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4:06 am
February 18, 2011
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A Data-Driven Path To PM Optimization

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Detailed work-order analysis can reduce inspection frequency and improve PM effectiveness.

Your mandate is to optimize your preventive maintenance (PM) program. But where to start? How effective is your existing PM program? What does “optimization” really mean and how can it be done in a cost-effective way? What tools are available? Should you consider Reliability-Centered Maintenance (RCM)? How effective is Weibull analysis? What other methodologies might work? Where do you get your data?
This article explains a data-driven, statistically sound process that can be used in real-life situations for optimizing the fre-quency and scope of inspections/PMs. Statistical jargon has been translated into terms that can be easily understood by maintenance personnel—which, in turn, will help lead to easy implementation of the described processes.
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Table 1. Sample Spreadsheet of Pareto Analysis Results (Click to enlarge)

The optimization process
In the course of their duties, maintenance and reliability personnel need to cost-effectively improve plant performance and equipment reliability while they also eliminate waste. Many methodologies can be used. RCM, with its derivatives, is probably the most prevalent. But because RCM is a resource-intensive process, many industries and plants stay away from it or apply it on an ad-hoc basis.

What many maintenance managers may not realize is that they have a simple, effective process based on inspection frequency at their disposal. The only requirement is for the organization to have a working CMMS/EAM system in place with equipment and work-order history. The inspection-frequency (or intervals) optimization is a structured, multistep process with simple, well-defined tasks in every stage. These steps are:

  • CMMS/EAM analysis:
    • 1. Work orders listing
    • 2. Sorting work orders
    • 3. Pareto analysis of work orders
  • Modified MTBF determination
  • Calculation of optimum inspection intervals
  • Inspection scope review
  • Cost/benefit determination

CMMS/EAM analysis
The goal of the CMMS/EAM analysis is to concentrate on the most costly inspections first, as these will bring most benefit to the plant. Start the process by exporting inspection work orders from the CMMS/EAM system into an Excel spreadsheet. Important fields to include are: equipment name, area, inspection name, inspection and frequency.

Next, modify and sort the table. Modification includes adding a column showing the total number of hours per year required to execute a particular inspection. This allows for sorting of the entire table by effort required, essentially by performing a Pareto analysis.

Although the next step depends on plant procedures, it makes sense here to follow the 80/20 principle and concentrate on the top 20% of the most costly (most hours required) inspections. Table 1 shows results of such an analysis.

Determining modified MTBF
To calculate inspection intervals, two parameters are required:

  • MTBF (Mean Time Between Failures)
  • Required equipment reliability

It is now useful to introduce a new term: “modified MTBF.” The addition of “modified” distinguishes this type of MTBF from its commonly accepted definition calculated for equipment. For our purposes, MTBF will be defined as a mean time between corrective actions performed on the inspected equipment over the time of analysis. Corrective actions will include:

  • Work orders (WO)—planned work resulting from a work request or inspection.
  • Corrective work orders (CWO)—work performed that was not planned the day before. Both WO and CWO represent breakdowns or failures.
  • Delays (DWO)—events, usually serious, stopping system.

We will calculate the MTBF using the following formula:

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where:
MTBF = modified Mean Time Between Failures (days)
En = number of pieces of equipment in analyzed inspection
Ta = period analyzed (days)
Nb = number of corrective actions per period analyzed

Nb is calculated by adding all WO, CWO and DWO for equipment under analysis for the analyzed period. A CMMS will be the source of this information. Table II (created for illustrative purposes only), demonstrates this process:

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Based on an inspection of 30 unique pieces of equipment, and given the data in Table I, MTBF would be calculated as:

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where:
30 = number of pieces of equipment inspected during inspection
365 = number of days in a year
4 = number of years analyzed
801 = number of corrective actions performed during period analyzed

Hence:

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Calculation of optimum frequency interval
As noted, to calculate the optimum inspection interval, the previously calculated MTBF and the required equipment reliability level are needed. The formula for the failure-finding interval (FFI) is as follows:

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where:
FFI = failure-finding interval in days
A = required system availability level in %
MTBF = system Mean Time Between Failures as calculated above

Another way to find the optimum inspection interval is to use Table III—which shows the relationship between desired system availability, system MTBF and FFI. In training personnel and applying this process in real life, the table may prove more convenient than the mathematical formula used to create it. This data has the potential to demonstrate to management that to achieve higher levels of system availability, either MTBF needs to be increased or FFI decreased.

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Table III. Relationship Between Desired System Availability, MTBF and Failure-Finding Interverals (FFI). (Click to enlarge)

Inspection scope review
Now that we’ve calculated the optimum interval for inspection, it’s important to concentrate on the scope of the inspection. The following questions should be asked:

  • Is the inspection description detailed enough?
  • Do we say what to inspect?
  • Do we say what to look for?
  • Do we describe typical malfunctions?
  • What indicators are recorded?
  • Are the indicators relevant to the process?
  • Are the indicators utilized in any way?
  • What can be removed/changed in the inspection scope?

The goal is to ensure that the right equipment is inspected in the right way—and that only relevant information is recorded. It cannot be assumed that inspectors know what and how to inspect. Equally important is the consistency of the inspections, hence the detailed checklists. This way, inspection effectiveness and efficiency can be assured.

Cost/benefit determination
The inspection-frequency optimization process leads to optimized cost of performing inspections. The benefit can be calculated in the form of saved time and reduced cost. The first step is to calculate time benefit. This can be easily calculated by comparing yearly labor requirements for the original inspection and the modified/optimized one.

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This example can be expanded by attaching dollar values to the time component. If the inspector’s hourly rate is $40, savings in the above example would be as follows:

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This value may appear small, but it reflects the analysis of only a small area. There is also the possible added benefit of applying freed-up resources to perform other tasks.

Granted, there could be cases when actual inspection frequency increases (which would increase cost). But by performing the optimization exercise, it’s possible to prove to management why the inspections are important and why a particular frequency is needed. And everything is based on data—not a “gut feel” or preference for a long-followed procedure. Thus, maintenance personnel can gain credibility in the eyes of management and talk the same language: business language.

Conclusion
Assigning and optimizing inspection intervals (whether for visual inspections or condition-monitoring routes) need not be difficult. By employing simple, innovative techniques, the task can be performed by most maintenance personnel. Furthermore, these techniques will allow for quick adjustments should business requirements change. All of this can be accomplished without expensive and difficult-to-use software. The end result will be business-based, optimized inspections and condition-monitoring routes. MT


As principal consultant for Siemens Industry, Inc., Kris Goly is responsible for the development and implementation of business-based asset-management and improvement programs across a range of industries. He has 30 years of experience in this field and is a Certified Maintenance and Reliability Professional (CMRP). E-mail: kris.goly@siemens.com.

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