Today’s volatile markets are shaped by environmental, political or regulatory compliance as well as by rising prices for components, raw materials and fluctuating demand. Industries affected include the energy and chemical sectors. The long-term business plans that drive industry still focus on mass-producing low-cost items as the most viable option. How can organizations adapt to the above-mentioned external factors, while remaining nimble and cost effective?
The potential solution may well be with the convergence of operational and information technology, which has long been considered a huge opportunity for industrial organizations. The merger of these typically separate managerial and operational silos could result in greater collaboration and hence enhanced operation efficiencies, reduced costs and greater profits. Operational Technology (OT) is the domain that controls the automation of machines and processes while Information Technology (IT) caters for the batch processing of enterprise data requirements essential for data analysis and management oversight.
To the layman, these seem to be similar disciplines but they are as different as baseball and cricket.
The problem is that diversity in the industrial enterprise has come about because OT and IT have taken distinct evolutionary paths determined by their unique characteristics. OT is machine and process orientated and this requires microsecond-swift response, deterministic-performance, continuous-operation, high-availability, reliability, with robustness and safety foremost. On the other hand, IT is practically the reverse, as it is orientated towards batch processing of historical data, where only reliability and user convenience are of concern. These distinct operational differences represent profoundly divergent approaches to troubleshooting, maintenance and support.
“””The promise of IoT in the industrial scenario is that it provides the convergence of data we collect from machine sensors that may well provide some insight into the machine’s health or the processes under its control.” -Dr. Chris Caplice, executive director, Massachusetts Institute of Technology, Center for Transportation & Logistics
Dr. Chris Caplice, the executive director of the Massachusetts Institute of Technology’s (MIT) Center for Transportation & Logistics (CTL), is known for his research in this field, and for his work on the determination of machine failure in a merged OT and IT environment via predictive analysis. For his project titled “Voice of the Machine,” Dr. Caplice faced several challenges in the merging of OT and IT. “Machines and operational processes work with real-time data streams. The difference with IT is that it is batch orientated, so not real-time, so they take different protocols,” he says. “The data volumes were once different but that is not so much an issue today,” Dr. Caplice explains.
He notes that during his project, matching real time streaming data with batch IT processed data in a common language, and then predicting in real time when a failure might occur was an issue. The difficulty, he says, was getting machines that generate streams of one type of data to speak to IT systems that generate another and there were also wiring, interface and timing differences to surmount. “However, once we could get them talking, we could run self-diagnostics on the machines and then analyze the data on the IT systems successfully.”
Despite potential operational hurdles, industrial organizations should still strive to achieve convergence because of the promise that it holds.
As Dr. Caplice expands, “The promise of IoT in the industrial scenario is that it provides the convergence of data we collect from machine sensors that may well provide some insight into the machine’s health or the processes under its control. If that data could be understood and ingested by the IT systems then the possibilities for utilizing big data, within other industries such as tracking vehicles and then when combined with artificial intelligence and machine learning algorithms to gain valuable process visibility and insights, are limitless.”
“””Utilising machine data proactively can lead to better forecast accuracy and, in turn, potentially result in higher service levels with less inventory.” -Dr. Chris Caplice, executive director, Massachusetts Institute of Technology, Center for Transportation & Logistics
Many such improvements can be attributed to IoT, data analytics and convergent OT-IT. Dr. Caplice explains, when considering predictive analysis for machine maintenance, “Improving the demand forecast for repair parts can lead to significant inventory reductions but it is notoriously difficult. This project has shown that utilizing machine data proactively can lead to better forecast accuracy and, in turn, potentially result in higher service levels with less inventory. So although individual signals and indicators may be weak when determining the health of a particular machine the aggregated machine data the system collected was able to reliably predict machine failures across the board.”
Nonetheless, it wasn’t perfect on a machine-to-machine basis but the opportunity for merging the vast data processing and analysis capabilities of IT systems with the OT deterministic real-time domain requires a common tiered architecture, which will result in greater agility and control. Indeed, that is the promise of the Industrial Internet of Things (IIoT). As Dr. Caplice states, it is the IIoT that has enabled streaming data to converge with IT batch data analysis systems for data analysis. “Without them we would still be unable to track real-time processes or understand how processes worked,” he says.
Dr. Caplice points out that sharing data has come about through the IoT, which has enabled real-time data processing to merge with IT batch style processing. As a result, at the IT layer, OT-IT convergence produces linked data, which creates value when it is shared throughout the value chain. The linked data can be synthesized by all manner of intermediate stages in the value chain and business units such as finance, human resources, R&D, stock control, sales, production management, logistics and quality control amongst others.
Yokogawa Electric Corporation, a leader in advanced industrial instruments, automated control technology and data analysis solutions, identified the need to adopt this approach, and through its 2016 acquisition of strategic consulting and leading simulation technology company KBC Advanced Technologies, expanded its capabilities from operational technology and information technology to business management. This is achieved through a business concept they call ” Synaptic Business Automation,” which aims to deliver profitable and sustainable growth.
Dr. Caplice says the IoT, coupled with OT-IT, has enabled visibility into the supply chain beyond anything we’ve seen before. “By merging the two disciplines of stream and real-time data, where you could have one and not the other, now we can have both and so with IT analysis this has enabled greater visibility but also has allowed many other players to enter the market.”
Elaborating on this point, Dr. Caplice says, “That real-time analysis of unstructured data, such as GPS, will open up the market for new start-ups with new software products aimed at the supply chain that can change many industries, even finance, as data can be processed in real time. This is something that was not possible before. It will be the traditional players in the market that operate on structured data that will need to look out for these new innovative competitors that are providing products that are essential in meeting the demands of industry.”
Yokogawa also leverages OT-IT convergence as a solution. The company merges IoT, OT-IT, advanced analytics and deep domain knowledge to deliver value through enhanced products and services. It offers domain-expert business consulting that delivers sustainable growth and optimizes plant-wide production processes. As a result of their domain knowledge, simulation technology and business expertise, Yokogawa can contribute to the integration, collaboration, and optimization of the customer’s entire value chain.
However, the benefits of OT-IT convergence do not stop there. There is also the potential for sharing linked data with subsidiaries, partners, vendors and even customers, which provides both visibility and allows elements and systems to ‘talk’ to one another throughout the supply chain. This can result in tremendous opportunities for innovation and in reducing both costs and risks while improving the operation efficiency of the entire supply chain. Indeed, sharing this linked data with the supply chain creates the ability for automation of Supply Chain Management and ‘just in time’ ordering of components or raw materials from vendors.
As Dr. Caplice says, “The convergence of the IoT and business has provided visibility into every area of the business and in real time. Now we can track any process as it happens and control that process in real time.”
Finally, asked to contemplate what was the most beneficial and value generating aspect of converging OT and IT, Dr. Caplice remarks, “I don’t consider them to be different any more. The IoT has merged them, so I would consider them to be the same, especially from a supply chain and transportation perspective and also from all other industries. That is how important it is, as it provides real time visibility into processes.”
As we have seen, creating sustainable value through OT-IT convergence is challenging but possible if there is sufficient domain knowledge combined with agile and adaptive management. Yokogawa recognises this challenge, spurring their launch of the business concept they refer to as “Synaptic Business Automation.” They consider that OT-IT convergence combined with strategic business planning and domain expertise is the key to success.
Consequently, Yokogawa’s Synaptic Business Automation concept seeks to expand its function beyond the provision of control-level automation solutions to address the full range of an enterprise’s activities, and will provide comprehensive solutions to realise operational excellence, that cover plant operations, business processes, and supply chains.
One recent example of this approach being applied can be seen in the late-2017 partnership between KBC and General Electric subsidiary Baker Hughes (BHGE). The two companies collaborated to integrate KBC’s process simulation and modelling technology with BHGE’s suite of digital solutions, allowing – for the first time ever – the oil and gas industry to create a ‘digital twin’ of a plant, refinery or rig, incorporating end-to-end process, operational analytics and machine learning. Monitoring a virtual clone such as this gives companies the ability to compare real-life performance to expected outcomes, analyze problems and opportunities, take predictive actions, and free up personnel to focus on critical operations. KBC and BHGE are confident their joint solution will offer customers not only greater operational efficiency, productivity and safety, but also increased sustainability.
Founded in 1915, Yokogawa Electric Corporation is a leading global provider of industrial automation and data measurement technology. Yokogawa’s innovative solutions and expertise in the areas of AI-driven industrial processes and the Industrial Internet of Things (IIoT), allow partners in sectors such as oil and gas, power, and chemicals, to maximise production efficiency and data analysis in smart manufacturing, equipping them to successfully navigate the coming Fourth Industrial Revolution.
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