Thank you for the post, @nikolasra! I think your idea of distributed maintenance centers enabled by additive manufacturing would be really fascinating. This would certainly give them a competitive advantage in being located close to the customer. But if they were to build out such centers, what are some of the challenges that they would face? My sense is that the 3D printing of spare parts may result in pieces with varying levels of strength / reliability due to environmental conditions where you are printing the pieces. For example, if one of your maintenance centers is located in Hawaii, how will the printing process be affected by the warmer ambient temperature and higher humidity levels? If your printing mechanisms are sensitive to changes in atmospheric conditions, then you may have to put a lot of resources into doing quality checks on the spare parts. But if this isn’t a large concern for the printing of spare parts, then I’d have fewer reservations about the distributed maintenance centers.
@Rohanmal, thank you for sharing this article on AIG’s approach. I was intrigued by your second question, regarding how to prepare the underwriters. I imagine that credit-extending institutions are also facing similar questions about how to train their teams making credit decisions. A shift in process to a machine-learned model will require individuals with deep lending knowledge to help train and develop the model. But one of the limitations of machine learning, is that at a certain point you can’t ascertain why or how the model made it’s decision. My sense is that this will be a big obstacle for insurance and credit firms as they start deploying machine learning in their decision models. According to a Quartz article last month (link below), regulatory bodies are already starting to raise concerns over discrimination on the basis of gender and race. How do think this will affect AIG’s decision to invest in machine learning and AI?
CBH, thank you for sharing this perspective on NRG’s predictive maintence implementation. As you pointed out, the value that machine learning can bring, is tremendously increased when the model is fed a large volume of data to train the system. The OEM’s like GE have suggested maintenance schedules, and in some cases these maintenance guidelines must be followed to be compliant with regulatory guidelines. Thus, there are probably situations in which the predictive maintenance model would propose waiting longer for a maintenance activity than is advised by the OEM. Additionally, I imagine nuclear plants have more stringent guidelines than those of a combined cycle plant, because the implications of a failure are more significant. As such, I’m curious about how OEMs and regulatory agencies are approaching this trend. Operators across the industry are certainly looking for ways to minimize cost, though I’m sure the OEMs and agencies may push back on it.
Thank you for sharing this article, Patrick. While not a perfect analogy, this open innovation approach at Eli reminds me of NASA’s Exoplanet Explorer program. NASA’s program works by crowdsourcing the search for habitable planets to citizen scientists (link below). In NASA’s case, the data is provided from one source, the Kepler space telescope. From my understanding, NASA’s cititizen scientist program is one of the better known programs due to the news following big finds in Kepler’s data. NASA has also devoted a large volume of resources to simplify participation in the exoplanet exploration program, which further encourages participation. I imagine that the academic institutions you mentioned are mostly universities, right? Is there a way to make this research more accessible for younger and aspiring scientists? By breaking the process into smaller steps, could high school students take on research projects to contribute to this?
Ali, thank you for your article! I hadn’t thought about SoundCloud looking to rely more heavily on its userbase as a source for innovation. SoundCloud certainly has a unique position and user base as you had pointed out. I was especially fascinated by the creator-focused product that just launched in October. As they work to monetize their solution, I think there may be additional value in doubling down on the tools that SoundCloud offers its creators. Kerry Trainor, the new CEO, was quoted saying that it “is built from the creators, out”. Aside from the features that you mentioned, are there some products that SoundCloud could co-develop with creators that would aid in music production? Or features that would help them better collaborate with other creators?
Enjoyed your article, Carlos! I agree that it’ll be interesting to watch how this investment in AM plays out with their competitors.
One other question that comes to my mind was around the material inputs as a key success factor. Not only are companies thinking about making these materials for AM more economic, but also how they compare in terms of mechanical strength and reliability. I wonder whether there is space here for GE Aviation to think about devoting more materials science research into these raw material inputs.