The advent of IoT technologies--and the more general move to digital tools that support operations, communication, analysis, and decision making in every part of the modern organization--won't change the fundamental purpose of production systems. With the introduction of comprehensive, real-time data collection and analysis, production systems can become dramatically more responsive. Highly integrated, digitally enabled production systems won't just work differently from today's--they'll be built differently, too. Automated optimization systems will adjust manufacturing sequences and speeds to help balance lines and match production more closely to customer demand.
Deep learning and IoT are two game-changing technologies that have the potential to revolutionize the stakes for oil and gas companies facing profitmaking pressure in the face of the dramatic drop in price of oil. Deep learning algorithms can automatically detect pixel signatures from drone footage for cracks and leaks that humans can miss, thereby minimizing infrastructure risk. While providing remote diagnostic services to industrial assets, the conventional form of interaction is through traditional dashboard communications. With the advent of natural language processing algorithms powered by deep learning, field technicians can interact with the asset diagnostic applications through voice interactions just as bots help in customer service.
The term "industrial Internet of Things" has a more muted-sounding promise of driving operational efficiencies through automation, connectivity and analytics. The company has integrated sensors to tools and machines on the shop floor and given workers wearable technology -- including industrial smart glasses -- designed to reduce errors and bolster safety in the workplace. Gehring uses the same cloud-based real-time tracking to reduce downtime and optimize its own manufacturing productivity through monitoring its connected manufacturing systems, visualizing and analyzing data from its machine tools in the cloud. While it offers an IoT platform known as Lumada, Hitachi also makes a plethora of products leveraging connected technology, including trains, which the company is beginning to sell as a service.
Chief financial officer (CFO) at global recruitment company Airswift, Tim Briant says artificial intelligence is going to disrupt finance departments completely, with bots replacing people. Chief financial officer at cloud accounting software company Xero, Sankar Narayan says artificial intelligence is powering his company's growth and enabling finance teams to spend more time conducting analysis. Technology has changed the role of the chief financial officer and artificial intelligence could erase it completely, predicts Todd Ford, CFO at spending management software company Coupa. The role of the CFO is to be steward of the company's capital and make decisions that maximise shareholder value CFOs of public companies need to embrace technological change to deliver shareholder value.
The circular economy concept requires that any resource is optimized in terms of renewability (energy used), reusability (cycling valuable metals, alloys and polymers beyond the shelf life of individual resources) and recyclability (compostable packaging). Within the energy sector, the circular economy would be powered by an "Internet of Energy" shaped by the imperative of decarbonisation (supported by transitioning sectors that today rely heavily on fossil fuels, such as heat and transport, to electricity-based power). The Internet of Energy would feature distributed generation with a high share of renewable energy, empowered by storage in all forms (grid, behind-the-meter and electric vehicles), demand response supported by smart grid assets down to "white goods"--and a fully transparent cost and value structure that takes into account levelized cost of energy (LCoE), cost of externalities, time and location of generation, value of ancillary services and storage, opportunity cost, etc. A suitable setup for an Internet of Energy could be a centralized electricity system with large-scale renewables, storage and flexible backup power interconnected to a decentralized electricity system with distributed generation, combined heat and power, electric vehicles, smart white goods, etc.
This digital computer, adapted for the control of manufacturing processes for General Motors, provided a means to generate and transmit digital information, so that hardware devices could digitally communicate with other interfaces and no longer had to work in isolation. Traditionally this had always required some kind of central computer to hold a rule set and act as a command and control server. In the oil and gas industry IoT sensors have transformed efficiencies around the complex process of natural resource extraction by monitoring the health and efficiency of hard to access equipment installations in remote areas with limited connectivity. By embracing near edge processing technology instead of the cloud, the resource industry can now process a significant amount of the data that is generated from the sensors they use in low power, small computers close to the physical location of the sensors themselves.
The Isaac robot simulator advances these tasks by providing an AI-based software platform that lets teams train robots in highly realistic virtual environments and then transfer that knowledge to real-world units. Under the hood, it demonstrated the ability to apply previously learned models of tool affordances, tool classification from vision, automatic tool pose detection, object segmentation and full/empty hand classification to achieve its task. By utilizing quadrotors attached to a mainframe via passive spherical joints as rotating-thrust generator, this SmQ (Spherically-connected multiple Quadrotor) system is fully-actuated (e.g., can resist sideway wind without tilting) and also backdrivable (e.g., impedance control possible for compliant interaction). With design optimization to address the tight weight-thrust margin of current rotor and battery technologies and proper control design, the ODAR system can exhibit such capability for "real" manipulation as 1) downward pushing force larger than 6kg (much larger than its own weight of 2.6kg) and 2) peg-in-hole teleoperation with radial tolerance of only 0.5mm, all unprecedented by other aerial manipulation systems (e.g., drone-manipulator).
"The largest inefficiency that most manufacturers face is inflexibility," says Jim Lawton, Chief Product and Marketing Officer of Rethink Robotics, maker of collaborative industrial robots. In virtually all factories, poor demand forecasting and capacity planning, unexpected equipment failures and downtimes, supply chain bottlenecks, and inefficient or unsafe workplace processes can lead to resource wastage, longer production periods, low yields on production inputs, and lost revenue. Faster feedback loops enable factories to tackle unplanned downtimes, low yield (percent of units that pass quality control), and low productivity (time it takes to make a product). Investments in technology have led to 81% reporting reduced cost and increased revenue, 76% reporting error reductions and accuracy improvements, and 95% reporting better customer service.
In view of falling oil prices and the resulting squeeze on cash flows, the oil and gas industry has been challenged to adapt and optimize its performance to remain profitable while maintaining a long-term investment and operating outlook. Additionally, geoscientists can better assess variables such as the rate of penetration (ROP) improvement, well integrity, operational troubleshooting, drilling equipment condition recognition, real-time drilling risk recognition, and operational decision-making. AI can help to create tools that allow asset teams to build professional understanding and identify opportunities to improve operational performance. By using AI software to analyze the company's large collection of historical well performance data, the company is drilling in better locations and has seen production rise 30% over conventional methods.
The effort shows how low-cost drones and robotic systems--combined with rapid advances in machine learning--are making it possible to automate whole sectors of low-skill work. Avitas uses drones, wheeled robots, and autonomous underwater vehicles to collect images required for inspection from oil refineries, gas pipelines, coolant towers, and other equipment. Nvidia's system employs deep learning, an approach that involves training a very large simulated neural network to recognize patterns in data, and which has proven especially good for image processing. It is possible, for example, to train a deep neural network to automatically identify faults in a power line by feeding in thousands of previous examples.