It has been argued that the industrial internet of things is one of the most groundbreaking developments in the history of manufacturing operations. In fact, according to a recent report, the global IIoT market is expected to reach approximately $232.15 billion by 2023, up from $145.81 billion in 2017.
Adding sensors to equipment and systems is not new to the industry, but recent advances in analytics capabilities and decreasing sensor costs have empowered many manufacturers to use the advantages of IIoT, making improvements in productivity and quality without breaking the bank.
While predictive analytics in particular has been used to help avoid machine failure and downtime, manufacturers must also be able to stream data from IIoT-connected devices and equipment in real time to not only help improve productivity, operational efficiencies and reduce costs, but also gain important insights that help shape current and future business decisions and assess risks.
Overcoming analytics barriers for IIoT
IIoT is characterized by high sensor density at a site or at the edge (a typical IIoT architecture has thousands of sensors), and with IIoT on the rise, the volume of data coming from IIoT-connected devices and equipment is exploding. With thousands of sensors at sites generating rapid measurements at sub-second intervals, data volumes can quickly reach terabytes of data per day.
The large volume of sensor data can be overwhelming for manufacturers, and most sensor data has a limited time period before it loses value, necessitating real-time analysis to capture business value that can be immediately acted on. This requires an end-to-end IIoT architecture that can support real-time streaming analytics and scale to handle large volumes of data.
Benefits of real-time analytics in IIoT
Today’s organizations across industries need actionable insights faster than ever in order to compete, meet customer expectations, reduce risks and capitalize on time-sensitive opportunities. And IIoT is no exception. By immediately connecting insights with action using real-time analytics, manufacturers can improve productivity, operational efficiencies and reduce costs. They can also avoid losing important insights that help shape immediate and future business and financial decisions, and assess risks in real time.
Some of the industry’s biggest players rely on real-time analytics every day. CGI has a smart metering IIoT system with over 50 million meters (electric, water and gas) in the UK that use an in-memory database management system to process large volumes of continuous messages sent by a network of electricity and gas suppliers. The system retrieves information from the smart meters, as well as provides real-time analytics for downstream decision making. Similarly, Mitsubishi Electric utilizes real-time analytics through speed data ingestion for swift analysis and rapid decision-making for 6 million smart meters deployed in Hokkaido and Shikoku provinces in Japan.
As the amount of data generated by IIoT devices continues to climb, and technological advancements directly affecting the industry, such as 5G and artificial intelligence, continue to roll out, having a nimble, end-to-end IIoT architecture that can both support real-time streaming analytics and scale to handle large volumes of data will only become more critical. At the end of the day, not only do real-time analytics help manufacturers prevent unplanned production downtime, optimize material flow and improve product design, but also execute at the speed and capacity needed to stay ahead in the today’s digital economy.
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