Thanks so much for the post – it was fascinating to see that open innovation is being applied to an industry that is so often seen as being traditional and slow-moving. A concern that I might have with Singapore Airlines’ initiative is thinking about the immense pressure that its customers may apply to the company to implement suggested improvements. Airline customers are notoriously particular, and quick to air their grievances online. If a customer believes that Singapore Airlines is dragging its feet on implementing a promising new customer-service innovation, and posts about it online, how will Singapore Airlines respond?
This is very interesting – thanks for submitting. I’m a bit short on cash these days so maybe I’ll see if I can find an engineer to submit to the Bell Labs Prize with me! My biggest concern with this issue would echo what CambridgeCappuccino wrote above; I worry about Nokia’s ability to successfully execute on commercializing this long pipeline of innovative projects. It seems that Nokia needs to develop the internal capacity to effectively filter which projects can be actionable with the company’s existing capabilities, and which will require additional internal growth to support.
This is a fascinating article – and I love to have another TOM project related to beer! This article actually brings to mind another experiment which took place in the aging process of whiskey (https://qz.com/907619/the-many-scientific-ways-to-age-whiskey-many-years-in-just-a-few-days/). It seems to me that as we are able to break down the components of taste into their scientific precursors, the ability to artificially build high-quality products for quicker and cheaper will accelerate. However, I wonder if consumer tastes will evolve to fit this new paradigm. Craft beer, for example, is often a very emotional purchase in which craft beer aficionados purchase beer that they know has been created over a long development process by a master brewer. Convincing mustachioed hipsters in Somerville or Brooklyn to buy beer that was developed by an algorithm may be the most difficult challenge for a company like Carlsberg going forward.
Really interesting article – thank you for writing! One question that came to mind as I read this article was whether or not big data can truly bring down cost to a level that is comparable to other countries which have a single payer (typically government-run) health insurance. I’d be very interested to see if machine-learning and big-data algorithms see greater efficiency gains in countries that have less layers of bureaucracy and fewer diffuse actors in healthcare administration.
I find this to be fascinating – the ability of AM to compress the military supply chain seems revolutionary and the author’s article is enlightening. As I read the case and think about some of the questions surrounding this technology, I would echo the concerns of some of the earlier commenters who noted that perhaps the technology has to mature and have a lower error rate to be able to be used reliably in the context of the Marines. Additionally, one concern that I might have is related to the supply chain for the inputs for 3D printers. I presume that 3D printing components are more portable and less bulky than pre-manufactured goods, but I wonder if this will still leave a small supply chain which will have to be defended at considerable cost, depending upon where the Marines are operating relative to the bulk of US and allied forces in the field.