When looking at the obstacles that limit the success of predictive maintenance (PdM) initiatives in the Plant Services survey, it’s not surprising that four of the six noted are implementation related.
Based on the last 200 companies I’ve visited/analyzed, more than 70% of companies point to culture and implementation as the major roadblocks to reliability & maintainability (R&M) and PdM/CBM technology success. The following are comments on what I frequently observe regarding the four impediments:
- Undefined financial benefits – Maintenance needs to do a better job monetizing the tangible and intangible benefits.
- Undefined operational benefits – Maintenance needs to do a better job integrating with production data.
- Poor program execution – More clarity is needed on how the technologies support the R&M strategy by doing all the analysis and fixing what the PdM finds, understanding that it’s a socio-technical process.
- Lack of executive support – It’s difficult to get long-term support in a culture incentivized to short-term results.
Figure 1 is the 2014, 2018, and 2020 data on PdM technologies deployed.
Figure 2 plots the average values by technology of Figure 1. Note that oil analysis is the highest deployed and with vibration analysis and infrared as the top three used.
The data was further divided in half, based on reactive maintenance percent. High reactive maintenance average was equal to 77% and low reactive maintenance average was equal to 26%. As shown in Figure 3, it was found that the high reactive maintenance half was at 19.4% of assets on PdM and the low reactive maintenance half was at 38.6% of assets on maintenance. Improvements in % maintenance cost/replacement asset value were also evident.
Figure 4 shows that every technology when performed on more assets, contributed to lower reactive maintenance.
Figure 5 illustrates that companies are more satisfied with PdM program results when reactive maintenance is lower. However, more improvements are most likely tied to all the roadblocks mentioned earlier.
Of course all of this only works if you are doing both the finding and analyzing/fixing from those PdM finds. Also, if you are doing CBM, are you collecting the correct sensor data and performing the appropriate analytics? Otherwise, expecting better results without having changed anything (doing the same maintenance over and over again) parallels Einstein’s quote/definition of insanity, or as I refer to it – maintenance insanity. Figure 6 shows that companies requiring less reactive maintenance also had greater satisfaction in their PdM programs.
In Figure 7, the same group of companies is not seeing the benefits of their CBM program (based on levels of reactive maintenance). So, it’s not surprising that overall satisfaction in PdM programs is low.
Predictive technologies and condition-based maintenance capabilities improve every year. However, successful implementation of PdM/CBM is still too elusive for too many facilities. It’s more about getting better at application and implementation. All of the data will not help, if practicable ways to make better daily “running the business” decisions is not an outcome. Seventy-five percent of North America is not doing enough PdM /CBM finding, fixing, and learning. So take the next steps – work on those implementation roadblocks and use reliability and maintainability as a competitive advantage.