Reliability engineering is often associated with maintenance engineering; however, a reliability engineer’s skill set is more diverse, driving business strategies to achieve goals through a well-structured path utilising vast amounts of data. The maintenance engineer’s skills are used for day-to-day fire-fighting activities to ensure assets that have failed are brought back into service in the shortest amount of time, without compromising on quality.
In attaining business maturity through reliability engineering, an organisation must define goals. The existing systems will go through an evaluation process, to determine their suitability for integration, as organisations tend have vast amounts of useful data on various platforms. The goals will determine which data has the quality and integrity to be transformed into information, providing insight into asset performance and reliability. Analytics are performed on the information and interpretations result in effective decision making, with recommended actions. The results are then presented on dashboards and business intelligence reporting visualisations.
The reliability journey begins with a risk-based perspective, comprising an understanding of criticality, risk, failure-modes, effects analysis, predictive maintenance technologies and analytics. Furthermore, elements such as business goals, asset reliability, KPI selection and asset life cycle modelling play a crucial role.
When considering support for the business goals, there has to be alignment between the following elements:
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Financial factors: Return on investment and capital investments.
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Customers: Serving customer needs and reducing product delays while maintaining quality.
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Internal processes: Enhanced operational efficiency, availability and reliability.
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People: Developing the workforce with training and structured growth paths.
An asset reliability KPI selection should incorporate the following:
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Evaluating the effectiveness of a condition-based maintenance tactic by measuring work orders generated by a CMMS.
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Managing the risk of critical asset failures, utilising Weibull analysis techniques.
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Monitoring the impact of changes to the maintenance and business strategy on reliability.
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Understanding the maintainability, availability and reliability of an asset through performance comparisons and trending.
Asset life cycle modelling KPI selection should incorporate the following:
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Identify underperforming assets based on utilisation, availability and running costs.
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Monitoring performance after modifications or changes related to failures (MTTR and MTBF).
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Identifying assets that show signs of premature ageing or approaching end-of-life.
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Evaluation of a repair versus a replacement strategy.
An example for the measurement of overall equipment effectiveness is adopting a condition-based maintenance tactic such as vibration analysis, oil analysis, or thermography surveys, which can be evaluated with the asset’s mean time between failure (MTBF) and mean time to repair (MTTR). These will have a direct effect on business goals and the alignment thereof.
When utilising these basic techniques and data analysis, the overall plant effectiveness will prove sustainable. Over time, the data quality will incrementally be improved, which will result in improved information quality and reliable decision making. The adoption of a continuous monitoring programme utilising data analytics reap the benefits of improved asset performance with increased return on investment.
With our integrated solutions, Martec is perfectly positioned to assist companies with the improvement of their asset value maturity.
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