This article looks at sophisticated new metrics and methods to unlock the hidden maintenance workforce in your plant.
The Maintenance Productivity Factor (MPF) was created by Daryl Mather in 2002 and is used widely in productivity audits, shutdown efficiency reviews and developing ongoing plans to improve efficiency. For information on Reliability Success services in this area please send an email.
Increasing challenges for maintainers
After decades of evolving in virtual isolation, physical asset management now attracts interest from corporate management, institutions, regulators and government bodies.
Asset managers feel the impact of this attention in two areas:
- First, in the increasingly sophisticated expectations. High confidence, defensible budgetary submissions to regulators, accurate whole-of-life cost forecasts for shareholders, and confidently managing asset risk to tolerable levels.
- Second, in the increased level of pressure to increase the return on capital through traditional areas of efficiency and cost savings. (Labor and materials)
On the other hand it has also created a significant issue for asset managers.
After decades of cutting costs using traditional methods such as reducing staffing and inventory levels, maintainers find further reductions in this area are either limited or nearly impossible.
In the rush to embrace reliability methodologies, it is often forgotten that reliability initiatives are not effective unless they are implemented correctly and executed efficiently.
For example; a routine predictive maintenance task, generated from an RCM study may call for 3 monthly, say, vibration analyses of the bearings of critical motors. (Such as the ventilation fan motors in incineration plants)
In defining this strategy the approach used follows the criteria set within the Maintenance Algorithm.
However, if we do not perform the predictive task at the desired time, then we run the risk of missing the warning sign that it is designed to detect.
Once or twice is not an issue generally speaking, but when a task such as this is routinely performed late, deferred or not carried out at all, then we are facing the very real threat of having an unpredicted failure on what we have defined as a critical asset.
So how can asset managers improve their efficiency even more than they have over the past couple of decades? And do so without causing the performance of the assets themselves to suffer?
Many companies are implementing methods such as LEAN as it was originally applied in manufacturing environments, often with good results. This brings with it measures such as Overall Equipment Effectiveness (O.E.E) for uncovering the hidden plant.
In other words, a metric to reveal areas of asset underperformance we can target to increase throughput, availability or yield in some fashion.
OEE and the Hidden Plant are concepts that are widely acknowledged and understood, but what about the Hidden Workforce? That part of the working day that is unusable due to issues of workmanship, poor workforce management, or inherent delays and inefficiencies?
In a workforce that is already feeling the effects of decades of efficiency drives, highlighting additional areas of improvement takes careful study and evaluation.
Revealing the “Hidden Workforce”
The metric for revealing the Hidden Workforce is the Maintenance Productivity Factor (MPF) and its associated metrics.
Maintenance Productivity Factor (MPF) combines efficiency in execution, quality of maintenance work and organisational effectiveness to measure the productivity of the maintenance workforce.
The formula is:
Estimated Work Time (ET) x Quality of Work (QW) x Organizational Effectiveness (OE)
Hours Worked (Paid)
Estimated Work Time (ET)
As the name suggests this relates to the estimated labor hours for a task. In order for MPF to be a useful measure the work and labor estimates contained in your job card system or in your CMMS need to be relatively accurate. Often when companies embark on a productivity measurement exercise this is one of the first areas they focus on.
What this almost immediately uncovers is that work estimates are, in many cases, woefully inaccurate or even non existant. (Yet the organisation still tells itself it prizes planning)
Work order estimates, their value in planning, and their vital role in annual and multi-annual budgeting should never be overlooked, yet this is a key area where many maintenance companies fail regularly.
The important thing here is to start with the top twenty (say) repetitive work orders, develop broad outlines, and improve these over time.
Even if your work estimates are not to a high level initially, MPF will still provide a productivity indication based on what you think it should take.
Any effort to calculate workforce productivity without accurate or passable estimates is bound for failure...
Quality of Work (QW)
Rework is a very emotive term that sometimes leads improvement initiatives into the blind alley of ego, recrimination and accusations.
THis doesn't have to be this way. Instead of Rework (which is a term i Used to use a lot) I now use the term "return to service" faults. Because this is what we are referring to really, and they cover a far broader spectrum than just "the tradesman made a mistake".
Return to service covers areas such as:
- Start up failures of the machinery, a quality of work issue either in terms of maintenance not done, failures not anticipated or work done incorrectly.
- Start up failures due to failures in the maintenance effort. Unfinished tasks, missed steps and an entire range of errors in performing maintenance.
- Later failures caused due to poor maintenance such as misalignment of bearings during fitting etcetera.
This is notoriously difficult to get agreement on, although the use of "return to service" as a definition helps a lot.
Later failures can only be included within productivity calculations when there has been a comprehensive audit performed of any failures, including a detailed investigation of the causes of any human errors. (E.g. Slips / lapses / Violations and Mistakes)
Organizational Effectiveness (OE)
Here we look at how effective the organization is at allowing people to do the work assigned to them. Within MPF this is usually defined as hours of delay time. Many companies first come into contact with how effective their corporation is once they start with basic initiatives such as Capacity Scheduling for example.
For example, waiting for parts, equipment availability, labor resources, attending meetings, permits and a range of other day-to-day issues that you would be aware of where you work.
All of these represent the lost time that occurs each and every working hour, a large part of the hidden workforce that we are trying to uncover.
Most work order systems available today have the ability to record work delay codes in some form or other, if you are not doing so now then this will need to be built into the work processes for the maintenance teams.
Hours Worked
Even with all of the technology available to us today many companies have great difficulty in accurately tracking hours worked per task.
In most cases this is just an effort in applying easily used systems and processes in a disciplined manner.
However, sometimes there are road blocks in terms of changes to information systems in particular.
Although surprising in the 21st century, there are still some enterprise level systems that cost an army of consultants to make minor configuration changes. (See The ERP Game)
A Worked Example
Let’s take for example a strip out and repair of the drive end bearing on a centrifugal pump within a process plant.
The pump is a redundant item so there is no direct impact on production, hence any issues related to productivity are normally lost as there is no lost production opportunity (LPO).
However, the time to repair the pump and return it to service is 8 hours, 2 hours more than the 6 hours on the accurate work estimate.
During that period there was a need to remove the new bearing because it was installed in a way that had done damage to the outer races.
This took an estimate of 90 minutes, which is roughly 20% of the total time allotted.
In the course of carrying out the work there were also a number of delays in finding the correct parts, the correct tools, and then in finding some bench space to work in due to other large scale projects currently underway.
This also took an estimated 90 minutes of the total time. (another 20% of the overall 8 hours)
In this case our formula would be:
6 x 0.8 x 0.8
8
8
Where:
- Estimated Work Time was 6 hours
- Quality of Work (Return to Service) = 8 hours minus the 90 minutes that were required for rework purposes. In percentage terms this is 80% good quality work during that time. (or 0.8)
- Organisational Effectiveness is given as being a total of 8 hours minus the 90 minutes that were spent looking for parts, space and tools. In percentage terms 80% of working time was not impacted by these delays. (or 0.8)
- Hours worked (Hours Paid) is given as being 8 hours.
6 x 0.8 x 0.8
8
8
3.84
8
Is equal to a MPF of 0.48 or 48%
This means that only 48% of the hours paid are actually useful in productive work.
To take this a step further we can use the metric Maintenance Productivity Cost (MPC), which is:
Rate of Pay
MPF
Or
$35 (say)
0.48
So, the actual cost for every productive hour of work for this task in this example is $72.96.
A startling figure when the company is only paying $35 per hour for labor.
This is where part of the concept of the hidden workforce comes from, there are hidden costs associated with poor productivity that we know about, but often we are not able to accurately measure.
Most companies do not need to go into the actual costs of productivity (MPC) and will be able to make use of the Maintenance Productivity Factor figure as a useful guide to their levels of efficiency.
Yet once a company does make the relationship between productivity and cost it often provides a pretty powerful indication of the level of hidden productivity that exists in their plants.
We like to think we can eliminate inefficiency in one blow, yet it is cyclical in nature and is related to the ebbs and flows of the workforce itself.
As older more experienced people leave the organization, newer and less experienced people enter. The probability of rework, which is normally limited anyway, begins to rise, as does the probability of work delays due to unfamiliarity.
It is sometimes the case that MPF is equal to or greater than 1. In the cases where the author has observed this either the workforce is working at maximum levels of efficiency in an effective work environment, or the work estimates are too conservative.
Using either MPF or MPC we can establish pretty quickly how productive our maintenance efforts are for each task that we do.
But if we want to find out how productive the workforce is as a whole then we need to look at not only the time when we are doing work, but also at the times when we are not doing work.
Workforce Productivity - A quick measure
For example, due to delays in gaining access to equipment, or due to lack of planning resources, a workforce may not be fully utilized during a given period. In these cases the metric Workforce Productivity Factor (WPF) is useful and is calculated below.
Paid Hours
Utilized Hours
A problem with this is honesty in recording, and again we come against the problem of cultural issues in measuring maintenance performance. To do this we need to again focus on accurate records of maintenance delays.
Here there is a strong chance that even when the workload is light or non-existent, people will book hours to other tasks, confusing the picture for all of these areas.
This is particularly true when we are measuring the productivity of a contract workforce.
In this case we will say that for the period we had a total paid time of 5000 hours, and total utilised hours of 3000 hours. Giving us a Workforce productivity factor of 1.6, meaning we have paid 1.6 times what we utilized.
The final indicator, at a workforce level, is Workforce Productivity Cost (WPC) as detailed below:
MPC x WPF
Or
$72.96 x 1.6 = $116.74
This means that at a workforce level we are paying $116.74 per productive hour worked, even though the figure on the accounts is a mere $35 / hour.
Summary
The Maintenance Productivity Factor family of metrics represents a sophisticated index to drill down into already challenged maintenance departments to find areas where even greater efficiencies can be made. These indicators are used regularly in asset intensive industries in applications such as:
- Driving out poor working practices in the quality of maintenance work; such as identifying frequently occurring human errors and other intangible causes of rework.
- Comparing outsource service providers and internal labor on a like for like basis. (Rather than on hourly costs alone)
- Targeting and improving delay reduction initiatives
- Highlighting poor organisational practices that are leading to poor workforce productivity.
It goes without saying that the MPF goes hand in hand with a rigorous data based root cause analysis technique. So that as problems are observed they can be addressed immediately.
As organizations become more familiar with using MPF figures and formulas it can also start to be applied to various sections within the maintenance workload such as determining the productivity of routine work, or of corrective or reactive work and so on.
However, even though all are useful, a company need only start with the initial Maintenance Productivity Factor to begin to highlight the amount of productivity they are getting out of the hours they pay for.
Bibliography
- The Maintenance Scorecard, Daryl Mather, Industrial press ISBN 0831131810
- Asset Maintenance Management, Dr Alan Wilson, ISBN 0831131535
- Unleashing the power of O.E.E, Robert Hansen, MT-Online article