Over the last 10 years, there has been a paradigm shift that has seen maintenance become almost synonymous with achieving reliability. To truly understand this shift, it is important to know the definitions of a couple of common terms:
Maintenance. The dictionary defines maintain (verb) as follows: to hold, preserve or carry on in any state; to sustain, to keep up; to support, to provide with means of living; to keep order, proper condition or repair; to assert, to affirm, to support by reasoning, argument, etc.
Reliability. In its mechanical sense, reliability can be defined as the probability of a device to perform its functions adequately for the period of time intended under the operating conditions encountered.
The role of the maintenance professional is to maintain equipment at peak operating reliability in the most cost-effective manner. Equipment manufacturers make machines for really only one purpose - to make money. Likewise, customers generally buy them for that same reason.
The cost of purchasing a piece of equipment tends to be relatively fixed. The actual cost of operating the equipment can be highly variable. The main aim of the maintenance department is to ensure that operating costs do not exceed income. This is achieved by minimizing downtime and repair costs.
Maximum output is now required from minimum input and this has resulted in reliability being optimized rather than maximized. This must be a strategic and discriminating process that considers both the cost of reliability and the consequences of unreliability. Companies that have achieved this optimization have invested heavily in people and equipment for effective asset management. Fundamental to this is education throughout the whole company.
As technology has advanced, machines have become more complex and expensive to build. Maintenance engineering has been required to develop along with the technology. From being nonexistent, maintenance has developed from a passive to an active philosophy. Proactive maintenance techniques now give some organizations their only edge over their competitors.
Oil analysis is both predictive and proactive and is probably the most cost-effective maintenance technique available. It is not, however a panacea. It is merely a tool, an effective one, but still only a tool. To carry the analogy further, a tool needs a tool box. It is pointless to implement an oil analysis program if there is not a mature overall maintenance program already in place.
Evolution of Maintenance Philosophies
Maintenance philosophies have evolved over the past few years. The following describes how oil analysis fits into today’s overall maintenance picture.
Breakdown Maintenance
Breakdown maintenance involves fixing things if, and only if, they break. This was common enough 50 years ago but with the current cost of equipment, labor and downtime it is no longer a commonly viable option. It is cheap to implement but the consequences are dramatic. It should be pointed out, however, that all maintenance philosophies have their proper place.
Each piece of equipment should be treated on its own merits and the most cost-effective (optimal) combination of philosophies and techniques should be employed. The factory manager does not schedule light bulb replacement in the plant on a calendar basis. Rather, the bulbs are replaced when they blow - this is breakdown maintenance and is the optimal strategy in this case.
Preventive Maintenance
The preventive maintenance philosophy evolved because it was soon realized that breakdown maintenance was not the best way to look after most pieces of machinery in industry. Preventive maintenance involves the service, overhaul and replacement of plant items based on a scheduled time interval such as operating hours or kilometers, or on a calendar basis.
This was certainly a step in the right direction but problems arise because the “maintenance interval” is based on an average. This means a percentage of machines will fail before receiving attention and a percentage of normally functioning units will be disturbed. There is a lot to be said for the maxim “if it ain’t broke, don’t fix it.”
Predictive Maintenance
Predictive maintenance evolved from preventive maintenance for the reasons outlined in the previous paragraph. This is also where condition-monitoring techniques come into their own because this philosophy involves using as many nondestructive testing methods as is necessary to determine the health of a piece of equipment. Maintenance decisions are then made based on these results. This practice originated in the aircraft industry during the early 1960s and was known as maintenance “on condition.”
Proactive Maintenance
Proactive maintenance naturally grows out of the other three philosophies. It is concerned with the analysis of all maintenance and condition-monitoring techniques to determine what causes failures and how these situations can be prevented. Root cause failure analysis is central to proactive maintenance and it is certainly the way of the future if organizations want to become world-class players.
Oil analysis has one foot firmly rooted in the predictive camp, the other in the proactive camp. Proactive maintenance delivers value where the failure rate can be effectively reduced and is most productive when root causes of failure can be easily and inexpensively controlled and improved. Predictive maintenance delivers value when early warning systems can substantively impact the severity of the failure event.
The benefits of proactive maintenance are a reduction in failure rates and a reduction in operating costs. The benefits of predictive maintenance are a reduction in the severity of failure and an increase in planned activities.
Oil Analysis Cost Savings and How to Calculate Them
Once an effective oil analysis program is up and running, one very important question needs to be answered: How is the oil analysis program affecting the bottom line? Up-front money has been spent and there are ongoing costs to run the program and pay for the service, so what is the maintenance department getting in return? What is the return on investment (ROI)?
It is a failure in the engineering, scientific and technological communities that scientists, engineers and technologists do not speak the same language as the financial gurus. Unfortunately, these are the people who control the purse strings and they are the ones who will have to approve the up-front and running costs for the oil analysis program.
The fact remains, however, that there is always money available for investments that produce a healthy profit. Failure to obtain such funding more often lies in the presentation. So, it’s important to speak the one truly universal language…money! It is a lot easier for an engineer to talk dollars and cents than it is for the financial manager to talk technically. Scientists still have to balance check books, however, accountants do not need to know how to change the CV joints on their cars.
Return On Investment from Oil Analysis
Unfortunately, finding a ready formula for calculating Return On Investment (ROI) is surprisingly difficult. There appears to be a dearth of information on the Internet and even the condition-monitoring wizards have little to say on the matter. The reason for this is that, like so many other aspects of oil analysis, each case or organization has to be treated on its own merits.
Even though a handy formula or computer program would be a tremendous boon to many companies, an informal survey by Wearcheck of its various customers who perform such calculations revealed that each company has a different way of calculating ROI. Following are some guidelines and ideas developed to help companies formulate their own methods for calculating the ROI on their oil analysis program.
There are three main costs that need to be considered: labor, parts and downtime. The first two are easy to determine, the third is almost impossible to calculate. There are, however, secondary costs involved as well. These costs include lubricants, energy consumption, quality, production and risk-based costs.
In this exercise, labor and parts are considered, and an attempt is made to put a realistic figure to downtime costs. As is so often the case, it is easier to do this for a fixed plant because many of the variables that apply to mobile equipment do not usually apply to machinery that does not move around. The fixed plant scenario will be explored first and then it will be applied to buses, trucks and bulldozers.
As has already been mentioned, there are two maintenance philosophies associated with oil analysis: proactive and predictive. Proactive maintenance attempts to control the forcing function or root cause that leads to failure and can be used to preemptively reduce the failure rate of a component or system. Quantifying this simply amounts to dividing the current failure costs by the life extension factor.
Proactive maintenance makes money because it reduces the number of failures over a given time period. Loss of production or downtime costs are easier to calculate for fixed plants. These costs are the retail value of the total products not produced less the operating costs for the same period of time.
For example, if it has been determined that reducing the amount of dirt in a conveyor gearbox by a certain factor will double the lifetime of a particular bearing, then every dollar spent on that particular type of failure per year is reduced by 50 percent (Table 1).
This is a highly idealized example but shows that for this particular failure mode every dollar spent on oil analysis represents $7.60 saved.
The other maintenance aspect of oil analysis is predictive maintenance. There is an inherent complexity in quantifying this aspect of oil analysis in that its objective is either the production of a nonevent or a reduction of a particular failure’s impact or severity.
This is achieved either by early repair before catastrophic failure occurs or by being aware that alternative plans need to be made. The worst outcome cannot be assumed for every situation. For example, water entering the above gearboxes will not always result in a catastrophic failure.
There is a statistical way of working around this problem as long as one keeps in mind that proactive maintenance saves money by reducing the number of failure events, while predictive maintenance saves money by reducing the impact of each event that remains. A costing exercise similar to the example above can be performed but outcome probabilities need to be considered and weighted accordingly (Table 2).
Again, this is a highly idealized situation but it does act as a starting point to illustrate how cost savings and ROI can be calculated in a reasonably realistic manner. The same exercises can be performed with the mobile plant but two other factors need to be brought into the equation.
First, what is the age of the component that may fail? If the component is brand new, then the savings in terms of parts is the full new value whereas, if the component is close to overhaul or replacement, the parts savings would be negligible. The possibility that this problem could have been detected by other means must also be considered.
Loss of production is a lot harder to quantify. What is the total impact of one motor grader being out of service for two shifts on a road-building project involving three such units and another 10 items of plant? How are penalties factored if the job is not finished on time? What about loss of good will? (“I will never use that construction company again; they finished the job six weeks late.”) These things are almost impossible to determine, yet they represents major cost savings when using oil analysis.
One way to apportion some cost of downtime is to simply find out what it would cost to rent an equivalent piece of equipment (Table 3).
There are a couple of points to look for in this example. If the component had reached 25 percent of its expected lifetime then the parts need to be multiplied by a factor 0.75 which represents 75 percent of the remaining lifetime of those parts. Once the total parts, labor and downtime bill has been calculated, then this total also needs to be multiplied by 0.75, which represents the likelihood that oil analysis would have been the only way that this impending failure could have been detected.
Once again, this is an idealized situation but it can be used as a model for calculating realistic savings and ROI. If an oil analysis program is best-in-class then the overall ROI ratio should be approximately 10-to-1 - $10 saved for every $1 invested.
The measurement of ROI not only proves to the accountants that the system is working and is cost-effective; it can also act as a key performance indicator (KPI). KPIs are useful management tools in that they can be used to make staff aware that their time and effort are having a positive effect on the company’s bottom line. A poster on a bulletin board showing the current state of affairs along with targets that need to be met can be highly motivating for the work force.
Oil Analysis Savings Case Study
A Wearcheck customer used annual savings from oil analysis in just such a manner. The first year showed a loss due to upfront costs and time taken to establish a system that actually worked. The second year was pretty much break even. The next four years showed a steady climb in savings, and after about seven years there seemed to be a plateau. However, after about 10 years, the inflation adjusted savings appeared to drop off (Figure 1).
Figure 1. Net Savings Attributable to the Oil Analysis Program
What was being observed was a truly world-class maintenance system of which oil analysis was the keystone. After 10 years, most failure modes either had been eliminated or were being controlled - in other words, maintenance had been optimized. What oil analysis was now doing was maintaining that high level of availability, productivity and, of course, profit.
Unfortunately, as is the case with many aspects of oil analysis, the problems and their solutions tend to be multifactorial and there are no clear-cut or simple answers. The information presented here is not intended to provide those answers but to point people in the right direction and to provide some ideas that either may be applicable or can be modified to suit the individual needs of customers.
About the Author
John Evans is diagnostic manager of mobile equipment for Wearcheck Africa.
