Justifying instrumentation- and automation-related projects in process manufacturing operations typically involves quantifying expected returns in reliability, efficiency or reductions in risks associated with safety and emissions. As Industrial Internet of Things (IIoT) technologies have emerged, the need for project justification remains as strong as ever.In a Smart Machines & Factories article, Securing a return on investment from IIoT, Emerson’s Travis Hesketh describes ways that IIoT deliver quantifiable operational performance improvements and financial returns.
…many companies have become stuck in decades-old work practices that fail to take advantage of the advanced digital technologies now available.
He highlights the performance of process manufacturers in the top quartile versus average performers. They:
…incur one-third as many safety incidents as their average industry peers. They spend half as much on maintenance, one-third of the industry average on energy costs, and 20% less on production-related expenses. They also achieve 15 days more production availability per year and produce 30% less CO2 emissions.
IIoT-based applications help improve operational performance by allowing companies:
…to empower their experts with the additional information they need for decisions and action that can facilitate operational performance improvements. As well as being performed in-house, either on site or remotely, analysis and decision-making can, for the first time, also be completely outsourced to third party domain experts.
Key elements to the digital ecosystems incorporating IIoT include:
…the provision of rich, real-time operating data from intelligent sensing and automation technologies across the business; secure transport of that data to where it’s needed anywhere in the world; robust, scalable software to convert the data into actionable insights; and domain expertise, either in-house or external, to make the decisions and drive the actions that will lead to improved performance.
Read the article for more on each of these key elements and examples which have generated returns on investment. Here’s one of the examples from the article:
- Diagnostic data is being used to identify potential control valve failures before they cause significant interruptions to operations. This service employs time series trend analysis to generate predictive data and has already helped a major chemical company identify failure conditions on a critical valve that would have caused a multi-day plant shutdown resulting in millions of pounds of lost production.
For more on ways to improve safety, efficiency, reliability and emissions and achieve top quartile performance, visit the Top Quartile Performance site.