Predictive Maintenance Implementation: Advantages and Disadvantages

A predictive maintenance approach strives to detect the onset of equipment degradation and to address the problems as they are identified. This allows casual stressors to be eliminated or controlled, prior to any significant deterioration in the physical state of the component or equipment. This leads to both current and future functional capabilities.

Basically, predictive maintenance differs from preventive maintenance by basing maintenance needs on the actual condition of the equipment, rather than on some predetermined schedule. Recall that preventive maintenance is time-based. Activities such as changing lubricant are based on time, like calendar time or equipment run time. For example, most people change the oil in their vehicles every 3,000 to 5,000 miles traveled. This is effectively basing the oil change needs on equipment run time. No concern is given to the actual condition and performance capability of the oil. It is changed because it is time.

This methodology would be analogous to a preventive maintenance task. If, on the other hand, the operator of the car discounted the vehicle run time and had the oil analyzed at some periodicity to determine its actual condition and lubrication properties, he or she may be able to extend the oil change until the vehicle had traveled 10,000 miles. This is the fundamental difference between predictive maintenance and preventive maintenance, whereby predictive maintenance is used to define needed maintenance tasks based on quantified material and equipment condition.

Advantages

• Provides increased component operational life and availability
• Allows for preemptive corrective actions
• Results in decrease in equipment and/or process downtime
• Lowers costs for parts and labor
• Provides better product quality
• Improves worker and environmental safety
• Raises worker morale
• Increases energy savings
• Results in an estimated 8% to 12% cost savings over which might result from a predictive maintenance program

Disadvantages

• Increases investment in diagnostic equipment
• Increases investment in staff training
• Savings potential is readily seen by management

There are many advantages of using a predictive maintenance program. A well-orchestrated predictive maintenance program will all but eliminate catastrophic equipment failures. Staff will then be able to schedule maintenance activities to minimize or eliminate overtime costs. And, inventory can be minimized, as parts or equipment will not need to be ordered ahead of time to support anticipated maintenance needs. Equipment will be operated at an optimal level, which will also save energy costs and increase plant reliability.

Past studies have estimated that a properly functioning predictive maintenance program can provide a savings of 8% to 12% over a program utilizing preventive maintenance strategies alone. Depending on a facility's reliance on a reactive maintenance approach and material condition, savings opportunities of 30% to 40% could easily be realized. In fact, independent surveys indicate the following industrial average savings resulted from initiation of a functional predictive maintenance program:

• Return on investment: 10 times
• Reduction in maintenance costs: 25% to 30%
• Elimination of breakdowns: 70% to 75%
• Reduction in downtime: 35% to 45%
• Increase in production: 20% to 25%

The down side of using a predictive maintenance approach are its initial costs. The up-front costs of starting this type of program can be expensive. Much of the equipment requires expenditures in excess of $50,000. And, training of in-plant personnel to effectively utilize predictive maintenance technologies and practices will require substantial additional funding. And, beginning a predictive maintenance program requires an understanding of the facility's predictive maintenance needs and the approaches which need to be undertaken. It is also essential to have a firm commitment, by management and all facility staff and organizations, to make it work.