This blog has been repurposed from the August 2022 edition of InTech.
Manufacturing processes have become extremely complex. One manufacturing line can generate hundreds of thousands of data points from various sensors, programmable logic controllers, motors, motion drives, and other devices. The data points for each device are often only used once for a small task within a larger process, with the data overwritten during each machine’s cycle. Communicating across hundreds of different proprietary protocols over wired networks to access and send that data to a centralized system for filtering and analyzing has been a challenge.
Enter the Internet of Things (IoT) and cloud and edge computing. These information technology (IT) concepts enable distributed network architectures by moving data and computing tasks normally processed in a centralized location to the cloud or to the edge of the network. IoT edge technology decentralizes data processing and storage by performing various functions at the edge of a network, closer to individual devices and machines.
Edge computing brings the advantages and power of cloud computing closer to where data is created and acted upon to improve efficiency, security, or compliance. Edge devices and sensors are where data is generated and collected, but they do not always have the compute and storage resources needed to perform advanced analytics.
Edge computing is used on premise in facilities to handle real-time data processing for important smart factory applications like predictive maintenance. Cloud computing, on the other hand, is done in a remote environment hosted by public cloud companies like Microsoft, Amazon, or IBM to support more data-intensive and less time-critical processes.
According to Forbes, public cloud providers are increasingly focusing on a hybrid cloud model. Hybrid cloud computing allows manufacturers to store and analyze their proprietary internal process information locally on their own servers, while offloading resource-intensive applications like machine learning to a public cloud to take advantage of scale and cost efficiencies.
Private cloud architectures exist too, in which an industrial company’s IT department handles its own cloud storage for data like production recipes, customer information, or even process knowledge of each product being manufactured. Public clouds are most effective in terms of cost per compute and long-term storage. They may be used for applications like storage of machine schematics, computer-aided design drawings of parts or machines, and other information that is not critical or time sensitive.
According to IoT Analytics, the number of connected IoT devices is expected to reach 14.4 billion by the end of 2022. Allied Market Research states that the global value of IoT in manufacturing products was $198.25 billion in 2020, with predictive maintenance applications commanding the largest share. Thanks in large part to advancements in smart sensors and virtual and augmented reality, the demand for real-time asset monitoring will boost the overall value to $1,495.65 billion by 2030, at a compound annual growth rate of 22.6 percent from 2021.
With exponentially more IoT devices collecting, analyzing, and processing more data and information, the need for edge computing can only increase in an effort to reduce latency and increase speed. With the edge orchestrating, aggregating, and trimming data on the fly, companies will rely upon the cloud for long-term storage and cost-efficient compute. The toughest decision corporations will face is choosing which data points are worth real-time analysis on premise, which need long-term storage and analysis in the cloud, and, most importantly, which data needs no analysis at all.
Many experts believe enterprises will deploy edge platforms and 5G wireless communications together in the coming years. A recent article from The Enterprisers Project indicates that the “next big thing” will be private 5G networks. 5G adds ultrareliable, low-latency communication (URLLC) that has previously only been offered on Class-C Ethernet technology.
With the increased data transfer speeds, decreased latency, and higher capacity of 5G technology, factories will be able to scale without relying on proprietary Industrial Ethernet protocols via standard wired network infrastructures. Combining edge computing with 5G will allow more flexibility in on-premise deployments by extending the range and reach of data collection to assets normally unreachable through wired deployments or bandwidth constraints.
Another trend is the need for faster and more agile cloud deployments. The Gartner Research 2022 Planning Guide for Cloud and Edge Computing stresses the importance of “cloud teams” that play an important role in supporting CIO- and executive-driven initiatives to accelerate cloud deployments. Speed of deployments through business agility and ability are the cornerstones of deploying effective cloud solutions.
Cloud teams must have more than architecture and technical ability; they need to understand the overall business and business challenges they are addressing. If teams are too slow to adopt, they may never catch the moving target of digital transformation. If teams do not understand the core challenges or reasons behind the digitization of a project, their solution may not be adaptive enough in future improvements and require more nonrecoverable engineering costs.
Factories deploying edge platforms experience faster network speeds and low latency, which contribute significantly to better decision making and production optimization. This ultimately improves return on investment. Using edge and cloud computing, manufacturers are improving productivity and identifying revenue opportunities from the efficiencies and capabilities of smart systems. In particular, real-time overall equipment effectiveness (OEE) visualization tools provide full visibility into every aspect of a factory’s efficiency.
OEE was invented in the 1960s by Seiichi Nakajima, the founder of the Total Productive Maintenance (TPM) system. OEE is a key performance indicator calculated to measure machine and overall manufacturing performance. It is not a static, individual measurement of success, failure, or mediocrity, but rather a living metrics combination that points operational technologists to the levers they must pull to improve business performance.
The first and most common challenge for an effective OEE implementation is data collection. Good data analytics start with good data availability. For manufacturing environments that use hundreds of types of machines and gather data from multiple industrial protocols, real-time data collection from machines it critical. Advanced industrial IoT data management edge platforms provide this. Most offer core capabilities that include:
Inherently, edge computing is done at or near devices and offers more security, lower latency, and more bandwidth capacity and reliability than cloud services. The most efficient IIoT edge platforms for data management include a large library of protocol converters enabling machine data collection using open and private protocols such as OPC UA and OPC DA, Modbus, MTConnect, BACnet, and Siemens. For networked machines, the edge makes data collection a simple process, particularly for choosing the driver and appointing it to the IP address. All the tags are auto-enumerated. For machines requiring analog or digital I/O data collection or external instrumentation, edge platforms can collect data from sensors or from a hardware adaptor for legacy machines.
With the machines connected, algorithms can be set up by mapping the variables to a trigger. The logic was previously written in traditional programming languages, however newer platforms provide a low- or no-code environment for a visual logic workflow to facilitate application creation, maintenance, and calculations. OEE calculations can be done machine by machine and consolidated by line, shift, and plant level to improve management visibility. Alarm functions for monitoring specific machine and process conditions can be added and customized to automated actions.
With robust OT/IT edge integration software, it is possible to automate machine setup and reduce setup time, as the software’s low latency is critical to avoid hidden downtime in the machine. With improved process visibility, managers have better information for planned stops and can calculate the machine OEE and schedule maintenance based on machine information.
Every manufacturer knows that unscheduled downtime is expensive. If one machine component fails, it can halt the entire production system, resulting in the loss of production time, raw materials, and more. Edge platforms can be deployed to learn expected machine behavior and fully automate preventive maintenance with triggers identifying anomalies in the production cycle, such as power consumption, vibration and noise, and temperature. Given that the most common challenge for smart factories is usually interoperability with infrastructure implementation, it is well worth prioritizing IIoT architecture that uses edge intelligence to integrate legacy machines and sensors into IT systems.