Of all the things companies need from their assets, reliability is perhaps the most valuable. The ability to deliver on expectations is one of the best indicators that assets are performing to the highest level. Reliability management is essential for any enterprise in a capital-intensive field.
Recent developments in asset performance management and technology have enhanced the industry’s ability to track reliability and make informed decisions about production, quality and costs. Making educated choices allows a company to mitigate risk overall.
Manufacturing downtime is expensive and stressful. Fixed-cost assets effectively cost more the longer they remain idle, as they aren’t providing a return on investment when they aren’t running. Every enterprise would ideally like to reduce downtime to zero, but even if this were possible, it would be prohibitively expensive. In fact, maintaining redundant equipment in case there is a failure costs as much as building a second asset without using it. It would be impossible to justify the cost of maintaining this equipment.
Reliability management accepts the real-world trade-offs between production and equipment failures. Instead of attempting to deliver unrealistic amounts of uptime at all costs, reliability management means attempting to deliver a given amount of uptime consistently for a given budget.
Companies have to make decisions about what is really important to the organization; it makes sense to want minimum downtime, but is there a budget for the higher asset maintenance costs?
No asset can be expected to operate continuously forever. Even solar panels, the majority of which have no moving parts, degrade over time and need to be replaced. Knowing when, and under what conditions, an asset may have to be serviced or replaced is of enormous benefit to a company.
Reliability management needs a realistic directive from the organization. For example, a company could stipulate that they can afford a 2% drop in production if it means a 5% increase in uptime.
The struggle to understand asset maintenance requirements has led many organizations to gather mountains of data about their systems. Asset performance monitoring through sensors and specialized software provides reliability managers with a trove of information, which they can use to mine for insights into how and why assets perform the way they do. This information is crucial for building a reliability management strategy.
The operational data collected by a company can be mined for insights into how to best manage the assets of the company. Machine learning algorithms and artificial intelligence software can find patterns and links between different operational outcomes.
Prescriptive analytics utilizes these machine learning models to allow an organization to determine which steps need to be taken to achieve different objectives. If a company is able to concretely articulate what things they need to prioritize, prescriptive analytics can find and propose ways of achieving them.
For instance, a company might decide that having a critical piece of equipment operate for 10% longer between servicings is worth a 3% drop in total productivity. But the company may be unable or unwilling to make those changes in the real world without somehow testing them. A prescriptive analytics model would provide the company with a list of changes to make to meet that goal, as well as a ‘digital twin’ on which to test the changes.
It is easy to see how prescriptive analytics can be such a powerful tool in reliability management; being able to increase reliability digitally reduces the risk of disrupting production.
What is system reliability?
System reliability is whether a system performs the way it is expected to perform.
How do you calculate system reliability?
System reliability can be measured as failure rates over time, often represented as mean-time-between-failures (MTBF), or average runtime between forced stops. System reliability can also be represented by how long a system remains inoperable when it fails, or mean-time-to-repair (MTTR).
How can equipment reliability be improved?
Maintaining and operating equipment within set limits can improve equipment reliability. These limits can be established through the analysis of historical patterns of failure.
How do you calculate equipment reliability?
Equipment reliability can be measured according to the amount of uptime the equipment has in a given period; mean-time-between-failures minus mean-time-to-repair equals uptime.
What is reliability optimization?
Reliability optimization is adjusting the operation of assets and equipment to maximize reliability. Instead of maximizing peak output or minimizing operating costs, reliability optimization seeks to make sure assets or equipment perform within expectations.
What is a reliability management system?
A reliability management system is a management system whose primary concern is measures of reliability. A reliability management system attempts to deliver a stated level of reliability for an operation, asset or piece of equipment.
The Aspen Fidelis Reliability Difference