1. 1 Decision Making ADMI 6510 Forecasting Models Key Sources: Data Analysis and Decision Making (Albrigth, Winston and Zappe) An Introduction to Management Science: Quantitative Approaches to Decision Making (Anderson, Sweeny, Williams, and Martin), Essentials of MIS (Laudon and Laudon), Slides from N. Yildrim at ITU, Slides from Jean Lacoste, Virginia Tech, ….

2. Outline Basics of Forecasting ERP/SMC and Forecasting Quantitative Forecasting Time series methods Smoothing models Regression model Forecasting accuracy Seasonal model Forecasting process Problems with forecasting (web) 2

3. Basics What is forecasting? – Prediction of the future : almost always wrong – Key of forecasting – selecting the model that is “less” wrong less on the average or less worst case What do we forecast for? The role of forecasting in business. – Short term : determine inventories, capacity – Medium term : place orders, hire/ layoff – Long term : develop products, open new facilities 3

4. ERP /Supply Chain and Forecasting Forecasts of customer requirements are derived by the Sales and Marketing components of the ERP. This information feeds the planning processes of the ERP Mfg/Production and HR components. 4 Manufacturing and Production Finance and Accounting Human Resources Sales and Marketing ES Integrated Business Processes Customers Suppliers Govt/Regulations

5. Quantitative Forecasting Quantitative methods are based on an analysis of historical data Time series methods use a set of observations measured at successive points in time or over successive periods of time. 5 if you know its here, where is it going next?

6. Quantitative Forecasting 6

7. Time series methods Smoothing methods/models Trend projections: regression Trends and seasonal effects 7

8. Components of a time series Trend component: direction of the time series. Cyclical component: a regular pattern of sequences of values above and below the trend line. Seasonal component: regular patterns of variability within certain time periods, such as over a year. Random component: short-term, unanticipated and non-recurring factors that affect the values of the time series. 8

9. Smoothing methods Simple methods used when the item has no significant trend or seasonal effect. Methods used to average out the random effect. Three methods (more out there, but we will cover 3) – Moving average – Weighed moving average – Exponential smoothing 9

10. Smoothing methods Simple moving average – Control parameter: n = number of past periods to include. – F t = (A t-1 + A t-2 + …. A t-n )/n Weighted moving average – Control parameters: n = number of past periods to include Weight to each period = w 1, w 2, …, w n. Sum of w’s = 1. – F t = w 1 A t-1 + w 2 A t-2 + …+w n A t-n Exponential smoothing – Control parameter:  = smoothing constant (proportion of past error). 0 to 1. – F t = F t-1 +  (A t-1 – F t-1 ) 10

11. Regression A statistical technique for estimating the relationships among variables: time would be the independent variable (x). – typical method is least squares: generate the line with the smallest sum of the squares of the errors (distance from the line to the actual points) In Excel is very easy to do and it automatically generates multiple versions 11

12. Forecasting Accuracy Need to select one from many possible forecasts. Objective: Minimize error. – Forecast (F x ) versus actual (A x ). – Several methods: Mean Square Error (MSE) and Mean Absolute Deviation (MAD) are two of them. MAD = AVG x  Y  A x – F x  where Y is the set of all the time periods with both a forecast and actual data. 12

13. Seasonal Forecasts Used when data has a seasonal effect, in most cases “dispersed” over a year. – Process requires estimates of the number of seasons to use. – Several are done (based on observations of the raw data) and MAD used to select the proper grouping. 13

14. Seasonal forecasting steps 1.Group data into seasons (a forecasting parameter). 2.Calculate the centered average (full year) = CA 3.Calculate the seasonal irregular = Actual/CA = SR. 4.Calculate the seasonal index = Average of the SR = SI. 5.Normalize the seasonal indexes = SI/avg(SI) = NSI. 6.De-seasonalize the data. 7.Estimate the trend using the de-seasonalized data. 8.Use the trend equation and normalized seasonal indexes to create the forecast. 14

15. Forecasting process Look at the data. Plot it! Determine if there is “strange” data. Outliers? – Should they be removed? Must be well justified. Trends, seasons? Select the models based on your answers to the above. – Select parameter ranges. Determine the errors. Select the model and parameters with the smallest error. Finally, does it make sense? Be sure to look at the value – do not ignore intuition and possible errors. 15