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On November 15, 2018, cah commented on Kevin Stein at TransDigm Group :

It seems like TDG has the early mover advantage over GE in additive manufacturing. I’d be curious to learn how GE plans to build out its AM operations. Since they are a legacy business, they will be afraid to displace existing processes. They will likely move cautiously into full-scale AM manufacturing, leaving TDG a long runway to innovate and leap ahead. TDG should move fast and try to replicate or enhance GE’s core products and yet-to-be-created AM-only products to built their “moat.” With the advantage of time, more experimentation and the talent/resources to back AM, TDG will likely beat them to realize the potential of this technology.

On November 15, 2018, cah commented on Open Innovation at NASA: Impact in Culture :

The threat of open innovation to traditional R&D roles is not one I had considered before reading your post. To be honest, I am surprised that NASA would continue to run traditional R&D given the effectiveness of their open source contests. As an American taxpayer and thus sponsor/beneficiary of NASA’s work, I would encourage them to rely more heavily on open innovation because of the benefits it can yield in speed & cost to otherwise hyper-challenging problems. Perhaps in the R&D group, they can “innovate” by adopting a hybrid approach that maintains regard for national security. Rather than release questions to the entire public, and give small awards, they can appoint a group of 100-1,000 “insiders” and share problems inside that community, offering much higher reward incentives for contributing. In an ideal world, they could even bring in these insiders to collaborate/hone ideas. This would hopefully improve the allure/prestige of participating in open source innovation, while also minimally impacting the time to solve problems.

On November 15, 2018, cah commented on Adding Value In Nike’s Production Line :

Your post is fascinating and eye-opening as far as the applications of 3D printing. I imagined that Nike was building entire shoes from the sole up with a single machine. But their process makes 3D printing seem complementary to existing manufacturing processes, which are driven by assembling numerous pieces together. The soles are made in one place, the fabric sides & upper in another. I wonder if Nike could save cost and continue building their assortment through leading the charge in 3D printed fabrics. Are there particular textiles – and at a higher level, consumer benefits like insulation/comfort/breathability- that can only be achieved through this method? I am excited to see how else Nike embraces this technology moving forward.

On November 15, 2018, cah commented on Great Scott! What’s next for open innovation at LEGO? :

The toy industry seems particularly well-suited to open innovation, since kids demand an unending stream of new stimulants and parents don’t want to ramp up on dozens of different brands as their children mature. Much easier to rely on the same brand to continually undergo radical but age-appropriate innovation. I wonder how else LEGO could incorporate this approach – could they look outside their existing set of materials (plastics, metals) for example and build soft toys using this model? Could they use the “crowd” to generate toys for difficult-to-crack segments, like “girls interested in science”, etc? Seems like the applications are endless and this could be a sustainable source of new ideas going into the future.

I am interested to learn about the application of ML to an industry whose operations are not in the digital/technology space. But of course this makes sense! I wonder what variables they are capturing about the CnF and what (at present) is the best-predicting variable of the production rate. Is it the structure of particular wells? Or the material they are ‘made of’? Or is it the chemical makeup of the oil itself? I would be curious to learn where their analysis started and where they ended up after recruiting ML to the problem? What other variables do you think they should consider incorporating into the ML to improve the predictive power of their model?

This product is amazing – I think Sephora may have used similar technology to execute their own AR makeup application tool? I like how here, the product 1) detects skin tone 2) selects SKUs and 3) can virtually apply them. How much easier could the purchase decision get? I do wonder though how l’Oreal will make use of this tool since going direct to beauty brand .com and mobile apps is uncommon versus using retailer apps with broader product selection and better delivery/returns service propositions. It would almost have been more natural for an Amazon or Sephora to bring this company in-house. And I question L’Oreal’s ability to manage data infrastructure and talent vs. a technology firm… But power to l’Oreal and I hope (as a consumer) they make this a popular & well-integrated part of their experience!