Data mining for MRO process optimization

The aircraft maintenance process is often characterized by unpredictable process
times and material requirements. Data mining seems to be a promising way to tackle
the problem of unpredictability in Maintenance Repair and Overhaul (MRO).
An applied research project was organized across 30 case studies for eight different
aviation MRO companies. The main research question is: How can SME MRO’s use
fragmented historical maintenance (and other) data to decrease maintenance costs
and increase aircraft uptime?
Three categories of data mining approaches where developed:
1. Visualization, for example development of KPI’s and dashboards to identify
maintenance tasks that are executed long before they should be performed
according to the maintenance instructions.
2. Statistical Data Mining for example prediction of the remaining useful lifetime
parts and prediction of required man hours in MRO tasks using time series
analysis.
3. Machine Learning, for example the identification of the factors related to low
maintenance lead-time accuracy. Other examples are the evaluation of the
prediction accuracy of seven machine learning methods, and the analysis of
maintenance records using automated natural language processing.
This research delivered important conclusions and recommendations: Maintenance
companies should organize their data not only for compliance goals but also for
prediction. The availability of external data from airline operators, suppliers and
OEM’s have to be improved by addressing confidentiality and ownership issues.
Sensors should be added to measure certain characteristics that are related to the
failures of the components in an aircraft. Data visualization is a natural starting point
in data analytics and has proven to be very useful. Companies should introduce data
scientists into their organization and train operational management and mechanics.
Overall, the ‘Data Mining in MRO’ process optimization research project delivered
promising proofs of concept and pilot implementations.

In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process.

To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry.

This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved.

This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.