Autonomous Operation – When Traditional Becomes Intelligent
Published on : Sunday 06-12-2020
Oren Yahav elaborates on the new discoveries and developments for intelligent plant operation.
A major game changing Autonomous Operation is approaching in the world of traditional industrial manufacturing, mainly focusing on process industries like Engineered Wood, Pulp, Chemicals and others.
At the same time, this is a major challenge because it requires the implementation of AI based technologies in traditional industries that ‘talk’ differently.
Industrial plants are dynamically influenced from different deviated factors like raw material, environment, machines, human behaviours and others. Those factors frequently change production values and create unstable manufacturing processes through cost, quality and productivity.
New Fourth Industrial Revolution solutions include AI emerge to ensure optimal operation, mostly offering different visual platforms for real time suggestions (‘near real time’). The concept is to delegate the insight to local operators for faster response.
In reality human cognitive behaviour is far away from being optimal, high performance deviations between shifts, lack of capability to track vast numbers of changes and different learning curves between local professionals. Additionally, traditional industries have played safe by restraining adoption of new technologies.
This disruption potential has evolved over time from Industry 4.0 concepts, by using generally well-integrated and well-applied digital automation technologies. It will also require a smart approach in order to adopt emerging cognitive computing in the industry and implement it on premise with a specific industrial toolkit, for increasing the willingness of local professionals to collaborate with new technologies.
Cognitive technologies are products in the field of artificial intelligence. These technologies are capable of performing tasks that only humans have been able to perform until now, for transferring the ‘suggestions’ into a ‘real time autonomous operation’. Examples of cognitive technologies include computer vision, machine learning, natural language processing, speech recognition, and robotics.
Those technologies are aiming to understand how humans can most effectively augment machines, how machines can enhance what humans do best, and how to redesign business processes to support the partnership. In practice, enhancing collaborations on production will provide clear value in increasing performance and minimising losses between shifts.
The ambition for this innovative approach is focusing on major cost reduction, combining multidisciplinary capabilities to make a real change in production performance and quality, by enhancing the existing capabilities.
Furthermore, bridging new cognitive methodologies to traditional technological concepts on the production floor can revolutionise the industry. Basically, delegating high level concepts into the manufacturing floor and amplifying on premise technical capabilities and collaborations.
These plant-level technologies all fall under the domain of operations technology or ‘OT’ for implementing real time advanced autonomous operations, as distinguished from more enterprise-level information technology, or ‘IT’. In general, cognitive technology focuses on safe, deterministic, and high availability for reliable, nonstop operations, combining IT elements of data integrity and security.
Through such collaborative intelligence, humans and AI actively enhance each other’s complementary strengths. The power and value of these new technologies will advance stable and safe operations over process manufacturing (e.g., mechanicals, chemicals and other materials) and improve overall business value by multiplying performance improvement as the collaboration principles adopted and implemented in the plant.
In addition to minimising the inevitable human errors, enhancing automation could go toward helping solve the growing issue of finding qualified personnel by offering something new to those traditional industries, and to assist humans running the production lines in a full potential optimal performance of cost, quality and productivity.
From the above it is clear that, connection between AI technical domains and the traditional industries will have to create a ‘Win Win’ situation, to push forward the potential for enhanced goals and allowing new discoveries and developments for the plant operation.
The article is a first in line, exploring real time cognitive approach for simple implementation of data and AI driven technologies on the production floor. Answering the questions concerning collaboration of local personnel in order to provide peace of mind in the field of technology scaling, with the right approach for short integration for time to value!
Oren Yahav is an Industrial Innovator, Specialist & Mentor – Automation, Robotics, IIoT & AI Based Technologies. He is an experienced business and technical consultant with more than 15 years' experience in the field of industrial manufacturing, entrepreneurship and leading new technologies into the market. Oren is an innovative and committed expert who has a proven history of aiding and significantly improving the efficiency and success of numerous businesses; and an individual who prioritises working alongside clients in order to achieve the desired results in a quick and efficient manner.
Familiar with all stages of project design and implementation in order to train or educate employees as necessary. Oren nowadays manages the CRP (Cost Reduction Program) domain at Smartech. The company provides smart, game-changing technology solutions for the manufacturing sector. Previously, Oren served in some key roles at Siemens, Bechor (Rockwell automation’s distributor) and co-founded KDsignage, a startup developing advanced IIoT, data analytics & AI, including machine learning and image processing algorithms. Where he established the new potentials and drove technologies and business processes.
Oren holds a BSc in Electrical Engineering from the Technion, Israel Institute of Technology