By Robert Barr
Becoming aware of risk, how to measure it and how to use it to establish priorities are important keys to successful maintenance management. The basic premise of risk-based maintenance management is that risk can be quantified and then prioritized. The results of this risk-based focus can be used to establish capital and expense allocations to preventive, predictive and reactive maintenance management decisions.
A facility engineer’s staff can usually communicate the most important equipment in the facility—but what about the next most important piece and the one after that? The further down the list one travels, the greater the demand for a way to objectively analyze the importance of each piece of equipment in maintenance management decision-making.
How does this translate to the physical plant? Facility engineers abide by one cardinal rule: keep the important equipment running at or above capacity. As facility engineers progress from hands-on equipment maintenance to maintenance management positions, determining the allocation of resources becomes increasingly more challenging. The further removed one is from hands-on equipment maintenance decisions, the more difficult it is to establish maintenance priorities. The challenge lies in not only determining which piece of equipment is important, but also determining its level of importance.
It is relatively easy to quantify the importance of the equipment at the point where revenues are generated. An example of revenue-generating equipment for an office building would be the equipment that is used to light, heat and cool the space, which tenants generally pay for. For a service industry dealing in e-commerce, the revenue-generating equipment would be the equipment used in customer communications, such as telephones and computers. For a manufacturing facility, this would be the equipment that develops the product. In all cases, each group of revenue-generating equipment, and likewise, each critical piece of equipment within that group needs to be assigned a quantifiable level of importance.
As a simplified example for grouping equipment and assigning risk priorities for a service industry such as office buildings, consider this scenario. A building has many floors of offices with different tenants occupying varying sizes of floor space and some paying a premium for the “best” offices. A simple method for risk-ranking the equipment at the point where revenues are generated by the facility manager is to assign maintenance priorities based on the relative revenues generated by each tenant. This ranking becomes a method for resolving conflicting maintenance priorities, but this is too simplified. This example risk-ranks building equipment based solely on the consequences of lost revenues caused by lost tenants. There is another dimension to risk-ranking equipment that has not been considered: the total maintenance costs required to maintain the equipment to assure the minimum possibility of failure.
Here is a manufacturing example that incorporates both dimensions of risk: if the cooling fan on the hypothetical line #3 drying tower B were to fail, it would take six hours to replace. Line #3 will be down 50 percent while the facility manager takes necessary steps to replace it. As the worst-case scenario, this will cost $20,000 in lost revenues (that will have to be somehow made up), plus $1500 for the replacement installation and repair. Testing to prevent failure costs $150 per year. The same applies to drying tower A. Therefore; the importance (risk consequence value) of the cooling fans in both drying tower A and B is the sum of all three costs, $21,650. At the end of this process, it should be relatively easy to make a risk-ranked listing of the other revenue-generating equipment based on the consequences of failure and the costs of maintenance.
But what about the rest of the equipment under the facility engineer’s purview—specifically, the equipment used to support the revenue generating equipment? What about the central HVAC systems, electrical distribution systems and other facility support systems? Developing relative importance for these support systems is challenging when you consider that, as in the case of mid-voltage electrical distribution equipment, one piece of equipment can affect numerous other systems that are at the point where revenues are generated. How can the support equipment be ranked in terms of its relative importance tothe facility? To answer these questions, let us focus on one facility support system.
Risk-based assessment as applied to electrical distribution equipment
Because things tend to get muddled when attempting to establish the relative importance of facility support equipment, programs have been developed that will assist in this process. One particular program focuses on mid-voltage electrical distribution systems (EDS). These methods were initially developed for a major manufacturing company at their request, where the efforts focused on their specific requirements. However, the success of this initial project fostered a more broad-based program that is applicable to a wide range of service and manufacturing industries. The basic methodology used for analysis of the EDS remains the same when applied to other settings.
Beginning the process
Quantifying the relative importance of the equipment begins with a walk-through of the facility while determining which equipment exists at the point where revenues are generated. Once this is established, another walk-through determines which EDS equipment affects other equipment used directly in generating revenues. As you go through the EDS, beginning at the point where revenues are generated up to the point where the utilities feed power to the facility, a trend becomes apparent. Each individual piece of equipment takes on increasingly more importance because it controls electrical current to increasingly more pieces of equipment that are at the point where revenues are generated. The EDS data gathered during this second walk-through is input to the programs used to analyze the EDS. The program then begins the process of quantifying the total risk for each individual component and connected group of components in the EDS.
The method quantifies the two aspects of risk by applying the probability of downtime caused by failure of an EDS component and the costs of the equipment, maintenance and consequences to the facility if the equipment goes down. The consequences to the facility are the aspects of risk that are often overlooked when making maintenance management decisions. As an example, suppose a circuit breaker controls critical equipment in a facility that, if failure should occur, would shut down 100 percent of its revenue-generating capacity for 16 hours. The exact same type of circuit breaker located next to it controls critical equipment that, if the same failure occurred, would cause the plant to lose 50 percent of its revenue-generating capacity for 16 hours.
All else being equal, the probability of the two identical breakers failing is the same. The dollar value to replace the two identical breakers and the time to replace them are also the same. The difference is the consequence to the physical plant. The breaker that would have the entire revenue-generating capacity of the plant down is more important to the facility than the breaker that would have half of the plant revenue-generating capacity down for the same amount of time.
Often, as previously stated, the consequences of failure can have a much higher dollar impact on a facility than the direct dollars to repair or replace the equipment involved in the failure. The program developed for the evaluation of a facility EDS determines the maintenance management priorities through interviews with key personnel in a facility, and the replication of the EDS in a computer-simulated model. Based on the data gathered and the EDS simulation, the two components of risk are applied to each piece of EDS equipment, and an analysis is made by computer simulation of every possible combination of failures of single and multiple components of the EDS. This analysis determines the likelihood of each failure scenario and the consequences. The model then develops a risk-based listing of EDS equipment ranking each in its relative importance to the facility.
Using this risk-based priority list, the model evaluates preventive and predictive maintenance corrective actions and does a cost-benefit analysis to determine the final list of recommended actions required to minimize failure of the facility EDS. This is what is called “the unconstrained list of recommendations.” Because the EDS is computer-simulated in the model, it is possible to conduct numerous “what if” scenarios to fit any particular budget constraints. For instance, if the unconstrained model recommends a list of activities that should be conducted and the maintenance budget can support only half of them, the model will determine the most advantageous recommendations to reduce the probability of failure of the critical equipment in the EDS.
Other facility support systems
This model is expandable to include other components of the facility support equipment such as HVAC equipment and roofs. By using the integrated model, the facility engineer gets the advantage of equipment prioritization at the facility level and not just individual components of the facility support equipment. This integrated approach makes it easy to compare, for example, the importance of a specific piece of HVAC equipment to a component of the EDS. This allows the most effective cost-benefit analysis in the allocation of resources to assure that all of the facility support equipment does not fail.
Quantifiable risk-based decision-making to support the preventive, predictive and reactive maintenance of a facility assures optimum usage of the available resources to reduce the probabilities of a breakdown of facility support equipment. It is the next step in optimizing maintenance management.
About the author: Robert Barr has been an account engineer with The Hartford Steam Boiler Inspection and Insurance Co. for four years. Prior to that, he worked as a plant engineer, production manager and plant manager in food plants for Frito Lay, among other Fortune 500 Companies for 20 years. He is currently working with the HSB Engineering Department in the implementation of the electrical distribution equipment model discussed in this article.