2. B. I NFORMATION T ECHNOLOGY (IS) CISB434: D ECISION S UPPORT S YSTEMS Chapter 1: Introduction to Decision Support Systems

3. L EARNING 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 2

4. I NTRODUCTION D EFINITION 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 3

5. I NTRODUCTION TO D ECISION S UPPORT S YSTEMS MIS and DSS

6. M ANAGEMENT I NFORMATION S YS. 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) 5

7. M ANAGEMENT I NFORMATION S YS. T YPICAL MIS R EPORT 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 6

8. D ECISION -S UPPORT S YSTEMS DSS 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 7

9. D ECISION -S UPPORT S YSTEMS S TRUCTURES OF P ROBLEMS ProblemsSolutions Types Solution Provider Structured Repetitive and routine Known algorithms provide solutions MIS Unstructured Novel and nonroutine No known algorithms. Discuss, ruminate, brainstorm to decide DSS Semistructured Midway Midway between the above solution types DSS 8

10. 9 E XAMPLE OF A S TRUCTURED AND S EMISTRUCTURED P ROBLEM 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?

11. 10 E XAMPLES OF S TRUCTURED AND S EMISTRUCTURED P ROBLEMS

12. I NTRODUCTION TO D ECISION S UPPORT S YSTEMS Types of Decision Support System

13. T WO T YPES OF DSS M ODEL -D RIVEN 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 12

14. T WO T YPES OF DSS M ODEL -D RIVEN : E XAMPLE 13

15. T WO T YPES OF DSS D ATA -D RIVEN 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 14

16. D ATA -D RIVEN DSS OLAP Traditional database queries provide one- dimensional data analysis OLAP supports multidimensional data analysis, and complex request for information 15

17. D ATA -D RIVEN DSS D ATA M INING 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 16

18. D ATA -D RIVEN DSS D ATA M INING I NFORMATION 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 17

19. D ATA -D RIVEN DSS D ATA M INING I NFORMATION 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 18

20. D ATA -D RIVEN DSS D ATA M INING I NFORMATION 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 19

21. D ATA -D RIVEN DSS D ATA M INING T OOLS Data mining uses statistical analysis tools neural networks fuzzy logic genetic algorithms rule-based systems 20

22. D ATA -D RIVEN DSS K NOWLEDGE D ISCOVERY 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 21

23. I NTRODUCTION TO D ECISION S UPPORT S YSTEMS Components of DSS

24. C OMPONENTS OF DSS DSS D ATABASE 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 23

25. C OMPONENTS OF DSS DSS S OFTWARE S YSTEM & UI Software tools for data analysis OLAP tools data mining tools mathematical and analytical models User interface easy interactions supports dialogue Web-based 24

26. C OMPONENTS OF DSS M ODELS 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 25

27. C OMPONENTS OF DSS S OME DSS M ODELS 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 26

28. C OMPONENTS OF DSS S OME DSS M ODELS 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 27

29. I NTRODUCTION TO D ECISION S UPPORT S YSTEMS DSS Applications

30. DSS A PPLICATIONS S UPPLY C HAIN M ANAGEMENT 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 29

31. DSS A PPLICATIONS C USTOMER R ELATIONSHIP M ANAGEMENT Uses data mining to guide decisions Consolidates customer information into massive data warehouses Uses various analytical tools to slice information into small segments 30

32. DSS A PPLICATIONS C USTOMER R ELATIONSHIP M ANAGEMENT 31

33. I NTRODUCTION TO D ECISION S UPPORT S YSTEMS Web-based Customer DSS

34. W EB - BASED C USTOMER 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 33

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