Lindsey Macleod's Profile
Few organizations have the capability and reach to make scientific strides like NASA. Tasked with the most difficult and abstract problems, I would want every tool at my disposal – including outside sources. I do understand the hesitancy to accept solutions from unproven, external sources. Outsiders have a freedom to develop solutions using their own methods outside of the prescribed framework in which NASA operates. Those within the institution may feel at a disadvantage needing to follow strict regulations and guidelines. However, I do think standard approaches and open innovation can coexist. The organization will likely need to reframe the external contributors as partners rather than competition. It would also likely help to get direct input from the engineers on how they think they can better utilize open innovation internally. While it may be difficult, I agree that NASA should continue slowly and steadily incorporating open innovation. Particularly in an environment where NASA’s funding is no longer considered a priority, the ability to incorporate open innovation could unlock significant value.
To your second question, I think their ability to perform better than traditionally produced sportswear will depend on their ability to quickly incorporate multiple material types and generate sufficient demand. While it sounds like they have successfully expanded with new materials, they will likely need to push this further to offer the same product variety currently available in the highly competitive market for shoes. To really compete with traditional production, the new AM process needs to maintain product quality while introducing cost savings – cost savings alone likely won’t cut it. The product variety can also likely help increase demand for the new products. I agree with you that partnering with athletes will also lend credibility to this new line and increase demand. This seems a critical step to gaining traction with customers and closing in on their goal to sell 50 million pairs. While a fascinating and potentially lucrative endeavor, I think you’re right to approach the question of market demand, at least in the short term, with caution.
This was a fascinating application of AM given the potential for customization in the toy industry. I was particularly intrigued by their ability to print soft objects, which, as you point out, has not been explored to the same extent as hard materials. So, to your first question, I think Disney has an opportunity to move quickly and capture a large market in the soft materials AM space and should work to incorporate that technology now. Waiting could allow competitors to enter the space first and Disney could struggle to compete on product quality and cost-effectiveness. Continuing to acquire smaller companies seems to be their best option right now – they can capitalize on existing technology and bring the teams in house to continue development. You also make a good point that they should pursue additional trademark protections to fend off both potential competition and theft which pose a large threat. It is unclear how long this research will take to come to fruition – but I can’t wait to see what they have planned with the “3-D printed, interactive, soft robot”.
The application of machine learning to reduce traffic and CO2 emissions is intriguing but I question how quickly it can be implemented. As you’ve pointed out, there is a clear unmet need and a potential solution where many parties benefit. It has also been implemented in smaller areas and seen initial success. However, so much of machine learning depends on training the algorithm that the complexity and scope of LA roads could pose a larger challenge. It could require many iterations over a long time to see results. I also wonder what guard rails need to be implemented to ensure safety. How does the system react to different kinds of accidents like an overturned car or a fire? How does it predict how quickly an unusual event will dissipate? While humans are not an ideal alternative, we should ensure there are appropriate controls in place before utilizing machine learning, particularly when safety is on the line. If we could accomplish that, you could be correct – there could be enormous opportunities within transportation.
I agree that NASA should continue to advance cautiously given the many “unknown unknowns” that impact even a relatively simple component. The risks associated with moving too quickly and using AM for mission-critical components is too high. To your point – no one wants another Challenger disaster. That being said, it could be highly useful for NASA to test the validity of AM in-space on non-critical components. If that is the ultimate goal and their process is incremental by design than they would benefit from starting the learning process earlier. Start small as they have with on-earth components but at least start since gathering data is critical to their steady, gradual approach. The data they gather from in-space AM could also further inform how AM works on earth. Of course, the question of funding for these many projects is another issue entirely and could potentially limit NASA’s ability to test AM both on earth and in space.