Thanks for the great read Patrick! The astronomical cost to bring a new drug to market is truly astounding, so I applaud Eli Lilly for sharing their compound library in an attempt to bring down development costs and encourage new innovations. I continue to wrestle with how to answer your first question. There seems to be a direct relationship between the size of a chemical library and the chance for innovation and saving lives, but given the fierce competition in the industry, I struggle to see the vision of a shared database come to fruition. However, I wonder if passing control of a common chemical library to a central authority (e.g. a regulatory body) would encourage more industry participants to leverage the platform. By doing so, I could envision the significant increase in scale exponentially reducing drug development costs and speeding up the product development lifecycle. Given your prior experiences, do you think a centralized, open chemical library would ever be feasible in this industry?
Additionally, do you have a sense of how Eli Lilly is monetizing the OIDD platform? I would imagine this is a key determinant of whether external parties choose to engage with the portal. Finally, do you believe OIDD will be a sustainable competitive advantage for Eli Lilly or will competitors develop similar programs?
Thanks for the great read Justin! Regarding your second question, I think the primary complication that arises in applying open innovation to the opioid crisis is the level of regulation surrounding the medical industry. While all stakeholders are aligned on the objective of eliminating drug addiction and related deaths, the process of testing and iterating on new ideas can be slowed by restrictions around patient safety and data confidentiality (i.e. HIPAA). For this reason, I would suggest that HHS remain in control of idea selection and continue with innovation tournaments. The sheer number of constituent groups involved in this decision – patients, families, doctors, nurses, hospitals, insurance providers, and regulators among others – reinforces the importance of having a centralized decision-making body to help validate and share innovations across the industry. However, I do not believe that idea selection must be exclusively internal or external. Toward that end, I would also recommend experimenting with voting functionality to judge market support for certain innovations (similar to a like / dislike button on Facebook). The resulting data from these surveys could provide useful guidance on market sentiment to HHS as they make decisions.
Thanks for the great read Ian! I really appreciated your vivid description of a situation where additive manufacturing could have tangible benefits beyond cost savings and reduced product development lifecycles (i.e. saving lives). Additionally, your point regarding the ability to manufacture out-of-production parts resonated with me given the emphasis on maximizing the useful life of equipment in the military due to the constraint of the Department of Defense’s budget. Furthermore, your scenario of preventing soldiers from having to travel across IED-laden roads drove home the safety benefits of eliminating transportation costs, which I had previously overlooked.
In response to your question regarding IP protection, I would suggest employing a subscription-based model where companies would receive an upfront fee for selling the schematics of their product design to be programmed into the 3D printer and then receive a per-part fee for each printed piece. This would offer the dual benefits of allowing product designers to receive profits for their inventions and providing soldiers access to the parts they need. The largest remaining hurdle in my mind is the implementation cost of supplying bases with 3D printers, given their upfront cost and physical size. Do you think supplying bases with 3D printers is feasible today or is this an aspirational goal only achievable down the road?
Thanks for the great read Carlos! As you laid out, there are substantial benefits to support a shift toward additive manufacturing – namely cost savings, an ability to prototype rapidly, lightweight design, and tool-less production. However, the downside risk of product failure is enormous. Customers are likely to demand vast amounts of safety / performance data before feeling totally comfortable leveraging new manufacturing technologies such as 3D printing. Their reservations are likely to elongate the consumer adoption curve as a result (especially considering the importance of regulatory bodies in this industry, e.g. FAA). One potential solution to the adoption problem would be to combine additive manufacturing and predictive maintenance machine learning algorithms to report on product functional status in real-time. Doing so would help mitigate customer fears of a catastrophic failure. Lastly, do you have more information about the cycle time to develop products under additive manufacturing vs. traditional methods? I’d be curious to learn how quickly 3D printers can construct these parts and whether a potentially longer cycle time in this process could shift the bottleneck for product development.
Thanks for the great read JHarvard! Predictive maintenance appears to be an exploding field for machine learning applications as similar technologies / algorithms are used in the aerospace industry, by firms such as GE, to ensure proactive engine maintenance. In terms of your question on outsourcing vs. insourcing, I suggest Caterpillar review aerospace firms’ approaches as peer benchmarks to guide their decision. Caterpillar’s investment in Uptake struck me as an interesting middle ground that I am surprised others have not emulated. Given that big data analytics is not most firms’ core competency and data security is a massive risk, do you think more firms would be better served pursuing an acquisitive strategy rather than building in-house or outsourcing to an independent third-party? Lastly, I question how this market will be impacted by autonomous vehicles. Are we headed toward worker-less job sites? If so, will Caterpillar’s portfolio of IoT-enabled products become a competitive differentiator?
Thanks for the great read Charlotte! Your first question is particularly thought provoking as every company that is considering utilizing machine learning must be wrestling with the tradeoffs between developing internally and outsourcing to a 3rd party. Personally, I would outsource the development of the machine learning algorithm to a 3rd party for a few reasons: (1) Burberry’s core business is product design and it would be difficult to structurally change the organization to become a technology company, (2) 3rd party vendors have likely completed multiple machine learning algorithm implementations and can leverage prior experience, and (3) the war for talent (particularly for data scientists and engineers) is fierce and I struggle to believe that Burberry would win this battle.
One additional question that came to mind for me was – how can machine learning be used to drive traffic to physical stores? With approximately 240 retail locations, Burberry is still highly levered to physical retail and I wonder what applications could be developed to make the in-store experience more interactive and personalized. Do you know of any competitors focused on machine learning applications in stores (as opposed to online)?