Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. It involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets. Demand forecasting may be used in making pricing decisions, in assessing future capacity requirements, or in making decisions on whether to enter a new market.
Planning is an integral part of a manager’s job. Forecasts help managers by reducing some of the uncertainty in planning. A forecast is a statement about the future value of a variable such as demand.
Features of Forecasting
- Forecasting in concerned with future events.
- It shows the probability of happening of future events.
- It analysis past and present data.
- It uses statistical tools and techniques.
- It uses personal observations.
Importance of Forecasting
- Forecasting provides relevant and reliable information about the past and present events and the likely future events. This is necessary for sound planning.
- It gives confidence to the managers for making important decisions.
- It is the basis for making planning premises.
- It keeps managers active and alert to face the challenges of future events and the changes in the environment.
Disadvantages of Forecasting
- 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.
- 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.
- 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.
- Forecasting requires proper judgment and skills on the part of managers. Forecasts may go wrong due to bad judgment and skills on the part of some of the managers. Therefore, forecasts are subject to human error.
A forecast error is the difference between the actual or real and the predicted or forecast value of a time series or any other phenomenon of interest.
In simple cases, a forecast is compared with an outcome at a single time-point and a summary of forecast errors is constructed over a collection of such time-points. Here the forecast may be assessed using the difference or using a proportional error. By convention, the error is defined using the value of the outcome minus the value of the forecast.
The forecast error is the difference between the actual value and the forecast value for the corresponding period
where E is the forecast error at period t, Y is the actual value at period t, and F is the forecast for period t.
Measures of aggregate error are
|Mean absolute error (MAE)|
|Mean Absolute Percentage Error (MAPE)|
|Mean Absolute Deviation (MAD)|
|Percent Mean Absolute Deviation (PMAD)|
|Mean squared error (MSE) or Mean squared prediction error (MSPE)|
|Root Mean squared error (RMSE)|
|Forecast skill (SS)|
|Average of Errors (E)|
Various methods are used for forecasting which can be classified on the basis taken for forecasting and are as
Qualitative assessment Prediction Methods
These are forecasting methods based on expert opinion and includes –
- Personal Insight
- Panel Consensus
- Delphi technique
- Market Surveys
- Scenario Analysis
- Forecast by Analogy or Historical Analogy
Quantitative Data Prediction Methods
- Reference class forecasting
- Neural networks
- Data mining
- Causal models
Based on Time Series Projection
- Moving Average Method
- Exponential Smoothing Method
- Trend Projection Methods
- Growth Curve
Other Casual Methods
- Chain-Ratio Method
- Consumption Level Method
The above methods are illustrated as
- Judgmental Approach – The essence of the judgmental approach is to address the forecasting issue by assuming that someone else knows and can tell you the right answer. That is, in a judgment-based technique we gather the knowledge and opinions of people who are in a position to know what demand will be. For example, we might conduct a survey of the customer base to estimate what our sales will be next month.
- Experimental Approach – Another approach to demand forecasting, which is appealing when an item is “new” and when there is no other information upon which to base a forecast, is to conduct a demand experiment on a small group of customers and to extrapolate the results to a larger population. For example, firms will often test a new consumer product in a geographically isolated “test market” to establish its probable market share.
- Relational/Causal Approach – The assumption behind a causal or relational forecast is that, simply put, there is a reason why people buy our product. If we can understand what that reason (or set of reasons) is, we can use that understanding to develop a demand forecast. For example, if we sell umbrellas at a sidewalk stand, we would probably notice that daily demand is strongly correlated to the weather – we sell more umbrellas when it rains. Once we have established this relationship, a good weather forecast will help us order enough umbrellas to meet the expected demand.
- “Time Series” Approach – A time series procedure is fundamentally different than the first three approaches we have discussed. In a pure time series technique, no judgment or expertise or opinion is sought. We do not look for “causes” or relationships or factors which somehow “drive” demand. We do not test items or experiment with customers. By their nature, time series procedures are applied to demand data that are longitudinal rather than cross-sectional.
In one sense, all forecasting procedures involve the analysis of historical experience into patterns and the projection of those patterns into the future in the belief that the future will somehow resemble the past. The differences in the four approaches are in the way this “search for pattern” is conducted. Judgmental approaches rely on the subjective, ad-hoc analyses of external individuals. Experimental tools extrapolate results from small numbers of customers to large populations. Causal methods search for reasons for demand. Time series techniques simply analyze the demand data themselves to identify temporal patterns that emerge and persist.
Judgmental forecasting methods incorporate intuitive judgments, opinions and subjective probability estimates.
This type of forecast is created by taking an industry expert’s opinion relying solely on their opinion, bias, mood and personal judgment. Although this method is very flexible and widely used, it is unreliable.
Forecast is formed from taking the opinions of several industry experts through an open panel discussion. Although this method is a bit more reliable since it involves a group of experts but there are still some things to beware of. In some cases, some panels may not work well together due to personality traits. There may be difficulty in getting the panel to discuss openly and combine their different views into a non-bias, non-partial consensus so you should be cautious when you use this method.
Survey methodology studies the sampling of individual units from a population and the associated survey data collection techniques, such as questionnaire construction and methods for improving the number and accuracy of responses to surveys.
Forecast based on data collected from a representative sample of your customers or potential customers through analysis of their views. This tends to get good results but is time consuming and expensive. However, there are still instances when a poorly conducted market survey causes poor results due to failing of accurate customers sample, poorly worded questions, and inaccurate analysis of the data and / or invalid conclusions.
Statistical surveys are undertaken with a view towards making statistical inferences about the population being studied, and this depends strongly on the survey questions used. Polls about public opinion, public health surveys, market research surveys, government surveys and censuses are all examples of quantitative research that use contemporary survey methodology to answer questions about a population. Although censuses do not include a “sample”, they do include other aspects of survey methodology, like questionnaires, interviewers, and nonresponse follow-up techniques. Surveys provide important information for all kinds of public information and research fields, e.g., marketing research, psychology, health professionals and sociology.
Scenario analysis is a process of analyzing possible future events by considering alternative possible outcomes (sometimes called “alternative worlds”). Thus, the scenario analysis, which is a main method of projections, does not try to show one exact picture of the future. Instead, it presents consciously several alternative future developments. Consequently, a scope of possible future outcomes is observable. Not only are the outcomes observable, also the development paths leading to the outcomes. In contrast to prognoses, the scenario analysis is not using extrapolation of the past.