Where digital meets physical innovation: Reverse salients and the unrealized dreams of 3D printing
Thierry Rayna, Joel West
kHUB post date: August 07, 2023
Originally published: June 09, 2023 (PDMA JPIM • Vol 40, Issue 4 • July 2023)
Read time: 60 minutes
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For more than three decades, enthusiasts have predicted that direct manufacturing enabled by 3D printing would inevitably supplant traditional manufacturing methods. Alas, for nearly as long, these utopian predictions have failed to materialize. One reason is a flawed assumption that hybrid digital-physical systems such as 3D printing would advance as rapidly as purely digital innovations enabled by Moore's law. Instead, like other examples of cyber-physical systems (CPSs), technological progress in 3D printing faces inherent limitations that are emblematic of the differences between CPSs and purely digital innovations. As with any complex CPS, improved performance of a 3D printing system has been limited by that of its key components—the sort of limiting problem previously defined as a reverse salient. Unlike previously studied technologies, several reverse salients for 3D printing performance have neither resolved nor signs of resolving soon. Here we analyze these key reverse salients, and show how they have hampered the suitability of 3D printing for direct manufacturing and other predicted applications. We contrast predicted versus actual capabilities for 3D printing-enabled transformation in six key areas: product innovation, mass customization, home fabrication, distributed manufacturing, supply chain optimization and business model innovation. From this, we suggest opportunities for greater realism in future 3D printing research, as well as broader implications for our understanding of CPSs and reverse salients.
- 3D printing points out the risk of planning around utopian predictions of technology performance.
- We show how persistent reverse salients can slow the performance and adoption of a hybrid digital + physical technology.
- We offer a framework for identifying which systems and systems components are most likely to face these persistent performance limitations.