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Incremental steps to creating value with industrial data analytics
In this article, Elliott Middleton, director of Product Management at AVEVA, sets out seven steps to data-driven decision making.
However, companies are not limited to taking large and costly decisions when it comes to exploring the possibilities. There are smaller, incremental steps that may be taken along the journey of digital transformation. Until recently, more mature process industries
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have pursued AI and ML initiatives, while others are only using some basic levels of automation. The unrelenting pressure to keep production lines moving, especially at a time when businesses are responding to changes driven by the COVID-19 crisis, means there is little time or headspace to consider implementing new technology. Automating the collection of sensor data, spotting problems and patterns can result in faster troubleshooting and major process improvement for all sizes of operation. It is possible to start with small incremental steps that add immediate value. The journey to adding value through
data analytics Here are seven steps to generating incremental value with industrial analytics and ML:
1
AuToMATe dATA coLLecTIon froM sensors
This is a critical prerequisite. An infrastructure for automated data collection requires multiple sensors to feed through the data that is needed for meaningful analysis.
2
record MeAsureMenTs froM THe sensors over TIMe
The challenge for many organisations is collecting the data and making sure that data is captured throughout the process so that the workforce has the option to draw some useful inferences at a later stage. You may not have an immediately obvious use case but if you start collecting all the data, additional insights are likely to arise.
3 AcceLerATe dIAgnosTIcs
once you have collected the data, diagnostics can greatly reduce the time required to pinpoint and correct operational problems—and lead you to make process changes that prevent them from happening again in the future.
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here is a lot of buzz surrounding industrial analytics, artificial intelligence (AI) and machine learning (ML).
4 MAke THe dATA More AccessIbLe
As the key efficiency metrics are better understood, it is important to make them readily available to a broader group within the operational staff, so they can independently monitor and react to potential problems earlier. Many sites use a large screen display showing a dashboard of live metrics or an automated email report. others take it a step further and deliver alerts to mobile devices. Add alarm history to the process
history to give more context and significance to the data Traditionally, data historians just record
sensor values, they do not record the alarm state. but combining sensor and alarm data makes it easier to understand the potential impacts on quality, safety, cost or the environment.
5 Add operATIonAL conTexT
In some applications, differences in recipes, equipment, or personnel can further complicate identifying root causes or improvement opportunities: was the yield lower because it is a different product, operator, or production line? by including information about this kind of operational context, you can begin to consider these other factors in your analysis.
February 2021 Instrumentation Monthly
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