Most companies have already figured out how critical a good measurement system can be for managing a business. This realization has been facilitated by the wealth of information available through CMMS, ERP and shop-floor data collection systems. In fact, there’s a staggering amount of data that can be collected from so many sources, analyzed in numerous ways, and reported in countless possible formats. Many companies, therefore, must consider the permutations and combinations to determine:
- Which data should be captured.
- How it should be analyzed.
- What information should be reported in what format to each stakeholder group.
- How to best translate the information into management decisions.
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One secret to success in accomplishing these daunting tasks is identifying a small but powerful set of measures that will trade off for a given area of focus. For many manufacturers, especially in asset-intensive environments, a key area of focus is asset management, given the improvement potential lying therein. Selecting the right measures enhances your decision-making capabilities greatly. In turn, this leads away from the usual fire-fighting mentality and more toward a planned environment, with better control over assets. Moreover, choosing a small but powerful set of measures that trade off ensures that maintenance and operations personnel are motivated and aligned with what management has defined as success for asset performance management.
Measurement challenges
If you choose too few measures or those that don’t trade off properly, you may find that improving one results in degrading another. For example, the success achieved by focusing solely on reduced equipment downtime may be at the expense of product quality if more output needs to be scrapped or reworked. In other words, be careful what you wish for.
Equally ineffective is selecting too many measures. The greater the number of measures, the more data that needs to be collected and analyzed, and, therefore, the more likely it is that errors will occur along the way. Also, the more measures you track, the more expensive and time-consuming it is to collect and interpret the numbers. Finally, too many measures means there’s no narrow focus across the organization.
Another challenge is managing the risk associated with using the wrong measures. A worst-case scenario would be a major safety or environmental failure that could have been avoided if a certain measure had been tracked. Other risks range from reduced efficiency that causes financial loss or a total plant shutdown resulting in the inability to ship product. It’s imperative to choose measures that minimize operational risk.
Metrics for asset management
A small but powerful set of asset management measures is described below, including an indication of how they trade off. Be aware that there’s a lot of discrepancy in how these measures are defined and used within a given company, let alone across industry. So whatever measures you settle on, ensure their meaning is understood consistently across your enterprise.
What to maximize
Start with availability, a term that refers to the percentage of time that operations can use an asset productively. The opposite measure, asset downtime, implies the reason an asset is unavailable to operations is that it has failed. If production equipment isn’t operating because the operator or raw material isn’t available, then it’s production downtime, not asset downtime. The importance of availability is undisputed -– if the equipment isn’t available, there’s no production possible.
The next candidate for maximization is utilization. Just because an asset is available doesn’t necessarily imply it’s being utilized. Utilization, expressed as a percentage, is the number of hours an asset is used for production divided by the total number of hours the asset is available. There are differences in how companies interpret the numerator and denominator. Starting with the numerator, an asset is either producing or it’s not producing because of a setup, changeover, cleanup, adjustment, inspection or other factor that may involve the maintenance department. The asset may not be working for reasons unrelated to maintenance, such as poor line balancing, poor design, operator absenteeism or operator inexperience. Companies vary on which factors are considered in the numerator.
The denominator is typically the availability of the asset as defined above, but some companies ignore asset downtime and use only the number of hours in a given time period (hour, shift, day, week). Regardless of how it’s defined, poor asset utilization has a strong negative effect on operations.
Maximize the performance. When an asset is available and being used, it still might not be performing efficiently, yielding insufficient output per unit of time. Thus, the third key measure is asset performance, which is defined as the production output rate or throughput divided by the expected throughput. This measure, too, is open to interpretation, depending on how you define what is expected. Some companies say the denominator should be determined by the equipment vendor’s engineering specification. My preference is to use an engineered work standard or some industry standard.
The most popular definition of the term expected is historical average, but if you have poor historic efficiency, you risk building it into your expectation. To some people, expected really means ideal, best practice or world class, whereas to others it means realistic, given the equipment age, operator skill and experience, raw material quality and environmental conditions.
The notion of “expected” is less important when determining the effect of a given change on performance or when comparing multiple approaches. For example, you could compare the relative throughput as a function of different vendors’ parts, or when a run-to-failure policy is replaced with condition-based monitoring.
Reliability is another issue. Every asset will deteriorate eventually. Some are more predictable than others, but all will exhibit a rate of deterioration or failure. Mean-time-between-failures (MTBF) is a typical measure of reliability. Just because the equipment has a low MTBF doesn’t mean availability is necessarily high. For example, a catastrophic failure may translate into several weeks of maintenance, thereby affecting availability adversely, even though MTBF is low. Reliability is a subset of a measure called dependability.
Finally, there’s quality of output. Even if the four measures above are fully maximized, equipment could be producing poor-quality output in the form of scrap, rework, re-feed, yield loss, returns, shrinkage, warranty repairs and so on. In some companies, the cost of poor quality can be as high as 30% to 40% of product costs, albeit not always as a result of maintenance-related causes. Regardless, it’s imperative to establish a trade-off between the measures above and the quality of output so as to maximize the five measures.
What to minimize
There’s only one measure that must be minimized -- total cost of ownership -– because it offers the biggest potential trade-off of them all. This cost is equal to the sum of all costs associated with a given asset, including original purchase price, related capital expenditures, maintenance labor and material costs, other costs of owning the asset during its lifetime, net of salvage value.
As you strive to maximize the five measures above, watch that you do so while minimizing total cost of ownership. For example, be wary of purchasing an asset that promises greater asset reliability and performance if the benefits are more than offset by a higher total cost of ownership.
E-mail Contributing Editor David Berger at [email protected].
