Top Features of Demand Forecasting

When we talk about business planning, demand forecasting is one of the most frequently used terms. It is an estimate to predict the future demand for products or services. Based on this prediction, the planning of business operations, supply chain, and inventory management is done. Traditionally, the data analysis for creating a forecast was done using spreadsheets which had limited efficiency and lack of real-time data accessibility. But now with the technological advancement and introduction of technologies like artificial intelligence and machine learning, now planners can use new methods to predict demand and plan their operations better.

There are various methods used for creating a forecast. These methods include the Consumer Survey Method, Collective Opinion Method, Delphi Method, Market Experiment Method, Time Series Analysis, and Regression Analysis. The choice of method depends on the requirements and objectives of the forecast. Each method has its own features that differentiate them from others and hence can be used for achieving different objectives.

Here are some prominent features of demand forecasting methods that can help choose the right way ahead, ensuring minimum marginal error in the forecast data.

What are the Features Of Demand Forecasting?

1. Time HorizonThe timespan or which the forecast is being done has a great impact on which method to use for forecasting. Generally, when the time span increases, the probability of marginal error in the forecast decreases.
2. Level of DetailThe level of detail required in the data for the analysis is another feature of the forecasting method that must match with the decision-making unit for which the forecast is being done. If the forecast is being done for production planning or sales planning, then there might be a need for the forecast at the individual product level whereas when it comes to corporate planning, a high-level aggregate demand forecast will be required by product category.
3. StabilityWhen forecasting situations that are in constant flux, a different method will be used than for the situations that are relatively stable with time and need less attention. For stable conditions, the existing pattern is presumed to be continued in the future. For such conditions, planners can easily extrapolate past patterns into the future.
4. Data PatternFor any forecast, there is a requirement for data, and it needs to be available on time. Each forecasting method uses an underlying assumption about the data that is being used for analysis. As different methods have variable abilities to identify patterns, it is beneficial to choose the data pattern according to the methods.
5. Type of ModelIn each forecasting method, there are other assumptions made that are required to fit under the situation to be considered. The management must understand the method used completely to get quick and meaningful results.
6. CostWhen a company adopts a forecasting procedure, there are several costs associated with it. The difference in cost can be a major factor when deciding the forecasting method. There needs to be a proper balance between the benefits against the extra cost of getting improved forecasting.
7. AccuracyThe accuracy of any method is measured by the level of deviation between the past forecast and the actual performance. If the predicted state is close to the actual state, then it makes the forecast method reliable.
8. Ease of ApplicationAny method chosen by the planners needs to be easily understandable to the users within the given time. This will allow management to properly interpret the results. The level of complexity in handling the method can be a deciding factor in selecting a forecasting method.

Every demand prediction method has its own features, merits, and demerits.  Planners can choose the methods as per their requirements and objectives. Used correctly, demand forecasting can easily help businesses plan their operation more effectively and productively, providing them with a competitive edge in this fiercely competitive market.