How Berco uses AI-supported demand forecasts with up to to better predict market developments in their aftermarket business

Berco Aftermarket, a leading aftermarket-parts supplier of undercarriage systems for mining machines, tracked construction vehicles and further applications operating a large warehouse in Bologna, Italy, has partnered with to improve its data capabilities by integrating AI-powered forecasting solutions.

As a pioneer in its industry, Berco Aftermarket has recognized the need to use advanced technologies to optimize its operational efficiency and reduce costs. In collaboration with, the company launched a project to optimize its S&OP processes by significantly increasing forecasting accuracy.

At the heart of this is the use ofa specially developed AI tool that is tailored to the specific forecasting requirements of Berco Aftermarket. By harnessing the power of machine learning algorithms, this innovative technology enables the company to achieve unprecedented accuracy in forecasting demand.

By seamlessly integrating AI in to its operations, Berco Aftermarket expects significant improvements in performance optimization and inventory management. This transformative initiative not only increases operational efficiency, but also drives cost reduction and positions the company for sustainable growth and competitiveness in the dynamic aftermarket business.

By using's machine learning technology, we are able to significantly improve the accuracy of our demand forecasting, optimize planning processes and promote sustainable growth.

Diego Buffoni - Managing Director @ Berco Aftermarket

Berco Aftermarket faces challenges when it comes to forecasting sales due to the complexity and diversity of its product portfolio, offering ~4,000 distinct product variations to customers in 52 countries.

Key Challenges of Aftermarket Business:

1. Demand Uncertainty: Fluctuations in demand for undercarriage components pose a significant challenge, leading to overstocking or stockouts, resulting in inefficient inventory management and increased costs.

2. Complex Supply Chain: Berco's diverse product range and global customer base contribute to the complexity of its supply chain, making it challenging to anticipate demand variations accurately.

3. Working Capital Optimization: Suboptimal inventory levels tie up valuable working capital, limiting Berco's ability to invest in growth initiatives and innovation.

Despite continuous refinement in day-to-day business, these forecasts occasionally deviate from actual market developments, which has an impact on both production planning and inventory management. This discrepancy weighs particularly heavy as Berco has strategically decided to expand its aftersales business. To succeed in this plan, it is of immense importance to Berco Aftermarket to avoid out of stock situations and reduce capital tied up in inventory. Both of these strategic objectives highlight the urgent need for an improved forecasting accuracy.

To meet this challenge, Berco Aftermarket has launched a strategic initiative to use modern AI technologies in collaboration with By integrating AI-supported forecasting solutions, the company is increasing the accuracy of its sales forecasts while maintaining the flexibility required for production and inventory planning.

This initiative is a crucial step towards optimizing operational efficiency and reducing costs by better aligning production and inventory levels with actual market demand. By utilizing AI, Berco aims to drive strategic decision-making and maintain and further expand its competitive advantage in the dynamic global market.

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The use of machine learning algorithms in demand forecasting is essential for Berco Aftermarket in order to significantly reduce the deviation between forecast and actual sales at tonnage level. This technological advance makes a decisive contribution to optimizing production planning and improving the efficient allocation of capital used in inventory management.


1. Enhanced Demand Forecasting Accuracy: By harnessing the power of machine learning algorithms, enables Berco to generate highly accurate demand forecasts. Advanced statistical models analyze historical sales data, market trends, and other relevant factors to predict future demand with highest precision.

2. Optimized Sales and Operations Processes: Improved demand forecasting facilitates streamlined sales and operations processes. Berco can align production schedules, inventory levels, and distribution strategies more effectively, ensuring that the right products are available at the right time and in the right quantities.

3. Inventory Optimization: Machine learning-based demand planning enables Berco to optimize inventory levels dynamically. By accurately predicting demand fluctuations, Berco can maintain optimal stock levels, minimizing excess inventory while mitigating the risk of stockouts. This optimization reduces carrying costs and enhances overall operational efficiency.

4. Working Capital Efficiency: Through better inventory management and reduced holding costs, Berco can unlock trapped working capital. Freed-up funds can be reinvested in strategic initiatives, such as product development, expansion into new markets, or improving customer service capabilities, driving long-term growth and competitiveness.

5. Customer Satisfaction and Loyalty: Reliable availability of undercarriage components enhances customer satisfaction and fosters long-term loyalty. Berco's customers can rely on timely deliveries and consistent product quality, strengthening relationships and positioning Berco as a trusted partner in the aftermarket segment.

To increase the accuracy of the generated forecasts, external industry datasources such as data on mining- and raw material prices will soon be included as input parameters. This maximizes forecast accuracy and enables Berco to make data-driven decisions with confidence and flexibility in a highly competitive market environment.

The forecasts are seamlessly integrated into a dedicated frontend that provides a user-friendly interface for internal users. In the interactive user screen, various user groups can intuitively calculate the forecasts according to their specific needs.

Launched in December 2023, the project aimed to provide a more accurate forecast for the year 2024 and predict the total amount of tons sold by Berco Aftermarket worldwide and in different market segments. The project is carried out in a three-stage process in collaboration between Berco Aftermarket and

The most important factors influencing this forecast include historical sales data, market trends, customer behavior patterns and fluctuations in product demand. External factors such as the economic situation, geopolitical events and changes in industry regulations also have an impact on delivery volumes and will thus be subsequently added to boost forecasting accuracy even further.

Through rigorous data analysis and machine learning algorithms, Berco Aftermarket and have successfully navigated market complexity and provided insights into sales volumes for 2024 and beyond.

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Ganz unabhängig davon, wo Sie gerade stehen. Unser Team steht Ihnen gerne in einem kostenlosen Erstgespräch zur Verfügung. Gemeinsam schauen wir in knapp 30 Minuten auf Ihre Herausforderungen und unsere Lösung.