An effective UOA program provides essential information on three aspects:
Most programs operate some kind of traffic light system that breaks the results down into, typically, three categories, normal (green), early warning (amber) and critical (red). Different programs may use different terminology, but the intent should be the same, a simple way to prioritise what needs attention.
Naturally anything in the most severe category, hopefully, gets acted on quickly though often the guidance from the lab is all too often followed blindly, without a real understanding of the results. Lab comments are often generic and lack an understanding of the actual application and its environment. As such they are often quite generic and lack the subtlety and finesse that someone with a greater awareness of the specific application may have.
Time and other pressures often dictate that the normal (green) results are just filed away, without review or understanding of the data. This can lead to missed opportunities to get early insights into a potential problem as data can be significantly different from previous, but the magnitude is insufficient to trigger an amber or red status.
Shown graphically below is some fictitious data that illustrates the problem described above. Initially the data is around 2 – 4 but then sample five jumps to a value of 18, a significant change from previous values but still below the early warning (amber) limit. Sample six is at 28 and almost at critical (red) limit. Hopefully this will be reviewed but time has past since the problem first started at sample four.
Providing focussed training for technicians might allow them to take a more proactive role in managing UOA data and relate specific events to the data. Involving them in the review of the data can also make them more invested in the UOA program, rather than just taking samples but never seeing or understanding the consequences of what they are doing.
Alternatively consider having a specialist review data on a periodic basis.
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