Edge Technologies Powering Industry 4.0’s Data Revolution

Automated data gathering technologies are driving the fourth industrial revolution, known as Industry 4.0 and transforming manufacturing. These technologies enable smart factories and warehouses to continuously collect and share massive data sets through connected devices and distributed infrastructure. By automating data collection, manufacturers can analyze the data to improve processes, maintain systems, and address issues in near real-time.

Advanced multi-purpose sensors are improving quality control and monitoring maintenance components in manufacturing. Industrial robots are more autonomous and capable of seamlessly communicating with manufacturing systems. The key challenge for manufacturers is harnessing the full potential of these data-driven technologies, such as the Internet of Things (IoT), artificial intelligence (AI), machine learning, and robotics to boost productivity, streamline processes, and increase flexibility.

The manufacturing industry has been recognized as an early adopter of edge computing, leveraging this technology to capitalize on things like those mentioned above. Arguably, it has existed in manufacturing for several years. Many plants already use significant processing power on-premises, processing power, such as programmable logic controllers (PLCs), embedded machine controllers themselves, and even on-site data centers.

Manufacturers face increasing pressure from global competition and a growing need for flexibility and cost efficiency in plant operations. This has driven significant efforts toward digital transformation, also known as Industry 4.0. Edge computing fits into this wider context by allowing manufacturers to use flexible, standardized hardware and open-source software to access and share data beneficial and relevant to their specific processes.

Three use cases highlighting the edge in manufacturing

1.  Predictive maintenance

This involves using data analytics to pre-emptively detect when a part of a machine or the machine itself will likely fail. This allows the user or manufacturer to mitigate the failure by conducting maintenance before the potential breakdown, ideally during planned downtime maintenance cycles, increasing the machine’s Overall Equipment Effectiveness (OEE).

Although predictive maintenance has been in the industry for years, many manufacturers struggle to implement it effectively, especially manual data collection forms that reliably work. Challenges often stem from reliance on inconsistent data collected from antidotal operator observations and maintenance staff, as well as difficulties integrating operational technology data with IT systems like ERP platforms. Other issues tend to come from the inability to predict outcomes effectively because there are insufficient measured variables (e.g., limited sensor data) or the maturity of machine learning platforms, which fail to generate actionable insights.

Like condition-based monitoring, edge computing processes and filters data at the source, eliminating the need for manual data entry and interpretation. It also reduces the bandwidth costs of transferring large amounts of data to a central data center or remote cloud, ensuring reliable access to critical information. For predictive maintenance to be effective, it requires extensive data input –problems can only be predicted accurately when enough data parameters are considered.

2.  Condition monitoring

Manufacturers face a challenge in simply accessing data from their machines, processes, and systems without interfering or putting extra burden on their control architecture. Traditional manufacturing systems, such as PLCs. are often proprietary and don’t communicate well with IT systems. Operational technology (OT) still relies on outdated standards, highlighting the need for more IT/OT convergence. One challenge to extracting data from all these machines in the factory is that it results in huge amounts of raw data, which would overload a controller and maybe even the central server infrastructure. Edge computing helps by filtering data to reduce the amount sent to a central server – on-site or in a cloud – while preventing strain on the machine’s controller. 

3. Precision monitoring and control

A key goal of Industry 4.0 is to use data from multiple machines, processes, and systems to adapt the manufacturing process in near real-time, depending on cycle and throughput times. This precision monitoring and control of manufacturing machines, assets and processes relies on huge amounts of data (also known as “big data”) and can use machine learning (ML) to determine the best course of action based on the insights from the data.

Edge computing is crucial not only for collecting, aggregating, and filtering data but also for implementing AI and ML strategies in the future. In some cases, depending on the power of the edge devices, they can be used to train and execute ML algorithms. Given the high processing demands of AI/ML in a production line or plant-based schema, manufacturers may benefit from distributing processing across multiple processor edge devices rather than relying on a central processing center or in the cloud.

The IIoT and edge computing are paving the way for smart manufacturing. In the realm of manufacturing, these technologies are pivotal for improving operational efficiency and enabling real-time analysis. By strategically placing intelligent, multi-purpose sensors and edge computing devices within plants, data can be processed close to its source, significantly reducing delays, especially compared to manual data collection. It fosters a true proactive approach to preventative maintenance and supports faster, more informed decision-making. This shift not only bolsters production up-time but also advances manufacturing towards the sophisticated benchmarks set by Industry 4.0, cultivating a manufacturing landscape that is more nimble, smart, and interconnected.