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Wow, I can’t believe this has been used since 1997! This is an application where 3D printing makes perfect sense (or, as you say, is the only way it’s even feasible). The ability to heavily customize is really crucial here. I’m surprised about how low the barriers to entry are now; basically then Invisalign only became famous and successful because they were able to take advantage of a still-unknown technology? And essentially their relative competitive advantage can easily be eroded? Yikes…

I definitely agree then that lowering prices could be crucial. If anyone comes in and undercuts them, this seems very high risk. I would disagree though that they should reduce reliance on advertising (think about Casper which took a not-the-most innovative product but made it seem hip and changed delivery/channels alongside cheap prices). Especially if they move into a D2C model, refreshing the advertising to consumers might actually be crucial. I would think though that having strong relationships with orthodontists would be most effective to potentially keep out other competitors. I can’t imagine not going to one at some point to get these made… Does Invisalign employ any of their own? Wonder if they have considered a telemedicine model where you can connect over the internet and somehow get everything made. Really interesting!

On November 14, 2018, Boreal commented on Taste the Future: 3D Printing Chocolate at Hershey :

Wow, this definitely reminds me of Embr Wave, except at least there was a plausible reason for what need they thought they were addressing. I’m curious if the author found any more information about how 3D printing can change (/ improve) the nutritional profile of chocolate products. If 3D printing remains a gimmick that helps Hershey entertain guests at their theme park, I don’t think they should invest in it any further, much less try to scale. Maybe this is the wrong assumption, but I also imagine that manufacturing costs of chocolate bars are not so astronomical that it would make sense to invest in 3D printing to drive down costs. The only way I can see this being an interesting development is on the nutrition side… and even then I question why 3D printing would need to be involved, versus a change in recipe that could be implemented in their normal manufacturing processes.

On November 14, 2018, Boreal commented on Crowdsourcing snack food trends at PepsiCo :

I agree with TOM 199 above that asking for full-on recipes (even at an early stage) would potentially limit the open innovation to a narrower set of people. I also think that this could limit the amount of innovative ideas that come out; by artificially asking those with much more limited resources / food science backgrounds to develop things within their potential capabilities.

I wonder what drives the discontinuations of the flavors? To me that signals that Pepsico is not serious about the products and that this whole thing is a gimmick, not a true source of innovation. Have there been any examples of the health food / substitute crowd-sourcing being successful and actually brought to market? I wonder also how Pepsico filters through all the ideas that come through such a channel. Did they create any teams dedicated to the evaluation of crowd-sourced ideas? How do they filter that to their product development teams? Also – without the Facebook-style voting for new flavors, how do they determine what are actually good ideas versus the idea of one individual?

On November 14, 2018, Boreal commented on Crowdsourcing at Amazon: Democratization of TV / Film Content :

I think this a really unique perspective of how Amazon is using some of these innovation trends unsuccessfully. I would argue that “example 3” is not that far from what already exists today; I was under the impression that pilots are shown to potential audiences and production companies move forward the ones that have best reactions. If the main difference was the ability to have many people in a room give quick feedback versus waiting for a video product to be watched on others’ own time around the world, it feels like there could be different changes to make that turnaround time shorter.

Just a general thought around “example 1”; this is a real shame as I think open-submission has the potential to find some really incredible and unique stories. I hate the Marvel-esque lack of original content for blockbusters and the industry trend of sequels & remakes. I wonder why Amazon didn’t decide to leave the open submission open, just so that they have an option to look at them? And then they would have to bid to produce one that was really good, similar to how it works outside of the crowd-sourced platform. I guess we would need to know how many submissions they were getting vs. the number of quality ones that are worth looking into further… Maybe that’s a future machine learning challenge.

On November 13, 2018, Boreal commented on Machine Learning: Dating’s Saving Grace? :

This is an incredibly interesting application of machine learning; thank you for doing the research and sharing! This could be genius, but also I have a ton of reservations about where such technology could go. Dating definitely feels like an area to me where ‘input in’ can have a dramatic and potentially negative impact on output. I wonder if / how Hinge has thought about biases in dating preferences and what role they play in potentially re-enforcing them. I’m imagining the potential for, for example, fewer partners of a different race being presented. Are there ways to correct for this (or is Hinge correcting for this already)?

I’m also curious about how much of the input is related to the way users interact with the platform (does it really matter that this person regular likes photos instead of quotes?) vs. other ways an algorithm could be taught to assess compatibility (for example education and work, or potentially some type of machine learning algorithm to judge attractiveness). It seems right now it’s more of the former, but if Hinge found success with the latter, would they move toward it or incorporate it into their algorithm? This seems like an interesting, potentially slippery slope to me. For example, does that reinforce some level of class-ism? Does that even matter since people are probably doing this effectively already through their own assessment process?

This is a really interesting application of machine learning. What’s not fully clear to me is where the $86M in savings thus far come from. I would expect what the author describe as “active inventory management” to be the way cost savings are achieved, but I suppose it’s currently more of a static way of tracking based on inputs? I would be curious to understand the distinction further. I wonder how Walmart will be able to leverage this to even influence growers on the supply side. Did the author find any information about how this could address overproduction as well as a way to reduce waste, or has it been mostly viewed to manage distribution?