Page 20 - Intro Predictive Maintenance

Impact of Maintenance 11

problems. The remaining 83 percent are totally outside of the traditional maintenance
function’s responsibility. Inappropriate operating practices, poor design, nonspecifi-
cation parts, and a myriad of other nonmaintenance reasons are the primary con-
tributors to production and product-quality problems, not maintenance.

Predictive technologies should be used as a plant or process optimization tool. In this
broader scope, they are used to detect, isolate, and provide solutions for all deviations
from acceptable performance that result in lost capacity, poor quality, abnormal costs,
or a threat to employee safety. These technologies have the power to fill this critical
role, but that power is simply not being used. To accomplish this new role, the use
of predictive technologies should be shifted from the maintenance department to a
reliability group that is charged with the responsibility and is accountable for plant
optimization. This group must have the authority to cross all functional boundaries
and to implement changes that correct problems uncovered by their evaluations.

This approach is a radical departure from the traditional organization found in most
plants. As a result, resistance will be met from all levels of the organization. With the
exception of those few employees who understand the absolute need for a change to
better, more effective practices, most of the workforce will not openly embrace or vol-
untarily accept this new functional group; however, the formation of a dedicated group
of professionals that is absolutely and solely responsible for reliability improvement
and optimization of all facets of plant operation is essential. It is the only way a plant
or corporation can achieve and sustain world-class performance.

Staffing this new group will not be easy. The team must have a thorough knowledge
of machine and process design, and be able to implement best practices in both opera-
tion and maintenance of all critical production systems in the plant. In addition, they
must fully understand procurement and plant engineering methods that will provide
best life-cycle cost for these systems. Finally, the team must understand the proper
use of predictive technologies. Few plants have existing employees who have all of
these fundamental requirements.

This problem can be resolved in two ways. The first approach would be to select
personnel who have mastered one or more of these knowledge requirements. For
example, the group might consist of the best operations, maintenance, engineering,
and predictive personnel available from the current workforce. Care must be taken to
ensure that each group member has a real knowledge of his or her specialty area. One
common problem that plagues plants is that the superstars in the organization do not
have a real, in-depth knowledge of their perceived specialty. In other words, the best
operator may in fact be the worst contributor to reliability or performance problems.
Although he or she can get more capacity through the unit than anyone else, the
practices used may be the root-cause of chronic problems.

If this approach is followed, training for the reliability team must be the first priority.
Few existing personnel will have all of the knowledge and skills required by this
function, especially regarding application of predictive technologies. Therefore, the