Manual reporting is still quite common within aviation, although the amount of data made available in systems as well as being produced daily by not only sensors of aircraft but also by us humans has increased tremendously the last years. In many other industries data driven decision making has become one of the top priorities as:
- Data is unambiguous; Data shows the big picture, the facts! Decision-making can be made on facts rather than a gut feeling. By analysing data, we are provided with objective answers.
- Data driven decisions ensure that any efforts and resources are heading towards the right goals & objectives and incraese revenue.
- Data can be used to improve processes to increase efficiency and save costs.
In order to be able to get most out of your data, data integrity and accessibility is rather important. In our blog 'How to refine data capture and collection to implement predictive maintenance' we have highlighted several steps you can take in order to achieve this.
If data integrity and standards are not an issue for your airline, the opportunity of applying predictive maintenance is within reach, hence doing data driven decision making in aircraft maintenance.
Predictive maintenance within aviation is a rather important topic as it is seen among airlines as a means to combat rising material costs and improve labour productivity and shortage in the future.
Based on years of working with airlines and aircraft data, within EXSYN we characterize three different types of airline:
- Reporting airline
- Monitoring airline
- Data driven airline
Each of these three types of airlines stand on different success levels for adoption of predictive maintenance - doing data driven decision making in aircraft maintenance
Let’s have a closer look at the three types of airline:
The reporting airline
The reporting airline is characterized by producing monthly reliability reports, drawing from manual work of retrieving the various information such as aircraft utilization, Pilot reports, Maintenance findings and component removals and then consolidate this data into Excel.
This typically is a very manual labour-intensive process and puts the reporting airline in a situation that they are able to produce the reports on monthly bases but not action on these reports.
The monitoring airline
The monitoring airline, stands 1 level higher on the adoption success scale of becoming predictive. This type of airline typically already moved away from excel reports and uses an analytical tool and mostly also dashboards. This allows the airline to act more quickly on rising issues and monitor the fleet than rather reporting the status of their fleet.
However, it still draws on the same set of data, the SPEC2000 chapter 11 reliability data. Which ultimately prevents the airline of adopting any predictive maintenance models as data sets are to limit to become reliable enough for operational usage.
The data driven airline
The data driven airline, has the highest level of adoption success for predictive maintenance models. It used all character traits of the monitoring airline but has recognized the need of having access to larger sets of data beyond its own airline data, such as industry reliability data, Flight Data Recorder information, weather data as well as ADS-B transponder data. Ultimately the data driven airline implements a data driven platform in its organization that provides information to its operational units such as maintenance operations control to make informed decisions in day-of-operation itself.
The question is where are you currently standing, are you ready for predictive maintenance and doing data driven decision making in aircraft maintenance?
Take the questionnaire to figure it out:
Any airline wanting to make use of the benefits that predictive maintenance brings to its operation will first need to start with the evaluation of which type of airline they are today. The outcome of this question will determine the roadmap that will need to be laid down to get to the point of highest success adoption of being able to implement predictive maintenance principles in the airline business.