To add to the debate around consumer preferences, I think 3D-printed jewelry fits nicely into the millenial desire to have greater customization and a greater transparency around sourcing. Within the wedding ring industry in particular, customization is key. Everyone wants something that is completely unique, both because there is a trend of diverging preferences, and also because the wedding ring is such an intensely personal and meaningful item. Knowing that a ring was custom-made (and no other exactly like it exists in the world) should lead a potential pricing premium opportunity. If costs do come down, that would lead to a very attractive margin. There may also be a way to re-create a special customer experience that appears to be so critical in the wedding ring industry by having an intimate setting for talking through the design and creating a special process for receiving the wedding ring.
Fascinating! It sounds like NASA has done an excellent job leveraging crowd-sourced innovation. One thing I do worry about is open innovation within government as part of a strategy to do more and more with less and less. It seems like there is sometimes a perception that open innovation is the secret sauce that can allow government agencies to thrive despite rapidly shrinking budgets. Unfortunately, I think this is largely a fallacy. While open innovation can be a useful tool, it relies on a strong and well-funded organization to truly thrive. If open innovation at NASA is to succeed long-term, they will need the ecosystem, in the form of highly trained PhDs and researchers that are able to devote their entire capacity towards these resources. While open source innovation should play some role in that process, it cannot replace it. Hopefully policymakers will not treat open innovation as a silver bullet, but rather think of it as a multiplier that can make existing efforts more effective.
To respond to a couple of the previous comments (@Marybeth and Marius) around the advantages of 3D manufacturing (i.e. what justifies the investment/higher production cost), I would not underestimate the ability to price at a higher price point given the higher levels of customization. Being able to have your name on your shoes like NBA or soccer players do is something that people have a willingness to pay for (currently this service for soccer cleats is typically in the $60-100 range). Additionally, there should be few limits to how creative people can get around customization in color and form. This is potentially an area of overlap with open innovation, as you could have a decentralized development of 3D manufacturing designs that customers can chose from. This is potentially an absolutely massive market and seems like an excellent value proposition relative to existing options.
One day, we may actually be purchasing shoes as IP (kind of like TV shows on demand) and just printing them on our home 3D printers – imagine that!
This is very cool! In addition to some of the sources of alternative data you mentioned for credit scoring, there is also a lot of exciting work happening around psychometric credit scoring that is highly relevant for the SSA context. Psychometric measurements can make a lot of sense in data-poor environments, where traditional sources of data (or even alternative sources) are less readily available, and can often be used in combination with other data sources. The way it works is essentially that a loan applicant will take some kind of test that includes behavioral questions and the traditional types of loan questions (income, assets, etc. ). Based on the results of the test, but also how much time is spent on each question, what key strokes you make (e.g. how you use an income slider), and other seemingly irrelevant data, the algorithm generates a credit score that can be used for loan decisions. An important consideration here is that it does open up the possibility of applicants trying to game the system. For this reason, it’s often necessary to switch up the questions and/or scoring on a regular basis. EFL (https://www.eflglobal.com/) is a company that has successfully applied these types of credit scoring methods across Latin America, Africa, and Asia.
Love it! A couple of thoughts:
First, I completely agree that motivating contributors is critical. The royalties definitely play some role in this, but a recent study suggested that for open innovation it’s often the combination of monetary and non-monetary incentives that is the most effective in motivating contributors (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.628.9113&rep=rep1&type=pdf). At LEGO, this appears to be happening through credit on the box, as well as through recognition within the online community. Additional brainstorming could focus on whether there are other non-monetary forms of compensation that might be effective (and cheaper for LEGO). This could include some kind of gamification element, or access to additional LEGO events/inside information.
Second, I’m a big proponent of the third party limited edition circulation similar to the Iphone developers model. Decentralization does carry some risk, as you lose a certain amount of control over the process. However, there is a tremendous benefit that comes from the more engaged developer base. As long as there are some limits around what can be created (quality standards should still be maintained to protect the LEGO brand), this could be the future of open innovation at LEGO.
Fascinating! What I find particularly appealing about Kaa is the ability to perform A/B testing. One of the limitations of machine learning, both in agriculture and more broadly, is the ability to generate exogenous variation to help generate a causal estimate. Even with lots and lots of data, if you don’t have sufficient variation, your estimates will lack precision, and if this variation is driven by confounding variables/if you have concerns about omitted variable bias, then your estimates may not even be accurate. For this reason, the ability to do A/B testing is a substantial innovation, as it allows experimental causal estimate to be generated that should be both precise (assuming a large enough sample size) and accurate (assuming the experiment is correctly run).