Forecasting starts with certain assumptions based on the managements experience, knowledge, and judgment. These estimates are projected into the coming months or years using one or more techniques such as Box-Jenkins models, Delphi method, exponential smoothing, moving averages, regression analysis, and trend projection. Since any error in the assumptions will result in a similar or magnified error in forecasting, the technique of sensitivity analysis is used which assigns a range of values to the uncertain factors (variables). A forecast should not be confused with a budget.
Forecasting is a process of predicting or estimating the future based on past and present data. Forecasting provides information about the potential future events and their consequences for the organization. It may not reduce the complications and uncertainty of the future. However, it increases the confidence of the management to make important decisions. Forecasting is the basis of premising. Forecasting uses many statistical techniques. Therefore, it is also called as Statistical Analysis.
It is the use of historic data to determine the direction of future trends. Forecasting is used by companies to determine how to allocate their budgets for an upcoming period of time. This is typically based on demand for the goods and services it offers, compared to the cost of producing them. Investors utilize forecasting to determine if events affecting a company, such as sales expectations, will increase or decrease the price of shares in that company. Forecasting also provides an important benchmark for firms which have a long-term perspective of operations.
Stock analysts use various forecasting methods to determine how a stock's price will move in the future. They might look at revenue and compare it to economic indicators, or may look at other indicators, such as the number of new stores a company opens or the number of orders for the goods it manufactures. Economists use forecasting to extrapolate how trends, such as GDP or unemployment, will change in the coming quarter or year. The further out the forecast, the higher the chances that the estimate will be less accurate.
Features of Forecasting
Peculiarities, characteristics or features of forecasting are as follows:-
1. Forecasting in concerned with future events.
2. It shows the probability of happening of future events.
3. It analysis past and present data.
4. It uses statistical tools and techniques.
5. It uses personal observations.
Steps in Forecasting
Procedure, stages or general steps involved in forecasting are given below:-
1. Analysing and understanding the problem: The manager must first identify the real problem for which the forecast is to be made. This will help the manager to fix the scope of forecasting.
2. Developing sound foundation: The management can develop a sound foundation, for the future after considering available information, experience, type of business, and the rate of development.
3. Collecting and analyzing data: Data collection is time consuming. Only relevant data must be kept. Many statistical tools can be used to analyze the data.
4. Estimating future events: The future events are estimated by using trend analysis. Trend analysis makes provision for some errors.
5. Comparing results: The actual results are compared with the estimated results. If the actual results tally with the estimated results, there is nothing to worry. In case of any major difference between the actual and the estimates, it is necessary to find out the reasons for poor performance.
6. Follow up action: The forecasting process can be continuously improved and refined on the basis of past experience. Areas of weaknesses can be improved for the future forecasting. There must be regular feedback on past forecasting.
Importance of Forecasting
Merits, significance or importance of forecasting involves following points:-
1. Forecasting provides relevant and reliable information about the past and present events and the likely future events. This is necessary for sound planning.
2. It gives confidence to the managers for making important decisions.
3. It is the basis for making planning premises.
4. It keeps managers active and alert to face the challenges of future events and the changes in the environment.
Limitations of Forecasting
Demerits, criticism or limitations of forecasting involves following points:-
1. The collection and analysis of data about the past, present and future involves a lot of time and money. Therefore, managers have to balance the cost of forecasting with its benefits. Many small firms don't do forecasting because of the high cost.
2. Forecasting can only estimate the future events. It cannot guarantee that these events will take place in the future. Long-term forecasts will be less accurate as compared to short-term forecast.
3. Forecasting is based on certain assumptions. If these assumptions are wrong, the forecasting will be wrong. Forecasting is based on past events. However, history may not repeat itself at all times.
4. Forecasting requires proper judgement and skills on the part of managers. Forecasts may go wrong due to bad judgement and skills on the part of some of the managers. Therefore, forecasts are subject to human error.
Characteristics of Forecasts
They are usually wrong!
A good forecast is more than a single number
Includes a mean value and standard deviation
Includes accuracy range (high and low)
Aggregated forecasts are usually more accurate
Accuracy erodes as we go further into the future.
Forecasts should not be used to the exclusion of known information
Forecasting Techniques
Primary forecasting techniques help organizations plan for the future. Some are based on subjective criteria and often amount to little more than wild guesses or wishful thinking. Others are based on measurable, historical quantitative data and are given more credence by outside parties, such as analysts and potential investors. While no forecasting tool can predict the future with complete certainty, they remain essential in estimating an organization's forward prospects.
Delphi Technique
The RAND Corporation developed the Delphi Technique in the late 1960s. In the Delphi Technique, a group of experts responds to a series of questionnaires. The experts are kept apart and unaware of each other. The results of the first questionnaire are compiled, and a second questionnaire based on the results of the first is presented to the experts, who are asked to reevaluate their responses to the first questionnaire. This questioning, compilation and requestioning continues until the researchers have a narrow range of opinions.
Scenario Writing
In Scenario Writing, the forecaster generates different outcomes based on different starting criteria. The decision-maker then decides on the most likely outcome from the numerous scenarios presented. Scenario writing typically yields best, worst and middle options.
Subjective Approach
Subjective forecasting allows forecasters to predict outcomes based on their subjective thoughts and feelings. Subjective forecasting uses brainstorming sessions to generate ideas and to solve problems casually, free from criticism and peer pressure. They are often used when time constraints prohibit objective forecasts. Subjective forecasts are subject to biases and should be viewed skeptically by decision-makers.
Time-Series Forecasting
Time-series forecasting is a quantitative forecasting technique. It measures data gathered over time to identify trends. The data may be taken over any interval: hourly; daily; weekly; monthly; yearly; or longer. Trend, cyclical, seasonal and irregular components make up the time series. The trend component refers to the data's gradual shifting over time. It is often shown as an upward- or downward-sloping line to represent increasing or decreasing trends, respectively. Cyclical components lie above or below the trend line and repeat for a year or longer. The business cycle illustrates a cyclical component. Seasonal components are similar to cyclicals in their repetitive nature, but they occur in one-year periods. The annual increase in gas prices during the summer driving season and the corresponding decrease during the winter months is an example of a seasonal event. Irregular components happen randomly and cannot be predicted.
Moving Averages Method:
Simple and exponentially smoothed moving averages can often predict the future course of a time-related series of values. Moving averages are widely used in time-series analysis in business and financial forecasting. Rolling sums have other widely used financial uses.
You can use the Moving Average transformer to calculate the following values:
•A simple moving average
•An exponential moving average
•A rolling sum for N periods of data, where N is specified by the user
An exponential moving average is also known as an exponentially smoothed moving average.
Moving averages redistribute events that occur briefly over a wider period of time. This redistribution serves to remove noise, random occurrences, large peaks, and valleys from time-series data. You can apply the moving average method to a time-series data set to:
Remove the effects of seasonal variations.
Extract the data trend.
Enhance the long-term cycles.
Smooth a data set before performing higher-level analysis.
The Moving Average transformer uses a warehouse target table as a source. The table that you use as a source must contain a primary key. If you use a target table that was generated by the Data Warehouse Center, you must assign a primary key to the table before you use it as a source. The transformer writes to a table on the warehouse target. Before you define this step, link the warehouse target to the step in the Process Model window, with the arrow pointing towards the step.
Advantages of Moving Average Method
Easily understood
Easily computed
Provides stable forecasts
Disadvantages of Moving Average Method
Requires saving lots of past data points
Lags behind a trend
Ignores complex relationships in data
3-Year Moving Average
First average:
Second average:
YEAR
No. of cars SOLD
Forecasted demand (Ft â€" 1)
Ft
1
1324
-
-
2
1605
-
-
3
1486
1471.66
-
4
1567
1552.66
1478.835
5
1687
1580
1559.835
6
1021
1425
1633.5
7
1424
1377.33
1223
8
986
1143.66
1400.67
9
1529
1313
1064.84
10
1425
1313.33
1421
1369.17
