The fundamental purpose of Reliability, Availability, and Maintainability (RAM) modeling is quantifying system performance, typically in a future interval of time. A system is a collection of items whose coordinated operation leads to the output, generally a production value. The collection of items includes subsystems, components, software, human operations, etc. For example, an automobile can be considered as a system with sub-components being the drivetrain, engine, gearbox, etc. In RAM models, it is crucial to account for relationships between items to determine the final output of the system. In various industries, RAM models have proven to be effective as cost avoidance or decision-making tools, as well as their ability to confirm or counter stated assumptions by internal stakeholders.
This paper highlights a non-exhaustive list of seven diverse solutions that a RAM model can bring to the organization in terms of decision-making advantages.
1. Contract terms and conditions
A contract between parties is often negotiated without evaluating the shared risks and potential liabilities in the event that the contractual agreement is not fulfilled. In the case of an industrial operation, where output is precisely measured, the same output can be the basis of the contractual agreement, and therefore a RAM model can be of great value. It can assist both parties in understanding each other’s capabilities and projected performance and set the terms of the agreement on realistic performance rather than hypothetical ones. An example of such agreements would apply between an oil producer (e.g., a bitumen upgrader) and a pipeline shipper when the producer commits to providing the pipeline with commodities to ship, and the pipeline operator commits to shipping all the products provided.
2. Mix-max levels
Stocking and restocking spare parts for an industrial operation can be an expensive exercise. Stock too much, and money just sits on the shelf and is lost when items go past their shelf life. Stock too little, and you are putting the plant at risk if there is a need for parts. In this last case, one might have to accelerate (hotshot) a restock, which can be very expensive. A RAM model can help establish the exact number of spares required on the store shelf as well as reorder amounts. This exercise can also evaluate the options to stock spares onsite or offsite, where the vendor carries the cost of insurance and other warehousing fees.
3. Maintenance task optimization
The “M” in RAM stands for Maintainability. It measures the impact of maintenance on the system’s performance. The RAM model allows the decision-maker to assess the cost or effectiveness of a maintenance task on an asset. If a maintenance task, for example, is deemed too long, a new maintenance procedure can be developed and implemented. However, those changes in procedure come with a cost (e.g., if new diagnostic tools are required), and management needs to understand the benefits of the decision. The RAM model can assist in evaluating the projected benefit and justify at least a trial run before full implementation of the new procedure. In some instances, the model can suggest the optimized or most economical frequency to perform the task.
4. Establishing a criticality list
A criticality list establishes the hierarchy of assets and their ranked impact on the operation’s output. It helps the operator allocate resources to the equipment with the most impact on the revenue stream and, conversely, avoids wasting resources on other assets with lesser impact. Because RAM models quantify the lost production contribution of each asset included in the model, they are very useful processes to help build a criticality list. However, the RAM model does not quantify other risk variables that make up a criticality assessment, such as safety, environment, or reputation. Those have to be assessed using other processes.
5. Justification of additional equipment
Adding redundant or spare equipment (e.g., pumps in parallel and standby configurations) can be costly. Redundancy helps boost production and mitigate the impact of equipment failures. Estimating the need for redundancy can be done using RAM models both at the design stage and post-commissioning. It might be much cheaper to evaluate the need for redundant equipment on the design blueprint of a greenfield project where, for example, geographical space is not a constraint, at least on paper. However, adding redundant equipment once a plant is built and in operation can be extremely costly. Installation might need the plant to be shut down, there might be a need for rewiring or reprogramming of PLCs, there could be space constraints requiring reshuffling of equipment locations, let alone buying a piece of equipment by the unit rather than getting some bulk discount. This is when the RAM model is crucial, as it looks at the entire and detailed cost of installation versus the projected benefits. It can also help with the economical evaluation, such as IRR or other accounting metrics key to making sound financial decisions.
6. Optimal equipment overhaul timing:
Equipment overhauls can be quite expensive in terms of the work itself but also regarding the logistics involved. For example, if a pump has to be overhauled at an offsite location and removed from a building, cranes and transportation are required. Disconnection, reconnection, commissioning, and testing tasks are also major expenses. Another common myth is that overhauls lead to “as good as new” equipment, which is not always true. Therefore, overhaul frequencies need to be carefully evaluated, and a RAM model is a perfect tool for this exercise. If all the costs regarding the overhaul are known and entered, as well as the impact on the operation (e.g., risk of not having the pump), then the overhaul frequency can be calculated precisely. Alternatively, if an overhaul is not cost-effective, then other options can be recommended, such as replacement in kind or maintenance in situ.
7. Maintenance workforce management
Some companies have tight cash flows and want to budget future spends well in advance. When it comes to human resources, such as maintenance techs, a RAM model is a tool of choice to quantify the needs in terms of maintenance resources in a future interval. In the same way, the model can highlight the gaps in the workforce and warn management about the impact of such a shortfall. The model can also set up workforce pools that can be shared between different units of a plant to optimize the workforce output. Ultimately, managing people comes down to good human resource management practices and processes, but the RAM model can still assist in the decision-making exercise.
RAM models offer a lot more than the above list. The author has provided some idea of the diversity in benefits of a model. Once a model is built, it is a great go-to tool to check assumptions, evaluate ideas generated by the workforce, and subsequently make sound business decisions.