What is demand forecasting?

Demand forecasting refers to the process of planning and predicting the demand for goods and materials to help businesses remain as profitable as possible. Without robust demand forecasting, companies risk holding wasteful and costly surplus—or missing opportunities because they have failed to anticipate customer needs, preferences, and purchasing intent.

Demand forecasting professionals possess specialised skills and experience. When those skills are enhanced with modern supply chain technologies and predictive analytics, supply chains can become more competitive and streamlined than ever.

Why is demand forecasting important for modern supply chains?

In today’s rapidly changing supply‑chain environment, companies are operating in an exceptionally fast‑moving business climate. Demand forecasting is essential to help companies stay ahead of shifting customer needs and expectations, and constant market change. Accurate forecasting keeps teams aligned around what’s coming next—so they can plan materials (via DDMRP), production, logistics, and budgets with confidence.

How does demand forecasting work?

At its best, demand forecasting combines both qualitative and quantitative forecasting, both of which rely on the ability to gather insights from different data sources along the supply chain. Qualitative data can be curated from external sources such as news reports, cultural and social media trends, and competitor and market research. Internally sourced data—such as customer feedback and preferences—also contributes greatly to an accurate forecasting picture.

Quantitative data is typically mostly internal and can be gathered from sales figures, peak shopping periods, and web and search analytics. Modern technologies employ advanced analytics, powerful databases, and use artificial intelligence (AI) and machine learning to analyse and process deep and complex data sets. When modern technology is applied to qualitative and quantitative forecasting and predictive analytics, supply chain managers can provide ever-increasing levels of accuracy and resilience.

Demand forecasting methods

Depending on the industry, the customer base, and the volatility of the product, demand planning professionals use the following forecasting methods:

• Macro-level demand forecasting: Macro-level demand forecasting examines general economic conditions, external forces, and other broad influences that may disrupt or affect the business. These factors help inform businesses of regional and global risks or opportunities, and keep them aware of general cultural and market shifts.
• Micro-level demand forecasting: Demand forecasting at the micro level can be specific to a particular product, region, or customer segment. Micro-level forecasting is especially attuned to one-off or unexpected market shifts that might lead to a sudden spike or plunge in demand. For example, if experts are predicting a heatwave in New York and your company manufactures portable air conditioners, it may be worth the calculated risk of pre-emptively increasing your inventory buffers in that area.
• Short-term demand forecasting: Short-term demand forecasting can be at the micro or macro level. It is usually done for a period of fewer than 12 months to inform day-to-day operations. For example, it may involve consulting with the company’s sales and marketing teams to see if they are planning any promotional or sales events that might cause a surge in demand.
• Long-term demand forecasting: Long-term demand forecasting can also be micro or macro, but typically looks ahead further than one year. This helps businesses make better-informed decisions about matters such as expansion, corporate investments, acquisitions, or new partnerships. When businesses allow themselves a year or more to analyse and test markets, they can gain a more robust picture of the kind of demand trends they can expect when they set up shop or launch products in new countries or regions.

Factors influencing demand planning and forecasting

Silos are the enemy of accurate demand planning and forecasting. For supply chain planning to be at its most accurate and efficient, it requires very different areas of the business to be connected in real time and to be continually contributing data and insights. When equipped with as much data as possible, demand forecasters are better prepared to tackle these factors:

Seasonality and stock forecasting

Products such as sunscreen or Christmas trees have a very obvious seasonal increase. But seasonality can also apply to anything that causes customers’ behaviour to change during the year. This could include unexpected weather events or even something like the pandemic, which caused people to stay at home and be indoors more than they normally would during the summer months.

Competition as it relates to demand forecasting

Today’s supply chain competition is being reshaped by rapid shifts in customer expectations, industry modernisation, and the adoption of AI business planning tools. Companies are moving faster to deliver shorter product lifecycles, real‑time responsiveness, and more personalised experiences—raising the bar for everyone.

At the same time, cloud business networks and next‑generation planning platforms are becoming standard, intensifying competitive pressure as organisations race to improve visibility, agility, and decision‑making. In this environment, demand forecasting becomes a critical differentiator: it helps businesses anticipate market changes, optimise resources, and respond more intelligently than competitors.

Types of goods and demand estimates

Demand forecasting can vary greatly from product to product, even within the same product category. For example, demand for black T-shirts may change and suddenly start outstripping demand for white T-shirts. The trick is not to spot that it changed, but to spot why it changed. Lifetime customer value, average order value, and product purchase combinations also vary greatly and sometimes change suddenly.

With demand forecasting tools, you can better understand and predict these trends and their causes. This helps businesses learn how to customise, promote, or bundle items to drive more recurring revenue and to better see how one SKU affects or drives demand for another.

Geography

Traditionally, many businesses have managed with only a few regional warehouses and distribution centres serving wide geographical areas. However, largely due to the Amazon Effect, customers now expect same- or next-day deliveries. This means that businesses have had to establish fulfilment centres all over the country to achieve the proximity necessary for these new demands. Furthermore, this is no longer exclusively a B2C challenge. Increasingly, B2B businesses are also feeling the pressure of delivery speed.

This phenomenon has caused enormous upheaval in traditional demand forecasting processes. Where once supply chain planners had only to worry about stock levels at a few locations, they now must establish accurate buffers and stock levels at sometimes hundreds of small distribution centres. And obviously, this leads to increased risk and potential loss. It also means that demand planning professionals are more reliant than ever on cloud-connected supply chain solutions to deliver the intelligence and informed real-time data to help them be highly accurate with their now smaller and more widely dispersed inventories.

Three steps to get started with demand forecasting

Here are three simple steps to help you establish good supply chain planning strategies and demand forecasting best practices:

1. Let demand forecasting be what it is. Demand forecasting is an important backbone in the supply chain planning process and underpins many other processes. It can therefore be tempting for businesses to let demand forecasting become a catch-all practice that is bent and wedged in to support various other supply chain planning functions. When used properly, demand forecasting has a clear purpose: it predicts what, how much, and when customers will purchase. Other supply chain functions—such as S&OP, inventory optimisation, and response and supply planning—provide complementary capabilities within an integrated business planning system. If these tools are used for the specific functions they’ve been designed for, demand forecasting tools can get on with what they do best.
2. Demand forecasting software loves data, data, and more data. When supply chain technologies—particularly those dealing with demand and inventory forecasting—are powered with AI and machine learning, they become better, more accurate, and more insightful the more data you provide them with. Don’t rely solely on retrospective data such as past sales or previous product performance. Refer to additional sources such as news, politics, social trends, and customer insights. Today, data does not have to be linear and simple to be analysed effectively. Modern data management tools can curate and process large and complex data sets. And AI and machine learning bring speed and intelligence that not only allow for advanced and predictive analytics, but also learn from experience and cumulative data input.
3. Budget and plan accordingly to optimise demand forecasting. Supply chain planning requires a realistic and strategic approach to be at its best. Legacy practices and workflows are difficult to adjust, and people tend to resist change. But in the end, improved demand forecasting and supply chain planning can increase profitability and reduce risk and loss while providing your supply chain team members with a more streamlined and efficient working experience. By allocating budgets and team resources early on, businesses can help support better buy-in and a smoother rollout of their supply chain optimisation plans.

Become more competitive with predictive analytics and demand forecasting

Every step you take towards the digital transformation of your supply chain brings you that much closer to the visibility and efficiency you require in today’s competitive business climate. Work with supply chain managers and team leaders across your business to start breaking down silos and learning where the biggest risks may be hiding—as well as the greatest opportunities for long- and short-term successes. Then speak to your software supplier to learn more about integrating supply chain planning solutions into your operations.