The driving philosophy behind IIoT is that smart machines are better than humans at accurately and consistently capturing and communicating data.įor companies to succeed in achieving their goals, getting the big data to a granular level is the key. It acts as a bridge between the existing operational technology within a plant and the enterprise network, so that valuable data can be shared reliably and securely to improve plant productivity and efficiency. Process Automation Controllers like the EZRack PLC focus on the interconnectivity and utilization of powerful data in a manufacturing environment. The challenging part is converting it to actionable insights.
The question is how to go from concept to reality. The level of insight and intelligence the technology delivers brings a significant competitive advantage to early adopters. If properly executed, it can decrease downtime, enhance productivity, reduce operating costs, and support predictive maintenance. Industrial Internet of Things (IIoT), Industrie 4.0 is making news for a good reason.
Measure: Once the data is collected, if it is not measured then that data is useless.Consistency: Check to identify if there is a pattern to the data or if the data is consistent.Completeness: Check if the data is complete or missing any values.Collect data: Define how frequentlly data needs to be collected.Define data: It is important to know what data is important to achieve the company goals.Raw data can be easily turned into actionable information by following some simple basic steps: Companies can use real time data to satisfy their customers demand for a particular product by changing their production schedules on the fly. In process industries, by monitoring equipment behavior and identifying malfunctions, companies can maximize their operating uptime and output. Real time data is time critical and one has to act upon it almost immediately. If it is not used or not properly analyzed, it is useless. Storing this information for future analytical needs helps management to not repeat the same mistakes again. It captures process variables over a period of time which can be used for various calculations and estimations. Historical data provides a clear view of the past that can offer solid insights into where improvements need to be made.
There are two types of data: historical data and real time data. So data must be segregated to help different departments to achieve the company’s common goal. Quality Control needs data to verify and control the quality of the manufactured product. Plant supervisors need data to help plan production. Plant managers need data which helps to analyze productivity, target, and goals. They can use it for preventative maintenance or scheduled maintenance. Different departments need different data for analysis.įor instance, maintenance departments need data from sensors and switches which they can analyze to determine machine health and safety. Data collection is simple, but if one does not know what to collect, how to analyze, and act on it, then that data is not useful.Īnother important aspect of data is that not everybody needs everything. Manufacturing plants also generate large amounts of data that can be collected and stored for data analysis and profiling.
With the technological advancements and increase in plant floor automation and connectivity to enterprise networks, todays process industries have a plethora of sensors, switches and transmitters which have data and can provide us with some useful information. You cannot take action on data if you don’t know what it means or why is it significant. The key to turning data into actionable insight comes from establishing context and knowing the business goals. Different people can interpret the same data differently. But it can sometimes easily lead to data overload, not being looked at, or being ignored. Turning Raw Data into Actionable Informationĭata is very important in process industries.
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