1. BUS 173: Lecture 10 Forecasting for Businesses

2. Outline  What is a forecast?  Why do we need forecasting?  What are the common tools of forecasting?  Basic Tools  Plain Average  Regression  Assessing Forecasts

3. Forecasts – Example Suppose you want to start a new garments factory. Your product will be woolen sweaters, which you will be exporting to Sweden and Norway. After deciding on the capital expenditure, loans, where to establish the factory and who would be the buyer, your operations manager asks you about the possible future demands of the woolen sweaters for next 7 months. You ask your friends, who have already been established garments manufacturers, and they provide you with the demand for sweaters for the past 16 months, shown in the next slide:

4. MonthsDemand (in thousand) December ‘12 25 January ’13 29 February ’13 31 March ’13 30 April ’13 26 May ’13 24 June ’13 20 July ’13 17 August ’13 16 September ’13 20 October ’13 23 November ’13 28 December ’13 31 January ’14 34 February ’14 37 March ‘14 33

5. Some Questions  How do you use this data to forecast for the next seven periods?  How do you determine that your forecasting method is accurate?  How do you make sure that you are accounting for data seasonality and cyclical data?

6. Forecast - Logic  The logic behind forecasting  No model is the ideal model.  Each model will depend on the situation.  The accuracy of each model will vary from time to time.  There will always be a certain degree of error involved.

7. Simple Forecasting Tools (1)  The Average  Take all the past data and find out the average value from them  For our example, average is:  26.5  This means that for the next 7 months, the demand will be 26.5 on average

8. Assessing Forecasts  Step 1  Looking into the trend of data  Step 2  Understanding the forecast method to use  Checking the reliability of the method used  Step 3  Generating forecasts for existing data and checking for deviation

9. Step 1 – Looking at the data

10. Deviation Checks - MAD  Mean absolute deviation - MAD  Step 1 – Difference: Forecasted Data – Actual Data  Step 2 – Absolute Difference: Convert ALL values to positive values  Step 3 – Average of the Absolute Differences

11. Deviation Checks - MAPE  Mean absolute percentage error - MAPE  Step 1 – Percentage Difference: (Actual Data – Forecasted Data)/Actual Data  Step 2 – Absolute Percentage Difference: Convert ALL values to positive values  Step 3 – Average of the Percentage Differences

12. Deviation Checks – MSE  Also called Mean Standard Error - MSE  Step 1 – Difference: Forecasted Data – Actual Data  Step 2 – Difference Square: (Forecasted Data – Actual Data)^2  Step 3 – Average of Difference Squares

13. Decision Rule  Whichever forecasting method has the lowest MAD/ MAPE/ MSE is the most appropriate forecasting for a particular scenario  Keep in mind  NO ONE FORECASTING METHOD IS THE BEST

14. End of Presentation THANK YOU