Learning Machine's Profile
Thanks for the interesting article! I actually wasnt surprised to see the crowdsourcing approach coming from a Scandinavian country – I believe it is in their DNA to share and develop as a society. I really like the fact that they engage fans and incentivize them to be creative and share their ideas. On the other hand it seems that Lego still manages the rest of the funnel itself and inform the idea owners at the end. I would also integrate the owners of most promising ideas back into the funnel at some steps to be able to diverge again (like IDEO) and assess and co-develop the product together. In addition, Lego could organize events, e.g. “Legothon”s, to have fans come together and compete in creative designs. This can increase engagement and also serve as a nice marketing campaign.
To address your question, I believe crowdsourcing cannot be the only input for product development, it should be a supporting tool. Therefore Lego still needs creative, innovative and local teams to be present.
There are around 20k-30k parts in a passenger car. Most of them are very small parts, such as bolts and nuts, and they are cross-overed between different models, therefore they require very high volume production. I do not see that additive manufacturing would be less costly and more convenient for those small parts going forward since they are produced in millions, however for the very large and complex parts (e.g. parts of the engine, exhaust, shafts) it can be a game changer for the suppliers in the medium run since tooling is very costly.
I agree that safety will be the main concern of the OEMs going forward. On the other hand, with mass adoption of additive manufacturing, costs will go down and quality will increase with higher competition in printing hardware manufacturers. In addition, developments in material sciences will play a key role in finding the right material (durable, low-cost and sustainable) to produce auto parts that will transform the industry.
Thanks for the interesting article. Not only Tesla, but most of the auto OEMs are facing these challenges and opportunities and it seems that most of them invested significantly in autonomous vehicles but future is dependent on many external factors as well. With more connected cars on the road, we will have much more data and we can use simulations to leverage machine learning; however, there are so many decision scenarios that we cannot find and “hardcode” in the car. Therefore, although Elon Musk is promising a fully autonomous vehicle for 2019, I believe we need to be more patient to get the full buy-in of the whole society. This requires a total transformation, it is not only about cars, it is a car-human interaction and we need to learn how to live that way.
Let’s think about the fact that 39k people died in the US alone from traffic accidents last year and 94% were due to driver related factors such as impaired driving, distraction, and speeding or illegal maneuvers. Autonomous driving is aiming for 0 casualties, but even the current state is promising vs. status quo. On top of increasing safety, its other benefits such as converting travel time into productive time and increasing car utilization, makes autonomous driving the future of transportation.
Lastly, Tesla has been producing its whole ecosystem “in-house” and is away from any partnerships (e.g autonomous driving development, own electric vehicle charging technology/network). However, all the other OEMs have been partnering up with tech companies and many startups. I believe collaborations to build mobility ecosystems will be key, so Tesla should look for some answers outside as well.
Thanks for the interesting article! I agree with you and above comments regarding the risks of using historical data for predictive purposes, especially in analyzing criminal records. It might be helpful to understand the common traits among criminals using data analytics, and that can help to define preventive actions; however in my opinion labeling people even before they commit a crime as potentially guilty is not going to work. This is a very sensitive topic, on which even human thinking is sometimes biased.
Nevertheless, I believe security enforcement can benefit from other areas of technological developments and AI. For example, there are many companies emerging that use machine learning to analyze visuals from surveillance cameras to detect “suspicious activity”. Using machine learning will then much less resources to monitor and analyze real-time videos and interfere if needed. Data privacy concerns will be still there, but I believe this will improve day-to-day security operations in a positive way if used well.
Enjoyed reading the article! I agree that additive manufacturing not only provides flexibility in production but can also reduces costs through enabling closer production proximity to demand, lowering fixed costs and shortening product development cycles. Nevertheless, I believe traditional mass production would still be a significant portion of the mix, especially in the geographies where supply and demand are closely consolidated, at least until cost of additive manufacturing can leverage economies of scale. Moreover, Adidas needs to think about addressing certain risks that come with a new way of producing high quality products. Lower entry barriers increasing competition, complexity in choosing the right material used in production, and ensuring comfort and durability are some of the risks I see going forward.