AI Articles and Analysis in Heavy industry

This week, we speak with arguably one of the best-known folks in the domain of neural networks: Jurgen Schmidhuber. He's working on a lot of different applications now in heavy industry, self-driving cars, and other spaces.

Accenture forecast the Industrial Internet of Things could contribute $10 trillion to the global economy by 2030. The report also suggested that sensors, material tracking mechanisms, 3D printing, automated product design, robotics, and wearables could help manufacturers reduce costs and increase productivity. Predictive asset maintenance could potentially reduce equipment and machinery maintenance costs by up to 30% and result in up to 70% fewer breakdowns.

McKinsey reported that most oil and gas operators have not maximized the production potential of their assets. A typical offshore platform, according to the 2017 report, runs at about 77% of its maximum production potential. Industry-wide, the shortfall comes to about 10 million barrels per day, or $200 billion in annual revenue.

With the entrance of artificial intelligence and its capabilities of recognizing temperature, vibration, and other factors from sensors pre-built into machinery and vehicles, business leaders in heavy industry might be interested in the possible opportunities of predictive and preventative maintenance applications.

Asset and wealth management firms are exploring potential AI solutions for improving their investment decisions, and making use of their troves of historical data. In fact, according to our AI Opportunity Landscape research into the banking industry specifically, approximately 13.5% of the AI vendors in banking offer solutions for wealth and asset management.

Garment and textile manufacturing have historically been labor-intensive industries, as seen in how many of world’s largest fashion, clothing and apparel brands seem to have a significant portion of their products manufactured in Asian countries such as China, India, Bangladesh, Vietnam, etc.

Supply chain management (SCM) is critical in almost every industry today - but it hasn't received as much focus from AI startups and vendor companies compared to healthcare, finance, and retail. Businesses are showing increased interest in AI applications, from its benefits to fully leveraging the vast amounts of data collected by industrial logistics, warehousing and transportation systems.

Over the past decade, the industrial sector has seen major advancements in automation and robotics applications. Automation in both continuous process and discrete manufacturing, as well as the use of robots for repetitive tasks are both relatively standard in most large manufacturing operations (this is especially true in industries like automotive and electronics).