2022 Digital Factory Update

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By Ryan Martin | 1Q 2022 | IN-6416

The evolution of factories is seeing the emergence of digital twins and other Industry 4.0 solutions, supported by the adoption of 5G.

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Market will be Worth More than $950 billion in 2030


Spending on smart manufacturing will grow from US$345 billion in 2021 to more than US$950 billion in 2030 (12% Compound Annual Growth Rate (CAGR)) as manufacturers advance their digital transformation initiatives. Today, most of the revenue is attributed to hardware; however, a greater reliance on analytics, collaborative industrial software, and wireless connectivity (Wi-Fi 6, 4G, and 5G) will drive spending on value added services—connectivity, data management, and platforms—to more than double over the forecast. At the same time, factories will increasingly adopt Industry 4.0 solutions, such as autonomous mobile robots (AMRs), asset tracking, simulation, and digital twins.

The Shift to Digital Threads


Manufacturers and their technology partners are acutely focused on establishing and supporting digital threads for better data management and enrichment throughout the manufacturing lifecycle. A common data backbone allows manufacturers to operate more efficiently across teams and departments, and more companies have started to realize these benefits thanks to recent investments in enabling technologies such as cloud and, increasingly, private wireless. As of mid-2021, there were more than 120 private wireless deployments in manufacturing and 44 included the use of 5G (see MD-IICT-107). ABI Research forecasts more than 76 million 5G connections in manufacturing in 2030, with 53% attributed to ultra-reliable low latency communications (uRLLC) use cases.

From a requirements standpoint, commodity applications like asset tracking are at one end (low bandwidth, latency flexible) while complex scenarios involving autonomous mobile equipment, such as AMRs, are at the other (high bandwidth, low latency, high reliability). In the middle is everything from Augmented Reality (AR)-assisted training and product assembly to condition-based monitoring for predictive maintenance. The idea is that each of these production assets will have a comprehensive digital twin that functions first on its own and second as a part interwoven with the broader fabric of plant operations. These integrations create a more dynamic and elastic production framework through better use of data.

Automotive Plants Showcase the Possibilities


Today, only 9% of digital factory connections are wireless (excluding asset tracking), and many of these connections use Wi-Fi. While the benefit of Wi-Fi is compatibility with Ethernet-based automation networks, such as PROFINET or MODBUS, the drawback is that it uses frequencies on the Industrial, Scientific, and Medical (ISM) bands, which are used by one in four industrial devices and are, therefore, highly susceptible to interference.

Devices using the ISM bands also employ “listen before talk” or “listen before transmit”, and, as a result, operate with an additional source of inherent latency. Industrial Wi-Fi and proprietary protocols help combat issues of determinism and Time-Sensitive Networking (TSN). The expectation is that 5G will also deliver these capabilities.

The automotive industry leads in terms of automation, having automated close to 50% of operations. In fact, 22 of the 30 largest factories in the world are in the automotive industry. The largest is Volkswagen’s (VW) Wolfsburg Plant in Germany at 6.5 million square meters and 60,000 employees (MD-MMD-101). Nokia and VW recently announced 5G trials for wireless upload into vehicles and robotics use cases (among others) at this location and all other major automakers including Ford, GM, and Toyota are evaluating these technologies in some way.

Manufacturers are also exploring new operating paradigms, such as having autonomous vehicle chasses driving to different stations to have components fashioned onto them, allowing for greater flexibility than a traditional fixed assembly line. And then there are tools that autonomously layer and compare different data sources (machine, product, spatial) in real time for more contextual and predictive operations. The most advanced manufacturers are starting to think along these lines while the majority have started their digital transformation journey but are not yet to fully scale.



Companies Mentioned