The statistic in this article that was most surprising to me was that the combined annual output of the two Speedfactories will be less than 0.3% of total volume of Adidas sneakers. Given that the capital cost of investing in additive manufacturing is very high, I wonder how scalable this strategy is for Adidas. In determining whether there is a path to commercial viability, Adidas needs to determine which type of sneakers additive manufacturing is most beneficial for and what percent of total volume those types of sneakers account for. Depending on the shoe type and the volume of that product sold, it would be interesting to run a break-even analysis to see whether traditional manufacturing or additive manufacturing makes more sense. While I understand the importance of speed to market, it is also important that Adidas has a clear understanding of the unit economics of producing shoes through additive manufacturing.
In terms of whether or not to continue partnering with Carbon, I think Adidas should assess which markets it is crucial to have speed to market and “fast fashion” sneakers in order to stay competitive with other players in the market and open Speedfactories in those markets.
Interesting article – thank you for sharing! In my opinion, one of the biggest business risks to Takeda of using open innovation is that it will be difficult to keep their R&D pipeline confidential. The value placed on the R&D pipeline is very important for pharmaceutical companies. By crowdsourcing R&D ideas through COCKPI-T, I fear that Takeda may find it more difficult to sustain a competitive advantage if its competitors can see the type of R&D ideas being presented to the company. While I’m sure Takeda has confidentiality measures in place as R&D projects progress to the next stage, the fact that Takeda’s R&D ideas are open to the public is worrisome. I’d also like to better understand how the decision-making process is made when selecting crowdsourced R&D ideas. Has Takeda formed a team of R&D specialists, lawyers, quality and marketing professionals to ensure that the R&D idea is assessed from all angles before proceeding?
Fascinating article! It was interesting to read about the merger of beauty and technology. To address your question around whether the customer experience in the beauty market will always be predominantly offline, I think that Shiseido needs to invest in educating the consumer and to be more proactive in making the benefits of machine learning in beauty known. What I find appealing about MatchCo is that the system is using an image that is unique to the consumer to give a recommendation. Having recently wasted over thirty minutes in Sephora looking for the perfect concealer match, I value the efficiency and ease that Optune offers. In order to convert consumers to buying beauty products online, Shiseido should consider arming its sales people in the stores with Optune, MatchCo and its other machine learning technology so that they can demonstrate to consumers the benefits that machine learning brings to the decision-making process when buying beauty products.
Having spent the past six years in finance, I am very intrigued by this article! BlackRock’s Scientific Active Equity group has a very impressive track record, especially when compared to the stock-picker managed funds. One of the concerns I had while reading this article was along the lines of the question posed at the end of the article around ensuring BlackRock’s machine learning approaches are not falling prey to algorithmic bias. Therefore, it is critical that BlackRock puts in place some type of system that continues to test the algorithms and ensure they are still working correctly. I’d be curious to know how often BlackRock monitors the performance of its SAE products and if there is some mechanism in place to alert professionals when performance dips below a certain threshold so the situation can be investigated. Furthermore, it would be interesting to see how the performance of BlackRock’s SAE products compare to the performance of the SAE products of its competitors. If BlackRock’s performance is lagging, that raises a red flag to look into the algorithm and see if any changes need to be made. Finally, when investing in stocks, a factor that should be considered is the strength of the management team. Many times, the best way to form an opinion on a management team is through-in person meetings. In thinking about how far machine learning can go, that seems to be one area that is better left to humans to decide.
Very interesting article! While reading this article, a few thoughts came to mind. First, one of the key participants in this crowdsourcing model is the designer. As the company tries to scale and compete with other retailers and Amazon, it will be interesting to see if Betabrand will be able to attract a diverse set of designers to their community and continue to receive product ideas that consumers are excited about. It would be interesting to know how Betabrand is compensating the designers for their clothing designs and if this will be enough to keep them contributing ideas to the site as opposed to finding work with other brands and retailers. Additionally, when I originally read this post, I had a similar thought as Jane Harvard (above comment) that Betabrand’s online community may fall into group think and vote on clothing of a similar style. However, I took a look at the Betabrand site and was actually very surprised at the range of different designs on the site. This site definitely does not cater to one style of dressing. After seeing the wide variety of styles on the site, I worry that it may be difficult for Betabrand to garner loyalty among consumers. Given most consumers have their own “style”, I worry that Betabrand may have trouble attracting a loyal consumer base given that the site does not seem to be a “one stop shop” for consumers but rather a site for consumers to order one or two items. In order to enhance the consumer experience on the site and increase loyalty, Betabrand may want to consider adding in some filters on the site so consumers can easily filter for clothing in their “style” whether it is preppy, punk, modern, classic, etc.