Forecasting-1.ppt.Brod Forecasting Methods
- 1. Quantitative forecasting These types of forecasting methods are based on mathematical ( quantitative) models, and are objective in nature. They rely heavily on mathematical computations.
- 3. Time series methods Time series refers to a set of observation measured at successive periods. The objective of time series method is to discover a pattern in the historical data and then extrapolate trends into the future
- 4. In the time series forecasting method, past times are the best indicators of future trends. By reviewing historical data over time period, we can better under stand the pattern of past behavior of a variable and better predict the future behavior.
- 6. Components of time series demand
- 11. Simple Moving Average A moving average is a technique to get an overall idea of the trends in a data set; it is an average of any subset of numbers. The moving average is extremely useful for forecasting long-term trends. We can calculate it for any period of time. For example, if you have sales data for a twenty-year period, you can calculate a five-year moving average, a four-year moving average, a three-year moving average and so on.
- 14. Using the data given in the Illustration 1 forecast the demand for the period 2015 to 2023 using a. 3- year moving average and b. 5- year moving average If we want to check the error in our forecast as Error = Actual observed value – Forecasted value find which one gives a lower error in the forecast.
- 15. Year Demand 3 year MV 5 year MV forecast error forecast error 2015 100 - - - - 2016 105 - - - - 2017 103 - - - - 2018 107 102.6 4.4 - - 2019 109 105.0 4.0 - - 2020 110 106.3 3.7 104.8 5.2 2021 115 108.6 6.4 106.8 8.2 2022 117 111.3 5.7 108.8 8.2 2023 - 114.0 - 111.6 -
- 16. Weighted Moving Average Whereas the simple moving average gives equal weight to each component of the moving average database, a weighted moving average allows any weights to be placed on each element, providing, of course, that the sum of all weights equals 1.
- 18. Choosing Weights : Experience and trial and error are the simplest ways to choose weights. As a general rule, the most recent past is the most important indicator of what to expect in the future, and, therefore, it should get higher weighting. The past month's revenue or plant capacity, for example, would be a better estimate for the coming month than the revenue or plant capacity of several months ago.
- 19. However, if the data are seasonal, for example, weights should be established accordingly. For example, sales of air conditioners in May of last year should be weighted more heavily than sales of air conditioners in December.
- 23. Exponential Smoothing In the previous methods of forecasting (simple and weighted moving average), the major drawback is the need to continually carry a large amount of historical data. As each new piece of data is added in these methods, the oldest observation is dropped, and the new forecast is calculated.
- 25. The Effect of the Parameter A smaller makes the forecast more stable A larger makes the forecast more responsive
- 27. 1. The demand and forecast for February are 12000 and 10275 respectively. Using single exponential smoothening with smoothening coefficient value 0.25. Find out the forecast for the month of March.
- 28. 1. The sales of a product during last four years were 860, 880, 870 and 890 units. The forecast for fourth year was 876 units. If the forecast for the fifth year, using simple exponential smoothing is equal to the forecast using a three period moving average, find the value of exponential smoothing constant.