L Wallace

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On November 15, 2018, L Wallace commented on Chariot: Changing Transit through Crowdsourced Co-Creation :

Interesting post. I am curious how Chariot / Ford evaluates efficiency and profitability before and after implementation of each contemplated route. ~49 riders per route seems like a low bar… As Chariot has become more established, did the criteria to establish new routes rise significantly? If the barriers to innovation increase, does the user eventually lose confidence in the open-source model, and become disillusioned with Chariot overall?

A very topical post! Given the circumstances, I think that the company cannot afford to lose market share within aeorospace, and further investment in AM appears critical to achieving this goal. Under the former CEO Jeff Immelt, GE tried to create conglomerate synergies by applying the work of its corporate research and development group across a broad array of its industries. Obviously, this strategy did not work out. However, as GE retrenches, I wonder if the company’s particular expertise in AM could be applied selectively across similar industries, and re-establish GE as a leading innovator?

Your post made me re-think the delivery ordering experience. It sounds like DeliveryHero’s use of machine learning will make its sales employees’ lives easier, and allow them to allocate their time more efficiently. However, I wonder if the machine learning algorithms would be able to identify new-concept restaurants that don’t fit previous trends for an area. The oversight could result in another delivery service signing the restaurant first, and a loss of future revenue for DeliveryHero. I think that human intuition and judgment could still be relevant and necessary in such cases, and DeliveryHero should make sure that their sales reps don’t stop acting on these kinds of intuition.

I found this article very interesting. I think that ConforMIS offers high value proposition for the unique, critical cases. Unfortunately, my mom is one person who has had multiple TKRs and hip replacements over the course of her life, and has not regained full flexibility of her knee. Given this personal experience, I think that a custom ConforMIS implant plus 3-D printing of bio-compatible cartilage could be very attractive to those patients like my mom who have tried the standard procedures to no avail. To widen its target market, could ConforMIS use its 3-D printing technology for other kinds of implants as well? Before patients are willing to undergo another surgery, however, ConforMIS needs to guarantee that the 3-D printed product has the same longevity as the traditional implants.

On November 15, 2018, L Wallace commented on Choosy: Give the People What They Want…To Buy :

Very interesting post! To get a better sense of Choosy, I just looked at the company website and saw a close copy of a $500+ Self-Portrait dress that went viral after Meghan Markle wore it. So, in addition to solving problems for the brand, it seems like Choosy can make the customer’s buying experience much more efficient. Instead of the customer seeing a dress on Instagram and then trying to find it or a look-a-like, Choosy simply makes the dress for them. As reliance on open-source innovation increases, I wonder to what extent Choosy will have to contend with potential copyright issues from designers whom they are copying? Does this limit the kinds of open-source requests they can respond to?

On November 15, 2018, L Wallace commented on Iberdrola, machine learning and the future of offshore wind :

Interesting article, thank you for sharing. As you mentioned, machine learning’s preventative maintenance application has high value for the offshore wind sector, especially given the high O&M costs in such rough, remote environments. Any innovation that allows these turbines to operate more efficiently with less human interface will help drive down costs across the industry. I agree with Daichi that sharing the risk with ROMEO consortium is beneficial as the industry comes up the curve on offshore technology. However, I do think that the data could provide Iberdrola valuable insights on asset management (costs, forecasts, curtailment, etc.), which could give them a competitive advantage as the lowest cost operator. Is there a trade-off between generating data from as many competitors’ turbines as possible and maintaining proprietary control over the insights?