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Amidst the COVID-19 pandemic, leaders in the industrial sector are moving towards one of the most influential changes in recent history, Industrial Digital Transformation (Industrial DX). However, before we jump on the bandwagon, it is important to understand what Industrial DX really entails, starting with its building block: operational data (OT data).
Imagine a factory that converts toxic waste into organic fertilizer. If a slight temperature change occurs during the production process, the neutralization of the active agent would be affected and consequently damage production capacity. In the past, inspections and subsequent adjustments had to be made by on-site staff. However, this method has long been proven to be ineffective in responding to sudden and immediate changes. Therefore, accurately predicting temperature changes within six hours before an actual fluctuation is crucial to make the necessary adjustments to maintain the optimal neutralization process. Several kinds of data are required to achieve accurate predictions, including equipment operation data, controller data, field temperature data, and weather forecast data. These types of operational data (OT data) are the fundamental building blocks of industrial DX.
To create effective business intelligence, OT data not only needs to be collected but also analyzed to formulate a relevant strategy. Hence, Industrial DX can be viewed as a process to find and interpret the value of the existing OT data. A task that is easier said than done. As Moxa has discovered from more than 30 years of experience in connecting OT data, when the ‘value’ of the OT data increases (i.e., We expect more out of the data), the difficulty to connect it also rises. As a result, OT data connectivity technologies’ original responsibilities—securely collecting, processing, and labeling the data prior to transmitting it in a timely fashion—are now divided into more precise steps. To respond to the influx of incoming data, great strides have been made regarding the method and speed of transmissions. OT data connectivity is now a hybrid specialty integrating domain knowledge and the latest technological capabilities. These advancements in "OT data connectivity" are carrying the key elements of success in Industrial DX. Here are some notable changes:
Pushing From Simply Monitoring the Present to Optimizing the Future
In the past, the purpose of collecting OT data was simply to monitor and control the existing operational system. OT data is used to ensure that machines are operating stably by keeping track of the devices’ current state on the factory floor. It can also be used to control the flow rate of an oil pipeline to comply with oil production targets. In other words, it only focuses on maintaining the "right now". However, Industrial DX takes it one step further into the future. Obtaining OT data is no longer for the sole purpose of monitoring and controlling the present; Integrating data to analyze the future is the main aim. By finding key factors that affect the operating efficiency, optimizing, and even creating new business opportunities, OT data has allowed many early industrial adapters to create brand-new business models. Take a leading power system integrator as an example. The company applied the data it collected from the historical usage of methanol in hydrogen energy batteries to estimate the future energy usage of every customer. A new personalized charging plan was then developed for each customer. The original charge-per-usage plan was upgraded to a Machine-as -a-Service monthly charging plan, creating a win-win transaction model for both parties.
Transitioning From a Single Digit to Actual Value
The line between IT and OT is significantly blurred when OT data is used for further analysis. In the past, ensuring the reliable transmission of data was the only requirement. Nowadays, it is necessary to also ensure the quality of the data. This has become one of the biggest obstacles for Industrial DX. The life cycle of industrial equipment is usually very long, which means it accumulates a lot of OT data that is incomplete or in unrecognizable formats. It is then up to IT professionals to perform additional data cleaning and conversion before it can be used. The best-case scenario here would be that data scrubbing takes a little longer, which costs money and precious time but the data is useable. The worst-case scenario, however, is that the data cannot be understood and becomes useless all together. For instance, if the output data shows "5" without any labeling, it is impossible to decipher what this number represents. Without further investigation, we may never know that the number actually indicates the machine speed. This miscommunication is often caused by a different format, not recognized by the IT system. Since this phenomenon is very common, one solution is to preprocess such data by converting it into the required format through a built-in program in the OT data connectivity device. Thus, data preprocessing gives the data context and makes it recognizable. The process of turning OT data into usable OT data—allowing it to have "analytic usability"—is an important step at the onset of the OT data revolution.
Complex and Diversified Needs-Diversified Data Sources and Types
The traditional control system already relies on a multitude of OT data to maintain daily operations. Simple data, such as the position of the water tank gate, the daily oil production, etc., shows basic information about the operating status. Complicated data, such as production recipes or processes, also gets generated. However, the industrial DX requires more. Take the renewable energy industry as an example. To quickly remove the shadows or stains on solar panels, more data is required. In addition to monitoring power generation, it needs environmental information such as temperature and humidity. This data coupled with the live feeds from surveilling drones and analysis by an AI platform can find the more precise position of the contaminated solar panel. Armed with this information, real-time and precise maintenance can be scheduled. Hence, large volumes of OT data from diversified sources reduce traditional capital expenditures and vigorously improve production efficiency.
Changing From Linear Control to Circular Feedback in Real Time
Traditional automation systems place great emphasis on real-time control. OT data is often used as an indicator of a specific time slot on the linear control process. The data’s purpose ends when the specific process is over. However, industrial DX emphasizes a different type of real time, by focusing on the "OT data collect/analysis/feedback” loop. With the new big data processing technology, faster networks, and maturing industrial computing capabilities, IT can now analyze uninterrupted OT data and provide immediate feedback to the operational equipment after analyzing the data. This loop of receiving data, analysis, and feedback allows enterprises to perform real-time adjustments. Take small to medium-sized manufacturers served by KPMG as an example. To reduce wasting manpower hours and material resources caused by defective products, more OT data, such as vibration, temperature, speed, current, etc., is collected, uploaded, and analyzed by the AI platform. Through analytics, we have learned that when the tool current frequency of a certain machine is too high, this means the tool is worn out. The tool can then be replaced in advance to ensure a high-quality output.
There Will Only Be More Not Less
In the era of Industry 4.0, large-scale automation systems (such as the distributed control system in an oil refinery) are capable of processing large volumes of OT data per second. However, this data is only used while the equipment is running. Once the operation is over, interpretation of the data also ends. OT data is thus only used to interpret the present. However, industrial DX takes it one step further. Using a larger amount of data, simulations and analytics can be performed to quickly improve real-time operation efficiency and control operational risks. For instance, to avoid overly crowded carriages during the pandemic, Taiwan's railway company installed pressure sensors on its trains to measure carriage loads. Before a train enters the station, the sensors will send information along with the feed from the onboard CCTV of each carriage to the control center. This way the control center will have an accurate view of how congested each carriage is and provide this information to the passengers waiting on the platform or notify management to help evacuate crowds.
Data Security Equates Enterprise and National Security
Although cyberprivacy is not usually the main concern when talking about OT data, it is a big priority for Industrial DX. Much of the OT data comes from critical infrastructures (such as equipment monitoring in water plants and power plants) or important operational information in key manufacturing facilities (such as oil refineries and semiconductor factories). Such information if maliciously altered, could cause immeasurable and monumental losses. In February 2021, hackers successfully entered the SCADA system of a public water treatment plant in the U.S. by taking advantage of outdated versions of Windows operating systems and poor network security. The plan was to raise the sodium hydroxide content in water to a range that could harm humans. Fortunately, the on-site operators immediately discovered the abnormality and prevented the threat from being carried out. As the threat of cyberattacks increases, cybersecurity should be prioritized as more industries may fall victim to such attacks, which could yield significant consequences.
Industrial DX is shining a spotlight on previously mysterious and unnoticed OT data. This transformation has directly catalyzed the integration of IT/OT in knowledge, operation, security, and even personnel mentality.