DeWalt Watson

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On November 15, 2018, DeWalt Watson commented on Crowd-sourcing the Secret of Life: 23andMe and Open Innovation :

Great topic that raises a whole host of issues around both privacy and accuracy. While privacy is of course a great concern, especially given recent hacks into databases such as Equifax, I personally have greater concern over accuracy and the potential to lead to incorrect treatment of given conditions based on what is a relatively cursory sequencing of patient DNA. I think there are 2 key potential sources of this inaccuracy. The first is the fact that patients are self-reporting results (as mentioned in the article and several of the comments above), which can not only be affected by cognitive biases, but also by simple lack of medical knowledge given the lack of provider involvement. Secondly, many of the correlation studies being performed fail to account for non-genetic factors that can be the more relevant drivers of a given condition, such as diet, lifestyle, etc.

Lastly, as the B2C market for genetic testing continues to grow, I wonder when the scale will be such that the company is in direct competition with more technically sophisticated diagnostics such as BRCA testing to identify genetic mutations linked to breast cancer (provided by companies such as Myriad Genetics) or companion diagnostics associated with the treatment of certain cancer mutations. Given the clinical data supporting companies such as the above, I question whether 23AndMe will hit a brick wall with respect to its sequencing capabilities and therefore validation for treatment suggestions derived from its database, and as a result I have a tough time seeing the company ever being much more than simply a consumer product.

On November 15, 2018, DeWalt Watson commented on Everything is Awesome: Product Innovation at LEGO :

Great article! While screen time is becoming an increasingly in child entertainment, I am a big believer that there will always be a market for a suite of products that allow children to physically build and play with a tangible item. My biggest concern with this open innovation platform is the challenges with integrating ideas from Lego’s core consumers – the kids themselves. Complexities around design, the required expertise, and the administratrive challenges discussed in the article (such as long waiting times and rigorous technical standards) are significant burdnes for even accomplished designers, and there is not a clear path to integrating kids’ ideas onto the platform.

In an ideal scenario, Lego could produce a software module that would allow kids to contribute their designs to a distinct platform (i.e. “Lego Ideas for Kids”). Perhaps a more realistic scenario, however, is to create a forum through which kids can be matched with designers capable of bringing their ideas to life and of navigating Lego’s review process.

While kids may get excited by the idea of new designs, I can only imagine the excitement that would accompany sets created by themselves or their peers (would even go as far as displaying an image of the child who came up with the design concept. I think buy-in from the kids themselves will enhance chances that they will choose Lego over digital / screen-based competition; otherwise, these competitive pressure may prove increasingly challenging for Lego.

On November 14, 2018, DeWalt Watson commented on The Perfect Fit? Adidas Sprints Ahead with 3D Printing :

Thanks for sharing – shoe manufacturing on the surface seemed like a home-run application for 3D printing, and I did not fully appreciate some of the operational challenges that you astutely point out in your essay (cost for mass production being the most critical). While Adidas likely has a first-mover and knowledge advantage given its investment in Carbon, I agree with some of the comments above that I’m not sure I see the mass benefit long-term. The ability to offer a higher level of customization and the potential for a no-inventory made-to-order model is attractive, I don’t view these issues as a major pain point for consumers and I think price and lead times will be a major barrier to adoption until Adidas is able to find some of the labor and cost efficiencies you mention in your article. Ultimately, while look is important, performance and comfort come first in many of the categories Adidas plays in, and it’s not clear there is a path to mass-producing products that are cost competitive with existing models.

In my opinion, the only way 3D printing becomes a competitive advantage for Adidas is if they are able to truly disrupt the market with product innovation to the point where they are effectively creating a new category rather than improving an existing one.

On November 14, 2018, DeWalt Watson commented on Can Machine Learning predict, prevent and diagnose Diabetes effectively? :

While machine learning is frequently discussed in the context of patient diagnosis and treatment across countless indications, given the scale I think diabetes will be a particularly important proving ground for the future of ML in healthcare. As you point out, the sheer prevalence is massive, and the problem is only getting worse. Abbott’s FreeStyle Libre and the data it collects is a great example of how data can be leveraged to improve disease management with limited physician interaction, and given there is less volatility in glucose levels for Type 2 patients there is less risk associated with using an AI-driven device. I believe Dexcom also recently received approval for a continuous glucose monitoring device that does not require calibration, and the data collected by these companies in the next several years would be massive.

The point Narmeen makes above regarding provider intervention is definitely valid, but I wonder if in diabetes there is a chance to massively minimize the role of the physician or replace it entirely when it comes to management of glucose levels (although of course imagine a provider would still be involved in diagnosis and general patient monitoring. There is a company called Intarcia (link to the website below) that is on file with the FDA for an implantable osmotic pump that can regulate glucose levels and only needs to be implanted/checked once per year – I wonder if down the road ML data collected from Abbot and Dexcom’s CGM devices can be leveraged for a fully autonomous treatment using the osmotic pump technology. Thanks for providing so much food for thought!

On November 14, 2018, DeWalt Watson commented on Rolls-Royce: Optimising jet engine maintenance with machine learning :

Will the type of data being collected change over time? Will the existing fleet need to be equipped with updated sensors? Challenges of having a separate digital team – will this slow adoption across other product-driven (rather than tehcnology-driven businesses?). Given alignment of incentives with airlines, any additional data that can be collected from the aircraft, pilot or airline?

Thank you Chris for sharing – very interesting to see how a manufacturing-based business is leveraging machine learning, a trend I originally thought was generally more limited to technology and software businesses. I found the point you raised on digital teams being separated from product teams particularly interesting. Given lack of information sharing, you effectively point out that this can slow adoption and innovation around how ML can be leveraged to extend duration between maintenance cycles. An interesting question comes to mind – given the organizational structure of manufacturing and product-based businesses, how easily can ML be adopted and innovated? It seems there are more barriers to adoption than in a typical software business. Perhaps the answer is in outsourcing, but this of course can be limiting, and the decision of whether or not to build these capabilities internally is especially interesting as you point out at the end of the essay.

One other question – is Rolls Royce using ML in its manufacturing processes? In a world where IoT is becoming commonplace in manufacturing facilities and where sensors are increasingly abundant, would be curious if these capabilities are being used at the plant level to monitor equipment maintenance to maximize uptime.

On November 14, 2018, DeWalt Watson commented on How Additive Manufacturing Can Unlock Boeings Biggest Challenges :

Love the fact that you were able to pick a topic that was relevant to our Boeing case in FRC! When reading the case, I was shocked to hear that Boeing used a methodology based on cost forecasting over the course of a given program. While the use of additive manufacturing in the supply chain for an aircraft was not surprising (in terms of using fewer parts, sub-assemblies, consolidating the supplier base, etc.), I did not appreciate how effective the concept could be when applied to product development. The ability to quickly re-design and re-manufacture components can speed up time to market (and be a game-changer from a competitive positioning perspective) and allow for an acceleration in innovation, a benefit I did not foresee.

Your question at the end of the article is interesting – I am excited to see how capabilities in additive manufacturing will develop over time and ultimately what % of components in aircraft manufacturing are candidates for 3D printing. The stats from the GE engine were shocking, and I didn’t appreciate that we were this far along.

One other question to think about – is there anything Boeing can do from a strategic perspective to leverage additive manufacturing to provide new value-add to its customers (rather than simply internal processes)?