Six ways IIoT expands manufacturing system capabilities
The Industrial Internet of Things (IIoT) is best used to expand process control and automation systems capabilities while letting them perform as designed. See six methods on how the IIoT enhances manufacturing.
The Internet of Things (IoT) 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 large amounts of data. It uses the Internet to connect everyday physical objects together. Of course, these physical objects must be embedded with sensors, processing ability, software and other technologies that allow them to connect and communicate with each other over the internet.
The most common and well-known application of the IoT is the smart home. IoT applications also can be found in hospitals, other medical and healthcare facilities, energy management, environmental monitoring, commercial buildings and more.
When the IoT is used in industrial and manufacturing applications, it’s called the Industrial Internet of Things (IIoT). Like the IoT, the IIoT connects devices and people and collects and shares large amounts of data. It also enables intelligent and autonomous machines to communicate with each other and with higher-level systems such as manufacturing execution systems (MES) or enterprise resource planning (ERP).
The IIoT has almost become synonymous with Smart Manufacturing and Industry 4.0. While there’s a lot more to smart manufacturing than just the IIoT, the IIoT has become a foundation technology for almost all Smart Manufacturing applications.
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.
The IIoT is best used to expand the capabilities of the process control and automation systems. It provides additional functions not often provided by those systems while still letting them do their jobs as designed. The idea is let the process control and automation systems do what they do best and let the IIoT do what it does best.
IIoT enhances automation systems and control systems in six ways.
1. Production monitoring, with context in real time
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, it’s now possible to get a bigger view of the production operations in real time.
For example, planned production can be compared to actual production. Orders can be tracked against batches being produced. Machine and line speeds can be monitored and tied back to product schedules. Waste and rework can be tracked and tied back to the orders and batches. Quality results can be collected and matched with batches in real time.
In-process inventory also 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 of this can be done without getting in the way of the process control and automation systems.
2. Asset performance tracking, wider views for manufacturing
Process control and automation also includes real-time asset performance tracking, but again, it’s often very local and focused on specific assets, work centers, unit operations and production 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 many KPIs. A few of the most common KPIs include availability, performance, quality, overall equipment effectiveness (OEE), mean time to repair (MTTR) and mean time between failures (MTBF). They also can be linked with orders, operations, batches and lots across the entire manufacturing operation. These KPIs also 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 going on with the assets and their performance.
3. Equipment maintenance, deeper analytics
Process control and automation systems are not often directly involved with equipment maintenance. However, 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. The IIoT is not directly involved with equipment maintenance; it’s relying 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 going on with asset maintenance.
Condition-based maintenance (CBM) alerts can be implemented using the IIoT. 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 maintenance’s impacts to individual batches by analyzing multiple batches before and after the maintenance was performed.
The costs of breakdowns also 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 preventive maintenance, postponing or even eliminating maintenance on key KPIs such as costs, on-time delivery and customer satisfaction.
4. Item identification: RFID, barcode integration
Barcodes and radio frequency identification (RFID) are the two most common identification technologies used in manufacturing operations. Process control and automation systems have often used barcode readers and RFID readers as part of their process control and automation activities, but the scope of those activities was often limited.
The IIoT expands the use of barcodes and RFID and integrates them with the process control and automation systems.
This allows for barcode and RFID applications for many manufacturing operations such as managing 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. Through the IIoT, barcode and RFID technologies are able to collect and share data on products, materials, work-in-process, locations, and movements while supporting the real-time management of these materials.
5. Continuous improvement: Product, process data aggregation
All manufacturing operations have a continuous improvement process that they follow. Lean manufacturing and six sigma are two common processes. Process control and automation systems support 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 by 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 those improvements worked.
6. Analytics: More useful, mapped
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 use 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 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 more.
IIoT supplements manufacturing processes
Process control and automation systems are here to stay. They are not going anywhere. They’re needed to provide real-time, closed-loop regulatory control of the manufacturing processes. The IIoT is means to supplement these processes rather than replace them.
The IIoT expands process control and automation systems by giving them added capabilities and uses their data in new ways and in conjunction with data from many other sources. This provides capabilities above and beyond what process control and automation systems can currently 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 systems, provide capabilities beyond what either could provide on their own.
John Clemons is a consultant for Maverick Technologies, a CFE Media and Technology content partner. Garrett Clemons is a pharmaceutical MES consultant at Rockwell Automation. Edited by Chris Vavra, web content manager, Control Engineering, CFE Media and Technology, firstname.lastname@example.org.
Keywords: Industrial Internet of Things, IIoT, smart manufacturing
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