Awesome article – thanks for sharing! I agree that Nike should continue to invest as competitors (namely Adidas) continue to make moves in the space of 3-D printing. I also love your suggestion that Nike should also use 3-D printing to prototype accessories (such as apparel and tennis rackets). As Nike works with top athletes to innovate the next generation of these products, 3-D printing allows the company to prototype, iterate, test and bring products to market more quickly and cheaply.
Awesome article – thanks for sharing! To answer one of your questions above, I think proper metrics and a robust feedback loop are key in successful OI programs. These ensure that the ‘right products’ make it through the product development cycle and eventually to market. These also avoid the scenario that @BuzzLightyear describes above, noise in consumer data and sabotaging competitors. I’d love to know more about how these work at Mondelez and competitors in the space.
Awesome article – thanks for sharing! I 100% agree that AirBnB should aim to reduce inherent biases in ratings and ensure that all hosts/guests are treated fairly on the platform. The research says it’s there and I agree it’s on AirBnB management to reduce it. At the same time, however, I struggle to understand how a machine learning algorithm could control for factors like race, gender, etc. How would it work in practice? I’d love to know more how it works at Uber (also in response to @Rio’s comment above).
Awesome post – thanks for sharing! I agree that crowdsourcing in Waze is important, but the app should enable more voice-activated functionality to notify other users of traffic jams, police cars, accidents, etc. Perhaps the app, in promoting safety, should disable users from inputting information when their vehicle is in motion? I have seen this work well before in car navigation systems and bluetooth-enabled cars. Also, connecting this to another megatrend, maybe Waze could even use machine learning to predict, based on drivers’ speeds, where accidents and traffic jams likely are without input from the user.
Awesome article – thanks for sharing! In answering your first question, I think machine learning will be important to stay competitive in the payments space. Churn is a key indicator of success in this industry, and as such, AmEx should continue to build out their capabilities to master this in house.
Additionally, your post made me think more deeply about how AmEx can use machine learning to also: (1) produce new products to existing customers (i.e., assist in new product development) and (2) predict how profitable new merchants and customers will be when signing them up for new cards.
Awesome post – thanks for sharing! I fully agree that an investment in additive manufacturing can be a way to solve Boeing’s program accounting woes. Airplane parts can be produced much more quickly and cheaply, enabling the company to start testing planes and incorporating learnings way sooner. This effectively shortens the new product development cycle and brings down the cost of the project – the potential benefits are huge.
On your second question, however, I also agree with the comments above. I think we are pretty far away from a world where airplanes are produced fully through 3-D printing given the FAA / the regulatory environment and low awareness of customers.