Really enjoyed reading your article! The price of bringing a drug to market is always astonishing! The idea that machine learning can not only help with the timing to market, but also help fill trials is very compelling.
To your second question: we saw in the Watson case that diagnosing through machine learning has some risks. The incentives also don’t seem to be aligned with what is best for the patient–in finding patients for a trial, it seems like there may be an incentive to misdiagnose healthy individuals with the disease in order to increase success cases for a drug. I wonder how Pfizer is dealing with this incentive.
Your point on how much more can be processed through ML in pharmaceuticals is a good one! Overall, the upside for using ML in this industry seems to be enormous.
Great article! I enjoyed how this article forced you to take a step back and evaluate how even though additive manufacturing may be the correct move for the company, there are deeply embedded cultural and skill constraints that need to be overcome before Rolls Royce can be effective in using the technology.
To your first question on what they need to do: while sometimes difficult to swallow, the reality is, and we see this in both their long and short term plans, current employees may not be able to learn the new tricks, so, to learn and adapt, new talent must be employed and old talent let go.
That being said, you raise a very interesting point on how then do they retain their culture in this changing environment. Moving forward will definitely be a balancing act for them, but one that involves risks that seem worth taking.
The benefits of 3D printing at GE are clear and compelling. While they’ve made what seems to be an enormous investment in additive manufacturing, I am a bit surprised given that the scope of what they can apply this technology to seems quite small. I think the point you make on the talent pool needed for this technology also being different is quite interesting. Bringing on this technology requires a huge investment and it will interesting to see how they will proceed in using it.
I think another interesting question given the current state of GE is whether it will keep its additive manufacturing division when times are tough. If it chooses to cut the division, that may be a clear indication that the investment was more of a trial and that the company does not see much of a practical future for the technology.
I enjoyed this perspective on the construction industry. To me it is really interesting how an industry that is so dependent on machinery can be so far behind in terms of technology. That being said, given the cyclicality of the business, it makes sense that companies are extremely risk averse and hesitant to make costly changes.
Given that Caterpillar does not seem to have the technological capabilities in-house and this problem involves many stakeholders, I agree that it may make sense for them to somehow gain those capabilities through open innovation. Although, it sounds like up until this point they have not been successful in their attempts.
In the long term, surviving in construction I think will be dependent on a company’s ability to progress technologically, so an interesting question arises to me of whether Caterpillar will be able to survive if they do not get this right. Whoever is able to innovate first will be at an extreme competitive advantage.
Very interesting article! Choosy’s solution to two critical inventory and design problems in retail is very clever. The costs implications of not having to design your own clothing is very compelling.
I wonder whether depending on a specific hashtag in an uncontrolled forum, like Instagram, will make it very difficult to develop a purely machine learning solution. The context of the Instagram post seems important to understand if this hashtag is being used because of the clothing items vs. for an irrelevant purpose (e.g., as a. joke, accident, goes viral for a different reason).
Overall, using open innovation combined with machine learning seems like the future of retail–especially for companies not trying to set trends.