Charlotte Chang's Profile
My view on 100% product personalization and 100% 3D printing production model is perhaps more optimistic than those above 🙂 From a quality perspective, I don’t see how, if the machine uses the same raw material and follows the same construction method, the product quality would be any inferior to that of manually produced shoes. In fact, I think 3D printed shoes would probably have higher consistency and adherence to the design. Second, from a cost perspective, I think it will only be a matter of time until the machines’ efficiencies surpass that of manual labor. Let us not underestimate the pace of technological advancements – I would not be surprised if 3D printing becomes the norm in manufacturing in the next decade. When the time comes, I agree with the observation made in the article that, countries like China and India will lose their appeal to manufacturers as low cost labor markets. However, considering that firms will want to produce close to their end consumers, and China and India both have large growing middle classes, the next generation of factories may very well remain in those countries.
Thank you for a great read, Sam. Super relevant trend in retail and these unmanned convenience stores have been popping up in China in large quantities over the last 2-3 years as well. To the question you posed, I think the key competitive advantage Amazon has and should maintain over Walmart is consumer data. Because of its ecommerce nature, Amazon knows significantly more about each unique consumer than Walmart, that allows them to make personalized offerings and promotions, driving up LTV per customer. As to your second question, I agree with your observation and think Amazon should refrain from overinvesting in brick-and-mortar. While some retail footprint is helpful to capture consumers with immediate grocery needs, overtime I can see Amazon transitioning towards a subscription model on most items wherein goods are shipped before consumers run out of stock, thus eliminating any need for grocery runs.
On balancing external vs. internal product ideation, I do think that if the open innovation process proves fruitful and effective, there is space for Lego to reduce the size of its in-house design team. However, once the redundancy is removed, Lego should put the remaining inhouse team in charge of evaluating externally sourced ideas and overseeing the production process. This arrangement could help ensure 1) seamless transition between open innovation and inhouse processes, 2) buy-in from inhouse design team, and 3) ownership of follow-up steps.
Thanks for a thought-provoking and interesting pieces. A few thoughts on the questions you raised –
On when is OI most impactful, I think the condition for success is two-fold: 1) the company needs to have an internal blind spot, and 2) the audience it opens up the question to needs to have unique insights into said blind spot. So for instance, crowdsourcing ideas for snack flavors from end consumers make a lot of sense, but you would not get valuable insights on inventory management from the same crowd. For the latter, all the information you need to make good judgments reside in-house, and opening the forum up to too many opinions will only prove counterproductive.
On how to measure the success of OI programs, I think back to the A/B testing methods we discussed in the Uber case. For a multinational like Mondelez, it would not be difficult to choose two similar markets (e.g., France and Germany) and run a controlled experiment to see how OI affects performance in a particular area. The challenge here is that, given the nature of the OI process, ideation to implementation will probably take considerable time, so running the test on a topic with limited scope would be beneficial.
Thank you for an interesting read. In addition to “the widest reach possible”, I think Mobileye should consider a few other factors in choosing their platform partner. For instance, which business will be their best thought partner from an R&D perspective? With whom can they have the most resources and freedom with experimentation/implementation? While time to market is a critical success factor here, ensuring long term, strategic alignment with the partner seems more important as the commitment is likely to be long-term. On a separate note, I wonder whether as the data environment matures for self-driving cars, platforms where companies can trade driving data will emerge. In that future, companies like Mobileye will be able to train their algorithms without having to rely on an install-base.