Maintenance optimization and your plant

July 5, 2005
David Berger explains some alternative maintenance approaches to consider, a simple optimization model to get you started, the optimization options for dealing with asset deterioration, and the future of maintenance optimization.

In the June 2005 column, I touched on management’s growing interest in the optimal mix of maintenance approaches -- failure-based, use-based and condition-based. Also, I described the key metrics that ought to be optimized (operational performance, output quality, availability, reliability and life-cycle cost). This month, I’ll explain some alternative approaches to consider, a simple optimization model to get you started, the optimization options for dealing with asset deterioration, and the future of maintenance optimization.

Alternative approaches
For many companies, optimization represents a long-term goal at best. Maintenance folks at these companies feel caught in a never-ending struggle to dig themselves out from under an ever-growing backlog. The pattern is familiar -– lack of time or technicians to deal with the problems, a shrinking budget, pressure from operations for improved service, and a growing pile of planned and preventive maintenance work that’s ignored or completed in a rush. For these companies, the strategy is simply finding ways to increase the proportion of work that is planned.

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As we all know, the “how” is not so simple. There might even be a low probability of success in the absence of management support. In my view, about 60% to 80% of companies at any given time are in this modus operandum.

A few companies have managed to inch much closer to a planned environment. These usually have an adequate CMMS in place to track various metrics. However, they thirst for something more than just a successful PM or PdM program implementation. These companies find few options for going forward except for Lean, Total Productive Maintenance (TPM), Reliability-Centered Maintenance (RCM) and a scant handful of other qualitative programs.

None of these approaches involve sophisticated analysis or optimizing quantitative data, but they do offer significant benefits, not the least of which is increasing the organization’s level of motivation and knowledge.

The most sophisticated of these, RCM, doesn’t offer optimization per se, but does focus attention on developing a more condition-based approach, thereby improving maintenance efficiency and effectiveness. Although RCM deals with multiple metrics, it tends to do so at the equipment and failure mode level rather than at multiple levels throughout an organization.

A few so-called scientific approaches feature a more quantitative and analytical bent. These options, however, tend to focus on the equipment or component level, and optimization is with respect to a single metric only -– usually cost.

The ideal optimization program can roll up or drill down through multiple levels, from components and equipment to a given plant and the overall enterprise. As well, the program should be able to optimize across any level, such as for all pumps across the organization.

A simple model
An example of a simple optimization modeling tool is the basic age repair/replace model shown in Figure 1. This model optimizes for a single metric, namely cost. The greater the mean time between maintenance (MTBM) for a given asset, the less preventive maintenance is being done. Hence, the lower the preventive maintenance cost (green line on the graph). At the same time, the greater the MTBM, the greater the probability there’ll be a failure, and thus, the greater the repair/replace cost (red line).

The sum of the costs the red and green curves represent is equal to the total cost curve (pink line). The lowest point on the total cost curve corresponds to the optimal MTBM for a given piece of equipment or component.

The problem with optimizing for a single variable -- in this case, cost -- is that you could come to the wrong conclusion easily. Optimizing for another metric, such as reliability or performance, might yield a different optimal maintenance policy.

Understanding asset degradation
Determining optimal maintenance policy requires collecting data for critical components and equipment. Even if you have little data for your facility, it’s surprising what can be gleaned simply through interviews with operators, technicians and their supervisors.

Only by understanding the asset’s degradation, deterioration or failure behavior, as well as the cost of possible remedies such as major and minor repairs, major and minor maintenance, and the like, can you determine the multi-metric optimal maintenance policy. The lower half of Figure 2 depicts the deterioration stages for a typical asset, from brand new to complete failure.

Degradation optimization options
The upper half of Figure 2 shows the possible remedies at each stage of deterioration, depending on the maintenance policy in effect. For example, if your policy was to let this asset run to failure, you’d make minor repairs at some stages (shown in pink), and a major repair at the final stage of complete failure (shown in red). However, optimization on the basis of reliability or availability might show that an alternative policy is superior. You might compare different condition-based maintenance (CbM) policies that differ in terms of mean-time-between-inspections (white line). Furthermore, CbM policies may differ in terms of the conditions that trigger a maintenance type (minor versus major) at which stage of deterioration. These options are shown in green on the upper half of Figure 2.

Thus, a good optimization program compares policies, from simple run-to-failure to online continuous CbM, all in the light of multiple metrics. More sophisticated modeling tools allow progressive overlays of greater and greater functionality, which continuously improves the optimal solution. Users should be able to

  • Experiment with the effect on several key metrics of changing the maintenance policy for a given component, equipment, line, facility or line of business.
  • Determine the cost/benefit of outsourcing one or more maintenance tasks.
  • Understand the trade-off in adding hand-held or online condition monitoring devices.
  • Determine the effect of changing the maintenance technician’s skill level.
  • See the effect of a given policy on product quality (damaged or lost items) at different degradation levels.
  • Perform sensitivity analysis to understand the effect of adjustments to various variables, so that strategic tradeoffs can be understood.

The future of optimization
It’s clear that the perception of many companies and their CMMS vendors is that we have a long way to go before optimization is a serious consideration. I believe this to be short-sightedness. Just as companies have been using simulation software, linear programming and even spreadsheet-based optimization models for years to solve complex operational problems, so too could they be using optimization to better manage plant assets. Thus, once the savings potential is recognized, the demand for optimization tools to get at those savings will grow, thereby sparking a flurry of activity by CMMS vendors and others to meet that demand. That will be good for you.

E-mail Contributing Editor David Berger at [email protected].

About the Author

David Berger | P.Eng. (AB), MBA, president of The Lamus Group Inc.

David Berger, P.Eng. (AB), MBA, is president of The Lamus Group Inc., a consulting firm that provides advice and training to extract maximum performance, quality and value from your physical assets, processes, information systems and organizational design. Based in Toronto, Berger has held senior positions in industry, including for two large manufacturers, and senior roles in consulting. He has written more than 450 articles on a variety of topics such as asset management, operations management, information technology, e-commerce, organizational design, and strategy. Contact him at [email protected].