With the price of crude oil skyrocketing, all of us are painfully aware that gasoline prices continue to rise, with gas set to remain an expensive commodity for the foreseeable future. Unfortunately, the same holds true for lubricants. Most lubricating oils are mineral based and, just like gasoline, are derived from crude oils. So as gasoline prices go, so do lubricant prices. Even synthetics are not immune to the issues. Most synthetics are made either from by-products of oil or natural gas and have seen similar price increases to mineral oils in the past few years. But in the scheme of things, does this really matter? After all, for most manufacturing plants, the amount spent on lubricant purchases typically is less than 1 to 2 percent of a plant's maintenance budget. Surely there are bigger things we need to be concerned with than the cost of a gallon of lubricating oil?
While on a commodity basis this may be true, it is not the cost of the lubricant itself that we should be concerned with, but rather the effects of the way in which the lubricant is applied (or, more commonly, misapplied) to our machines. So, how can we track how much poor lubrication is costing the organization? For most, this can be a difficult, often impossible task. The same reasons why many plants' maintenance practices are mired in mediocrity are precisely why we can't easily determine the deleterious effects of poor lubrication: poor record-keeping and little to no standardization of work leads to a lack of fundamental data upon which to base our decisions.
Instead, what's required is "educated guesswork". With this approach, we use a simple but realistic framework in conjunction with the knowledge and experience of those directly involved in maintenance to "guess" at a series of sequentially more telling questions (Figure 1). Here's how it works.
We begin by estimating, in round numbers, the current annual maintenance cost. In order to bracket the opportunity (provide a low and high estimate), we then review the maintenance budget history to establish a low and high limit. From this history, we can now project best case (costs are lower than expected), likely case (costs are as expected) and worst case (costs are higher than expected due to unplanned expenditures) scenarios.
Next, we determine the total annual downtime costs. This is where it can start to get tricky. While this figure is relatively straightforward in some plants, varying production schedules, market conditions (not all product is sellable) and poor record-keeping can make obtaining realistic estimates challenging. In obtaining estimated downtime costs, it's often a good idea to involve production and plant management; after all, it is these people to whom we are attempting to sell. Involve management in the process early on by seeking their best guess of estimated costs. This creates buy-in and ownership in the process. It also prevents cynicism when it's time to present the final cost benefit analysis. The "low", "likely" and "high" case scenarios are helpful here. Where downtime costs are well known, a fairly narrow window can be selected, bracketed by the low and high cost estimates. Where downtime costs are difficult to obtain, a fairly broad window can be selected.
The next step is to identify, from the maintenance and downtime figures, how much of these costs can be eliminated through a well-designed, well-executed lubrication program. It's advantageous to involve people with a vested interest in making this assessment. After all, it's difficult to argue against a number you've helped to determine in the first place!
Figure 1. Assessing the cost of poor lubrication.
In order to estimate the opportunity costs (maintenance and downtime cost reduction) attributable to repairs required as a direct result of poor lubrication, we need to estimate the following (see Figure 1):
Percentage of costs due to repair (A): This includes parts, labor, supervision and management, overhead, insurance, risk-based costs and incidentals.
Percentage due to wear or lubricated components (B): It is important to itemize and consider replacement costs for both lubricated and associated non-lubricated components (shafts, housings, cages, fans, couplings, etc.) affected by failure of lubricated components. This estimate should include all scheduled and unscheduled repair work, such as replacements and rebuilds, and follow-up work for commissioning and assessment of newly deployed equipment.
Percentage of wear problems due to poor lubrication (C): This is an estimate of the negative impact of current practices on lubricated components. Influences could include: incorrectly selected lubricants, too much and too little lubricant, incorrect relubrication frequency, ineffective contamination control (fairly to maintain best practice targets) and poor oil analysis practices.
Percentage of wear that could have been avoided (D): This can be a tricky number to estimate. However, a combination of the lubrication team's best guess plus case study-based information from other sources can be useful in "guesstimating" this number.
Once these estimates have been made, the opportunity costs resulting from the repair of lubricated components as a direct consequence of ineffective lubrication is simply: Opportunity costs (repair) = (maintenance cost + downtime cost) x A x B x C x D
The final cost attribute to consider is the cost of inefficiency associated with deploying a poorly designed lubrication practice. While the effect of poor design (for example, incorrect regrease frequency, volume and product selection) is accounted for in the repair opportunity costs, you must also recognize the wasted time and effort associated with that design. This value is derived by estimating the percentage of the maintenance budget associated with lubrication PMs and other lubrication-based non-repair activities (X, including labor and materials), and then estimating the percentage of this value that is unnecessary (Y). It is:
Opportunity costs (inefficiency) = (maintenance cost) x X x Y
Using the example in Figure 1, it is estimated that based on an annual maintenance budget (likely case) of $13 million and estimated annual downtime costs of $5 million, we have a total of $1,426,000 in repair and inefficiency costs that can be eliminated through a well-designed, well-executed best practices program.
So, what's the net result? When most companies go through this exercise, they are shocked by the results. Most companies (particularly in heavy industries such as steel, base metals, pulp and paper, etc.) realize that the losses due to lubrication amount to 10 to 20 percent of their maintenance budget - 10 times the cost of the commodity, the lubricant.