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On November 15, 2018, Soriano commented on 3D printing in Automobile: End of Invention from 100 Years Ago? :

I liked this post! I found your second question quite interesting: who would be the target customer if people could design their own cars? I dont believe there will be a point anytime soon where customers will be able to design their own cars. 3D printing might be able to offer a bit more customisation, since larger order sizes of the same parts are no longer important, but designing a new car is another story. The reason why I think that is because car manufacturers would need to thoroughly test the quality of each permutation of car rather than just producing a lot of units of a car that is already tested. The question for me is: how could this testing be speeded up if customers really wanted to design their own cars?

On November 15, 2018, Soriano commented on Future of Footwear? Additive Manufacturing at Adidas :

I love this post. As I read it, there are two points I would like to comment on:

1. On your point about customising shoes: I found very interesting that they will be able to do this. Even though most of the costs for 3D printing are variable costs, I assume that there are substantial additional costs for designing and developing a new shoe for a specific person (measuring, testing, etc.). Would the costs for this not be too high? What would be the price for such a shoe?
2. On your question about having a competitive edge: I dont think 3D printing will take any competitive edge away from Adidas. For Adidas, as well as for other sports brands, the main competitive edges that they have are design and brand recognition. None of this will change with the introduction of 3D printing. The operational costs might change, but I dont see that as a big differentiating factor.

On November 15, 2018, Soriano commented on Autonomous Stock Replenishment at Online Retailer OTTO :

I like this article! I was not aware that Otto was using machine learning to this degree. I find your second question (is this advantage sustainable) super interesting. Businesses are moving towards a world of automation in which the efficiency of a company is mainly driven by the data available and the quality of the AI/ML algorithms. Large companies will have more data and therefore an advantage (at least temporarily). But the main sustainable advantage will be the ability of a company to hire people who are able to develop superior algorithms. Companies will therefore need to switch from algorithms provided by 3rd parties to algorithms that they developed in-house and are better than the rest.

I like this comment and I think that Open Innovation can indeed help MBTA. What surprises me is the very low percentage of solution that MBTA is implementing. The article mentions that MBTA gets this ideas “for free”, which means that they are not offering any price to the people coming up with the ideas. I wonder if this might be the reason why the implementation rate is so low. Offering no price will not incentivise many people to participate in the competition and therefore the very good people who could deliver the best solutions will probably choose to take part in other online challenges, where they can actually win a price. I would suggest MBTA to start offering rewards.

On November 15, 2018, Soriano commented on Betabrand: Too Much Open Innovation? :

I liked this article and I think that this company is using Open Innovation in a very clever way. The only challenge I see is the very low barriers for any competitor to do exactly the same. Since it looks like Betabrand has no traditional design, all is coming from outside. Therefore, if another company decides to open a similar platform where the customers are the ones designing and buying the products, then I dont see what the benefit would be of using Betabrand over the other company. Therefore I do think that incorporating some more traditional design could help the brand. Maybe this traditional design could leverage AI to analyse past designs and come up with new designs that are not necessarily those proposed by the customers.

On November 15, 2018, Soriano commented on Printing the Future of Athletic Shoes at Adidas :

Loved this post! I found surprising that 3D printing can be cheaper than traditional manufacturing for large-scale production. I found the point about being able to design a unique shoe for each customer on-site extremely interesting. However, is this really an option? What would be the price that Adidas would need to charge to this customers? I assume that every customer requires new measurements, transferring data to the 3D printing, printing it, etc. and I cant imagine this being profitable at “normal” prices.

I agree that 3D printing can bring GE a lot of advantages in terms of prototyping. Many of the advantages mentioned in the article (e.g., shorter life cycles) referred to prototypes but not necessarily to large scale production. While 3D printing brings less cost advantages in very large scale production, I wonder what the threshold would be in terms of number of units to use 3D printing vs. traditional manufacturing (after the prototype is built). Would 3D printing work for the entire production or just for the prototyping? Thanks!

On November 15, 2018, Soriano commented on Barrick – mining data for gold :

I liked this post and agree that AI can offer a lot of benefits, especially seeing that currently many of the decisions are taken by humans just based on their based estimates. The only challenge I see with this is how to accurately link the change of the different parameters (variables in the machines) to the actual gold output. Since the input is probably not always the same (different levels of gold in the ground, different density, other materials in the ground, etc.), I assume that for different situations the optimal setup of a machine might be different. And Im not convinced that you can accurately assess in what kind of situation you find yourself using only machines. I guess my question would be: would AI work accurately in a setup in which the input is not always the same?

On November 15, 2018, Soriano commented on Machine Learning vs Poachers :

I liked this article and completely agree that AI can help to solve the issue of poachers. You mentioned that data volume is one of the key drivers to build good predictions of where poachers will be. In terms of getting more data, I see other alternatives. While the most evident one would be installing GPSs in some of the animals to track their movements and make sure that patrols are nearby, other startups are using alternative data sources, such as audio recordings ( ). I really believe that going beyond the use of drones can be very helpful to make the AI predictions more accurate.