Does Your Lube Program Measure Up?

Ken Bannister | February 17, 2017

Accurate measurement and trending of maintenance performance, including lubrication-related elements, is a crucial part of effective asset management.

Use performance measurements to focus and improve your lubrication program and overall uptime.

Peter Drucker, recognized as the 20th century father and inventor of modern [business] management, wrote “If you want it, measure it. If you can’t measure it, forget it.” Drucker was an untiring advocate of workers knowing who they are, what they do, and the impact—positive and negative—that their direct and indirect efforts regularly impose on organizations, colleagues, stakeholders, environment, and corporate assets. The ISO 55001 Asset Management Standard brings Drucker’s philosophy to the forefront as it emulates, incorporates, and supports many of his ideas within its framework requirements.

Ken Bannister discusses this article and more with
Editorial Director Gary Parr in the above podcast.

Most maintenance departments are instinctively aware of their impact on the organization and understand that a proactive approach improves equipment reliability and availability, which leads to reduced downtime and increased or sustainable production throughput. Unfortunately, instincts often aren’t good enough.

Countless maintenance organizations have trouble measuring and accurately defining their impact on themselves and the rest of the business—despite having easy access to data through their work-order-management systems. Instead, they continuously collect mounds of meaningless unrelated data (MUD) in the mistaken belief that the data might eventually be of value. In reality, data will only become information (and useful) in two ways: when mined and translated with context and meaning, and when used to make management decisions.

In today’s data-rich environment, performance-indicator reporting, typically based on KPIs (key performance indicators) is used to translate and make sense of data. KPI reporting delivers tangible evidence of change and allows users to assess the value and validity of their program(s) and the work that they, and others who work with them, perform within a time-based framework.

Benefits of performance measurement

A maintenance department represents one of the single largest controllable costs within any industrial operation. Poorly managed asset maintenance—in which lubrication plays a major role—will result in reduced throughput and equipment life that translate into waste and unnecessary cost. That’s why accurate measurement and trending of maintenance performance is so crucial. Performance measurement generates many immediate benefits for a maintenance department and, by extension, the entire business, including:

• the ability to orchestrate and align corporate and departmental strategic direction through improvement programs that include a lubrication-management program

• improved utilization of a CMMS (computerized maintenance management software system), or an LMS (lubrication management software) system set up to deliver meaningful reports through the use of KPIs

• fast recognition of improvement opportunities

• fast recognition of existing excellence that can be exploited

• establishment of a diagnostic baseline measurement from which to set target goals, develop improvement strategy, and trend continuous improvement

• the ability to benchmark with other businesses.

Anatomy of a KPI

KPIs aren’t usually provided as part of asset-management/maintenance-management software packages and, therefore will require some set up. Changes may be required to the work-order design to enable capture of specific data used to populate the software data-field registers (database search filters). A mix of standard and custom queries/reports is then used to fill in the KPI calculation requirements to attain the final KPI report.

For example, mean time between lubrication failure (MTBLF) is a popular lubrication performance indicator. A lubrication-specific derivative of mean time between failure (MTBF), this KPI is used to determine the level of lubrication-related reliability. It can be applied in a number of ways: a production line, machine, machine type, assembly, or component within a machine; a specific lubrication system or system design; and, in relation to the number of failure events due to ineffective lubrication for a specific time period, by work shift or by contractor — all from the same data set! The calculation for MTBLF is:

Specified # of operational hours or days/Total # of lubrication-related failures.

The answer will be a number that spells out the average number of hours, or days, between each failure event. The higher the number, the better the reliability.

This KPI is best used on a machine-by-machine basis—although it also can be leveraged in evaluating multiple production lines simultaneously to obtain a plant-wide MTBLF. Note that plant-wide reliability numbers will be much lower and depict a higher failure frequency than any single line or machine. This could paint a picture that appears worse than it really is—especially when a troublesome machine or line is allowed to skew the overall figure.

MTBLF is particularly valuable when it is trended to show an increase or decrease in reliability and used to demonstrate maintenance-service-level quality (internal staff or contractor), or to highlight change in reliability due to program implementation, product change (production raw material or lubricant), maintenance efforts, and procedural changes, among other things.

It can also help assess and compare the reliability of products, including lubricants and gearboxes, that are used in similar machines within a plant.

Processing the MTBLF calculation requires:

• The ability to search for work-order types in a facility’s CMMS/LMS system(s)

• Failures usually require reactive work to be captured on a demand or repair work-order type.

• The ability to search asset-failure classification in the CMMS/LMS

• To specifically separate out lubrication-related failures, there must be a failure-classification search filter that allows the demand or repair work order to have its failure classified prior to closing in the CMMS/LMS.

• Identification of what machine, line, or asset group is being reported on

• A specific machine would require its CMMS or LMS asset number.

• A production line would require all CMMS or LMS assets in the line to be rolled up to a parent-line asset number. If no parent/child relationship is set up, all machine assets in the line must be reported on and averaged together in a separate calculation.

• Asset types are put in searchable fields that group similar equipment together for comparative reporting purposes.

• The machine manufacturer (OEM) is identified in the nameplate data section of the CMMS/LMS asset section. (Most software makes this a searchable field.) This allows reporting on the reliability of equipment made by different OEMs and can help in purchasing decisions.

• Information includes whether the reviewed repair work was/is being performed by internal staff, contractors, or both. To make this distinction, the work order must record if the work was performed by internal or contract (external) resources and the data must be captured in a searchable field within the CMMS/LMS.

• In multi-shift operations, failure by shift can be reported on if respective shifts are noted on the work order and captured in a searchable field in the CMMS/LMS.

• Identification of the period of time on which the report is based

• Does the department (maintenance client) require the KPI report based on all work since the start up of the CMMS/LMS, or “from and to” a set of specific dates? This is a basic reporting parameter requirement of any report or query in a CMMS/LMS.

• Is the report required in hours or days? If there are many failure incidences and the answer is fractional days, the report should be changed to hours for better comprehension. Prior to final calculation, the number of days should reflect only worked operational days and/or hours within the specific time period. For example, If two shifts are worked, they would be counted as 16-hr. days.

Once the query/report has been run, the number of failure occurrences will need to be counted and manually divided into the number of days or hours to achieve the final KPI number.

If repair hours and costs are requested in tandem, auxiliary KPIs, such as Average Lubrication Related Failure Repair Time, Average Lubrication Related Failure Repair Cost, or Total Lubrication Related Failure Repair Cost, can be extracted from the same report. See the sidebar on p. 41 for how KPIs can be sub-grouped.

Keep in mind that performance measurement is a test of how well your CMMS/LMS systems are implemented and how much you can trust your data. The big question is “What are you going to do with your data?”

KPI Sub-Groups

Financial performance. These KPIs analyze the relational costs of services and functions. Typical financial indicators can include lubrication cost expressed as a percentage of total maintenance or operating cost.

Efficiency/effectiveness performance. These KPIs analyze program and department effectiveness, and areas affecting cost expenditure. MTBLF (mean time between lubrication failure) is a typical efficiency/effectiveness KPI.

Stand-alone performance. These KPIs must be broken down further or compared with other performance indicators to understand their true meaning. Consider, for example, the Lubrication PM Compliance indicator. By itself, PM (preventive maintenance) compliance is a weak KPI in that it doesn’t analyze effectiveness of a PM program or maintenance department. Considering this type of indicator in conjunction with MTBLF, however, leads to a better understanding of compliance relevance.

Correlated performance. These KPIs allow users to view specific information in different ways. For example, in an organization that wants to focus on program direction for all departments regarding equipment availability, it would be important to understand that the inverse of a KPI of 80% machine availability is 20% machine unavailability, or downtime, if the selected machine is required for production.

Ken Bannister is co-author, with Heinz Bloch, of the recently released book Practical Lubrication for Industrial Facilities, 3rd Edition (The Fairmont Press, Lilburn, GA). As managing partner and principal consultant for EngTech Industries Inc. (Innerkip, Ontario), he specializes in the implementation of lubrication-effectiveness reviews to ISO 55001 standards, asset-management systems, and training. Contact him directly at kbannister@engtechindustries.com, or telephone 519-469-9173.

“How to Begin Measuring Maintenance Effectiveness, Part I”