INTRODUCTION TO FORECASTING

What is forecasting???

Based on Wikipedia, forecasting refer to the process of making statements about events whose actual outcomes (typically) have not yet been observed. A forecast also is a prediction of what will occur in the future. (Operation Management, 6^th, Russell&Taylor). J E Beasly in his notes said the definition of forecast is the estimation of the value of a variable (or set of variables) at some future point in time. So, in business, we can conclude that forecasting is about prediction about financial, operation and management in future.

John Galt had definite the process of forecasting as provides a mechanism for soliciting participation from individuals who have knowledge of future events and compiling it into a consistent format to develop a forecast. The forecasting process concentrates defining how information will be gathered and reconciled into a consistent picture of the future.

Apart from that, forecasting can be broadly considered as a method or a technique for estimating many future aspects of a business or other operation. There are numerous techniques that can be used to accomplish the goal of forecasting. For example, our sample is bakery which has been in business for 3 years can forecast its volume of sales in the coming year based on its experience over the 3-year period—such a forecasting technique bases the future forecast on the past data.

What is sales forecast??

Sales forecast is a forecast of a company's units and Ringgit Malaysia sales for some future period. It is generally based on recent sales trends and economic prospect which includes nation, region and industry.

For information, many forecasting techniques use historical data in the form of time series. A time series is simply a set of observations measured at successive points in time or over successive periods of time. Based on Russell and Taylor, time series is a forecasting method that use historical demand data over a period of time to predict future demand.
Before start the explanation about time series, let’s understand about behavior of time series in general terms. Times series are comprised of four separate components: trend component, cyclical component, seasonal component, and irregular component. These four components are viewed as providing specific values for the time series when combined. In time series concept, the measurements are taken at successive points or over successive periods. It means that the measurements may be taken every hour, day, week, month, or year, or at any other regular (or irregular) interval.

TIME SERIES FORECASTING USING SMOOTHING METHODS

Smoothing methods are appropriate when a time series displays no significant effects of trend, cyclical, or seasonal components (often called a stable time series). The goal is to smooth out the irregular component of the time series by using an averaging process.
The moving averages method is probably the most widely used smoothing technique. In order to smooth the time series, this method uses the average of a number of adjoining data points or periods. This averaging process uses overlapping observations to generate averages. It means that this method use an average demand for a fixed sequence of periods. Based on Russell and Taylor the moving average is good for stable demand with no pronounced behavioral patterns.
The term "moving" refers to the way averages are calculated—the forecaster moves up or down the time series to pick observations to calculate an average of a fixed number of observations.

The sample of calculation use moving average as follows:

Suppose a forecaster wants to forecast the sales volume for United Kingdom-made automobiles in the London for the next year. The sales of UK-made cars in the UK during the previous three years were: 1.3 million, 900,000, and 1.1 million (the most recent observation is reported first). The three-period moving average in this case is 1.1 million cars (that is: [(1.3 + 0.90 + 1.1)/3 = 1.1]). Based on the three-period moving averages, the forecast may predict that 1.1 million UK-made cars are most likely to be sold in the United Kingdom the next year.

Weighted moving averages are a variant of moving averages. In the moving average method , each observation of data receives the same weight. In the weighted moving averages method, different weights are assigned to the observations on data that are used in calculating the moving averages.

TIME SERIES FORECASTING USING TREND PROJECTION

This method uses the underlying long-term trend of a time series of data to forecast its future values. The time series data on U.S. auto sales can be plotted and examined visually. Most likely, the auto sales time series would display a gradual growth in the sales volume, despite the "up" and "down" movements from year to year. The trend may be linear (approximated by a straight line) or nonlinear (approximated by a curve or a nonlinear line). Assume that the time series on American-made auto sales is actually linear and thus it can be represented by a straight line. Mathematical techniques are used to find the straight line that most accurately represents the time series on auto sales. This line relates sales to different points over time

TIME SERIES FORECASTING USING TREND AND SEASONAL COMPONENTS.

This method is a variant of the trend projection method, making use of the seasonal component of a time series in addition to the trend component. This method removes the seasonal effect or the seasonal component from the time series. This step is often referred to as de-seasonalizing the time series.

Once a time series has been de-seasonalized it will have only a trend component. The trend projection method can then be employed to identify a straight line trend that represents the time series data well. Then, using this trend line, forecasts for future periods are generated. The final step under this method is to reincorporate the seasonal component of the time series (using what is known as the seasonal index) to adjust the forecasts based on trend alone

CAUSAL METHOD OF FORECASTING

Causal methods use the cause-and-effect relationship between the variable whose future values are being forecasted and other related variables or factors. The widely known causal method is called regression analysis, a statistical technique used to develop a mathematical model showing how a set of variables are related. This mathematical relationship can be used to generate forecasts. Regression analysis that uses two or more independent variables to forecast values of the dependent variable is called a multiple regression analysis.

*VIDEO ~ Example of cash flow forecasting for small business