ENWPS follows a systematic approach when it comes to Data Analytics services. It begins with a process that involves several different steps like:
The amount of data collected is humongous. All this must be visualised, the actionable bits passed on to higher-level systems for processing to be useful and the rest archived for future reference.
The customised data analytics software provides health and diagnostic analysis from the inputs collected from all the industrial equipment. It crawls industrial networks, discovers assets, and provides analytics by transforming the data generated into preconfigured dashboards for quick access of the data needed to respond to issues effectively; it delivers status information through a browser-based interface. The system also delivers ‘alerts’ to the user’s smartphone or tablet if a device requires attention. As the application uncovers information, it starts to understand the system on which it is deployed to make prescriptive recommendations. All the useful data is stored for future reference and further analysis as and when required.
A typical plant has several areas of activity ripe for Data Analytics in the IIoT era.
All these are dots spread across the plant that can be connected into a seamless whole through Data Analytics to optimise the functioning of the plant, improving the overall efficiency and safety, leading to higher productivity. Common database functions such as stored procedures allow key performance indicators (KPIs) to be generated as needed, resulting in more optimised manufacturing processes.
As a company with more than two decades of legacy in providing Automation & Robotics solutions for the global industry, ENWPS has a wealth of experience in the engineering and manufacturing world. The company has implemented countless projects across industry verticals spanning discrete manufacturing and process automation, understanding the bottlenecks, addressing the pain points and offering satisfactory solutions with measurable outcomes. In automation, data was always sacrosanct but much limited earlier. All that has changed with the evolution of the Industrial Internet of Things (IIoT) and proliferation of sensors and devices in the connected plant environment.
The key to successful Data Analytics is in first understanding the rationale of the whole exercise, and the need for undertaking it. The next stage is identifying the correct data collection points and the devices to be used. The data thus collected must be displayed, monitored and recorded; appropriate alarms generated by fixing the parameters and critical levels, etc. All this needs expertise in relevant processes and industries and an understanding of the larger picture.
An in-depth understanding of the requirements of the engineering and manufacturing industries and the processes thereof.
Expert professionals from various engineering streams – mechanical, electrical, instrumentation, automation and robotics.
A thorough knowledge of sensors and the most appropriate IoT devices for data collection from specific machines and equipment.
A plant typically has a mix of new and old equipment – it is important to collect data from legacy equipment for effective solutions.
Now all data is useful or critical, hence it needs expertise in identifying data that is important for monitoring and analysis.
One of the major benefits of Data Analytics is, companies can better organise their operations by streamlining processes, removing bottlenecks, optimising procedures, having a better inventory management, etc., offering a better understanding of plant management.
As a result of data analysis, the processes will be improved and fine-tuned which will directly result in better quality of the output – production of parts and components, or consumables as the case maybe, with greater control on process parameters.
Another important benefit is maintenance activities can be switched to Predictive mode with continuous monitoring of machinery and equipment giving better insights into the state of wear and tear and facilitating planned maintenance, eliminating unexpected breakdown.
With the insights gained from data analysis, companies can improve their decision making regarding plant operation, expansion or downsizing as a result of better productivity and improved efficiency. This will also enable better utilisation of space and result in energy efficiency.
Finally, data analytics would help in making the plant safer for operations with better monitoring of networks for possible cyber threats and improve personnel safety with regular monitoring operator movements within the premises.