathematical and analytical models • User interface • easy interactions • supports dialogue • Web-based

25. Components of DSSModels • A model is an abstract representation to illustrate the components or relation-ships of a phenomenon • DSS is built for a specific set of purpose • It has different collections of models

26. Components of DSSSome DSS Models • Statistical models • full range of statistical functions: mean, median, deviations, etc. • ability to project future outcomes • help to establish relationships • Optimization models • use linear programming to determine opti-mal resource allocation, e.g. time or cost

27. Components of DSSSome DSS Models • Forecasting models • use to forecast sales • a range of historical data used to project future conditions and sales • Sensitivity analysis • what-if analysis • determines impact of changes in one or more factors on outcomes

28. Introduction to Decision Support Systems DSS Applications

29. DSS ApplicationsSupply Chain Management • Comprehensive examination of supply management chain • Searches for most efficient and cost-effective combination • Reduces overall costs • Increases speed and accuracy of filling customer orders

30. DSS ApplicationsCustomer Relationship Management • Uses data mining to guide decisions • Consolidates customer information into massive data warehouses • Uses various analytical tools to slice information into small segments

31. DSS ApplicationsCustomer Relationship Management

32. Introduction to Decision Support Systems Web-based Customer DSS

33. Web-based Customer DSS • Customers use multiple sources of in-formation to make purchasing decision • A Customer DSS • supports the decision-making process of customers • provides online access to databases, infor-mation pools and data analysis tools

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