The Industrial Internet of Things (IIoT) has been growing rapidly over the last several years to the point where it connects billions of devices and millions of people and collects and shares incredibly large amounts of data. One of the IIoT’s biggest advantages is it can form the foundation for a wide range of smart manufacturing solutions. Machine learning (ML), artificial intelligence (AI), big data, analytics, manufacturing execution systems (MES), enterprise resource planning (ERP), augmented/virtual reality (AR/VR), cloud and edge computing, digital twins, digital threads and many other applications can all be built on, or access, an IIoT foundation.

To take advantage of IIoT capabilities, many companies must first admit their technology and systems are old and not getting the job done. Coupling this with bureaucracy that’s too big, too hierarchical and too slow leaves manufacturers with processes that aren’t reliable, repeatable or predictable. What’s needed isn’t just new technology and systems, it’s creating a vision for the future with new business processes and capabilities.

Contrary to some opinions, the IIoT is not at all meant to replace traditional process control and automation systems. Far from it. Process control and automation will always be required to perform real-time, closed loop, regulatory control of manufacturing processes – something the IIoT was not designed to do.

Instead, the IIoT is best used to expand the capabilities of the process control and automation systems. It provides additional functions not typically provided by those systems, while still letting them do their jobs the way they were designed. In short, the idea is to let the process control and automation systems do what they do best and let the IIoT do what it does best.

Real-Time Production Monitoring

Process control and automation always includes real-time production monitoring, but it’s usually very local, focused on specific equipment, on specific unit operations, and specific lines.

With the IIoT, collecting and sharing large amounts of data from the process control and automation systems, from MES and ERP systems and from smart machines and equipment, it’s now possible to get a much bigger view of the production operations.

Planned production can be compared in real time to actual production. Orders can be tracked in real time against batches being produced. Machine and line speeds can be monitored in real time and tied back to product schedules. Waste and rework can be tracked in real time and tied back to the orders and batches.

Quality results can be collected and matched with batches in real time. In-process inventory can be viewed against the batches and orders. Manufacturing key performance indicators (KPIs) for quality, cost, and delivery can be monitored against the schedules, orders, and batches. All without getting in the way of the process control and automation systems.

Asset Performance

Process control and automation also includes real-time asset performance tracking, but again, it’s usually very local, focused on specific assets, work centers, unit operations, and lines.

With the IIoT using the data from the process control and automation systems, along with the data from MES and ERP, it’s now possible to elevate asset performance to a much greater level.

The performance of assets, work centers, unit operations and lines can be measured using a variety of KPIs. Availability, performance, quality, overall equipment effectiveness (OEE), mean time to repair (MTTR), and mean time between failures (MTBF) are just a few of the most common KPIs. But more than that, these KPIs can now be linked with orders, operations, batches, and lots across the entire manufacturing operation. In addition, these KPIs can be mapped against maintenance and cleaning activities and even individual people and crews.

This allows much broader analyses of asset performance against a wider range of potential impacts to performance. It also allows for a much deeper dive into the root causes of inadequate asset performance. It relies on data from the automation and control systems and combines it with data from other sources to provide a more robust perspective on what’s really going on with the assets and their performance.

Equipment Maintenance

Process control and automation systems are usually not directly involved with equipment maintenance. But they often collect large amounts of equipment data that is used by the enterprise asset management (EAM) system or computerized maintenance management system (CMMS) in the condition-based maintenance regimens. Likewise, the IIoT is not directly involved with equipment maintenance, relying simply on those management systems to do their jobs.

The IIoT’s expanded capabilities can be used to better link these systems together and provide more visibility into what’s really going on with asset maintenance.

Condition-based maintenance alerts are easily implemented using the IIoT. The IIoT is a key enabler for reliability-centered maintenance and especially for ML paradigms used to support predictive maintenance. Maintenance activities can be tied back to the production schedule and tied to specific orders, operations, batches, lots, and quality results. Deeper analyses are possible on the impacts of maintenance to individual batches by analyzing multiple batches before and after the maintenance was performed. This adds up to increased throughput, reduced downtime, lower maintenance costs, high machine reliability, and a much greater return on invested capital in the form of better machine utilization and output.

Additionally, the costs of breakdowns can be analyzed against a wider range of parameters and KPIs. It’s also possible to perform more detailed analyses of the true impact of preventative maintenance, postponing or even eliminating maintenance on key KPIs such as costs, on-time delivery, and customer satisfaction.

Item Identification with Barcodes and RFID

Barcodes and radio frequency identification (RFID) are the two most common identification technologies used in manufacturing operations. Process control and automation systems have pretty much always used barcode readers and RFID readers as part of their process control and automation activities, but the scope of those activities was usually limited.

The IIoT expands the use of barcodes and RFID and seamlessly integrates them together with the process control and automation systems. This allows for barcode and RFID applications across virtually all manufacturing operations.

This includes using barcode and RFID technology to manage raw material and packaging material inventories, work-in-process inventories throughout the facility, and finished goods inventories.

It also allows for barcode and RFID technologies to be much more ubiquitous throughout the manufacturing operation supporting material traceability at every step in the process. In the end, through the IIoT, barcode and RFID technologies are able collect and share data on products, materials, work-in-process, locations, and movements – all while supporting the real-time management of these materials.

Continuous Improvement Processes

All manufacturing operations have a continuous improvement process that they follow. Lean Manufacturing and Six Sigma are two processes that are quite common. Process control and automation systems have always supported these continuous improvement processes by providing them with data on the manufacturing activities.

The IIoT takes that to the next level by aggregating product data and process data, including the data from the process control and automation systems, the MES and ERP systems, and potentially many other systems, and getting that data to the right people. This is exactly what is required as part of the continuous improvement process to identify problems, analyze the root causes of the problems, implement the required improvements, and then confirm that those improvements actually worked.

Analytics for Operational Insight

Process control and automation systems have almost always had an analytics component. This usually takes the form of various charts and graphs of key manufacturing parameters like pressures, temperatures, flows, speeds and feeds.

With the IIoT, analytics can be so much more than just a few charts or graphs of some key data. Analytics can now include descriptive, diagnostic, predictive and prescriptive data, and utilize data from a very wide range of sources, including process control and automation systems, MES and ERP systems, quality management software (QMS) systems, EAM/CMMS systems, corrective and preventive action (CAPA) systems, and many, many others.

This allows these analytics to map manufacturing data against customer, inventory, order, schedule, maintenance, cost, labor and other data.

This provides a better overall view of operational performance, allowing analyses on actual time to market, cost variations, material variations, quality trends, customer satisfaction trends, supplier quality, supply chain performance, and much more.

IIoT Supplements Manufacturing Processes

Process control and automation systems are here to stay. They are absolutely not going anywhere. They’re needed to provide real-time, closed-loop regulatory control of the manufacturing processes. The IIoT is not meant to replace them, but to supplement them.

The IIoT expands process control and automation systems by giving them added capabilities. The IIoT uses their data in new ways and in conjunction with data from many other sources. This provides capabilities above and beyond what the process control and automation systems can do, while still allowing those systems to do what they do best.

Process control and automation systems and the IIoT working together are greater than the sum of their parts. The IIoT is a force multiplier and together with the process control and automation system provide capabilities far and above what either of them could provide on their own.