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  • By Peter Martin, PhD
  • Automation
Automation professionals' new role: converging IT and OT
Applying real-time automation principles improves manufacturing results

By Peter G. Martin, PhD

Business and operations functions within an industrial enterprise have traditionally been separated because of the design and functionality of their systems. Business functioned under the guidance of information technology (IT) departments; operations functioned under the guidance of automation and control systems, or operational technology (OT). However, recent technology advancements-Industrial Internet of Things (IIoT), Industry 4.0, and the like-have caused industrial organizations to rethink the validity of keeping IT and OT separate, suggesting instead that converging the two would be beneficial.

Many of the discussions around bridging the IT-OT divide have focused on the technology needed to connect these two domains. But connectivity, by itself, does not solve any industrial problem. Instead, the most effective way to converge the two might be to evaluate the functionality required to run the business and the functionality required to operate the plant, as well as to determine the best environment in which to execute each function.

Instead of thinking about separate technology domains, it might be better to partition activities into control domains: real time, i.e., decisions that need to be made within the time dictated by the dynamics of the process; and transactional, i.e., decisions that can be made on human schedules (e.g., weekly or monthly). If done that way, real-time decisions can be thought of as control (OT), while transactional decisions can be thought of as management (IT), which, as we will see, is critically important for the future of automation.

Increasing speed of business

As the speed of industry steadily increased over the past decade, critical business variables that were once constant, such as the price of electricity, have begun to fluctuate quite frequently, sometimes as often as several times an hour. When we entered the age of real-time business variability, industrial businesses went from being very well controlled to being completely out of control, mostly because they have been traditionally managed on monthly cycles. Today, by the time business managers receive their monthly updates from whatever enterprise resource planning (ERP) systems they use\, the information is no longer relevant to the decisions they need to make (or should have made). In other words, important business decisions, such as how to improve operational profitability, have slowly migrated out of the transactional/business management domain (IT) and into the real-time control domain (OT).

Business executives have been at a loss as to how to respond to this real-time business conundrum. Their first attempt was to seek help from their IT teams. Unfortunately, while IT teams and ERP software are well suited for managing transactional business functions, they are not equally suited for performing real-time control. That meant managers needed to look elsewhere, and they turned to the people in their organizations who best understand real-time control and have the needed expertise and knowledge: automation professionals. Real-time control is their domain; they are best able fill this industry gap.

As a result, it is becoming clear that automation professionals, who have traditionally focused strictly on improving the efficiency of the operation using real-time process and logic control, will soon become responsible for controlling real-time business performance too. However, because many of these professionals are not business trained, they are reticent to take the lead. The good news is that even without such training, by breaking down real-time business challenges into basic requirements, they are in an excellent position to succeed. That leaves many of them wondering what exactly their future looks like.

Developing real-time business control

Breaking down real-time business control problems into basic components is rather straightforward. As with any other control loop, the first requirement is to develop the measurements that need to be controlled. This might seem daunting at first, since so many measurements are used to manage the business. However, not every business variable fluctuates faster than monthly. Only those that do require real-time measurement and control, and there are really only three of them: energy cost, material cost, and production value. For our purposes, we define energy cost as the price of energy at the time it is consumed times the quantity consumed. Material cost is the price of the material at the time it is consumed times the quantity consumed. And production value is the value of the products at the time they are produced times the amount produced. Notice that, historically, consumption and production amounts varied in real time, but financial values were essentially constant for at least a month. Today, both the quantities and the financial values change in real time, which is the core business control challenge.

Once the real-time business measurements are developed for each cost and process point in the production, the next step is to provide a mechanism to control those variables. Automatic business control mechanisms are bound to be developed over time and through experience, but until that occurs, applying manual control makes the most sense.

Manual process control preceded automatic control, that is to say, manual process control evolved to automatic control. Operators used to look at gauges to determine the value of process variables. They compared that value to the desired set points, and, if there was an error between the set point and the measured value, adjusted hand valves to return the value to the set point. That is manual process control, and it worked quite well.

A similar manual approach could be developed for real-time business control, utilizing decision-support mechanisms that are powered by real-time business measurements. If the objective of decision support is to help manually close various control loops, such as process, reliability, and profitability, it can help close real-time business control loops too. Then, as soon as there is enough manual, real-time business control experience, understanding how to close these business control loops automatically is bound to follow, which is how control typically evolves.

Applying real-time business control

Automation professionals have a clear role when it comes to controlling business performance. It should involve, but perhaps not be limited to, applying real-time process and logic control to the real-time business variables that affect operational profitability.

As with any control strategy, the first step is to develop the appropriate measurement that can be made within a certain period (real time) to provide effective decisions support (control). This has been a challenge for automation professionals for the past 30 years, perhaps because they are averse to cost accounting. Automation engineers tend to work in the natural sciences, but by its very definition, cost accounting is an unnatural science, i.e., it is a man-made discipline.

Over the years, automation engineers have tried many times to develop their own key performance indicators (KPIs) to capture the economics of production operations. But because these KPIs do not often align with cost accounting measures, they have not been accepted by business management. After all, the finance team's job is to measure business results, so any real-time business measurement system must align to its cost accounting measures.

The good news is that cost accounting measures are typically based on reasonably simple algorithms that can be easily modeled, by combining process sensor data and business data, and then executed in real time within the process controllers. These measures can then be added to process historians to develop a real-time activity-based accounting database.

It is important that these real-time business measures are made as close to the process as possible, and for every cost and value point across the operation. Every piece of equipment, such as a compressor, contains cost and value points. By developing the real-time accounting models at the lowest level in the operation, both the business and operations teams can measure the profit impact of any performance-improving initiative executed in the operation. Because this approach lets the teams know which initiatives add the most value and should, therefore, be their focus, it enables them to better control the profitability of the operation in real time.

Once the real-time business measurements are in place, the next step in applying business control is building an effective real-time business decision-support system for all profit-impacting personnel in the operation. There are many approaches to effective real-time decision support. The feedback mechanism can be optimized according to the experience and knowledge of every person within the operation, and for his or her area of responsibility.

The most common approach deployed today is a visual dashboard through either a console or mobile device. These can be very effective, but there are other feedback mechanisms, including audible feedback and approaches such as vibration. These decision-support systems act as learning tools, giving each person in the operation immediate feedback about whether or not an action added business value. The entire workforce can then adjust its activities and actions to drive the most value-safely. This is the essence of manual real-time business control.

Everyone within the industrial operation will be able to use these systems to make more effective operating and business decisions. Operators can use them to learn what the most effective set points are for optimal business results. Maintenance personnel can use them to set schedules more effectively to yield better operations and business results. Engineers can use them to ensure their process control strategies are aligned with business objectives, and managers can use them to solve production problems more quickly and effectively. Real-time business decision-support systems driven by real-time accounting measures turn every person in the operation into business performance managers.

It is important to mention that business control must always be executed according to the constraints imposed on the process, including risks to the reliability, safety, and environment. To drive measurable operational profitability improvements safely, these risks must be measured in real time too.

Evolving role

The increasing speed of business has caused industrial manufacturers to lose control of their operational profitability, even while their processes are in control. Business managers are searching for a solution to this difficult business problem, but the answer is not to be found within their transactional ERP systems or the IT teams responsible for them. Instead, it is in the operations layer, the realm of real-time control.

That is why the role of automation professionals is evolving. They comprise the only talent base in most industrial companies that understands real-time process dynamics and control, the two disciplines required to develop real-time business measurement and control. In the world of the IIoT, they will be able to do more than control the efficiency of the process. Because of their expertise in applying real-time control, they will be asked to control the profitability of the operation too.

Automation professionals have a very promising future, as long as they step up and lead the development of the real-time business control systems that supersede real-time process control. The time has come. The key to driving and controlling measurable operational profitability, safely, is in the hands of automation professionals.

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About The Authors

Peter Martin, PhD is vice president, business, innovation and marketing, for Schneider Electric. He holds multiple patents, including patents for dynamic performance measures, real-time activity-based costing, closed-loop business control, and asset and resource modeling. Martin authored or coauthored four books, most recently The Value of Automation, and he received an ISA Life Achievement Award.