The Maintenance Productivity Factor

After decades of cutting costs through traditional methods maintainers are finding additional efficiency gains to be either limited or physically impossible. This article looks at sophisticated new metrics and methods to unlock the hidden maintenance workforce in your plant. Increasing challenges for maintainers After decades of evolving in virtual isolation, the discipline of physical asset management is now attracting interest from corporate management, institutions, regulators and government bodies. Asset managers are feeling the impact of this attention in two areas:

First in the increasingly sophisticated requirements that they generate. For example; the requirement for detailed and 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 one hand this has invigorated the interest globally in techniques and issues related to reliability, which is a welcome change to what maintainers have been used to in the recent past. 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 often find that further reductions in this area are either limited, or nearly impossible. And 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 is logical and follows the criteria set for selection of a predictive maintenance task. 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. Similar can be said for all of the different types of routine and proactive tasks generated in an RCM analysis. 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, and often with good results. This has also bought with it measures such as Overall Equipment Effectiveness (O.E.E) for monitoring plant and equipment performance. However, to adapt LEAN into the world of maintenance management requires a modified approach because the operating characteristics are fundamentally different from those of production. There are many reasons for this but within this article we will be focusing on the way that work is performed. Unlike their production counterparts, maintainers often work on a wide range of tasks. Some are routine, some are regular but infrequent, and sometimes they are confronted with tasks they have never come across before. Each day in the maintenance workforce is a different day that could bring with it any combination of these tasks, often under the pressure of lost profits, and requiring a combination of skills, technical knowledge, and information resources. If somebody set out to design an environment that provided continual and sometimes sudden logistical challenges they would be hard pressed to beat maintenance in asset-intensive industries. Revealing the “Hidden Workforce” If maintainers are going to effectively meet all of the challenges outlined above, then they are going to need a measure that unlocks the “hidden workforce” in the way that Overall Equipment Effectiveness unlocks the “hidden plant”, revealing a resource that can be tapped into to reduce the unit costs of maintenance. Such a measure can be found in the Maintenance Productivity Factor (MPF) family of metrics. Maintenance Productivity Factor (MPF) is a metric that combines efficiency in execution, quality of maintenance work and the impact of organizational effectiveness to measure the productivity of the maintenance workforce. The formula is:

Estimated Work Time x Quality of Work x Organizational Effectiveness 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 where they focus their attention. A tip here would be to make sure that your work estimates are not the best possible time, nor the average time, but the time the task should take if it is properly planned and scheduled and the right skills are available to do the task. 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. Quality of Work (QW) This refers to the level of rework required after maintenance is carried out; these are the losses due to things intangibles such as training and skill levels, knowledge levels, workforce morale levels, poor components and a range of other issues. This is notoriously difficult to get agreement on and then to measure. However, once there is a clear definition within the company, and then a clear and easily followed process to record this, it is a useful statistic for many additional measures of maintenance performance. It is in the recording of issues such as rework that we confront head-on the major stumbling block to good measurement systems, that of culture and corporate attitudes. For example, if somebody is aware that recording rework is going to get them clobbered, well, what would you do? Recording it is sometimes as simple as a code on the work order, or a link to a past work order indicating a connection. Either way, once it is accurately recorded it can be managed. 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 today with all of the technology available to us 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. 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 industry plant. The pump is a redundant item so there is no direct impact on production. 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 once as it had been 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. In this case our formula would be:

6 x 0.8 x 0.8 8 Where:

Estimated Work Time is given as being 6 hours
Quality of Work is a total of 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)
Organizational 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 is given as being 8 hours.

The result:

6 x 0.8 x 0.8 8 Is equal to a MPF of 0.48 or 48% This means that only 48% of the hours that are paid for are productive. To take this a step further we can use the metric Maintenance Productivity Cost (MPC), which is:

Rate of Pay MPF

$35 (say) 0.48 So, the actual cost for every productive hour of work 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. Although we like to think we can eliminate inefficiency in one blow, the fact remains that 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. 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. 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 utilized 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

$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 organizational 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 Performance Factor to begin to highlight the amount of productivity they are getting out of the hours they pay for. If you would like to receive a short article on how to implement the Maintenance Productivity Factor (MPF) in your plant please send me an email to [email protected] quoting this article. Bibliography