You make a compelling case for how Nike benefits from additive manufacturing. I do worry that there’s not much of a competitive moat with that, though. I see it as a way for the company to reduce costs (in selective applications), though Adidas et al certainly can do the same. I don’t think the consumer cares enough about whether or not their shoes are 3d-printed, therefore this innovation is only really meaningfully impactful from a cost standpoint, which will eventually be a benefit had by all competitors in the category.
I love the product side of this and see a ton of opportunity in what they’re doing. My concern is related to maintenance and upkeep costs, given part of what’s attractive about the vending machine model is that once distributed, they only really require replenishment and the occasional technical maintenance issue. In contrast, these machines would require materials replenishment in the same manner but it seems like any technical breakdowns would need a much more skilled type of mechanic to fix them. This is a scalability and cost concern for me, since I’m guessing the number and geographical distribution of 3d-printer engineers is sparser than traditional vending machine repairmen.
In response to your question at the end about whether non-profits are best-suited to help solve these challenges, what if the go-to-market phase is also left to open innovation? I realize that OI is typically reserved for science and technology projects, but if proper incentives are in place, I can’t imagine that more business-minded people wouldn’t also want to engage in the challenge. To the question of whether for-profits are best suited to run these projects, I don’t think there’s necessarily a difference as long as there is enough of a “carrot” incentive to innovate.
I think open innovation in a hardware-driven category is really risky. In answer to your question of whether they should “close” if they come across really disruptive technology, I think the answer is yes. Open innovation in a hardware-driven category is really risky, given much of the challenge is identifying how to do something – repeating it is less difficult. I think the “rising tide lifts all boats” adage is appropriate for software, but it feels like Tesla’s technology is part of what feeds it’s brand, therefore commoditizing that too much could dilute it’s brand.
I question your concern about whether it’s “too late” for H&M, given I’d argue it’s a brand people buy for a specific utility – aesthetic at low price – and not for the H&M brand itself. Consequently, if investing in ML can enable the company to bring more relevant apparel options to the consumer still at low cost, then as long as they can get that product in front of the consumer’s eyes (whether via retail or digital), they seem well-positioned to grow again.
Great choice of topic and a compelling illustration of how ML can unlock significant value. If 60% of business is from Tier 1 Cities (for which algorithms are already being worked on), it seems the other 40% would represent enough volume (if not too fragmented across geographies) to develop more comparable algorithms to solve for this issue. I’d also expect there could be some learnings from the Tier 1 markets that are transferrable. Overall, I push back on the concern about scalability for the reasons above, particularly paired with the Moore’s Law-esque improvements in ML technology and growing popularity of ML educational we’re seeing.