• Student

Activity Feed

I believe there are substantial benefits for medical device companies that use additive manufacturing effectively. Medical device product development is a very iterative process, and products going through FDA approval have to be shown to consistently perform within very tight statistical parameters. As a result, frequent modification and experimentation is needed until a design is satisfactorily proved out from a safety and efficacy perspective. Being able to iterate quickly will provide Stryker with a keen competitive advantage.

With respect to creating customizable products for individuals, I believe it best to have patient data staying with doctors / hospitals to the extent possible, as patient confidentiality protections are already in place with respect to medical professionals and institutions. I would think that sufficient data could be transmitted to medical device companies in a patient blind / non-identifiable way, so that only the details necessary for product specification and manufacturing were transmitted without patient identifiers (i.e. Social Security numbers, birthdays, etc.).

I think Comcast’s attempt to apply machine learning to the problems of cord-cutting and poor customer service makes sense, but I wonder if this is a case of too little too late. Unless Comcast is able to provide consumers with customized offerings that are at a substantial discount to their current content offerings, I am not convinced that customers will be willing to continue paying their high prices. I do think applying machine learning to their customer service problem will enable Comcast to create value for consumers by anticipating customer needs and providing effective solutions in a more timely manner. However, I am not convinced that you can fully remove the human element from customer service. Comcast would do well to supplement its investments in machine learning with human capital investments to further improve the human interactions inherent to the customer service experience.

On November 15, 2018, Publius commented on Great Scott! What’s next for open innovation at LEGO? :

As a huge fan of LEGO since childhood, I really enjoyed this post. I think LEGO’s idea is smart for several reasons. First, LEGO is able to effectively outsource its product development and its market research functions by letting consumers “control” (in a limited sense) both idea generation and idea selection (see also A. King and K. Lakhani’s article “Using Open Innovation to Identify the Best Ideas,” MIT Sloan Management Review, fall 2013, vol. 55 no. 1). Also, by giving consumers an interactive platform on which to develop / display designs and to vote, they have created an online user experience.

Regarding the loss of brick and mortar sales, I tend to agree with Camille. As long as LEGO continues to make compelling products, I think they can generate sufficient sales through online channels, as the world continues to move towards digital. As demonstrated in the above post, part of their strategy for creating compelling content is open innovation.

On November 15, 2018, Publius commented on Virgin Hyperloop One: Will Open Innovation Lead to Its Reality? :

Virgin Hyperloop One’s approach to open innovation appears to have attracted significant interest and been a great method for outsourcing its product development. What most interests me is the intellectual property protections that the participants were offered (if any). Oftentimes, people are hesitant to participate in open innovation for fear that sharing their ideas without guarantees of protection could compromise them for the long-term. Being able to clearly articulate this to participants may help Hyperloop One attract even more contributors for future open innovation contents (should they choose to hold any).

Also, I do not see any downside to hiring the finalists if Hyperloop One believes they can be long-term contributors. In this sense, one can view open innovation as not only outsourcing product development but also as a hiring / talent acquisition tool, yet another application.

Thanks for a great article, Phil. I am astounded by the magnitude of improvement in the product design process for Nike following the implementation of additive manufacturing. In addition, the company’s strategy of pursuing patent protection and partnering with HP helps to ensure their competitiveness in this particular space. The question raised about extending the 3-D printing technology into other product development areas is intriguing, as additive manufacturing would appear to align with many other products Nike produces (golf clubs, baseball bats, American football gear, etc.). I agree with several of my classmates in questioning the viability of mass production via 3-D printing, but I do think many other opportunities exist with respect to Nike’s product development.

Lastly, the post raises the question of whether the spread of 3-D printing and its increasing accessibility to the average consumer would create a threat of DIY substitutes for Nike. In my opinion, this is where the value of the Nike brand also serves as a mitigating factor. To the extent that I as a consumer identify with the Nike brand, I may decide to forego any available alternatives that 3-D printing affords.

I very much enjoyed reading this post. Intuitively, it makes sense that opportunities exist in the legal profession for machine learning to replace human lawyers, particularly in areas such as credit card contracts with a high degree of standardization and less judgment required. The benefits accruing to JP Morgan for utilizing this technology clearly exist, but I wonder if machine learning also provides an opportunity for law firms as well. Given their sophistication and expertise, presumably one of the larger law firms could partner with a technology company specialized in machine learning to develop their own in-house tool for assisting with contract review and other less sophisticated tasks. This would free up lawyers to spend more time on higher value services for clients.

Looking ahead, I do wonder to what extent the gap between human and machine work can be closed. I see a parallel to medicine, in that machines may be able to assist with the identification or diagnosis of issues as well as pattern recognition, but I am not convinced that the human element can entirely be removed. Also, as the author mentions, there are accreditation standards and other mechanisms in place that effectively serve as barriers to fully eliminating the human element. However, I would not be surprised to see more companies follow JP Morgan’s path, or for a technology company to develop and license a similar solution for use by larger corporations.