Patricia Andrade

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On November 15, 2018, Patricia Andrade commented on LEGO: Leveraging the Building Blocks of Open Innovation :

LEGO is clearly in a unique advantage to implement open innovation given its loyal fanbase. I would encourage them to continue investing in these processes as well. I am interested to see how they define their target market going forward using open innovation. Will using an existing fanbase to acquire ideas bias them to keeping that market, as opposed to trying to reach out to new demographics?

On November 15, 2018, Patricia Andrade commented on Teaching an Old Bank New Tricks: Open Innovation at Barclays :

This is a very interesting application of crowdsourcing. Something I was left wondering is how this might impact them in terms of competitors. If other (similar) banks begin to use the same process to drive product innovation, how would Barclays ensure a competitive advantage? By this I mean, if product innovation is coming from crowdsourced ideas, what would stop other banks from getting the same ideas from customers?

On November 15, 2018, Patricia Andrade commented on RIDGES Run Deep: How PepsiCo Delivered on Crunch through 3D Printing :

Very interesting! I think your question on organizational design is extremely relevant and it applies across organizations who are beginning to use new technologies (machine learning included). As new processes develop to change how we prototype, how we produce, or how we analyze data across functionalities, organizations need to be rethinking how their organizational design needs to shift to adapt to new needs. It is likely that, as mentioned, organizations are beginning to build divisions of innovation across units. However, as these processes become more entrenched in day to day processes there will likely be a need to have 3D printing experts imbedded within units as opposed to having an ad-hoc team.

This is such an interesting application of 3D printing! Innovating in this area has the potential of significantly giving the US a leg up in terms of military technology. I wonder if other countries are beginning to think about this as well. I would assume Russia and many European countries would be thinking about matching these capabilities the moment they found out about this. Of course, this requires significant capital investments.

I do think the point on the changes in talent acquisition and training that this implies is extremely important. Apart from having to train engineers to be able to operate these machines, they will also have to build up their legal teams as they begin to obtain the intellectual property of this equipment. Likewise, as the military adopts a larger role in producing materials as opposed to sourcing it from third parties, they will need to begin hiring experts in manufacturing. As the application of this technology is broadened it will be interesting to see a shift in profiles of people entering the military.

The research Freenome is doing is fascinating! Like Lindsey, I think that defining a control group for these studies will be their biggest challenge in developing an adequate algorithm. I am not very knowledgeable in this subject but my understanding is that we simply do not know enough about cancer, its causes, and like it is mentioned in the article its early stage symptoms, so clearing a group as “healthy” seems incredibly difficult, even with existing research — this is exactly the reason Freenome is doing this study to begin with! From reading this, I am left with the impression that Freenome needs to become very good at identifying patterns of biomarkers in the blood before they can actually use machine learning to enhance the capacity of doing this same identification. In a way, it is a cyclical issue: they need to be good at identifying the trends in order to feed good information to the study, which will become better at identifying trends based on the selection that was made. If they fail in doing this, their control groups will simply not serve their function.

Another question this leaves me with is how easy it would be to apply the same algorithms to other types of cancer in the future. Are types of cancer “similar” enough to be able to leverage the research or will they run into a “watson-type” problem when they try to broaden the applicability of their research?

Like Jerzey#305, I agree that ThyssenKrupp should begin thinking about applications of this technology beyond the elevator industry. The statistic you presented, “MAX delivered excellent results. According to ThyssenKrupp, “it is capable of cutting elevator downtime in half”” is pretty impressive and could have a significant impact in other sectors. My first thought is this could be applied to machinery in production lines, increasing efficiency of processes and eliminating variability that could occur due to breakdowns. I found an interview to the CEO of a machine learning company based in Israel discussing this subject (ml in factory machine breakdowns). The piece makes a good point in noting the applicability of this technology in settings that, like with elevators, there is not a spare machine that can be used while a repair is taking place. According to the interviewee, adoption in the sector is growing fast: “the level or the rate of adaptation or adoption, is increasing and we’re now at a point that if industry before was like something to speak about, now we see how the market is doing it. We have request for a query, request for information, and for quotes from people who are actively looking for this and I expect it will increase in the next 18, 36 months. It will definitely increase.”[1]