By Bernhard Langefeld

The additive manufacturing (AM) market is evolving quickly. Just a couple of years ago, AM users faced numerous challenges, including significant CAPEX investment for machinery, limited material availability and a prohibitive diversity of software programs. But as this report shows, new technology and new business models are now diversifying risk and driving innovation.

With machine-leasing and pay-per-use/part models, AM users can make gradual ‘make-or-buy’ selections from a range of options, shifting costs from CAPEX to OPEX. Thanks to this evolution, product innovation is becoming quicker and less capital-intensive.

No more one-size-fits-all

A growing portfolio of machines, processes and materials is enabling new applications for additive manufacturing.

And that’s not all: parts and products that were once unfeasible due to technical or financial restrictions are beginning to emerge. Soon, they could disrupt established markets or even help create entirely new businesses.

Greater flexibility and security for supply chains

The disruption caused by Covid-19 has left companies searching for ways to bring greater flexibility and security to their supply chains. AM can help by providing lean, print-on-demand services that lower inventory costs and shorten delivery times to meet harder to predict production demands.

As additive manufacturing becomes more established in mainstream manufacturing , AM service providers are joining the Tier-X supply chain, able to meet the same governance and quality criteria as conventional suppliers. Keen not to miss out, those same established suppliers are also adopting AM technology. The result: OEMs gain greater access to qualified parts, which can lower their costs.

Such rapid change makes additive manufacturing a complex market. There is significant potential within the new AM customer journey, but leveraging it requires extensive experience and expertise. EOS and Roland Berger can help you maximize the benefits.

This project was developed in cooperation with EOS. We would like to thank the colleagues Thomas Weitlaner , Detlef Scholz and Florian Lassan .