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Five Ways Production Data Drives Plant Efficiency

Production data will boost plant efficiency … once you embrace it.

In my last piece, Five Ways to Rethink the Production Data Discussion, I explained how to reframe the discussion on using production data to add more value, and getting buy in from the rest of the team so you can plan and implement a successful IIoT deployment. Once you have data management and analytics capability spreading across the line, how can it drive plant efficiency, though?

Here are at least five benefits you should see:

1) Achieve better defect detection

The key is to collect the waveform, or digital process signature, from cycle of each process or test. A process signature is a visual representation of everything that happened through every millisecond of that operation on a particular part.

It is easy to benchmark what a healthy signature should look like for the ideal process or test cycle. Hundreds or thousands of signatures from the same process or test can be visualized as a histogram to spot trends and patterns. If quality problems are identified in finished products, the team can quickly identify a few possible causes. This makes it possible to quickly trace the root cause of existing problems or spot the anomalies that point to new ones before quality suffers.  

2) Set more effective limits

This also gives your team the insight to optimize limits on a test, to eliminate false failures and reduces the risk of bad parts slipping through. On the process station, it provides the assurance that each part has met spec and that process cycle completed within the acceptable range.

3) Optimize test cycle times

A repeatable and reliable test is great … unless it takes too long and creates a bottleneck, forcing the need to fit up and staff parallel test stations. With the right data and the means to analyze it, your team can quickly and easily see how a test cycle can be shortened and with what impact.

4) Optimize the whole line

Parts production data can drastically shorten how long it takes to calibrate and verify new equipment or new stations, launch entirely new lines, or reduce the time to switch over and resume production on a line that is producing variants of the same part or model.

Because small but apparent changes in the typical signature for a process can be an early warning sign of impending quality defects, maintenance cycles on the line can be predictable and planned, rather than unexpected and disruptive.

5) Make what is good even better

All this has been in the context of discrete manufacturing, in which all parts in production are serialized. This makes it easy to archive all the part production data, indexed by part serial number, into individual birth history records. These records can capture all the data from any system in the plant that is relevant to that part’s production and quality assurance.
This provides a rich resource with which to experiment. All this data can be used to run various scenarios in a virtual test bed, to test new limits and gauge the impact of different variables without disrupting actual production.

It really is just the next logical step

Manufacturers of all kinds have been testing how they can use data to improve quality, yield and efficiency for decades. Through the 2000s, much of this focused on how various enterprise software systems could support the need. With Industry 4.0, manufacturers have a powerful new toolset that is much more granular, allowing parts in production to at last tell the uncensored story they have held all along.

Tim Williams is Vice-President of Global Sales at Cincinnati Test Systems. He has worked extensively with major industrial companies and manufacturers on the challenges of data utilization from the plant floor and deriving real business value from Industry 4.0.

 

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