MAINTAINANCE LABOR HOUR ANALYSIS: A CASE STUDY OF SCHEDULE COMPLIANCE USAGE. BY FORTUNATUS UDEGBUE M.Sc. MBA. PMP. CMRP


 Grant Anderson
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1 Introduction The objective of this paper is to draw our attention to the importance of maintenance labor hour analysis and the usage of one of the associated metrics Maintenance schedule compliance. This becomes very important to ensure that we measure what we need to improve our business and have a sound basis to bench mark this metric. According to R. Keith Mobley 1, surveys of maintenance management effectiveness indicate that about 33%of every dollar of all Maintenance Cost is wasted as a result of unnecessary or improperly carried out Maintenance. Keith further explained that more than 200 billion USD each year is spent on plant and facility maintenance, meaning that about 60 billion USD is wasted as a result of poor maintenance management. According to Dr. Alan Wilson 2 conservatively 42% of total maintenance cost is the maintenance labor cost. This having been said, then it is crystal clear that optimization of the maintenance labor will significantly reduce the total maintenance cost. Combining Keith s and Wilson s asserting leads to draw a conclusion that yearly at least 80 billion USD is spent on maintenance labor. My experience has shown that maintenance labor analysis has failed to yield the desired results because of several key factors, two out of these are as stated below: i.) ii.) Lack of Management s focus on labor utilization analysis and effective stewardship. Unavailability of Maintenance labor reporting guidance document The fact of life is that the people give attention to whatever management has clearly defined as very important. In most computer maintenance management system where the work order system is used to record maintenance history, recording of the man hours used for the work is weakly emphasized if ever it is emphasized. The alternative way to determine maintenance labor hour is the use of work studies which is very expensive compared to continuously reporting, auditing and analyzing labor hours through the computer maintenance management system. Research also show that organizations rarely carryout work study, so maintenance labor forecasting is mostly based on guess work 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. 1 An Introduction to Predictive Maintenance, 2 nd Edition, 2002 by Keith R. Mobley; Page 1. Published by Elsevier Science (USA). ISBN Asset Maintenance Management. 1 st Edition, 2002 by Dr. Alan Wilson: Page 82. Industrial Press Inc. 200 Madison Avenue New York. NY USA.
2 E.g. A technician who starts off his day by 7.00 am spends one hour to obtain work permit, two hour to gather tools and materials, waits one hour for isolation and access of equipment to be worked on, one hour travel to and fro equipment location, three hours on actual maintenance work on equipment, one hour for house keeping and thirty minutes on maintenance history update in the CMMS. With respect to the example above some technicians in the same plant could report the labor time as three hours the actual time spent on maintenance work, another technician could report six hours i.e. the sum of time to gather tools and materials, time to isolate and access equipment and the actual time spent on maintenance work, another technician could even decide to report the entire time, 7.00 am to when the work was reported in the CMMS i.e. eight hours thirty minutes. The challenge from the example above is that the analysis of the labor reported in the CMMS becomes impossible because the maintenance analyst does not know exactly what time is being reported, bench marking of maintenance labor effectiveness and efficiency will be impossible as the analyst is not able to compare appletoapple in other to leverage good practices or eliminate bad actors so as to improve labor hours. Maintenance labor reporting guidance document properly rolled out and entrenched in the maintenance organization will simply solve the problem and make maintenance labor reported data standardized in the organization as such easier to analyze. Maintenance Labor Bench Mark Determination using a CMMS If we must improve Maintenance labor utilization, then we must determine a bench mark for it. For this to happen we must establish a guidance document for reporting labor utilization and ensure that everyone is trained to use it and the organization is periodically audited to ensure that there is total compliance. My recommendation is that the guidance document includes (i) a separate column for reporting actual timeontool (wrench time). (ii) time spent on travel to and fro equipment location (iii) number of maintenance executioners (iv) Skill level of Maintenance executioners (v) Time spent on work permit (vi) Time spent on Isolation and access of equipment (vii) Time spent on post work execution house keeping (viii) 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 find out the category which is the bad actor or best practice so as to leverage on them for improvement efforts. Without this categorization the
3 bad actors and best practices are masked and we loose opportunity for improvement. The hypothetical cases below are examples to enable us to clarify further. HYPOTHETICAL CASE i. A technician who starts off his day by 7.00 am spends 1 hour to obtain work permit, 2 hours to gather tools and materials, waits 1 hour for isolation and access of equipment to be worked on, 1 hour travel to and fro equipment location, 3 hours on actual maintenance work on equipment, 1 hour for house keeping and 30 minutes on maintenance history update in the CMMS ii. A technician who starts off his day by 7.00 am spends 10 minutes to obtain work permit, 10 minutes to gather tools and materials, no waits for isolation and access of equipment to be worked on, 30 minutes travel to and fro equipment location, 6 hours on actual maintenance work on equipment, 10 minutes for house keeping and 30 minutes on maintenance history update in the CMMS. If for cases one and two the total labor were reported (9 hours thirty minutes) respectively, the assumption will be that both have the same labor hour! while this is true on the surface, we would have lost the following opportunities because the labor reporting was not categorized: i. Learn from case i the strategy they applied to achieve low timeontool (3 hours) and apply it to improve the extremely high timeontool (6 hours) for case ii compared to case i ii. Learn from case ii the strategy they applied to achieve low time on: a. work permit b. gathering of tools and materials c. waits for isolation and access of equipment d. post job completion house keeping, all totaling (1 hour 30 minutes.) and apply them to improve the extremely high time on: a. work permit b. gathering of tools and materials c. waits for isolation and access of equipment d. post job completion house keeping, all totaling (6 hours 30 minutes.) for case i compared to case ii. Maintenance Labor Hours Related Metrics i. Work Order Schedule Compliance: Most organization calculate this metric as the ratio of the count of completed work order to the count of the total scheduled work order. The draw back here is that since the dimension of this metric is work order count and not labor hour we are not being effective because when we speak of schedule our primary focus should be time. Schedule compliance should be used to measure how well work order labor hour adheres to the plan thus ensuring efficient use of labor hours. For efficient labor resource deployment actual execution labor
4 hour must not be significantly different from the planned labor hour else there will be under loading or over loading of the labor resources. Over loading or under loading is not desirable, the target is a balanced loading of labor resource. This can be determined from actual labor hour data which is correctly reported in the CMMS continuously being analyzed with the forecasted plan until there is a consistent indication of no significant difference between actual execution labor hour and the planned labor hour. Another advantage of this achievement is that labor hour forecast will be done with very high level of confidence thus making long lead planning realistic. Below are two cases to demonstrate a typical analysis of the effect of using work order count for schedule compliance and analyzing actual labor hour and planned labor hour to determine the existence of significant differences between them. a. E.g. Plant A. have ten work orders as follows work order 0001 to 0006 contains 8 hours of work each, work orders contains 16 hours of work each and work order contains 32 hours of works. If we schedule all ten work orders in a week, and only work orders 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 i.e. ((48/144)*100). The schedule compliant calculation in terms of actual execution work hours assumes that there is no significant difference between planned work hour and actual work hour and should be the goal of maintenance organizations. The Work hour planned into the work order system must be driven by a generally agreed guidance of what the organization agrees to mean work hour before we can get any meaningful improvement using the Schedule compliance metric else we will be wasting effort. b. The Planned work hour must not be significantly different from the actual execution work hour else our work order schedule compliance will not be considered effective, thus becoming in itself a waste of resources. If the basis for determining the planned work hour is different from those for determining actual work hour, Schedule compliance will be insignificant since you can not get any quotient from two oranges divided by two mangoes. Below is an illustration on how to determine the statistical significance of Planned Work hour and Actual work hour: The data in the table below shows planned work hour and actual work hour as built in and reported respectively in a work order. CAVEAT: 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
5 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 8 hours for a job and the actual execution time also becomes 8 hours!!! When you see this, it is a sign there is a foul play and you need to engage your maintenance executioners so as to eliminate reoccurrence. Training and oneonone mentoring is an approach that had worked for me in the past. S/N WORK ORDER NO PLANNED WORK HOUR REPORTED ACTUAL WORK HOUR TABLE 1: Table Showing Work order planned work hour and reported actual work hour To determine if the planned work hour is significantly the same as the actual work hour reported, we will have to carry out an hypothesis test using the paired t distribution. Hypothesis: H 0 : µ P = µ A S/N WORK ORDER NO PLANNED WORK HOUR REPORTED ACTUAL WORK HOUR H 1 : µ P µ A DIFFERENCE
6 TABLE 2: Table Showing Difference between planned work hour and reported actual work hour Mean of the difference =[( (Difference))/10] = (1) Standard Deviation of Difference = [ (((Difference)(Mean of Difference)) 2 / (n1)) 0.5 ] = (2) Test Statistics t =((Mean of Difference)*(n) 0.5 )/ (Standard Deviation of Difference) = (3) n is number of work orders = (4) Critical value of t, at 95% confidence interval for a two tailed test with 9 degrees of freedom implies t = (5) The value t was inferred from the Table of t distribution 3 herein referenced. Since the value of t < we fail to reject the null hypothesis H 0 : µ P = µ A CONCLUSION We therefore accept that the planned work hour is not significantly different from the actual work hour meaning that the schedule compliance metrics is a true indication of a balance scheduled that has neither been under loaded or over loaded. The result has also shown that the estimated planned labor hour can be confidently used for realistic labor resource forecasting. 3 Table of t distribution:
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