I find the article very interesting since, as someone with experience in the airline sector, I can relate very much to the huge potential that ancillary revenues bring to the companies. These benefits come not only in terms of revenue potential, but also in terms of high profitability, with airlines having been able to transform a cost center into a profit center. Usually the clearest example is the onboard food, area in which airlines stopped spending a pretty considerable portion of their ticket fares in this service, and transformed it into a revenue source, this way saving the associated cost while receiving a profit for it. Having a machine learning based system to untap the potential of these additional profit sources is something very impactful in an industry with low and shrinking margins.
Regarding the last statement about the lack of existing distribution channels and enough infrastructure to support this initiative, on the one hand I do agree that there can be many challenges in the travel agencies/e-travel agencies channel, not mainly related to the infrastructure but to the amount of information that JetBlue is eager to share about its passengers with third parties. On the other, I do not see bigger issues for implementing this system in JetBlue’s own platform, on the contrary, I see a significant opportunity to gradually implement the system, testing the results, and being among the firsts in the industry.
I find the article very interesting considering the challenges and technical expertise that machine learning can face when implemented in traditional organizations. I am aware of the strong focus that McKinsey has on this arena, having friends and family that have joined the company not as traditional Consultants but as “Data Translators”, as the McKinsey names this pretty new position. The quote that “The gap for most companies isn’t that machine learning doesn’t work, but that they struggle to actually use it.” is not only very illustrative but also very applicable to all kind of companies across industries and geographies, and reinforces the idea that machine learning is a tool for organizations and employees to make better decisions, which will be more or less valuable depending on the people’s level of understanding of the system, the data, and its potential. At this still early stage, positioning as consulting company that can thrive on data analytics and machine learning is without doubts a significant edge and hedge for McKinsey in the future, and a way to innovate in the status quo of the professional services industry.
I like how the article shows quantitative and qualitative benefits of the innovative production system both in the short and the long term. It was particularly interesting to see the recommendation of delaying the formal launch of the Futurecraft until the company has confidence that the product actually has the expected quality. In this line, I think that this recommendation should be more or less applicable depending on what the company is looking for with the Futurecraft shoes. Is Adidas mainly aiming to be the first mover with the “3D printed” sneakers? Or is Adidas using the technology mainly to become more efficient in terms of costs and development, production, and distribution times? If the latter is the main objective, I agree with the recommendation of delaying launch and mass production until the due diligence is fully completed, compromising time for quality. If the objective is the former, I think that Adidas should further expand what is currently doing, continuing launching the limited releases of the product in all the markets, heavily investing in quality control for this initial phase (compromising profitability for time and quality), and continue taking advantage of the first move in all its geographies.
The article provides very insightful and concise information on how the 3D printing is improving the time and costs of construction, but I found particularly interesting the reasoning behind how this technology is also helping to address two “non-numerical” but extremely important issues, such as corruption and environmental impact. Think about the future, the most important challenges seem to be related with the entry in new markets and the response on competition, but for me, another fundamental question related to whether this technology, as it is, is sustainable and long lasting, and if not, how much should the company continue investing in R&D to keep being at the forefront.
I like how the essay articulates the tensions between internal and external crowdsourcing at the company and opens the debate on what could be done to align the incentives between the parties. Given that the internal crowdsourcing has a significant economic incentive, I wonder if the solution proposed, by which the employees would be more involved with the initiatives proposed by external parties, would be enough to significantly reduce the tension, or if further incentives should be implemented. Thinking about the latter, and being aware of the existing economic award, a possible initiative could be setting targets related to the number of external initiatives implemented as part of the employees’ bonus or as part of an attractive career path that awards the openness towards collaborative innovation.
Nestle seemed to have launched this initiative with a much bigger scope than the one actually in place, and as soon as I started reading the first paragraph, I could see some of the challenges it may be facing to escalate it and achieve higher penetration in the “more technical” crowdsourcing. I feel that the compensation in place does not seem very attractive to more science-oriented startups that may be/have been working in the pursuit of high returns given the risk they incur (which is completely different than that of a marketing company). In the specific case of the PET alternative, I would tend to think that a company or individual developing such new technology would seek for something else than $ 50,000 and a “public recognition”.
I would love to hear more about the author’s recommendations on how to tackle this situation, but I feel that one possible solution could come from re-designing the incentives strategy depending on the type of project. Introducing for example some kind of royalties for those projects that bring innovative R&D technologies to the table can be a good way to start thinking about longer term partnerships with the “external” innovators.