AI Articles and Analysis in Heavy industry

DHL is a German logistics company that offers parcel delivery, express mail, freight forwarding, and third-party logistics. Its parent company is Deutsche Post (‘DPDHL’), the world’s largest logistics enterprise, operating in over 220 countries and employing more than 510,000 people worldwide. 

The manufacturing industry has changed in recent years. Humans on the shop floor have always used their senses and experience to anticipate machine failure before it occurs. Now, AI can be used in conjunction with human expertise.

ExxonMobil is the largest investor-owned company in the world and the largest oil company by revenue in the Western world.

While the steel industry may not be synonymous with AI adoption, steel manufacturers are not different from any other business in ensuring efficiency in their output and throughput. There are almost certainly opportunities for AI implementation across an industry worth hundreds of billions of dollars that currently lags behind in adopting AI technology.

Chevron is the second largest producer of oil in the United States (Exxon Mobil). The company is traded on the NYSE (symbol: CVX) and has a market capitalization of approximately $322 billion.  

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General Electric (GE) was founded in 1889 by J.P. Morgan and Anthony J. Drexel who came together to finance Thomas Edison’s research and merge their companies together. Originally, GE was an industrial and consumer products company but today, more than 130 years later, GE has transformed itself into a multinational, digital industrial corporation ranked as the 33rd largest company in the United States by gross sales in 2020, according to Fortune 500.

The hypothesis is simple:

Equipment breakdowns or downtime is extremely expensive (imagine a train broken down on isolated tracks, hundreds of miles from the nearest depot)
Heavy equipment (engines, wind turbines, manufacturing machines) produce various streams of data (heat, vibration, time-series, etc)
Machine learning could be used to detect "failure patterns" in that data, helping businesses to maintain equipment health more effectively