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IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 Model-Driven Business Intelligence Systems: Part II Week 9 Dr. Jocelyn San Pedro School of Information.

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Presentation on theme: "IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 Model-Driven Business Intelligence Systems: Part II Week 9 Dr. Jocelyn San Pedro School of Information."— Presentation transcript:

1 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 Model-Driven Business Intelligence Systems: Part II Week 9 Dr. Jocelyn San Pedro School of Information Management & Systems Monash University

2 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Lecture Outline  Trend Analysis  Seasonality Analysis  Multiplicative Decomposition of a Time Series  Causal Forecasting Models  Decision Trees  Influence Diagrams

3 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Learning Objectives At the end of this lecture, the students will  Have understanding of some models used in model- driven business intelligence systems  Specifically, have understanding of trend analysis, and seasonality analysis; decision trees and influence diagrams for decision modelling

4 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Trend Analysis  Fits a trend equation (or curve) to a series of historical data points  Projects this curve into the future for medium- and long-term forecasts  Trend equations – linear, quadratic, exponential, …

5 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Linear Regression  Least Squares Procedure  Fits a line that minimises the sum of the squares of vertical differences from the line to each of the actual observations – i.e. minimises the sum of squared errors  Least squares line: Y = a + bX  a is the y-axis intercept  b is the slope of the regression line

6 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1,

7 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Linear Trend Analysis- ExcelModules

8 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Seasonality Analysis  Recurring variations at certain periods (i.e., months) of the year make a seasonal adjustment in the time series necessary  E.g., demand for coal and oil fuel usually peaks in cold winter months; demand for sunscreen may be highest in summer  Seasonal Index – ratio of the average value of the item in season to the overall annual average value

9 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Example - ExcelModules

10 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Seasonality Analysis  Seasonal Index <1 indicates demand is below average that month  Seasonal index >1 indicated demand is above average that month  Use the seasonal indices to adjust the monthly demand for any future month  Example: If 3 rd year’s average demand is 100 units,  forecast for January’s monthly demand is 100 x = 96 units, (which is below average)  Forecast for May’s monthly demand is 100 x 1.309= 131 units, (which is above average)

11 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Multiplicative Decomposition of a Time Series  Breaks down a time series into two components  Seasonal component  A combination of the trend and cycle component (simply called trend)  Forecast is calculated a product of composite trend and seasonality components

12 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Multiplicative Decomposition in ExcelModules

13 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Causal Forecasting Models  Purpose is to develop a mathematical relationship between one or more factors affecting a variable  Example: sales of swimwear are likely to depend on average daily temperature, price, advertising budget  Sales – dependent variable  average daily temperature, price, advertising budget – independent variables  Most common methods  Linear regression – Y = a + bX  Multiple regression – Y = a+b 1 X 1 +b 2 X 2 +…b p X p

14 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Influence diagrams  An influence diagram is a simple visual representation of a decision problem  Influence diagrams offer an intuitive way to identify and display the essential elements, including decisions, uncertainties, and objectives, and how they influence each other.

15 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Influence Diagrams

16 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1,

17 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Example Influence diagram for R&D and commercialization of a new product

18 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Example - Genie

19 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Example - Genie

20 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Decision Trees

21 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Example – TreePlan Render, B., Stair, R. and Balakrishnan, N. (2003) Managerial Decision Modeling, Prentice Hall.

22 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, References Langley, R. (1970) Practical Statistics Simple Explained, Dover Publications, NY. Render, B., Stair, R. and Balakrishnan, N. (2003) Managerial Decision Modeling, Prentice Hall. Render, B., and Stair, R. (1999) Quantitative Analysis for Management (or any edition) Rowntree, D. (1981) Statistics Without Tears: A Primer for Non-mathematicians, Penguin Books. Useful online resources: Analytica Genie - www2.sis.pitt.edu/~genie/www2.sis.pitt.edu/~genie/

23 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, Questions? School of Information Management and Systems, Monash University T1.28, T Block, Caulfield Campus


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