How to Forecast the Futures of 3D Printing

3D printing (additive manufacturing) is pretty hype right now, anointed so not just by Gartner (see the top of the hype cycle below), but also by the proliferation of exuberant articles in mainstream publications over the last 18 months or so.  How are all these journalists and evangelists arriving at their images of the 3D printed future?  Truthfully, in most cases I’m not exactly sure, but I suspect that in a fair number of them folks have given over to expanding on intuitive extrapolations of what they’re seeing in front of them.

Gartner Hype Cycle Special Report 2013

Digital fabrication certainly looks poised to become a major component in our future economic systems, and additive manufacturing will clearly play a major role in any digital fabrication scheme.  Given the inherent appeal of 3D printing systems, how they decentralize (not democratize, as so many writers like to mistakenly say) the tools for fabrication and how they enable such freedom of creation, it is both easy and fairly logical to assume that 3D printing has some role in our future.

But how can one usefully think about the future evolution of 3D printing?  How can we generate useful images of 3D printing’s future? (by “useful” I mean both logical and insightful though not necessarily probable).

Well, let’s do a little exercise in forecasting.  In fact, let’s consider two different approaches to qualitatively forecasting the futures of 3D printing.  We will call the first method displacement analysis and the second coevolutionary forecasting.  The former method is based partly on assumptions about the expected uses of a new technology and focuses our thinking on the changes that have to happen for those uses to come about, and how our expectations themselves might have to change.  The latter method leaves the future more open-ended and focuses our attention on how a given technology will evolve in connection with related and/or necessary enabling technologies and systems.

The Displacement Analysis

In displacement analysis, we would first think about additive manufacturing in terms of the capabilities it presents and the applications for which people currently do and hope to use it.  Then we map these capabilities and applications against a picture of the systems and processes in place in society today.  What’s displaced?  What has to be reworked?  Where are the systemic connections that have to be made to make the technology work as hoped?  And socially and institutionally, what stakeholders are threatened, and which are empowered?  Where’s the push-back (and what forms would it take) and where’s the natural incentive for investment?

As a result, we redraw the picture of the industry or societal systems of which our new technology will be a part and we identify the types of changes that have to occur for that new picture to become a reality.  And in the process we are forced to think about how our expectations might be altered when confronted with the many forces and relationships in systems that influence both stability and change.  Having been forced to think so systemically, we can end up with a picture of the future different than the one with which we started out.

The Coevolutionary Forecast

Our second approach also asks us to see our new technology as part of an ecosystem, but rather than working backward from the systemic impacts of a new technology it progresses forward from today by asking how a new technology would coevolve with other developments.  In contrast with displacement analysis that is anchored by our assumptions or expectations for a technology’s future use, this method asks us to start by identifying related parts of the ecosystem and then to think about how these different but related technologies, institutions, and values would logically coevolve as developments and changes in each part feed back to the other parts.

Coevolutionary forecasting is therefore predisposed to producing timelines of change (what I have always thought of as coevolutionary change tracks).  In the process of doing this kind of forecasting we are often “following our nose” as it were, following sequences of interactions that can take us to fairly unexpected places.  And that really is the key value of this kind of forecasting: it provides a structure for finding unexpected or counter-intuitive pathways into the future.

And so, what then would be our two different forecasts for the future of 3D printing?  Well, that will just have to wait for second part of this piece…