Very interesting use of additive manufacturing in the footwear industry. Nike is quite unique in its ability to charge exorbitant premiums for its hottest shoes – whether Jordan’s or Kobe’s or anything else. Judging by the lines out the door of Niketown’s whenever there is a limited release event, its clear that demand for the most unique Nike products significantly outstrips supply. I think additive manufacturing poses the chance to extract a lot more value out of the marketplace, but it has to be balanced with the political and social optics of potentially displacing existing workers. To the extent that additive manufacturing is also additive to human labor, we can cheer it as a success. Anything less should be a cause for concern.
This is an excellent use of machine learning that brings accuracy and fairness to public sector assessments. Zillow and Redfin have long used machine learning to come up with expected values for millions of properties across the United States – it is well past time that local governments do the same.
I hope to see this model extended to other cities and states. In fact, it’d be best if the underlying algorithms and datasets are aggregated across municipalities to improve their predictive accuracy and reduce total operating costs to any one city government. The results of this data should be freely and easily accessible to the public, so that inequities in property taxes can be made more evident. My hometown of Los Angeles, by law, only charges property taxes on the last assessment conducted when a home was either sold or refinanced. If people truly understand the delta between actual taxes paid and current property values, we’d likely have fairer taxation systems.
Great article! I’m very interested in how NASA can leverage open innovation to free its employees to do more than they do today. Faced with declining budgets, our nation’s most inspiring agency has been forced to cut back on “moonshot” projects that would otherwise fill our imaginations. Open innovation has the potential to undermine the work and effort of existing NASA employees, but, if it is done right, I believe it could accelerate their potential. The best employees in a creative environment like this (sure its science, but its essentially creating new ways of doing things) are those who can ask the right questions and frame the hardest problems in an accessible way. NASA has enormous potential to benefit from more open innovation.
The comments above point to an interesting balancing act between the benefits of crowd-sourced stories and the potential for reputational decline if those stories are not accurate.
I agree that Buzzfeed has a believability problem – they’re relegated to interesting headlines without much substance beyond that, in my experience. However, their valuation indicates an ability to capture a large volume of eyeballs. Buzzfeed should continue its open innovation platform but also increase its credibility by publishing data on the proportion of stories that it accepts vs. rejects by news category. It could actually benefit from telling the public that only x% of stories make it through its filters while still encouraging people to send in new tips.
Using additive manufacturing in luxury automobile production is an interesting concept, but it has the potential to destroy value in the industry. With respect to obsolete parts for older cars, many of these classic cars have high values due to rarity. If you make replacement parts and the entire vehicle easier to replicate, there’s a chance that you increase unit volume while reducing value per unit. With respect to new cars, I suspect that people are willing to pay a significant premium for hand-made luxury cars. Rolls Royce, Bentley, and certain models of the highest-end German cars are carefully marketed as being hand-made and trade at large premiums to factory-line vehicles.
I’d suggest using your recommendations for lower-end, mass production cars, to maximize value.
Interesting article. I’d like to respond to several of your recommendations:
1. Integrating quant benchmarks in production contracts: the inherent difficulty in deciding what to renew vs. cancel is that once you set a content budget, every subsequent decision is made at the opportunity cost of another potential show or movie. Say a given show hits its pre-set “target” but 20 other shows dramatically exceed theirs – if Netflix only has money for 15 renewals, what should it do? You can’t really set targets in isolation.
2. Dividing content budgets: all decisions at Netflix are, as Ted Sarandos stated in Variety last year, a combination of data and intuition. Data on previous show performance can give you a sense for the overall market size for an idea/concept, but execution through showrunners, writers, directors, and actors is critical to the outcome. Judgement is necessary.