Felicity Jones

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On November 14, 2018, Felicity Jones commented on LEGO: Leveraging the Building Blocks of Open Innovation :

I love your pen name. Keep killing it.

I personally don’t see too many privacy issues here since this is all open source innovation and Lego doesn’t hide any advanced trade secrets that I know of. I’m sure the composition of its blocks and some of its manufacturing processes are proprietary but beyond that, the designs themselves come with each set. I definitely think open innovation in a space like this is the way to go. My concerns would be the cost of filtering through all these ideas and how many personnel you have to hire just to do that.

I think it is definitely worth it for the additional engagement with fans. Does Lego advertise on the box that this design was crowdsourced? I think that would be a great marketing strategy to drive further engagement.

On November 14, 2018, Felicity Jones commented on Open Innovation at NASA: Impact in Culture :

Super interesting stuff! I am a big fan of open innovation and I’m impressed at the culture change that it has affected at NASA. I think open innovation and traditional R&D can exist together. A couple of key points is that in classified applications you obviously cannot have open innovation so you have to keep that internal R&D talent in house at the risk of competing for talent at critical junctures. Another point I had is that the examples you list are successful uses of open innovation but are there cases where the public just does not care or have the right expertise? Another thought is how much time has to go into separating the bad ideas from the good ones? How many marginal ideas get through the screen and then require personnel to go through and evaluate each one. How does that trade off compare to just doing it on your own?

On November 14, 2018, Felicity Jones commented on Kevin Stein at TransDigm Group :

I agree broadly that TPG should consider using AM to compete with GE but the one concern I have is the safety issue and reliability of AM versus traditional machined part. My intuition is that parts made between the two processes could have different internal load characteristics even though the specifications on the outside may be the same. For example, two engine blades may both fit size, material, and finish criteria but one may be able to take stronger stresses because one was machined as one solid piece versus the other was made by adding layer and layer of material on top of another.

Especially in industries such as aerospace and defense, one part failure is catastrophic and I would be concerned of the risk of any one piece failing. Perhaps peace of mind may be worth holding 25 years of inventory…

On November 14, 2018, Felicity Jones commented on Lemonade reinvents the insurance industry with machine learning :

Super interesting topic. My major takeaway question walking away from this is Lemonade achieving cheaper premiums for people and are they taking on the majority of the risk? It seems they are an actual insurance policy so I see an enormous amount of risk with limited data. Insurance works by pooling risk and if it doesn’t have enough data to accurately predict the risk then I’m curious what a natural disaster or exogenous shock would do to its algorithm. My worry would be that it isn’t priced correctly.

I think there is opportunity to build the platform and partner with large insurance firm just for the capital, if nothing else. I imagine the difficulty of forming insurance company is the massive capital investment in order to be able to pay claims.

From my extensive experience as an intern in the medical device field at a competitor of Medtronics, my initial thought is that its good that Medtronic is taking a slower approach to personalized medical devices. The problem in my mind is regulations because you are putting these devices in people’s bodies. Each one is different so does that mean each one needs new regulatory procedures? Today’s market works because you obtain approval for one product to put in many people’s bodies. If you put different devices in each person, you increase risk of defective products that can cause harm. That would be my primary concern.

On November 14, 2018, Felicity Jones commented on Redefining the oil and gas industry through machine learning :

It seems this is a very unique application of machine data. I am curious what the statistic and results are from the predictive analysis on seismic movements and other natural events. Is it meaningful? What are the secondary implications of this advancement in technology? How are these passed on to other stakeholders? Are risks in the industry coming down?

In regards to your questions, I think data sharing will be coming very soon since this is such a fast growing field and there are many experts who have knowledge who are moving from firm to firm. Even if they don’t share that knowledge outright, the skills and internal knowledge will be diffused through time and as labor economies of scale grow.