“If you think the cost of training is high, try calculating the cost of ignorance.”
“What if we train them and they leave?”
“What if we don’t train them and they stay?”
“Training ain’t learning.”
When we see a magician pull off his head and stick it under his arm we are amazed; but when we know how the trick is performed, it becomes unimpressive. Technologies sometimes have the same effect on us. To see someone measure vibration on a machine and then be able to state that the inner race of a bearing has a flaw can be almost as amazing as a magician’s trick. It shouldn’t be, because all the stakeholders in machine reliability should be sufficiently trained to know how these “tricks” are performed. If stakeholders understand the basics of the technologies involved in maintaining machinery, the proper maintenance strategies are more likely to be developed. Many years spent in maintenance training reveals a most important concept: Frequently trainees have expressed a desire to have their supervisors present for the training. Unfortunately, they return to the job with high expectations of improving machine reliability only to discover that their bosses aren’t as thrilled about making the needed changes learned in the recent training. The saying, “We don’t know what we don’t know” comes to mind. Without training all the stakeholders, the full importance of what was learned by some is not understood by all. Consequently, the full value of the training goes unrealized. It is imperative that all stakeholders know the basics of the technologies used if the strategy is to be implemented successfully.
On-the-job training is a wonderful way to learn most jobs and should be part of the training in all jobs. Today’s predictive maintenance technologies are more complex and require more precision in order to be competitive in a world where machine reliability is a must for plant success. This new precision requires more than OJT because there may be some basic knowledge that OJT doesn’t address. Sometimes OJT teaches us to take shortcuts that may, in the long run, be harmful to machine reliability. OJT alone is not usually adequate in teaching the philosophy required for successfully maintaining machinery. Formal classroom training is the best way to learn the principles and standards required in order to keep machines running at peak performance. Proven, researched-based training provides adequate hands-on learning, as well as basic principles that apply across all technologies. These basic principles and standards embody the philosophy of successful machine management.
The author remembers teaching an electrical class where one of the participants declared, “Microfarads, picofarads… we don’t need all that theory stuff, we just need to know how to fix it.” What the student failed to realize was that “fixing it” is simply the application of theory. The application of any technology is putting theory to use, and theory is best learned in the formal classroom. A person with OJT can eventually learn to be an electrician on a specific job; but when that person is moved to a new location, he/she must learn the new job. Whereas, a person well-trained in theory can be a good electrician regardless of where he/she is placed, once the individual learns the locations of the equipment.
Today’s training must provide a thorough understanding of the theories of technology if we are to be successful tomorrow. Technology has provided improved learning opportunities surpassing what was available in the past. Computers allow us to simulate scenarios that may be too expensive or time-consuming to develop with physical components. This provides us with a greater learning advantage than was available in the past. Computers also allow us to easily individualize training for our particular needs and situations.
The knowledge base in all predictive maintenance technologies grows daily along with the data we collect on our machines. We will continue to find new parameters to measure as we improve our ability to keep our machines running. We can expect this trend to continue in the future because we can foresee a day when small devices will let us collect and share data with huge, smart data banks, making use of the collective knowledge in all maintenance fields. The new tools and technologies will always require a solid basic knowledge, well-grounded in theory, learned in the classroom. Data becomes meaningless if we don’t have the knowledge to sort and interpret what is needed in order to keep our machines reliable. The knowledge base will continue to grow, so we must continue to learn.
“The only true competitive advantage is the ability to learn faster than the competition.”