Demand Forecasting

What Does Demand Forecasting Mean?

Demand forecasting is an aspect of business analytics that focuses on predicting the level of need for a specific product or service in the future.

Demand forecasts can be either subjective or objective. Subjective forecasts, which are based on opinions and educated guesses, are useful for predicting demand when products and services are new and historical data is not available. Objective forecasts, on the other hand, are entirely quantitative. This type of forecast uses historical data—as well as statistical, data mining and machine learning software tools—to quantify forecasts with mathematical scores.

To handle the increasing variety and complexity of demand forecasts, three basic types of demand forecasting have developed in recent years:

1. Qualitative demand forecasting predicts future sales using the opinions and instincts of potential customers, sellers and industry experts.
2. Time series demand forecasting relies on historical data and assumes demand trends will not vary significantly year over year.

3. Casual demand forecasting takes external economic indicators into account and uses regression models to look for relationships between dependent and independent variables.

Demand forecasting plays an important role in supply chain management, resource allocation, logistics, inventory management and staffing. Inaccurate demand forecasts can lead to bloated inventory levels, significant cost increases, poor customer satisfaction and a loss of competitive advantage.

Techopedia Explains Demand Forecasting

The emergence of multi-source big data and improvements in machine learning have made demand forecasting easier than ever. Choosing whether to use one demand forecasting method—or a combination of methods—depends on the analyst’s business goals and ability to work with learning algorithms.

For each business goal, there are dedicated forecasting methods that tend to work best. Hybrid methods, for example, provide the strengths of both statistical and artificial intelligence approaches by combining the speed and simplicity of statistical analysis with the ability to analyze complex relationships, which AI provides. The selection of a particular method depends on many factors, including the:

• Relevance and availability of historical data.
• Forecast's business goals.
• Degree of accuracy the forecast can provide.
• Lead time required.
• Forecast's duration.

Common issues that prevent accurate demand models include:

• Lack of data.
• Poor data quality.
• Using the wrong forecast model for a particular task at hand.
• Failing to account for seasonal fluctuations.
• Neglecting to analyze historical data on a channel-by-channel basis.
• Failing to factor in the possibility/probability of unexpected events.