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The COVID-19 pandemic has highlighted, steered and illuminated how the Industrial IoT (IIoT), or Industry 4.0, can enhance organizational resilience in a state of crisis. Digital management tools and connectivity, for example, have enabled organizations to react to market changes faster and more efficiently.
Industrial IoT, or the Industrial Internet of Things (IIoT), is a vital element. IIoT harnesses the power of smart machines and real-time analysis to make better use of the data that industrial machines have been churning out for years. The principal driver of IIoT is smart machines, for two reasons. The first is that smart machines capture and analyze data in real-time, which humans cannot. The second is that smart machines communicate their findings in a manner that is simple and fast, enabling faster and more accurate business decisions.
Specifically, the market has seen the convergence of information technology (IT) and operational technology (OT) due to advances and synergies between the respective areas. This has resulted in the Industrial Internet of Things (IIoT), which is a solution that collects and centralizes mass amounts of machine data gathered from industrial environments. Applications built on these IoT platforms collect, analyze, and enable you to quickly act on the data to fundamentally boost operational efficiency and production.
Thanks to continuous streams of real-time data, it’s now possible to create a digital twin of virtually any product or process, enabling manufacturers to detect physical issues sooner, predict outcomes more accurately, and build better products.
While its output is a physical object, manufacturing inevitably begins with data during the design phase. That data is communicated to machines that execute designs—the point of transition between the digital and physical worlds. Increasingly, additional data is captured during manufacturing and eventual use of the final product. This data, in turn, can be extremely valuable for informing future designs and modifications, creating a virtuous cycle of innovation and improvement.
Put all those pieces together, and it’s clear that a digital “thread” of data now flows continuously. Aggregated and integrated in real time, it can be used to stitch together the physical and digital worlds, creating a virtual replica of a product or process that can reveal significant new insight. This digital thread can enable the digital twin by providing the data it needs to function.
The digital twin of a complex product such as a jet engine or large mining truck, for example, can monitor and evaluate wear and tear as the equipment is used in the field, potentially leading to design changes over time and informing predictive maintenance. The digital twin of a process can replicate what is happening on the factory floor (Figure 1). Sensors distributed throughout can capture data along a wide array of dimensions, from behavioral characteristics of the production machinery to characteristics of works in progress (thickness, color qualities, hardness, torque, and so on) and environmental conditions within the factory itself. Analyzed over time, these incoming data streams can uncover performance trends, potentially triggering changes to some aspect of the manufacturing process in the physical world.
Technologies enabling digital twins include sensors that measure critical inputs from the physical process or product and its surroundings. Signals from these sensors may be augmented with process-based information from systems such as manufacturing execution systems, ERP systems, CAD models, and supply chain systems. Those data streams are then securely delivered for aggregation and ingestion into a modern data repository, followed by processing and preparation for analytics. Artificial intelligence and other techniques can be used for analysis; the resulting insights can then be fed back to the physical world through decoders and actuators for implementation via additive manufacturing, robotics, or other tools.
A Real-World Example
An industrial manufacturer was facing numerous quality issues in the field, resulting in costly maintenance and high warranty liability. To address these problems, its engineering and supply network organizations pursued a digital twin approach. First, they combined the as-designed bill of materials (BOM) with all the analogous information produced during manufacturing (also known as the as-manufactured BOM), including procured parts details and assembly details. That step allowed them to run analytics and glean insights into production variations affecting quality. As a result, the team was able to improve the assembly process, reducing rework by 15 to 20 percent.
The manufacturer’s after-sales department is now planning to apply the digital twin process to information from products in the field (the as-maintained BOM) as well to better understand how process variation in field maintenance affects performance and to identify further potential improvements. All in all, capturing a variety of live measurements from the as-designed, as-manufactured, and as-maintained BOMs amounts to a cradle-to-grave digital journey, creating opportunities for better asset availability management, spare parts inventory optimization, predictive maintenance, and services.
“As a result, the team was able to improve the assembly process, reducing rework by 15 to 20 percent.”
The IIoT has already gained traction within countless industries, including manufacturing, food and beverage, oil and gas, healthcare, automotive, and more. For machine builders, it is quickly becoming a business imperative. According to an IDG and Siemens IoT survey, 53 percent of companies have started an IoT initiative. To keep pace with leaders in the industry, you need to start acting now.
