Brian Westlake

  • Student

Activity Feed

On November 15, 2018, Brian Westlake commented on 3D printing in automotive industry :

What is your view on the risk that large scale 3D printing in the automotive industry leads to rampant decentralization of automotive servicing and automotive repair? Currently, to a source a new part for a car requires ordering that part from the original equipment manufacturer. In a world where 3D printing exists at scale (e.g. your local car repair shop could have its own 3D printer) does this risk the traditional repair business models of car manufacturers? If this is the case, then what is the ramifications for quality and safety? My fear for a company like Ford is that any business improvement that comes from being able to personalize a customer’s car may be offset by a decline in servicing business as these very parts can be manufacturer with ease and installed under a decentralized system without control or profits passing through the automotive manufacturer.

On November 15, 2018, Brian Westlake commented on Open Research: Advancing Innovation in Public Health :

Your article made me think about how we can find an incentive model to best solve this problem of open innovation in research. Perhaps a revenue sharing model could work. Research could be licensed out to other prospective researcher with strict conditions that should the research in any way lead to discovery or invention, the original research would be entitled to share of the profits. Thus, rather than a prospective researcher having to pay a large up front licensing fee, they would only be required to pay if that research led to a successful outcome (and in turn they could pay for it out of profits). Under this model, innovation would flourish, researchers would be appropriately cited and (potentially) remunerated. Perhaps the greatest outcome could by the efficient use and investment in quality research backed by an incentive model that allows revenue sharing in any venture that is successful because of it.

On November 14, 2018, Brian Westlake commented on Shooting for the Stars :

You suggest that open innovation allows NASA to evaluate multiple ideas at once. You cite Jessica Day in suggesting this allows idea to achieve a vetted solution faster. In her article she points out that this may be a product of the military and intelligence histories of the organisation. {1} How can open innovation walk the fine line between crowd sourcing innovation and potentially sharing highly sensitive information with the public? As alluded to by an earlier comment, NASA still only hires US citizens suggesting that national security remains a key concern throughout NASA’s work. If this is true, what are the limits of open innovation at NASA and by extension what are the risks? Also does open innovation risk NASA losing valuable intellectual property to other nations when space programs and the innovation that stems from its remain nationally significant?

{1} Jessica Day. 12 January 2017. “How NASA Is Crowdsourcing Its Innovation Strategy.” IdeaScale, 12 Jan. 2017,

I do not share your concerns over the highly personalized nature of Alibaba’s services. What if we are truly destined to have similar tastes in snacks or to use Egyptian cotton bed-sheets! Jokes aside – I see personalization through machine learning as an efficient marketplace for allocation of our resources (time, money) to those areas where we perceive greatest value (e.g. willingness to pay). Where this really gets interesting is if that allocation is not truly efficient (e.g. the marketplace is pushing us to allocate capital in ways that do not satisfy our true interests in allocating capital). This seems to be an implicit assumption of your analysis but you never truly make the link as to why this is a bad thing. I would be interested to hear your thoughts on why app that distribute content should be designed as ‘content democracies’. This was an interesting read!

On November 14, 2018, Brian Westlake commented on Machine Learning at Emirates Airlines :

How has Emirates prioritized its machine learning initiative against its different research options? For example, what is the ultimate goal that Emirates is optimizing for, is improving customer experience, reducing cost in manufacturing or improving revenue through optimized pricing. This is important because there are non-trivial costs associated with wide-scale data collection and analysis – particular in a field that is still evolving {1}. Also how does Emirates communicate this approach to its consumers? To what extent should Emirates communicate this as a competitive advantage or should it be seen as a necessary business analytics tool? Finally, have you considered the downside risks of machine learning. You mention the possibility of over-indexing on safety results from predictive maintenance but are there any other potential downside the company should consider?

{1} M. Yeomans. What every manager should know about machine learning. Harvard Business Review Digital Articles (July 7, 2015).

On November 14, 2018, Brian Westlake commented on Will you marry me (if I ask with a 3D-printed ring)? :

One of the problems with the traditional wax-casted modelling is the high degree of wastage and redundancy that occurs from hand or machine fitting luxury metals such as gold, silver and platinum. The ability to reduce this wastage through the accuracy of 3D printing would remove this as one of the major cost components in jewelry manufacturing. If this could be reliably achieved at scale, then economics of 3D printing jewelry would become far more attractive. Where I see this technology becoming most disruptive in the jewelry industry is in changing the way consumers buy and shop for jewelry. Jewelry is still very much a ‘brick and mortar’ business with customers frequenting jewelry shops to inspect the product prior to purchase. Allowing customers the ability to ‘self-create’ possibility opens up the ability for retailers to shift to a more digitally distributed model.