Demand Forecasting: How accurate forecasts strengthen your competitive advantage

Want to know today what your customers will want tomorrow? Demand forecasting makes it possible! With accurate demand forecasts, you can avoid bottlenecks, use resources more efficiently and respond more quickly to market changes. But what exactly is demand forecasting and how does demand forecasting work? In this guide, you will learn what is behind it and how you can successfully integrate demand forecasting into your business. We will also take a look at the most important methods and tools, common mistakes and demand forecasting best practices.

Demand forecasting is an essential component of supply chain management. The aim of demand forecasting is to predict future demand for products or services as accurately as possible. Demand forecasts are based on historical data.

From car manufacturers to retailers to restaurants, demand forecasting offers companies in all industries the opportunity to better predict future demand. Instead of relying on guesswork, companies can use modern forecasting tools to better predict what their customers want – long before they say it themselves. This proactive approach brings numerous benefits that increase efficiency and secure competitive advantages.

Demand forecasting is the first and crucial step towards efficient resource planning in various areas of a company, particularly in production planning, warehousing, procurement and personnel planning. The forecasts created as part of demand forecasting form the basis on which further planning is built. By predicting future demand, companies can ensure that sufficient products, materials and personnel are available to meet demand.

However, demand forecasting not only plays an important role in manufacturing companies, but is also significant in the service sector. For example, restaurants can use demand forecasts to estimate the number of guests to expect. Based on these forecasts, decisions can be made about how many employees are needed in the kitchen and for service. Demand forecasts therefore not only ensure smoother processes, but can also save labour costs.

In addition, demand forecasts in the catering industry form the basis for better food planning. Precise forecasts allow purchasing quantities to be optimised. Ideally, this leads to less food waste and fewer sold-out dishes.

Efficient resource planning with the help of demand forecasting also helps to prevent production and delivery bottlenecks. Demand forecasts are used in production planning to order the necessary raw materials and components in good time so that production runs as efficiently as possible and without delays.

Demand forecasting also optimises inventory levels in warehousing, ensuring that overstocking and understocking are avoided. By storing only the quantities that are actually needed, storage costs can be significantly reduced. This also prevents production stoppages due to missing materials.

In supply chain management, demand forecasting enables companies to identify at an early stage which materials will be needed in the future. However, a good forecast alone does not automatically lead to optimal coordination within the supply chain. The actual improvement of the supply chain only comes about through the implementation of these forecasts in demand planning. This involves coordinating suppliers, manufacturers and distributors in such a way that the flow of materials and production planning are efficiently synchronised.

Demand forecasts enable companies to better predict future demand for specific products or components. In the automotive industry, for example, this means that a manufacturer knows in advance how many variants of a car model (e.g. with or without heated seats) will be required. These forecasts help to determine the right quantity of materials. The actual timing of orders, i.e. when exactly these components are delivered, is then the task of demand planning. Demand forecasting thus lays the foundation for demand-driven planning and improvements in the supply chain.

Demand forecasting enables companies to identify potential changes in demand at an early stage. These forecasts form the basis for using dynamic pricing to balance supply and demand. Dynamic pricing actively responds to changing market conditions by adjusting prices in real time, thereby influencing demand. In the aviation industry or the energy market, for example, demand can be controlled by lowering or raising prices. This helps companies maximise their revenues while offering competitive prices that reflect current market conditions.

Situations in which desired or needed products are suddenly unavailable can quickly frustrate customers – whether in an online shop or in a brick-and-mortar store. Such negative experiences can lead to customers shopping with the competition in the future. This is a major setback for any company.

This is exactly where demand forecasting comes in: good demand forecasts enable better planning of product (or ingredient) availability and better coverage of demand. This increases customer satisfaction by avoiding disappointment and meeting customer wishes and expectations more reliably.

Demand forecasting does not look the same in every company. Which method is right for you depends, among other things, on the data available to you. In practice, different methods are often combined to make the best possible forecast.

Introducing effective demand forecasting in companies requires a well-thought-out strategy and the right selection of technologies and tools. First, companies must ensure that the required data is available in sufficient quantity and quality. In addition, internal processes and systems should be aligned to seamlessly integrate data and incorporate forecasts into daily decision-making. Another step is to train employees in the new forecasting methods and tools to ensure that they are used to their full potential.

As we already know, AI and ML are powerful technologies in demand forecasting. There are also other tools that can be used for effective demand forecasting in production. These include:

• Enterprise resource planning (ERP) systems: These systems serve as an important data source for creating forecast models, as they provide access to relevant data from across the organisation. The collected data is used to train models and make forecasts.
• Advanced analytics and business intelligence (BI) tools: Tools such as Tableau, Power BI or QlikSense help to visualise and analyse data in order to identify patterns and trends. These tools are often used to analyse the data before training a model. Later, they help to prepare the forecasts graphically and present them to the specialist departments in an understandable way.
• Machine learning platforms: Platforms such as TensorFlow, Azure Machine Learning or Amazon SageMaker offer the possibility of developing and implementing customised forecasting models.
• Supply chain management (SCM) systems: These tools support the coordination and optimisation of the supply chain and help to integrate forecasts into procurement and logistics processes.
• Data management and integration platforms: Tools such as Apache Kafka or Talend are important for integrating and processing large amounts of data from different sources in real time.

Demand forecasting promises numerous benefits for your company, but introducing forecasting models also has a few pitfalls. Below, you will learn what is important when implementing demand forecasting.

The accuracy of demand forecasts depends heavily on the quality of the data fed into the system. To implement demand forecasting successfully, it is therefore particularly important to have reliable data and to maintain it continuously.

Several types of data are crucial for accurate demand forecasts:

• Past sales and production data: Historical data is at the heart of every forecast. It provides the basis on which trends and patterns can be identified.
• Market and industry data: Information about market trends, competitor activities and industry-specific developments is important for taking external influences on demand into account.
• Seasonal data and event data: Data on seasonal fluctuations and special events (e.g. holidays, promotions) are crucial for predicting changes in demand. External events (sports events, trade fairs, etc.) can also influence demand.
• External data: For many areas, weather data can play a major role. For example, temperatures are crucial for the catering industry or for providers of outdoor activities. But temperatures also have a major impact on business processes in the energy and water markets.
• Supply chain data: Information about the availability of raw materials, delivery times and possible bottlenecks helps to coordinate production planning precisely.
• Customer behaviour and preferences: Data on customer orders, feedback and purchasing habits can be used to better anticipate demand.

Even the best data is useless if you lack a fundamental understanding of it. As a developer, it is therefore particularly important to engage intensively with the available data and the various data sources. With a comprehensive understanding of the data, you can prevent false correlations from being assumed and, in the best case, even actively counteract them. A deep understanding of the data also allows you to evaluate the models you have developed more thoroughly. Close cooperation between technical development and the specialist department is therefore always a great advantage.

Demand forecasting should be seamlessly integrated into existing production and ERP systems. This integration ensures that data flows smoothly between different departments and that forecasts can be incorporated into decision-making processes in real time.

The forecasts should be presented in a simple format so that the responsible employees (demand planners) can process them quickly. This has a decisive advantage: if the forecasts are easy to understand and visually presented, demand planners can make more efficient decisions. For example, a graphical representation of demand trends with clear trends and deviations can help identify bottlenecks at an early stage.

An efficient demand forecasting solution should be scalable in order to easily handle growing data volumes and increasing requirements. At the same time, it should be flexible enough to respond quickly to changes in the production environment (e.g. new processes or technologies) or in the market (e.g. shifts in demand or seasonal fluctuations).

When demand forecasting is implemented in a company, employees must be trained in how to use the new systems. It is particularly important to create company-wide awareness of the importance of data maintenance and forecast accuracy. This also means that a basic level of trust in data-driven forecasting must first be established. This is often a particular challenge, as many employees have been accustomed to manual forecasting for years and are now suspicious of the new methods.

In order to make reliable statements, the quality of the forecasting models must be assessed regularly. Both a technical and a professional assessment are important here:

• Technical assessment: This includes criteria such as time performance (‘How long does the forecast take?’) or costs incurred (‘How much does a forecast cost when the service runs in the cloud?’).
• Subject-specific assessment: This involves checking whether the forecasts are logical and comprehensible. Particular attention is paid to whether the results appear reasonable in practice and whether there are any potential disadvantages (e.g. for certain production areas).

Quality testing plays a central role, especially in the continuous optimisation of forecast models. With each further development of the model, it must be ensured that it represents an improvement (or at least no deterioration) compared to the current state. This applies to both technical and professional quality.

A demand forecasting system is not a static solution. To maximise its efficiency, it must be regularly reviewed and continuously adapted to market changes. For example, if you last trained your AI model six months ago, this means that all data collected since then is not yet known to the model. In order for the model to work effectively and deliver reliable forecasts, it must now be retrained, i.e. adapted to the new database.

There is no general rule for how often a demand forecasting system should be adjusted. There are basically two approaches here:

1. If the market cycles are known, the forecast models are regularly adjusted to the new data. This is done at fixed intervals, for example hourly, daily, weekly or monthly, depending on the speed of market changes and the training time of the forecast model. Training large models can easily take several hours. In this case, hourly updates are not practical.
   
   
2. The input and output data are continuously monitored. So-called drift scores are calculated, which indicate how much the data changes over time. If the current data deviates too much from the training data, this is referred to as data drift. In concrete terms, this means that if the changes exceed a certain threshold value, the forecast model must be retrained to respond to these changes.

As you have just learned, there are numerous challenges associated with implementing demand forecasting. We will show you which mistakes you should avoid at all costs when creating a demand forecast:

Our collaboration with Siemens provides a best practice example of demand forecasting. The goal was to optimise production planning and reduce storage costs using an AI-based demand prediction platform. Together, we developed a machine learning system for time series forecasting, which was initially implemented for 100 products in one factory.

With the help of automated machine learning (AutoML), we were able to quickly find suitable models for the different products and shorten the test phase to a few weeks. The platform is now being used as a self-service solution in the plants, enabling production planners to generate reliable demand forecasts at any time. Thanks to its scalable architecture, the solution will be extended to other plants and the entire product range in the future. The combination of AutoML and customised algorithms ensures that Siemens can respond flexibly to market changes and thus further increase the efficiency of its production processes.

Demand forecasting is an indispensable tool for companies and offers decisive advantages, especially in highly competitive industries and tight markets. Companies that use demand forecasting to analyse data and accurately predict demand can meet their customers' expectations. This not only promises higher customer satisfaction, but also more efficient resource planning. By accurately understanding customer needs, companies can optimally manage their inventories, avoid bottlenecks and respond flexibly to changes. This provides a clear competitive advantage, especially in a rapidly changing market environment.

We believe that advanced forecasting tools and AI-powered models will further improve the capabilities of demand forecasting in the future, enabling even more accurate and automated predictions.