Top 10 Methods of Demand Forecasting

Demand forecasting is one of the essential prerequisites for any supply chain planner. This forecast gives an idea of the possible demand trends in the future. It is not just guessing what the future sales for any product or service might be but uses scientific and objective methods to estimate the trends. It hence helps planners devise a plan that aligns the resources, finances, and teams toward meeting the forecasted demand while maintaining the optimum inventory and minimizing costs. While predicting the trends, it is important to ensure that the correct technique is being used. Read this informative blog post that discusses the various methods of demand forecasting.

What Are the Methods of Demand Forecasting?

Demand forecasting methods greatly impact the accuracy of the prediction made for future trends. They help you make well-informed business decisions that drive profitability and productivity. There are many different methods of forecasting used by businesses that are being explained further.

Statistical methods are a commonly used approach for demand forecasting as they can provide accurate predictions based on historical data. Some of them used for predicting include Time series analysis, Regression analysis, ARIMA (Autoregressive Integrated Moving Average), and Exponential smoothing. The choice of method depends on the nature of the data and the business problem. A combination of these may also provide a more robust forecast.

The survey method of demand forecasting involves gathering data directly from consumers, customers, or market participants to make predictions about future demand for a product or service. This can be done through various means, such as telephone, online, focus groups, or in-person interviews. The data collected through surveys can provide valuable insights into consumers' opinions, attitudes, and buying behavior, which can be used to make informed predictions about future demand. The forecast's accuracy will depend on the size and representativeness of the sample, the quality of the survey questions, and the ability to generalize the findings to the larger population.

The Delphi method is a group consensus-based approach for demand forecasting. It involves collecting predictions and opinions from a panel of experts and then using an iterative process to reach a consensus forecast. It involves identifying a panel of experts, collecting initial projections from each expert, compiling, and aggregating the initial estimates, providing expert feedback on the overall results, collecting updated forecasts from the experts, and creating a consensus demand forecast. The Delphi method is helpful for complex forecasting problems where a single expert's forecast may need to be more reliable. It can help reduce the influence of personal biases and improve the accuracy of the final predictions.

The barometric method of demand forecasting is a technique that predicts the future trend for a product or service based on an analysis of external factors such as economic indicators, market trends, and industry-specific variables. It assumes that consumers’ expectation for a product or service is closely related to changes in these external factors. It involves:

• Collecting data on relevant indicators.
• Creating a model to establish the relationship between the indicators and demand.
• Using the model to forecast future demand.

The accuracy of the forecast will depend on the quality of the data and the interdependence between external factors and demand.

The econometric method of demand forecasting is a statistical approach to predicting future sales for a product or service based on past sales data and relevant economic and market factors. It uses regression analysis and other statistical tools to establish relationships between demand and independent variables such as economic indicators, market trends, and other relevant variables. The model is then used to predict future trends based on expected changes in the independent variables. The econometric method is considered highly reliable and accurate, especially for products or services with a long sales history. However, it requires a large amount of data and a good understanding of statistical techniques to implement effectively.

The expert opinion method of demand forecasting involves gathering opinions and insights from individuals with expertise in a particular product or market to make predictions about future demand. It typically relies on experts in a given field's experience, knowledge, and judgment, who can provide insights into market trends, consumer behavior, and other relevant factors. Expert opinion can be collected through interviews, surveys, or by consulting industry publications and reports. This method can be useful when data is limited or consumer behavior changes rapidly. Still, it can also be subject to bias and error if the experts consulted do not have a broad or accurate understanding of the market. The forecast's accuracy will depend on the expertise of the individuals consulted and the validity of their assumptions.

The trend projection method of demand forecasting is a statistical technique used to forecast sales based on past trends. It assumes that the demand will continue in the same direction as the past trend, with the same or a similar rate of growth or decline. This involves fitting a trend line to historical data and extrapolating the line into the future to estimate the trends.

Trend projection techniques use different mathematical models to fit the trend line to the data to minimize the difference between predicted and realized values.

The trend projection method is best suited for forecasting demand in stable, predictable environments where past trends are expected to continue. However, it is important to consider external factors such as changes in the market, competitors, or government regulations that may impact demand and adjust the trend projections accordingly.

The Sales Force Opinion method of demand forecasting involves gathering forecasts from the company's sales force, such as sales representatives and managers. These individuals are in close contact with customers and have direct insight into current and future customer demand for a product or service. The sales force's opinions are combined and analyzed to create a forecast. It can provide valuable information and help to identify regional, customer-specific, and product-specific trends. However, it may also be subject to biases and personal opinions, so it is often used in conjunction with other methods to increase accuracy.

The Market Test method of demand forecasting involves conducting a limited-scale launch of a new product or service in a specific geographic area or customer segment to gather info. The data collected from the market test is then used to forecast regarding the product or service in a larger-scale launch. This is advantageous when launching a new product or entering a new market. It provides real-world information on customer demand, which can be difficult to estimate using other forecasting techniques. However, it can be expensive and time-consuming, and results may not represent trends for other regions. This is often used with other forecasting methods to increase accuracy.

The demand forecasting machine learning method involves statistical algorithms and models to analyze historical data and predict demand for a product or service. These models leverage large amounts of data for modern businesses to create accurate forecasts. Some popular ML methods for demand forecasting include Time series, Artificial Neural Networks, Decision trees, and Random Forest. They can provide highly accurate forecasts, especially when combined with domain-specific knowledge and human insights. However, it is essential to carefully validate and test the accuracy of these models before implementing them in decision-making processes.

How can 3SC help you increase the accuracy of your demand forecast?

3SC's Intelligent Supply Chain Planning platform can help improve forecast accuracy by using different demand forecasting methods that leverage the power of advanced analytics, artificial intelligence, and machine learning algorithms. The platform can analyze various sources, such as historical sales data, market trends, consumer behavior patterns, and more, to generate accurate forecasts.