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On November 15, 2018, lumos commented on 3D Printing Toyota’s Headlights: The Sourcing Decision :

Well written piece! It appears the main driver in all sourcing decisions at this point boils down to cost and time, and in particular projected costs going into the future / how to maintain the competitive edge. I wonder if there are estimates on the timeline of development for AM technologies, and at what point the economics and production times will make sense. As AM technology develops and becomes economically feasible, it would make sense for Toyota to vertically integrate, especially as one of the main functions of suppliers (i.e., holding inventory) will no longer be relevant.

On November 15, 2018, lumos commented on Printer-to-Table: The Next Food Movement? :

This is such an interesting application of additive manufacturing! I do wonder about a couple things. Firstly, I believe the idea of a printer making food out of a capsule has limited appeal and is limited in functionality to a specific set of meal types (e.g., I see a lot of desserts and appetizers on Foodini’s website). Those meal types may not need to be freshly prepared in the first place, and so therefore the usage of Foodini may be rendered obsolete by restaurant / food / bakery delivery services. Secondly, I believe the target market for such a device will be quite niche given the limited range of options, and the fact that it does not solve a “burning need” that people have (at least in their homes). Perhaps for restaurants this may work once it reaches higher production speeds and lower costs, but even then it has the flavor of a “generically” produced meal that may limit the appeal of the restaurant. Customers go to restaurants for the experience that each dish was prepared uniquely for them by the labor of another human being. For now, Foodini lies in the realm of novelty.

These are some intriguing ideas of the application of open innovation! I am particularly interested by the idea of our generation “voting with our dollars” as opposed to other forms of civic participation. Especially due to the fact that dollars do not go by “one person one vote,” will that be an effective way of influencing the way cities pick their projects? Funding an existing initiative is one matter but influencing with dollars which projects get done is another matter entirely. This may also cause other externalities in terms of transforming municipalities into more “business-like” organizations; for instance, cities may start engaging in in marketing campaigns for particular projects, which may be another drain of resources and energy.

On November 15, 2018, lumos commented on Open Innovation in Pharma R&D :

This is a fascinating case of open innovation, particularly because this model seems rare in other industries. My main concern would be that the financial incentives of all of the players are not aligned to create a system of true collaboration in this case. I would imagine that most large pharma companies would have the resources and motivations to do their own development (at least in the U.S.), and that only particular drug types would be fit for such an open innovation model, especially for products that offer a less compelling financial return.

On November 15, 2018, lumos commented on EasyJet: Reducing Delays with Machine Learning :

This is such an interesting application of machine learning. I agree with the aforementioned comments that shared data could be anonymized to remove conflicts of interest; however, this may reduce the specificity and the quality of the dataset that would otherwise make it “filterable” and valuable. I don’t see the risks of sharing data as a competitive disadvantage, as it is a highly commoditized industry in which competitive edges are easy to recognize but hard to implement. It is truly how Easyjet decides to implement the findings of the data that will be interesting to observe.

Intriguing article! I would be curious to see what Fitbit does to integrate the data that exists across the healthcare provider landscape. One of the major challenges of the U.S. healthcare industry is the lack of coordination of data for any particular patient, both longitudinal data as well as data scattered across multiple providers. One area that Fitbit could focus on is becoming the central repository of the data on any specific individual through the course of their life, and play a key role in the penetration of outcomes-based payments.

The second question is also interesting, but in the realm of what Fitbit does, we are only talking about behavioral health outcomes, rather than genetic!