Product Carbon Intelligence

Use Case

Tier 1 supplier (electric compressors)

electric compressors, automotive parts, automotive, supplier, automotive supplier

The customer offers automotive parts, electric compressor units, as a Tier 1 automotive supplier. The Carbon Footprint-calculation for this customer is challenging due to a high number of components, suppliers and manufacturing locations. The customer's previous calculations involved regular manual data imports into a central Excel spreadsheet. This type of calculation is not only time-consuming, but also provides a Carbon Footprint that is too imprecise, and often results in a crash of the file. The customer wants consistency, a source where information is a single source of the truth, cloud-based, and holds the ability for a diverse product portfolio. The Carbon Footprint calculation for this customer is particularly complex due to the involvement of over 180 components, 200 suppliers, and multiple manufacturing locations. PCI provides the client with comprehensive capabilities including: - Accurate Carbon Footprint calculations, - Advanced analytics, - Efficient data export functions. This solution will streamline their processes, enhance precision, and significantly reduce the time and effort required for Carbon Footprint assessments.

Use Case

Leading German industrial conglomerate

industrial conglomerate, multi-industry, multi industry, material & services, services, industrial solutions, industry, industrial

A major German leader in Material Services & Industrial Solutions looking for a more transparent, scalable, and standardized solution to calculate carbon footprint. The previous Carbon Footprint analysis was developed in-house and based on broad product categories. The analysis of the in-house system only included weight-based calculations for their main product categories. The company seeks to delve deeper, expanding the analysis to individual products and services with various input units beyond simple category-based methods. We conducted a custom emission analysis of the customer's Spend Data Warehouse, focusing on the entries related to purchased goods and services to calculate Scope 3.1 emissions. By analyzing an extensive Excel file containing over 22,000 entries with various input units (such as kilograms, euros, and liters), we achieved a remarkable accuracy rate of over 90%, completing the analysis in a matter of minutes. We are currently in the process of integrating our calculation API into the customer's emission reporting system, which will further streamline their reporting process and enhance the precision and efficiency of their emissions calculations.

Use Case

Engineering company (industry furnace)

engineering, engineering company, industry furnace, furnace, industrial, industry

A medium-sized German company in need for calculation of its Product Carbon Footprint for industry furnace. The company wants to gain a competitive edge in public tenders and be a first mover in the sector.  They want calculations on product-level to be more precise, faster and scalable than their current excel-based system. They lack expertise and want results that are verifiable by a 3rd-party auditing process.   We employ advanced AI for rapid and precise data analysis, significantly reducing the time required for evaluation. Our calculations comply with ISO 14067 standards, resulting in a streamlined verification process that reduces costs and reinforces the credibility of the analysis. The comprehensive calculation includes an analysis of: - The components, - Transport from the Tier-1 supplier to the manufacturing site, - The production process, - Delivery (transport to the customer). Our implemented system also allows for scalable calculation of PCFs for other products in the future with ease, ensuring that the solution remains efficient and adaptable as the product portfolio expands.

Use Case

Forecasting methodology in the insulation material industry through AI integration

Dämmaterial, Dämmaterial Industrie, Insulation Material, insulation material industry, supplier, construction, construction industry

In the complex industrial landscape of insulation materials, in which multiple market segments and diverse product lines coexist, precise forecasting and strategic planning based on this is essential. For a multinational manufacturer in this sector, forecasting plays a critical role, as it is the basis for budget distribution across the various departments. Previous forecasting methods relied on manual calculations and the use of Excel-based solutions, which often only work with simple averages. This high level of manual and personnel effort not only limited the accuracy of the forecasts, but also their timeliness and frequency. The forecasting methods were also vulnerable to unpredictable market changes, which often led to delays in the supply chain. The introduction of an AI-based forecasting tool marked a turning point for the manufacturer. This advanced tool not only uses internal historical data, but also integrates external influencing factors such as special calendar events, past and future expected inflation indices and building permits into its analyses. As a result of this comprehensive data integration, the accuracy of forecasts was significantly increased to 91.4%.The increased forecast accuracy led to numerous positive effects on operations. A more reliable forecast enabled a more efficient and targeted budget allocation, which made it possible to achieve significant cost savings. Improved predictability and speed of response to market changes also contributed to a reduction in delivery times. The implementation of AI technology thus strengthened operational safety and sustainably improved the company's competitiveness.By using innovative AI technologies in forecasting practice, the insulation material manufacturer was not only able to optimize its processes, but also make it more adaptive and resilient to market fluctuations. This case study impressively demonstrates how technological advances can be used specifically to solve specific industry-specific challenges in order to promote operational efficiency and economic stability.

Use Case

Aerospace supply chain planning

Aerospace, industrial, industry, supply chain, aerospace industry, supplier

Customers from various industrial sectors, including original equipment and the aftermarket, are facing similar challenges. A specific example of this is a supplier to leading aerospace companies. It must predict the developments of over 4,000 material types in various market segments. For one of its main end customers, the planning processes were previously carried out manually and exclusively using Excel. In the past, this method of planning led to inaccurate results, which in turn led to both inventory shortages and excessive inventories. To overcome these challenges, pacemaker.ai provides a solution that provides automated, regularly updated forecasts. These forecasts serve as a basis for replenishment planning and help to precisely define the quantities to be purchased at the level of individual products.The planning is based on a forecast period of 18 months, during which the delivered materials are carefully reviewed. An important feature of the pacemaker.ai solution is the implementation of a five-level grouping structure. Within this structure, Cluster A materials are given priority, with a total of 215 articles being prioritized based on ABC/XYZ analysis.The current accuracy of pacemaker.ai's predictions is over 80%. Continuously refining and adjusting these forecasts is a key part of pacemaker.ai's commitment. This shows the company's efforts to constantly optimize its customers' supply chains and maximize their efficiency through innovative approaches in data analysis and machine learning.The integration of advanced, data-driven forecasting tools such as those from pacemaker.ai can significantly help solve traditional supply chain planning problems. The use of automated systems not only improves the accuracy and efficiency of inventory management, but also prevents costly overstocks and shortages. This represents enormous added value for suppliers in highly dynamic industries such as aerospace.‍

Use Case

Revolutionizing sales forecasting in the filter industry with AI-based solutions

filter industry, oil filters, air filters, automotive industry, automotive, filter, filters, filter supplier, suppliers, supplier

A leading supplier in the automotive industry faced significant difficulties in predicting monthly sales of oil and air filters for the B2B market in Europe, Russia and the UK. The volatility of the “call-off” data for these products was exceptionally high and showed fluctuations of up to 80%. The previous forecasting methods were based primarily on manual calculations by a team of four employees. This approach often led to inaccuracies, particularly for products with low sales volumes, which in turn was offset by high inventories. These inventories represented a significant capital commitment of billions. With these challenges in mind, the company turned to pacemaker.ai, a specialist in AI-powered forecasting technologies. Pacemaker.ai developed an advanced machine learning system that was specifically configured for the supplier's needs. This system integrated not only historical sales data, but also industry-specific influencing factors such as motor vehicle registrations, pollen count data and air pollution indices into the analysis. The introduction of the automated demand forecasting solution fundamentally transformed the company's sales forecasting. The forecast accuracy was significantly increased, which led to a reduction in forecast error of an impressive 41%. This improvement extended across a portfolio of 2000 products. In addition, manual planning effort was significantly reduced, which enabled employees to focus on more strategic tasks.Automation and increased accuracy of sales forecasts enabled the company to manage its inventory more efficiently and free up capital that had previously been tied up in oversized inventory volumes. These resources can now be invested in innovative projects and the further development of the product range. The success of this project demonstrates the potential of AI-based technologies to transform and sustainably improve traditional business processes.