Fight maintenance waste with labor analysis

Understanding the importance of labor-hour analysis and the usage of associated metrics.

By Fortunatus Udegbue, FOGAEC Electrophysics Technologies

In brief:

  • About one-third of every dollar of maintenance costs is wasted as a result of unnecessary or improperly carried-out maintenance.
  • Most managers would agree that they can’t control and improve things they can’t measure, and most industrial managers have not invested in the tools to measure and manage maintenance labor.
  • Documented guidance for reporting maintenance labor, if it’s properly rolled out and entrenched in the maintenance organization, will solve the problem and standardize maintenance labor data that’s reported, making it easier to analyze.

About one-third of every dollar of maintenance costs is wasted as a result of unnecessary or improperly carried-out maintenance, according to R. Keith Mobley, principal, subject matter expert, Life Cycle Engineering (www.lce.com). Since more than $200 billion is spent annually on plant and facility maintenance, says Mobley, poor maintenance management is something like a $66 billion problem.

Conservatively, 42% of total maintenance cost is maintenance labor cost, according to Dr. Alan Wilson, author of “Asset Maintenance Management.” Moreover, direction of maintenance labor has a huge impact on the cost of maintenance material. This means that optimization of maintenance labor has the potential to significantly reduce the waste in total maintenance cost. It is certainly a valid place to attack maintenance waste.

A brief look at the maintenance labor management situation in most industries helps us understand this opportunity better. Most managers would agree that they can’t control and improve things they can’t measure, and most industrial managers have not invested in the tools to measure and manage maintenance labor. This is partly because the data processing to do the job hasn’t been available very long and because maintenance cost is typically about 10% of a company’s operating cost. Most managers probably felt they had bigger fish to fry. For maintenance and reliability professionals, though, the realization that we have the opportunity to conquer a $66 billion opportunity is exciting. Below are some observations on where we can begin attacking this issue.

Maintenance labor analysis often fails to yield desired results because of several key factors, two of which are:

  • lack of management focus on labor utilization analysis and effective stewardship
  • lack of documentation of maintenance labor reporting guidance.

Employees give attention to whatever management has clearly defined as very important.

SMRP Conference

Fortunatus Udegbue, CMRP, PMP, CEO of FOGAEC Electrophysics Technologies, and Ricky Smith, CMRT, CMRP, principal reliability advisor at Allied Reliability Group, will present “7 Deadly Sins of Data Centric Maintenance Management” at the Society for Maintenance & Reliability Professionals Annual Conference in Indianapolis on Oct. 15 at 2:45 PM. The presentation will focus on seven major issues that can drive the wrong organizational behavior for managing the maintenance function in any organization. The presentation will show the cost/benefit analysis of maintenance data management, the data that is required for effective maintenance management of any assets, and what data is optional and why. In addition, remediation concepts will be introduced that would put an organization on the right path toward achieving effective and efficient maintenance management using a data-centric maintenance management approach. Learn more about the SMRP Conference.

In most computer maintenance management system (CMMS) software, where the work order system is used to record maintenance history, recording of the man hours used to complete the work is weakly emphasized, if it is emphasized at all. The alternative way to determine maintenance labor hours is the use of work studies, which is very expensive compared to continuously reporting, auditing, and analyzing labor hours through the CMMS. Also, organizations rarely carry out work studies, so maintenance labor forecasting is mostly based on guesswork without any effectively validated data.

In cases where utilized maintenance labor is reported, the reporting is usually not uniform because there is no company-wide guidance on what is the organization’s acceptable standard. Some report wrench time, while others report the entire labor time.

For example, a technician who starts off his day by 7 AM spends one hour to obtain a work permit, takes two hours to gather tools and materials, waits one hour for isolation and access of equipment to be worked on, travels one hour to and from equipment location, performs actual maintenance work on equipment for three hours, uses one hour for housekeeping, and then spends 30 minutes on the maintenance history update in the CMMS.

With respect to the example above, one technician in a plant could report the labor time as “three hours” — the actual time spent on maintenance work; another technician in the same plant could report “six hours” — the sum of time to gather tools and materials, time to isolate and access equipment, and the actual time spent on maintenance work; and another technician could even decide to report the entire nine hours and 30 minutes — from 7 AM to when the work was reported in the CMMS.

The challenge from this example is that analysis of the labor reported in the CMMS becomes impossible because the maintenance analyst doesn’t know exactly what time is being reported. Benchmarking of maintenance labor effectiveness and efficiency will be impossible as the analyst can’t make an apples-to-apples comparison to leverage good practices or eliminate bad actors. Documented guidance for reporting maintenance labor, if it’s properly rolled out and entrenched in the maintenance organization, will solve the problem and standardize maintenance labor data that’s reported, making it easier to analyze.

Maintenance labor benchmark

If we must improve maintenance labor utilization, then we must determine a benchmark for it. For this to happen we must establish a guidance document for reporting labor utilization and ensure everyone is trained to use it and the organization is periodically audited to ensure total compliance. The guidance document should include:

  • a separate column for reporting actual time-on-tool (wrench time)
  • time spent on travel to and from equipment location
  • number of maintenance executioners
  • skill level of maintenance executioners
  • time spent on work permit
  • time spent on isolation and access of equipment
  • time spent on post-work execution housekeeping
  • time spent on post-work execution documentation.

The content of the guidance document should be designed into the work order system in the CMMS for reporting during the work completion documentation. An audit process should be continuously carried out to ensure compliance. The categorization of labor time from work execution reporting will easily enable the analyst to find out the category that is the bad actor or best practice to leverage improvement efforts. Without this categorization the bad actors and best practices are masked, and we lose the opportunity for improvement.

Hypothetical reporting

Example 1: A technician who starts his day by 7 AM spends one hour to obtain the work permit, two hours to gather tools and materials, one hour waiting for isolation and access of equipment to be worked on, one hour traveling to and from the equipment location, three hours on actual maintenance work on equipment, one hour for housekeeping, and 30 minutes on maintenance history update in the CMMS.

Example 2: A technician who starts off his day by 7 AM spends 10 minutes to obtain the work permit, 10 minutes to gather tools and materials, no time waiting for isolation and access of equipment to be worked on, 30 minutes traveling to and from the equipment location, six hours on actual maintenance work on equipment, 10 minutes for housekeeping, and 30 minutes on maintenance history update in the CMMS.

The total labor for Example 1 was reported as nine hours, 30 minutes; and the total labor for Example 2 was seven hours, 30 minutes. The assumption then is that both have the same number of labor hours. While this is true on the surface, we would have lost the following opportunities because the labor reporting was not categorized:

  • Learn from Example 1 the strategy they applied to achieve low time-on-tool (three hours) and apply it to improve the extremely high time-on-tool (six hours) for Example 2.
  • Learn from Example 2 the strategy they applied to achieve low time on tasks other than the actual maintenance work (one hour, 30 minutes, total) and apply them to improve the extremely high time on the actual maintenance work.

Work order schedule compliance

Most organizations calculate this metric as the ratio of completed work orders to total scheduled work orders. The drawback is, since the dimension of this metric is work order count, and not labor hours, we aren’t being effective. When we speak of schedule, our primary focus should be time. Schedule compliance should be used to measure how well work-order labor hours adhere to the plan, thus ensuring efficient use of labor hours. For efficient labor resource deployment, actual execution labor hours must not be significantly different from the planned labor hours, or else there will be underloading or overloading of the labor resources. The target is a balanced loading of labor resources. This can be determined from actual labor-hour data, which is correctly reported in the CMMS and continuously being analyzed with the forecasted plan until there is a consistent indication of no significant difference between actual execution labor hours and the planned labor hours. Another advantage is the labor-hour forecast will be done with a very high level of confidence, thus making long lead planning realistic. The case below demonstrates a typical analysis of the effect of using work-order count for schedule compliance and analyzing actual labor hours and planned labor hours to determine the existence of significant differences between them.

Fortunatus UdegbueFortunatus Udegbue is CEO at FOGAEC Electrophysics Technologies, a Nigerian asset-management consulting company. Contact him at f.udegbue@fogaecelectrophysics.com.

Plant A has 10 work orders as follows: work orders 0001 to 0006 contain 8 hours of work each, work orders 0007 and 0008 contain 16 hours of work each and work orders 0009 and 0010 contain 32 hours of work each. If we schedule all 10 work orders in a week, and only work orders 0001–0006 are completed, following the traditional work order schedule compliance , we will be 60% compliant, while in terms of actual execution work hours, we are 33% compliant (48 hours/144 hours). The schedule-compliant calculation in terms of actual execution work hours assumes there is no significant difference between planned work hours and actual work hours and should be the goal of maintenance organizations. The work hour planned into the work-order system must be driven by a generally agreed-upon definition of what the organization means to be a work hour before we can get any meaningful improvement by using the schedule-compliance metric.

The planned work hour must not be significantly different from the actual execution work hour, or else our work order schedule compliance won’t be considered effective, thus becoming a waste of resources. If the basis for determining the planned work hour is different from those for determining actual work hours, schedule compliance will be insignificant since you can’t get any quotient from two oranges divided by two mangoes. If the number of planned man hours is not significantly the same as the number of actual man hours, then the use of planned man hours to determine capacity leveling will be useless.

Experience has shown that most maintenance executioners when reporting actual execution labor-hours simply replicate the planned labor-hours in the actual execution labor-hour record in the work order system. You need to watch out for this bad practice when you are analyzing data. If the data is too good to be true, ask questions. It is almost impossible for you to plan eight hours for a job and the actual execution time also becomes eight hours. When you see this, it’s a sign there’s foul play and you need to engage your maintenance executioners so as to eliminate reccurrence. Training and one-on-one mentoring is good approach.