The role of forecasting in demand planning: Strategies and methodologies

Demand is the driver of any business. However, when the perception of the level of consumer demand is far from reality, the business will face one of two troubles. The first is the cost of storing stock or, even worse, writing off unsold products. The second is the loss of income and, at the same time, customers due to a lack of goods on the shelves.

Both are quite annoying. Especially now that there are modern solutions on the market based on machine learning and artificial intelligence technologies that allow you to accurately predict demand fluctuations and help businesses make quick management decisions based on up-to-date data.

Companies are increasingly using such tools to gain a clear competitive advantage. As a result, the market for demand forecasting and planning software continues to grow. While in 2021 its volume amounted to USD 3.5 billion, in 2028 it is expected to reach USD 6.8 billion, which is almost double.

If you would like to learn more about how demand forecasting tools can help you prevent write-offs, please request a personalized presentation.

What key task does demand planning solve?

Demand planning is the starting point in supply chain and business management, affecting almost all KPIs of an organization. Like a nerve impulse, it sends a signal to all links in the chain, adjusting their workload in accordance with the expected sales volume. Procurement, supply, production, logistics, warehouse operations—everything is adjusted to ensure that there are enough goods to meet consumer demand while preventing excess finished goods. The key here is to maintain a perfect balance when there is enough stock, but this sufficiency does not turn into excess.

Maintaining such a balance is complicated by the fact that the market situation is constantly, and sometimes rapidly, changing. It can be influenced by macroeconomic factors and many other factors, including industry trends, competitors’ activities, the political situation, natural disasters, and high-profile events—down to a single post by an opinion leader on social media. All of this affects the already volatile and unpredictable consumer buying behavior.
Of course, it is impossible to take everything into account, but ideally, businesses should be able to react to these changes—or better yet, be ahead of at least those that can be predicted. That is why the accuracy of the forecast is particularly important in demand planning. It will determine whether the company will be able to provide optimal volumes of certain categories of goods in the right locations at the right time.

How can inaccurate demand forecasting affect financial performance?

An inaccurate demand forecast can have significant negative financial consequences for an organization, especially if the deviations are significant. For example, in cases where analysts fail to take into account factors that will lead to a significant increase in sales, this may at some point lead to the fact that the company will not be able to sell some of its goods because there simply will not be enough of them, or they will not be delivered in time at the right time and place.
Thus, the organization will lose the opportunity to make a profit from the sale of its products at the peak of its demand, and customers will be disappointed by the inability to make the purchases they need. And there is no telling what is worse. Customer loyalty is very easy to lose, but it takes a lot of time and marketing efforts to regain it or attract new customers.
Even if a company manages to fill product gaps by moving products from other locations, it still comes at the cost of additional logistics costs.
Thus, underestimating demand when planning sales can be costly. However, overestimation also has unfortunate consequences. Companies have to spend money on storing unsold products, paying for additional warehouse space, and the accumulation of excess inventory blocks working capital. In addition, organizations must reduce the price to sell the stock as soon as possible, and if the expiration date is over, they must write it off altogether. Product write-offs are the most annoying and common consequence of inaccurate forecasts in retail.

It is clear that the consequences for companies are particularly painful when they make serious miscalculations in demand planning. Minor mistakes lead to less noticeable problems, but when they happen on a regular basis, it still significantly harms the business in the long run.

Therefore, organizations that want to optimize their processes as much as possible and eliminate any causes of unnecessary costs are implementing modern demand forecasting tools. In today’s competitive environment, accurate forecasting and, as a result, efficient inventory management can be a decisive competitive advantage.

What benefits does a business get from accurate demand planning?

Forewarned is forearmed. Being aware of future demand fluctuations in advance allows companies to prepare for them, which means they can manage their resources more rationally and implement optimal product strategies. Let us look at the main advantages of an accurate forecast in demand planning.

Sales maximization

Timely knowledge of demand surges allows organizations to make the most of them by preparing the necessary amount of goods for sale and avoiding shortages. This leads to an increase in sales of an average of 15%.

Reducing the cost of storing surplus goods

On the other hand, anticipating downturns in demand helps to avoid excess inventory and thus the cost of storing it in warehouses.

Reducing the volume of written-off products

Maintaining optimal inventory levels and avoiding overstocks also ensures that fewer products are written off due to expiration.

Improved service

Accurate demand forecasting allows you to maintain the constant availability of products that customers need without unnecessary logistics and warehouse costs. Customer satisfaction increases by an average of 20%.

More accurate data for business decisions

A demand forecast is one of the starting points for making management decisions. The more accurate the forecast, the more successful the decisions will be.

More reasonable pricing policy

The level of demand is one of the key factors that should be taken into account in the pricing strategy. Accurate demand planning makes it possible to set prices that are optimal for the market, which is one of the important prerequisites for successful sales.

More efficient marketing

Marketing activities yield the best results when they take into account future fluctuations in demand for certain product groups, taking into account cannibalization. Thus, the effectiveness of promotions increases by 15%.

In general, an accurate forecast can increase business profitability by 25%. Of course, this figure varies from company to company. You can use a calculator to understand how much profit your organization can make from accurate demand forecasting.

What are the main methods of demand forecasting?

Traditionally, forecasting methods are divided into two large groups according to the level of formalization: intuitive and formal or statistical. The former, despite their name, are based not so much on irrational feelings as on the insights and experience of experts. That is why they are also called “experts.” They are divided into individual (when the forecast is made by individual specialists) and collective (when forecasting takes place during one-time or serial discussions).

Typically, intuitive methods are used as a supplement to formal methods and in situations of high uncertainty when there is a lack of data for analysis. For example, when a company has just entered the market or is opening a new business line. The advantage of these methods is that they are more versatile and can cover a wider range of factors than formal methods. The disadvantage is the subjectivity and accuracy of forecasts.

Formal methods are based on a mathematical approach and the ancient truth that the best way to understand the future is to study the past. These methods are used when a sufficient amount of historical data is available for analysis, primarily in relation to sales. By studying the trends of past periods, analysts extrapolate them into the future. In doing so, they use complex mathematical formulas and models that consider various factors and interdependencies that may affect demand.

The advantage of this approach is its objectivity and the fact that it produces results in quantitative terms. The disadvantage is that it is not suitable for identifying complex nonlinear patterns and that many factors are left out of the equations.

How can IT solutions improve demand forecasting?

Modern demand forecasting tools using AI and machine learning, such as SMART Demand Forecast, can be used as an effective alternative to traditional intuitive and formal methods or complement them. In any case, they provide businesses with advantages that were not available before.

At the very least, they significantly simplify and speed up the forecasting process by taking over complex analysis and calculations—something that required long and painstaking work of an entire analytical department. Moreover, as practice shows, such software is usually more efficient than traditional statistical methods.

One McKinsey Digital study showed that demand forecasting using artificial intelligence can reduce supply chain administration costs by 25–40%, reduce lost sales due to out-of-stocks by 65%, and reduce inventory by 20–50%.

How is this efficiency achieved? AI and machine learning software combine the precision of traditional statistical analysis with the ability to take into account a large number of factors inherent in expert analysis. These tools process entire arrays of both structured and unstructured data from various sources, and at the same time, they usually produce results with greater accuracy than experts because they are able to spot hidden patterns. In addition to the sales history of different types of goods in different periods, the following may be taken into account:

• macroeconomic indicators
• market trends
• competitors’ activities;
• survey results;
• own marketing activities;
• information from POS terminals and IoT sensors;
• data from suppliers and distributors;
• weather forecasts;
• information from social networks, etc.

Machine learning algorithms are able to process all these data sets and find both linear and non-linear correlations between them, “see” patterns and trends, including non-obvious ones that a human analyst may not notice. As a result, a predictive model is generated, which will be used for demand planning. Moreover, the model can learn and improve over time, and the more data is added to the system, the more accurate the forecasts become.

It is worth noting that much will depend on choosing the right algorithms. However, this is the task of forecasting solution providers. If you choose SMART Demand Forecast, our experts will help you with the right choice, testing, and further use of the algorithms that will provide the most accurate forecast based on the specifics of your business.

It is worth emphasizing that the fundamental advantage of AI-based demand forecasting tools compared to traditional statistical methods is their ability to take into account not only the past (historical sales data) but also many current factors.

Moreover, information can be fed into the system in almost real time, such as data from POS terminals, IoT sensors, and signals from websites and social networks. This makes the forecasting process continuous and more flexible, demand planning more adaptable to the changing circumstances of today, and the supply chain more closely aligned with real consumer demand.

Plans for success

For any company, demand planning is both a starting point and a benchmark. It determines the extent to which the organization’s activities are aligned with customer buying behavior and how effectively the business will respond to market demands. Therefore, it is crucial for companies to use effective demand forecasting tools. Solutions using artificial intelligence and machine learning algorithms demonstrate the greatest proven effectiveness and are replacing traditional tools.

If you would like to learn more about how to implement the best practices of demand forecasting in your organization, please request a personal presentation.