Industrial automation is based on operational technologies (OT) such as programmable logic controllers (PLC), supervisory control and data acquisition (SCADA), and distributed control systems (DCS). These systems are usually inflexible and do not allow easy reconfiguration. This is a significant barrier to the deployment of new automation technologies in manufacturing (for example, installing a 3D printer as part of a manufacturing process), as well as changing the configuration of industrial processes (for example, adjusting the output of a production line).
Regardless of the area they work in, IT support specialist job description will require strong analytical skills, along with familiarity with different operating systems, such as Windows, macOS or Linux, and proficiency in one or more programming languages.
IIoT facilitates the convergence of OT with IT and allows industrial automation processes to be reconfigured based on digital tools in a shorter time frame (for example, hours rather than weeks). Instead of reconfiguring complex OT systems, machine and tool configurations take place at the digital level of the IIoT system. Consequently, IIoT deployments are widely used to support mass production models in a faster and more cost effective manner.
Most industrial organizations service their equipment on a regular basis. However, prevention is often carried out earlier than required, resulting in suboptimal overall equipment effectiveness (OEE). To optimize OEE, enterprises are moving to predictive maintenance. With predictive maintenance, machines can predict their failures by accurately calculating parameters such as end of life (EoL) and mean time to failure (MTTF). IIoT technologies collect and consolidate datasets of equipment health such as vibration data, acoustic data, ultrasound data, thermal imaging data, oil analysis data, and more. By improving the efficiency of high-value equipment, industrial organizations see a direct ROI on IIoT investments.
IIoT and BigData technologies allow the collection of extensive data about industrial processes, which leads to accurate identification of quality problems at different times. For example, digital data on a production line can be collected and used to identify problems and inefficiencies while recommending corrective actions. This allows for quality management disciplines such as Total Quality Management (TQM) and ZDM (Zero Defect Manufacturing). While TQM and ZDM have been around for over two decades, it is the emergence of Industry 4.0 that enables them to be implemented reliably and cost-effectively.