Interesting read. One thought while reading this is the potential use of machine learning in tandem with open design innovation. Particularly, as the company continues to source and test consumer designs, the data generated from doing so (e.g. popularity, sales) could be used to run predictive analytics regarding both evaluating and tweaking future consumer designs, or even coming up totally data driven designs trained on consumer design data.
Interesting read. With regards to your question about target customer if they could design their own car, my starting point would be to consider car companies today that are closest to that: ultra-luxury manufacturers such as Rolls Royce or Lamborghini. What AM does to car manufacturing is reduce the cost, and thus price, of customization, and thus democratize customization in doing so. As with other technologies such as televisions, Personal Computers or air travel, that rode down the cost curve, I see the diffusion of customized cars not necessarily being tied to a “target customer” but rather a general trend affecting the population at large.
Really enjoyed reading your well-informed essay. It was fascinating to understand the data angle with regards to the aggressive push from Tesla to increase sales, even if at a lower price point through the Model 3. The ethical questions you pose at the end will be a key part of the debate on autonomous driving. The medical profession poses an interesting case study with regards to legal liability medical service providers carry with regards to their practice. I would further argue that legal responsibility is only a part of the overall picture, with the other major component being reputational damage. Even if the legal responsibility ends up coming out on the customer, the reputation impact of accidents can take a serious toll to the brand equity of the company.
Enjoyed reading the article – very interesting to see how the manufacturing business’ priorities are shifting as they move from traditional manufacturing to AM. I completely agree with the question you raise regarding defensibility as patents expire. As the product becomes more commoditized, I wonder whether the way to differentiation may be the service that forms part of the overall value proposition. You already touch upon a couple of areas in this regard, particularly turn around time and the overall customer experience. I can imagine cost and overall reliability of the product will also play a role. Lastly, another way scale may be helpful (beyond operating leverage for R&D and capex) is negotiating power vis-à-vis suppliers for raw materials (e.g. resins) which may play into overall product cost.
Interesting article! It was fascinating to see the various ways in which Netflix uses machine learning to acquire or produce content, and serve its customers. One thought I had reading this would be to understand to what degree are the datasets informing Netflix’s algorithms proprietary versus external. It is clear from the essay that Netflix has been successful at applying machine learning (in many ways) to conduct business, but the above inquiry would further indicate the relative advantage Netflix may have in doing so in comparison to competitors. The underlying assumption here being that external data, being publicly available, is not a source of differentiation, but proprietary data may be.