When exploring the S&OP processes, we often hear of demand planning and demand forecasting. In this article, we want to focus on demand forecasting, what that means, the different types, and more. Let’s get started!

What Is Demand Forecast?

Forecasting is the act of predicting future events. So, demand forecasting is similar in that it’s a prediction. However, it’s a prediction of the demand for a product or service. This process is undertaken by demand planners to predict the demand for a product in a week, month, or year's time. The objective is to apply the necessary barometers that measure demand and apply them over time to arrive at a forecast. It’s a highly data-focused approach that is essential for production planning.

Demand planners don’t work alone. They have to communicate and collaborate with sales and marketing teams and other relevant key stakeholders to gather the necessary historical information. This information includes sales numbers, growth rate, and real-time data about market trends, customer behavior, and more. This information is how they create models and techniques to arrive at a consensus forecast.

Why Is Demand Forecasting Important?

Demand forecasting is essential for companies as it can result in several valuable benefits:

• Better allocation of resources
• Saves money
• Reduce waste
• Increase sales
• Increase revenue

Applying demand forecasting means making more informed decisions that can save a ton of money and reduce mistakes. You’ll be organized to be in the right place and time with the right product.

Types of Demand Forecasting

There are three types of demand forecasting you need to know about:

Qualitative Forecasting

When a company applies qualitative forecasting techniques, it’s because they don’t have as much data available as they’d need to create more detailed forecastings. This might be because the business is fairly new, or the product has only recently been introduced to the market, which can leave demand planners with little historical data to use.

In the event of these shortcomings regarding historical data, they use several other measures to create their forecasts. These include expert opinions, comparative analyses, and market research.

Technology is often a frequent user of qualitative forecasting as products can be unprecedented, and customer interest is difficult to gauge pre-release.

Time Series Analysis

The time series analysis approach is used when historical data is readily available for products or product lines as well as clear trends. Businesses use time series analysis to prepare for demand forecasting, which aids demand and supply planning. This process analyzes historical data and trends and seasonal fluctuations, key sales trends, and cyclical patterns. This process is best used when there is several years' worth of data to refer to.

Causal Models

Despite its name, it’s the most complex and sophisticated forecasting tool available. Casual models use information regarding the relationship between variables that affect demand in the market. These variables include economic forces, competitors, and various socioeconomic factors. It takes these variables and uses historical data to create a casual model forecast.

Conclusion

Now you know why these aren’t competing ideas but intertwined practices all companies should follow when dealing with supply, demand, and supply chains. You can plan better with the right tools. Check out John Galt for the best supply chain planning software.