Statistical Process Control (SPC) is widely used these days by manufacturers across industries to better understand quality processes and drive continuous process improvement. This practice has typically been performed by quality experts and engineers.
Today’s leading manufacturers are using SPC techniques and data for another purpose – Enterprise Asset Management. Their idea is to leverage the manufacturing intelligence being gathered by the quality team, and use it to monitor equipment performance as part of their program to identify problems early and take preventive and corrective actions before costly breakdowns occur. It seems an obvious idea, but when I brought this up at a recent conference hosted by a major consulting firm, there were questions and interest from around the room. I’ll try to explain the concept briefly here.
As we know, SPC involves the collection and analysis of production process data to find correlations between planned process parameters and the actual execution of those processes. This data can be used to highlight out of control processes that can then be quickly identified and acted upon.
Let’s take a simple example, say the boring of a hole in a metal piece that will be part of an assembly later in production. In the typical manufacturing plant, the quality team will analyze the size of the hole, the variances that occur in the process, and then relate those factors to the quality of the product. They may conclude that a +/- 1% variance in hole diameter does not affect quality, but exceeding that limit results in negative quality consequences – for example, the assembly may fall apart. The plant will then take steps to measure that process in production to ensure that holes are always bored with no more than a +/- 1% variation from specification.
While that result is valuable, some manufacturers are finding they can do much more with their SPC data – they can use it to trend and monitor equipment performance. Staying with the same example, by looking at SPC trending rules, you can determine that your process is out of control, indicating a possible, eminent equipment failure. With this knowledge, the plant can then take an early repair action and avoid a costly failure and cleanup. The solution can be as simple as alerting someone to shut down a machine, or to trigger an automated sequence to halt production.
The point is statistical analysis can be related to many outcomes, not just to product quality. The same data and the same science can provide insights into the condition of the equipment and people who are executing a process. Without this SPC approach, maintenance might never realize that a production process is yielding vital clues about the status of the equipment, while the Quality team who has the data may attempt to relate it to machinery condition or issues not directly connected to quality, but is not done in an immediate, actionable time frame.
Implementing SPC-based preventive maintenance triggers requires that the data be integrated and readily shared amongst the various groups, including production, quality and maintenance in particular. This is easier said than done for companies with siloed plant floor systems. Those manufacturing enterprises that have invested in platform-based plant floor solutions that can easily share this information, perhaps as an embedded process, will fare much better. Making SPC-driven maintenance management work in that case depends mainly on recognizing its value, and making the decision to implement it.
We expect to be hearing more from customers who are taking this approach and the ROI data in its favor starts to accumulate. I would be interested to hear from you if you have ideas for other SPC-driven maintenance and asset management examples, or are trying it yourself.