About Deutsche Bahn (DB)

Deutsche Bahn (DB) is the largest railway operator in Europe. They are responsible for the operation of the national rail network and infrastructure in Germany, as well as providing international rail services. DB started using 3D printing in 2015 and is a global leader in this field for the rail industry. Whether plastic or metal, parts for vehicles and infrastructure or auxiliaries in their maintenance: over 100,000 parts have been engineered in more than 500 applications using around 12 different AM technologies. By 2030, around 10,000 different components are targeted to be stocked solely for digital manufacturing.

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The Challenge

To answer incoming Requests for Quotation (RFQ), the Additive Manufacturing (AM) team at DB previously had to perform a number of tasks manually, including:

• Performing feasibility checks to ensure that AM is the most appropriate manufacturing approach for a given part or assembly. Up to now, the team has primarily based its quotation management on empirical data and manual quotation requests from partners, which will no longer be sufficient for the future increase in projects.
• Estimating the cost of AM parts using a variety of simplifications and assumptions regarding material cost, machine time, labor costs and any pre/post processes.
• Comparing different AM technologies to select the most cost-effective and sustainable option.

These tasks are repetitive, time-consuming and error prone. In many cases, spreadsheet calculations vulnerable to errors have been used to carry out these tasks.

Implementation

DB implemented 3D Spark, a software solution that automates AM technology selection, costing and pricing based on the manufacturing reality of DB. 3D Spark uses artificial intelligence (AI) to analyse 3D CAD files, 2D drawings (PDFs) and metadata to generate accurate manufacturability, cost, lead time and CO2 calculations.

• Sales can directly receive RFQs from customers and then efficiently generate smart quotes based on real production costs and market price insights.
• Purchasing/Procurement receives instant-target prices from their listed suppliers, speeding up Make or Buy decisions and supplier selection. RFQ and quote bureaucracy decreases significantly both on the requester and supplier side. Procurement team is also technologically better informed for negotiations.
• AM management team has instant access to cost, lead time and CO2 savings in a responsive global dashboard. Reporting to higher management is streamlined.

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Results that matter

The implementation of 3D Spark will lead to a significant decrease in cost calculation time, while achieving an accuracy of ±5%. Namely, the implementation is expected to cut the cost calculation time by two thirds, so that their AM team now has more time to dedicate to actual customer challenges and generate revenues.

Using the 3D Spark platform, DB can now also track the amount of CO2 emitted while manufacturing a part and benchmark this data with other manufacturing technologies to easily identify the best manufacturing approach.

3D Spark’s built-in market price insights tool has given DB the ability to compare their prices with average market prices making the quotation process more efficient.

Benefits

The use of 3D Spark has provided DB with a number of benefits, including:

• Instant accuracy up to ±5% of AM costing estimates.
• Improved efficiency of AM workflows.
• Increased enabling to benchmark different manufacturing technologies.
• Reduced time and resources spent on manual tasks.
• Improved reporting and visibility into AM costs.
• Improved sensing of cost trends whilst optimising AM processes.
• Instant assessment of the carbon footprint of different manufacturing options.

‍Conclusion

The use of 3D Spark has enabled DB to improve the accuracy and efficiency of their AM costing processes. Benefits include instant cost estimation, improved workflow efficiency and reduced time and resources spent on manual tasks. In addition, DB can better track the cost of AM parts over time, which has enabled them to identify cost trends, discover further opportunities and optimize AM processes.

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