Edge Computing

Smart manufacturing encapsulates the use of sensors and entire systems that have become more intelligent, with increased networking and vast amounts of available data. Industrial Internet of Things (IIoT), Industry 4.0, and digital transformation are a few of the principal trends describing the ubiquity of devices and the increased connectivity to those data sources in the manufacturing environment. Add in advanced analytics and manufacturers can capture crucial insights that inform users on how to improve operational efficiency and reliability.

Midstream oil and gas companies can’t afford unplanned downtime. Studies have shown unplanned downtime can costs tens of millions of dollars per…

Edge AI is a technology that can put artificial intelligence on edge servers and perform predictive maintenance with artificial intelligence at high speed. Future development is desired as one of the key technologies to solve the shortage of skilled engineers. Here, we will explain the difference between cloud AI and the use of cloud AI and edge AI.

Digital transformation in oil and gas remains paramount in managing mission-critical operations, gaining valuable business insights, improving worker safety, reducing costs, and more. This is especially true for offshore oil and gas production, presenting a unique set of circumstances.

While automation is accelerating business intelligence across industries, many industrial operators lack the staff and experience to pilot new automation projects, relying instead on system integrators. By developing core competencies in emerging technologies like edge computing, system integrators can position themselves as trusted partners and drivers of innovation.

A unique challenge faced by the Midstream Industry is that most critical assets are remote and often unstaffed. For pipelines, the edge…

Edge computing is moving away from the “edges” to become a mainstream approach and an important consideration in industries where real-time data is needed to drive better business decisions. We look at four key factors driving the success of edge computing.

“Equipment maintenance” is a very important activity to maintain the productivity and efficiency of the production system. This section outlines equipment maintenance activities and, in particular, the importance of predictive maintenance, and finally introduces its relationship with edge computing.

It is no exaggeration to say that the success or failure of the development of the manufacturing industry lies in quality improvement activities. In this paper, we described classic TQM activities as specific examples of quality definition and quality improvement activities and explained the future relationship between quality improvement activities and IT equipment.

The connection between big data and AI has greatly expanded the possibilities and is expected to play an active role in various fields. How will big data and AI create new value? Introducing the fields of utilization of AI and big data, and the relationship with edge computing.