HI Saggitariutt Jefferspin, thank you for such an interesting article regarding BMW and additive manufacturing!! It was fascinating to read how BMW is taking advantage of this trend and how it is reducing costs for the company. My concern here is around liability – obviously, auto companies take on a lot of inherent risk associated with their products and can often be liable for their manufacturing standards, their specs, etc. How will this risk shift in the light of 3D printing? If there is an error in the printing, will it be the 3D printer’s fault? Or still the auto manufacturer’s fault? Or is this blame distributed in some other way thanks to this new technology that is introducing new potential machine errors but reducing human errors?
This is a very interesting article, thank you for this Natasha Romanoff! I think Lego is a perfect use case for Open Innovation, as their entire goal is to keep their products interesting, innovative, and ahead of the curve as far as the toy industry goes. They have an uphill battle to stay relevant, so it’s important for their competitiveness as a firm to have a very effective product innovation process. However, how do they ensure that they stay in touch with what their customers truly want through this Open Innovation platform? It’s a fascinating way to source new projects, but how do they know they’re not developing something that customers don’t actually want or are willing to pay for? Fans always think they want something but might not actually want it when the time comes to purchase – a reason why surveys asking about willingness to purchase don’t really work.
Phil Knight, thanks so much for this fascinating look into Nike’s use of 3D printing techniques! I very much agree with your open questions regarding mass production using 3D printing. Specifically, I can think of 3 considerations that might be impacted: sustainability, factory jobs and the impact on the economy, and patent technology.
1) I’m interested to see what implication 3D printing will have on jobs in Nike factories and their sustainability. Can sustainable materials be 3D printed just as easily, or will they have to make some sacrifice on their goals to be carbon-neutral in order to adopt this technology more broadly?
2) Additionally, Nike employs large number of workers at their factories with fair wages. Will 3D printing remove jobs for these workers and will it cause a ripple effect in other factories, potentially crippling the economies of the countries in which these factories are located?
3) Lastly, 3D printing technology is fascinating but also democratizes making things. If anyone could find the patterns for Nike shoes, presumably, anyone could print Nike shoes and sell them on the black market (more of a threat in countries where Nike does not have patent protection). How do think Nike is thinking about this potential threat to its revenues?
Hi Kentucky Freud Chicken, thank you for this interesting piece on mental health! I also wrote about a similar product, but noticed your essay had a much stronger focus on patient and data privacy. This is an interesting point, as Mindstrong will control effectively clinical data that is derived from people’s iPhones – to what degree to you believe this data should be HIPAA-compliant? What class of privacy should this data fall into and what kind of requirements should be put in place to encrypt it, etc. in order to prevent misaligned incentives for Mindstrong to eventually sell this data to health insurance companies or employers? Additionally, regarding your question around commercialization, what types of players in the healthcare system do you believe is most interested in this type of product? Would you market it to providers, or rather work with the payers (health insurance companies, Medicaid, Medicare, etc.)? I strongly believe it will be the payers that would be most willing to pay for this solution and make it feasible to bring to the broader market, but its the providers whose buy-in is needed to make this product most effective, a challenging obstacle.
Hi Nancy, this is fascinating!! What an innovative application of machine learning to reduce operating costs AND improve service (an elusive double win). My thoughts on this is that while there are significant benefits to having operations aligned and ready to meet potential demand, espeically predicted spikes in demand, does this also make them more susceptible to huge and costly operational errors (e.g., if they project a lot of demand for a certain event and ramp up operations for that, and this demand doesn’t materialize?) Or on the flipside, if they are projecting low demand on a specific day and didn’t account for a certain unforseen spike, could they be caught even more off-guard than if they hadn’t made that projection?