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On November 15, 2018, SkyDecktective commented on Chanel’s Foray Into 3D Printing :

Great post Arting. As you’ve called out, by utilizing 3D printing, Chanel stands to save time and money by forgoing traditional injection-molding manufacturing processes, increasing the opportunity for flexibility and customization. If we imagine a world in which Chanel is able to go to market very quickly, do they risk losing their luxury perception as they start to look more like a fast-fashion brand?

On November 15, 2018, SkyDecktective commented on Additive Manufacturing at GE Aviation :

Great post Carlos and many great points Carlos. I’m very curious to see how quickly GE can scale this technology not just for all the benefits you outlined above, but also for it’s potential impact on lead time for the broader aviation industry. Shorter lead times would significantly improve predictability upstream and downstream.

On November 15, 2018, SkyDecktective commented on Unlocking the Power of Open Innovation in Disaster Relief :

Great post Kaleigh and interesting comments Cathy. While it can be difficult synthesize crowd sourced information, I have to imagine there are ways that data aggregation can be standardized and streamlined.

On November 14, 2018, SkyDecktective commented on Football and Chess: How Machine Learning Can Improve Playcalling in the NFL :

Very intriguing post Derek. Can we expect to see this next year in intramural football? Jokes aside, I too am hesitant for the league to provide other team’s data to each other. I question where will it stop? I’d love to understand the algorithm’s deployed here a little better, and see how we could improve them. If I took this a step further (given the amount of data being tracked today), I’d love to incorporate as much athlete data into the to formulas as possible. Specifically fitness levels and current playing time to predict current performance levels. Would also like to overlay situational tendencies to further aid in selecting the “best” call.

Great post Samuel. As an admirer of Amazon and the innovation we continue to see from them, I’m excited about this proof of concept. However, I am skeptical of Amazon’s rumored plans to expand this format to 3,000 locations nationally for several reasons. First, as you’ve called out, this will require a significant capex investment in a very crowded competitive space. I’m not convinced the cost-benefit is there just yet. Second, a superior customer experience is not enough to make up for undesirable locations. Amazon is new to the brick and mortar game and will need significant expertise to build it’s retail footprint.

When it comes to Whole Foods, I anticipate a slower adoption timeline based on the results from the Amazon Go proof of concept. I’d expect to see a few pieces of technology focused on improving labor productive pop up in Whole Foods in the short-term.

Interesting post GlossierMasker. The data is clear – these crow sourcing efforts have done an effective job of creating engagement around the the brand and generating buss for Glossier. I’d be interesting in the financial impact of these efforts. How have crowd sourced ideas performed relative the rest of the portfolio?