1. Chapter 4 Forecasting Mike Dohan BUSI 2016

2. Forecasting What is forecasting? Why is it important? In what areas can forecasting be applied?

3. Forecasting What is forecasting? “art and science of predicting future events” Why is it important? allows for planning of future events, within a defined time horizon What should a good forecast be? ACCURATE Timely, interpretable, ability to detect trends In what areas can forecasting be applied? SCM – demand quantity from suppliers HR – labour demand for scheduling and planning Capacity – how many units to meet customer demand

4. Seven Steps 1.Determine the use of the forecast 2.Select items to be forecasted 3.Determine time horizon 4.Select forecasting model 5.Gather the data needed to make the forecast 6.Make the forecast 7.Validate and implement the results

5. Qualitative Approaches Jury of experts Delphi method Sales force composite Market survey

6. Quantitative Approach Time Series Models – “the future is a function of the past” Naïve Approach Moving Averages Exponential Smoothing Trend projection Associative Models – “what other numbers are related to demand” Linear Regression

7. Time Series Forecasting Future values predicted based on past values Four Components: Trend, Seasonality, Cycles, Random Variations

8. Naïve Approach Demand of next period is equal to last period Example Demand in January was 87 Therefore demand in February will be 87

9. Moving Averages

10. Weighted Moving Averages

11. Exponential Smoothing

12. Trends and “Lag”

13. Measuring Forecast Error of Time Series Models

14. Exponential Smoothing w Trend Adjustment

15. Trend Projection Find equation for trend line, use that for prediction Figure 4.4 Assumes linear relationship * * * * * * *

16. Variations in Data Seasonal Ie Swimming trunks sell more in the summer than in the winter Forecasting can incorporate a “Seasonal Index” SI = 3 year historical ave. demand for the month (ie all Januaries) / average monthly demand Forecast = (yearly demand / number of months) * SI More accuracy can be achieved by incorporating Seasonality and Trend to the forecast..

17. Associative Forecasting Methods

18. Correlation Coefficient r = -1 r = -0.5 r = 1 r = 0.5 r = 1

19. Multiple Regression

20. Monitoring and Controlling Forecasts Tracking Signal: used to make sure forecasts are acceptably accurate

21. Business Intelligence - the use of existing data in new ways is a popular area in industry and research Also: http://www.cio.com/slideshow/detail/119877http://www.cio.com/slideshow/detail/119877