Many of today’s industrial facilities use a suite of networked sensors to monitor the performance and conditions of its assets and capital investments. Industrial digitalization is the process by which information from these sensors informs decision-making about how a company’s industrial assets can be optimized to operate most efficiently. In other words, industrial digitalization is when information technology can be used to increase margins, extend the lifespan of assets, and reduce the environmental impacts of industrial operations.
Industrial digitalization can provide senior level executives both a holistic and fine-grained view of the company’s operations. Specialized software provides at-a-glance information about total resource flows, production of deliverables, and whether an organization is meeting its own targets. Management can also do a deep dive into the specific functioning of any given piece of equipment, empowering insights by reducing the friction between an idea and its execution.
In addition, organizations are beginning to realize that the information being produced by their network of sensors can do more than simply reinforce existing directives and goals. In the effort to truly understand how their assets and equipment operate, companies have unlocked new ways to refine their goals and operations, as well as refine their decision-making. Managers are empowering the data-driven process of industrial digitalization and letting the information revolution touch every part of the company.
The groundwork for industrial digitalization began with the digital transformation of the 1980s, when analog controls and readouts were replaced with inexpensive microprocessor-based solutions. The revolution that occurred in the workplace with internet-connected personal computers put another prerequisite for industrial digitalization in place.
The industrial internet of things connected small micro-processors with digital sensors and networking, delivering the information coming from a company’s digital sensor suite directly to its headquarters. The technology has also enabled the operating parameters of assets to be controlled remotely. The gap between strategic planning and ground truth data has been reduced, if not closed, allowing decision-makers to see the impacts of their choices in real-time.
If a company is looking to make digital models of its production line, the stream of information produced by the industrial internet of things is a great resource for process simulation software. In process simulation, a model of a production process is simulated in software. In order to be useful, accurate information about the actual functioning of the process in question is required. With industrial digitalization, the laborious data-entry of paper records, or the digital conversions required for information that is siloed, is eliminated by linking the data stream to the simulation.
At the same time, networking has matured to the point where an entire supply chain can be linked together for increased productivity. For instance, Toyota uses specialized industrial digitalization software to automate communications with suppliers. The car maker’s lean “just-in-time” supply chain strategy has been streamlined even further by using software to automatically anticipate which components will be needed to maintain the current pace of production.
Instead of relying on direct intercompany contact between staff to receive orders, the supplier simply checks a web portal, and the order information is there for them to process. This example of production optimization cuts out the two middle-men and demonstrates the benefits of Toyota’s adoption of industrial digitalization.
Industrial digitalization is a prerequisite step in a company’s journey to becoming a smart enterprise, where the organization is entirely data-driven in its strategic decision-making and day-to-day operations. Smart enterprises typically have a shortened lag time between their information on the ground and any adjustments needed to operations to meet their company goals.
The process of industrial digitalization can generate ample rich data for companies, a huge benefit for organizations looking to develop or ramp up industrial AI. Machine learning or artificial intelligence programs require large datasets to train on.
Supercharged industrial AI algorithms are able to crunch through huge volumes of information and determine relationships and connections between the various pieces of an industrial process. A company looking to implement an industrial AI can pipe the data generated by the firm to the machine learning software.
How is industrial digitalization different from industrial digitization?
Industrial digitization was the conversion from analog to digital controls and meters that occurred during the 1980s. Microcontrollers replaced existing control and monitoring paradigms; for example, electric and hydraulic control systems and readouts were replaced with electronic actuators and displays. This was fundamentally a retrofit of existing hardware and processes, and the transformation did not disrupt the decision-making process.
Industrial digitalization connects those electronic controls and sensors to the organization itself. It promises to usher in a new era of automation and autonomy, where even high-level decisions can be orchestrated by the software itself.
How is industrial digitalization related to plant digitalization?
Industrial digitalization includes the entire industrial process, including resource extraction, supply chain management, production, and delivery of goods. Plant digitalization is limited to the activities of a company’s physical plants. Thus, plant digitalization is contained within industrial digitalization.
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