What a great article! I think this example is a trend that can span across a variety of industries, but its success in the beauty realm could be hard to replicate. By targeting the demographics that historically have driven trends in the industry, it can be a great way to stay ahead on the trends. I would speculate that most of the people contributing to Natura are people that follow beauty trends set by some of the larger players in the industry. By that logic, I would expect this to be a great way to indirectly act as a collaborator with a variety of firms operating in the same space. It also allows Natura to leverage innovation from many different firms.
This was such an interesting article! I would have loved to be able to identify the different productions to compare the quality of crowdsourced ideas vs traditional ideas. It is interesting that it was shut down in 2018. What types of profits were generated from the crowdsourced media? I liked your question regarding whether Amazon should have kept the platform in order to act as a marketing device. I believe it would have kept customers engaged even if none of the ideas were used. Amazon could have just dedicated one of their productions to appear as a crowdsourced production and disregard the ideas that actually came through the funnel. However, that would also create potential integrity issues.
I really enjoyed reading this article. This is a great application of 3D printing. I would be curious to learn more about how material selection would affect the cost of the medical devices. Are there ways to optimize the printing technology around a specific material or does that not affect the time and cost required for the device?
Great job! This article was very clear and concise. I hope to see more automated solutions using AI when drilling both the vertical and horizontal portions of a well. In oil wells in particular, ensuring the vertical portion is as straight as possible is critical as the well ages and artificial lift mechanisms such as sucker rod lift are employed. A straighter well minimizes wear and tear on the sucker rods and reduces failure frequency. However as the speed of drilling the well increases, the wells often become less straight in the vertical. On the directional aspect of shale wells, it is imperative to stay within the pay zone of the formation being targeted. As these formations can be very narrow (<100 ft.) the importance of precision while still drilling quickly becomes extremely important.
What a cool article! I thought it was very interesting that the algorithms and hardware at the smart warehouses could replicate a human’s judgement when putting together packages. Your recommendation for utilizing ML in current operations prior to the implementation of the smart warehouses was also very astute. However, I wonder how much real impact is attainable with ML given Kroger’s current infrastructure. Perhaps in their existing warehouses Kroger could invest in some new robotic technology that would allow them to pilot and optimize algorithms prior to the implementation of their smart warehouse.
Great article! I am a dedicated customer of Barilla and did not realize how quickly they were able to produce a piece of pasta. It is also very interesting that they are using 3d printed in this space. I would be curious to see how structural changes to the pasta will change the total time required to create a piece of pasta? Are there any projections on changing the material that would reduce the time required to produce?
This was such a cool article! I would love to have the opportunity to plan my itinerary while I am booking my tickets. It is definitely painful planning vacations especially to new countries for more than a few days. However I was surprised to see JetBlue pursuing this application. Like the comment above I would have expected third parties to have more access to historical data that could utilize ML to develop trip recommendations. I am very excited to see how things progress with ML at JetBlue in the future!
Great article! I did not realize how large of a market there was for turbine maintenance. It is very interesting that predictive maintenance is going to be such a major effort across industrials in the coming future. I am excited to see how successful ML can be in building truly predictive failure models. I hope that ML is able to reach a stage where it can locate and combine all the data necessary to allow operators to generate predictive maintenance schedules.
Great article! I agree with Energy that there is a huge potential in developing predictive maintenance solutions as you also described under the “Manufacturing” section of the article. However, I found the application of ML in feedstock selection to be even more interesting. The opportunity for optimizing the differential between raw material and produced good pricing could be a massive savings at newer plants with multiple feedstock capabilities. It seems like right now managers may not necessarily have all the information they need to make the best decision when selecting which feedstocks to use.