Many in the manufacturing industry have accelerated their digital transformation journeys by deploying new technologies, including internet technologies like IoT (Internet of Things). In fact, manufacturing has been leading the IoT revolution, as the manufacturing industry has continued to make the largest IoT investments since 2016. As of 2020, 749 billion USD was estimated to be spent on IoT technology worldwide.
In the age of Industry 4.0, IoT deployment scenarios most closely align with digital transformation needs. The rise of IIoT (Industrial IoT) projects is also an indicator of the growing customer demand for improved visibility into full manufacturing processes, reduced workload for employees, increased automation, and more as companies seek to modernize.
The largest use cases for manufacturing include operations, monitoring, and maintenance. Most recently, edge computing applications have also become a leading deployment cause. Let’s explore how IoT is applied and what the benefits are in their adoption by the manufacturing industry.
IoT use for manufacturing operations
Across the board, operations come first as the largest manufacturing IoT use case. Business operations covers a wide range of components from the use of facilities (the machines and other assets) to people. Seen from this perspective, it is understandable that over half of the total IoT spend in manufacturing is spent on operations.
Manufacturing has become more “smart” and data-driven, bringing the elements of cloud computing, big data, and IoT to carry out everyday operations — many times with as little human intervention as possible. IoT in particular has increased the capability of manufacturers to automate operations to optimize and improve operational efficiency with less room for human delays and errors.
As manufacturing operations include the diverse parts of MOM (Manufacturing Operations Management), the possibilities of IoT use in production management, performance analysis, quality and compliance, and human-machine interface (HMI), for example, are virtually endless.
With IoT, the end result is usually to optimize resources. It is made possible with IoT devices like sensors that enable the collection of data, allowing manufacturers to receive actionable insights that have positive gains in resource consumption, quality control, and performance. Some popular examples of IoT use cases for manufacturing operations include:
● Optimizing production quality: Interconnected IoT devices in the production cycle of manufacturing can provide real-time data to monitor and improve operations and even increase ROI due to lowered production expenses.
● Reducing machine downtime: Alert systems aided by IoT technologies can ensure that manufacturers monitor system or equipment failures. With machine learning and greater implementation of sensors/actuators on critical machines or processes, IoT solutions can even respond to failures automatically.
● Providing optimal storage management: Assessing the storage conditions is important for manufacturers who need to stay on top of the quality of their inventory and respond to less than optimal conditions. An example of the importance of factory conditions can be found in electronics manufacturing plants, where the difference of a few degrees on computing enclosures can cause critical errors and catastrophic economic costs.
Monitoring and maintenance
Continuous monitoring of production assets is a significant aspect of manufacturing operations. As the second-largest IoT use case, the manufacturing industry IoT applications in production asset management and maintenance are huge. Mainly, IoT can serve to detect and resolve quality or performance issues and prevent potential damage or breakdowns.
Monitoring also includes the tracking that is involved in production asset monitoring as it relates to quality assurance, performance optimization, or even waste management. One good example is managing inventory using IoT technologies. Installing sensors to collect data on the expiration of certain food goods to minimize waste or monitoring inventory usage patterns can lead to better manufacturing decisions such as identifying delays and proactively reacting to low stock levels in real-time.
Predictive maintenance has come up a lot in the conversation of IoT in manufacturing. Consider this common affliction: Some of the most expensive investments in manufacturing operations are heavy equipment and machinery. Manufacturers seek to maximize the lifetime value of these capital assets including their uptime.
By taking a predictive maintenance approach using IoT, the lifetime of machines is lengthened, reducing expenses by preventing otherwise expensive repairs. Instead of relying on regular checks that look for faults in the supply chain, manufacturers can monitor the conditions of the machines.
Industrial edge computing nodes
In the manufacturing industry, IoT and IIoT are often used interchangeably. Regardless, the advent of AI technology in both IoT and IIoT requires powerful computing capabilities when utilized in manufacturing. Take tasks that require the use of big data for advanced fault prediction or forecasting. This is one example where applying edge computing can prove fruitful. Manufacturers of all sizes can use edge nodes to power their IIoT projects and reduce the latency or connection issues endemic to cloud-based computing.
According to Open Manufacturing, edge computing in the context of manufacturing is “a system of decentralized edge nodes, which reside near the physical origin of the data. Edge nodes must be able to run arbitrary containers and are managed centrally…and connect to both the cloud level and the production asset level and can temporarily run offline.”
This ability to access and share data relevant to manufacturing processes is crucial. One of the greatest benefits of edge computing is that it is able to provide a faster and more efficient way of accessing and processing data (information) at its source. This is incredibly important when manufacturing processes cannot risk having even a little bit of latency, which can have significant effects on efficiency and quality. Edge computing counters this issue because data is being processed in network proximity to data creation.
Applying edge computing in smart manufacturing has meant improvements in system performance, data security and privacy, and reduced operational costs. SDT is one company that is leading digital transformation with edge computing solutions for the manufacturing industry and has industry experience applying its edge devices and cloud expertise within the smart factory sector. For one client, SDT’s edge devices, which come equipped with on-device AI, proved to be critical in helping the smart factory take preventative measures for incinerator pump failures.
Conclusion
IoT has the ability to radically transform the manufacturing industry. The number of manufacturing use cases is essentially limitless, as they grow with new advances in IoT technology that operate on the different levels of operations, assets, and even people. There are processes that not only take place inside but also outside the manufacturing facility, including the service side of manufacturing.
As we enter the era of “cognitive manufacturing” — where IoT sensors, big data, predictive analytics, and robotics will forge the future of manufacturing operations, we will likely see more complex manufacturing use cases for IoT. Above all, the role of data will remain vital in deriving value from IoT devices. With an integrated IoT approach, manufacturers can gain valuable insights to offer the best products or services.
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Written for SDT by Karen Cruz
About the Author: Karen is a passionate B2B technology blogger. While studying at Georgia Tech, Karen first grew interested in cybersecurity and has since worked for several security and cloud companies as a global marketer. When she’s not freelance writing, Karen loves to explore new food trends.
