The following article on warehouse automation is written by Kobus Vermeulen, direct sales executive, process automation at Schneider Electric. It is Part 1 of a two-part series.
Kobus Vermeulen, direct sales executive, process automation at Schneider Electric. Schneider Electric.
What is the body without a brain? An empty vessel, uncoordinated and purposeless. The same can be said about industrial operations which require a DCS (Distributed Control System) to – in real-time – coordinate and control its process subsystems.
Like the brain, a DCS is a multitasking maestro, controlling and coordinating complex processes in a myriad of industrial setting such as large manufacturing plants, providing valuable top-down control.
The DCS communicates with subsystems such as sensors and other data collection devices, interpreting production trends to make automated decision and send instructions to individual controllers, actuators, and other industrial equipment, programmable logic controllers (PLCs), throughout the plant.
It is certainly marketplace that seeing considerable growth. According to research, the DCS Market size was valued at over USD 23.28 Billion in 2023 and is anticipated to reach USD 48.44 Billion by the end of 2036, growing at a CAGR of 5.8% during the forecast period between 2024-2036.
Key to the marketplace’s growth is the continued proliferation and acceptance of IoT technologies with DCS. The number of connected IoT devices worldwide is projected to reach nearly 76 billion by 2025.
Brainfood for the system
Considering the above, it’s clear, the DCS is growing marketplace. So, what is if one then adds Artificial Intelligence (AI) and Machine Learning (ML) to modern DCSs? For one, it acts as software-based brainfood to the DCS, optimising several key operations such as predictive maintenance, process optimisation, analytics and anomaly detection
A perfect example is predictive maintenance. Traditionally reactive, maintenance addresses issues after it occurs—or based on a scheduled timeline, which often leads to unnecessary downtime or unforeseen breakdowns.
AI and ML algorithms, however, analyse historical and real-time data from DCS sensors to predict potential equipment failures before it happens. This predictive maintenance capability enables industries to strategically schedule maintenance activities which in turn reduce unexpected downtime whilst optimising asset performance.
For instance, in the oil and gas industry, AI algorithms monitor data from pumps, compressors, and other critical equipment. By detecting subtle changes in vibration patterns or temperature, the system can forecast when a piece of equipment might fail, allowing maintenance teams to intervene early. This not only reduces maintenance costs but also enhances operational reliability.
