What learnings from F1® and aerospace can be applied to resources?

July 22, 2021

F1® and aerospace are two fields that are often compared to each other, less so with the resources sector – but are the similarities greater than the differences? All three industries operate complex machinery in conditions where safety, reliability and performance are continuously monitored and optimised. Minute operational inefficiencies can have a significant impact on performance and in the worst case, lead to serious accidents. This white paper will explore the differences and commonalities between the industries, and what aspects may be useful to resources.

Defining performance

In order to compare the industries, it is important to first define each industry’s key performance metrics.

The key takeaway is that operational risk is treated as a performance metric for all industries, but with different tolerance thresholds.

Racing cars and aircraft must be 100% reliable whereas the resources industry can accept a certain failure rate – a typical industry standard is 20%. Comparing the costs of downtime helps explain the performance priorities of each sector.

Although it would be technically possible to deploy F1® and aerospace level reliability targets across a mine site, the total cost of production would increase far too much, and it would not be economical. Failure rates have a negative impact on a mine’s balance sheet and performance, but they are harder to observe immediately whereas an F1® car failing to finish 1 out 5 races would be catastrophic for a team, its sponsors and maybe its existence. Likewise, passengers would likely avoid an airline that had a 1% failure rate. 

However, safety is treated seriously in resources. As a result, safety incidents in resources are recorded with the same level of detail as reliability incidents in F1® and aerospace. With that in mind, it is actually aerospace that has to achieve maximum results in all areas.

Incidents are categorised into multiple levels from potential and near misses to actual events. This is important because near misses can be more frequent than actual events, and including them in the analysis provides important insights. Each incident is therefore thoroughly analysed so that the appropriate measures and corrections can be made to prevent them from happening again.

The impact of safety for resources and reliability for F1® and aerospace explains why they are treated and optimised as performance metrics as opposed to risk metrics. To be clear, it is not that resources companies or aerospace are treating humans as machines for analysis, it’s the other way around – F1® are taking a multi-tiered approach to reliability improvement that is similar to safety in other industries.

Understanding reliability

Reliability in resources is a sub-metric of performance, with a significant impact on the bottom line, therefore improving reliability will reduce a mine’s cost of production. Given that the results of operational efficiencies are difficult to perceive by the operating/production teams in the short term, it is understandable why reliability can be less of a priority. Remember in some cases these resources companies are the living definition of “the coal face”, so it is understandable how production can override medium to long term maintenance.

This section will explore how the reliability practices across all 3 industries compare and what resources can learn from F1® and aerospace.

A lot of the differences between resources and the other two industries can be explained by the variation in data collection processes. F1® and aerospace trace the location, status and history of every single part, whereas in resources, parts are recorded in transaction data, typically only when they are “transacted” (note: there are some exceptions for expensive parts such as an engine in a truck, but even then there will typically be many sub-components not tracked). Consequently, reliability engineers in resources must reconstruct the history of each part by manually searching and sorting data – a laborious, error prone and ultimately unsustainable process.

Due to the fierce competition in F1® and aerospace, each racing team and airline have rigorous continuous improvement policies that set a certain standard and cadence for maintenance strategies. As a result, parts are typically over maintained in order to not compromise reliability or performance. Resources on the other hand is dictated by market conditions (i.e. low commodity prices = low $ cost of downtime), and that means their downtime cost is variable and maintenance plans may be varied as a result. 

Core to any continuous improvement process is benchmarking. An F1® team or an airline has direct and immediate measurement of their competitive position, including asset reliability. On the other hand, obtaining clear and unambiguous peer benchmarking for the asset performance of a resources company is more difficult. Let us turn the tables for a moment, and mention quantity of data that is sufficient to make statistical decisions. An F1® team has two cars, and normally there will be 20 racing cars of data across the industry. One large resources company alone may have > 300 of the same type of haul truck operating across all of their mine sites. In respect of data quantity, airlines OEMs are similar to resources companies and hence benefit from statistical analysis of large datasets.

Lastly, the maintenance work processes in F1® and aerospace are tightly refined – part of their standardised work methodology. This is demonstrated by 2 second pitstops, where variability is contained through standardisation which is a key driver of performance and savings. Maintenance processes in resources are not as rigidly standardised, and work variations are accepted. This can degrade reliability through quality of work and is problematic for benchmarking as described above.


Recommendations for the resources industry

It is easy to waste > US$20m per annum due to unoptimized reliability on a medium sized resources operation with approximately 30 trucks and associated equipment (dozers, excavators, etc). Improving and standardising maintenance processes can improve performance significantly and reduce variation in mine performance.

An important step is to better capture and monitor maintenance data. It is not currently economical to install sensors and barcodes on every single part, as in F1®. It is also not necessary if we consider the cost of downtime between industries. However, the transactional data that connects front line maintainers to reliability engineers can be improved significantly and easily – with fast results.

Independent third-party maintenance solutions like IronMan® can serve as a benchmark with regards to the best practices all the way from frontline data quality, through to optimising strategies and master data. IronMan® processes >10 million transactions every week and enables reliability engineers to keep track of every part and perform their analysis in minutes as opposed to weeks.

About Ox Mountain (OXMT) and IronMan®

OXMT was founded in Oxford, UK by engineering domain experts from F1®, aerospace and resources, who have deep understanding of the issues with asset management in capital intensive industries.

IronMan® operates as software as a service (SaaS), and requires minimal training, because it was designed by engineers for maintenance people. No matter what data quality issues you think you may have, IronMan® has always found meaningful insights and savings, and remember if you do not start improving, do not expect to see change. If you would like to extract actionable insights and value from your transactional data, contact us now.

© Ox Mountain Limited 2020


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