Great post! I agree with JJY that the main barrier to efficiency in the public sector is the inertia on the side of policymakers, but I do agree that open innovation can play a role. I see the main issue as misalignment of incentives and free information flow, both of which could be supported with public pressures via open innovation. I’ve worked with public figures who inherently see the value of a certain initiative, but cannot support it due to internal barriers and red tape, not because it is an inferior option to the status quo. By adding more transparency as to why certain initiatives or bills are held up, the public could be more critical of certain actions policymakers take, and bring light to these issues. For example, if a beneficial program for needy schools was held up because a certain policymaker would prefer to fund their friend’s less-effective initiative, there could be an anonymous and open platform where fellow policymakers make these facts public, and constituents would better know where to funnel their voices to add pressure in the most effective way.
Really interesting article! Crowdsourcing is certainly a great way for LEGO to garner ideas on product development, although I wonder if it goes far enough. Consumers may be able to contribute ideas related to things they’ve seen already and would like to experiment with, but I wonder how effective this channel is for spotting future trends. I’m worried that LEGO is relying too heavily on customers to inform future innovation, as oppose to investing resources into “defining the future” of toys and thinking up ideas independently. I could be convinced otherwise on this point, and I think it’s important to have a mix of crowdsourced ideas and internal R&D driving product development in order to ensure relevance and sustainability.
This article does a great job of explaining the benefits that 3D printing brings to manufacturing. My only cause for concern is the quality control issues that result from producing tools via 3D printing. I imagine that given the complexity of the production units (in this case, cars), even small variations in tools produced can lead to ineffective repairs, re-making of tools, lost time, and lower productivity. The cost savings may justify these issues, but if they are severe or occur too frequently, BMW and others should rethink their dependency on 3D printing for items with extremely high quality needs.
While 3D printing for prototyping seems like a natural fit for many products, I am less optimistic for its use for Nike products. Nike products are innovative largely because of the material composition of their products – whether lightweight, waterproof, breathable, etc. 3D printing has limited ability (if any) to incorporate these key material differences in prototypes, which could yield the prototypes ineffective and unrepresentative of the actual product’s performance. Given the fact that improvement is often at the margins of improving these key features, I’m reluctant to conclude that the investment in 3D printing for this use is worthwhile.
Interesting article! While there is a lot of discussion about customer retention and forward-focused analysis, I wonder how Amex is also thinking about using available data to better target future customers. If Amex can attract higher quality customers in the future (as measured by length of time the customer remains a cardholder), they will likely need to expend less resources on retention efforts down the road. It would also be interesting to better understand which card(s) Amex customers tend to switch to, such that Amex can gain a clearer sense of where their value proposition falls short for various demographics.
One caution I have with respect to tools such as the “risk score” is that it falls victim to the basic flaw inherent in most machine learning algorithms: identifying correlation is not the same as identifying causation. One of the reasons that our justice system experiences bias is because it is hard for humans to understand the root causes behind criminal activity. For example, while “on average,” lower income groups tend to commit crimes at higher rates (thus a high correlation between income and crime rate), there are likely underlying factors in individuals lives – such as addiction – that directly motivate theft. If addiction is actually a more direct cause of crime, then looking at income levels to predict someone’s chance of re-offense is flawed, because high income populations also experience addition. This is a grossly over-simplified example, but the point remains that with the complexity of human decision-making, there is a high risk that machine learning will spot historical correlations that are not accurate predictors of future behavior, and thus introduce even more bias into the justice system.
MagicBands are a great innovation that clearly improves the customer experience and benefits Disney with rich data. One additional machine learning application of MagicBands that came to mind was if Disney could use them to make recommendations to guests to further improve their park experience. One example would be to notify a park guest if the line for a certain ride is particularly short, and that the visitor would benefit by visiting that ride at the present moment instead of waiting until later. Another traffic control-related recommendation could be to implement “pop-up sales” at certain gift shops if lines got too long, as a way to encourage visitors to “spread out” and decrease congestion throughout the day. In this example, Disney’s software could monitor traffic around the park and offer perks at non-congested areas in real-time.