NAT2102

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Unlocking Efficiency Gains Through AI: Shell and Machine Learning
NAT2102
Posted on November 13, 2018 at 2:35 pm
Machine Learning has the potential to create billions of dollars of efficiency gains for oil and gas companies. Shell is the first big company to take the plunge. Will they succeed?

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On November 15, 2018, NAT2102 commented on The Beauty of Crowdsourcing :

I think the bigger question here is how to efficiently filter through the ideas that come from their crowdsourcing funnel. Brainstorming may give Natura a large number of ideas, but it won’t mean much if they can’t efficiently (and thoughtfully) filter through them.

On November 15, 2018, NAT2102 commented on Getting Swiggy With It: Your Favorite Foods, Faster :

I’m very skeptical that improving the company’s machine learning algorithms will improve it’s throughput times. It seems that food delivery services have reached a theoritcal limit of improvement – any mor.e improvement in delivery times would mean improving urban mobility in the countries where they operate.

On November 15, 2018, NAT2102 commented on Amazon Studios’ experiment in crowdsourcing scripts: were they right to abandon open innovation in developing new content? :

One can contrast Amazon’s foray into crowdsourcing TV/film with its success in publishing through their Kindle platform. The latter succeeded because of the low production costs of publishing books, while I believe the former failed because of the prohibitively high costs of producing TV/film. However, as production costs continue to fall, I believe crowdsourcing ideas will flourish.

On November 15, 2018, NAT2102 commented on Print-a-Part: How 3D Printing is transforming Medical Device Manufacturing :

The potential of Stryker products to revolutionize healthcare supply chains is stunning. I think back to the cases involving healthcare suppliers and distributors (i.e. O&M) and this article highlights the existential threat those companies face. I believe Stryker will face significant resistance from these companies, and they must continue to extend their technological lead to stay ahead of them.

On November 15, 2018, NAT2102 commented on Streamlining Humanitarian Aid with Additive Manufacturing at Oxfam :

Training villagers and providing 3D printers in humanitarian sites does not solve the logistical challenges here. It just pushes it earlier in the supply chain. The real challenge here is to find ways to supply 3D printer feedstock with locally sourced materials. Oxfam isn’t the right party to solve that problem, but they can partner with tech companies who might have the capabilities to do so.

On November 15, 2018, NAT2102 commented on Make Tonight Unforgettable with HotelTonight :

Machine learning capabilities are being commoditized rapidly. Unless HotelTonight finds a way to lead technological development in the field, I strongly believe that their competitive advantage will erode. They’ve done a remarkable job growing their user base by exploiting an overlooked problem, but what stops the other travel booking giants from copying HT’s features overnight? Either they make a compelling customer retention play, or they extend their technological advantage. Otherwise they should sell the company to one of the larger competitors.