Turn around time prediction of aero engines grouped based on specific repair

Abstract

Aircraft Engine Overhaul Divisions overhaul aero engines that have encountered

unexpected failure during flight operation. The process of overhauling damaged

parts sometimes doesn't involve only refurbishing damaged parts or component

replacement, but also investigating into the root cause behind failure. It is mandated

by aviation authorities to find out reasons behind an engine failure as flight safety

has to be accounted for. In an Engine Overhaul Division (EOD) every engine needs

to be overhauled within a certain fixed number of days or overhauling Tum Around

Time (TAT). But when engines need to be repaired for certain failure and

investigated into failure, the process can consume extra days from the fixed Engine

Tum Around Time (TAT) without keeping up with the target. This delay can have

an impact on engine availability for airline customers as airlines would have

planned flight operations based on availability of engines. Delay or unavailability of

engine can entail costs in terms of fleet management and also human factors, if a

failure repeats itself leading to catastrophe. This also reflects on the productivity

and competitiveness of the EOD.

This study focuses on predicting the delay encountered by engines which have a

specific repair and investigation requirement. The repair engines overhauled in an

Engine Overhaul Division are classified into six groups based on similar failure

symptoms. Based on past engine data, the delays in TAT of engines are calculated

and suitable forecasting methods are identified to predict future delays. Two

forecasting models, namely the Method of Moving Averages and Non Linear Grey

Bernoulli method are used to generate suitable forecasts. Accuracy of the forecast is

evaluated using error diagnostic Mean Absolute Percentage Error (MAPE). The

results are compared between the actual recorded values and the predicted values

generated by the forecasting methods. On comparing the results it was found that

both forecasting methods have performed well with reasonable forecasting power.

Also, one group of investigation demonstrated high MAPE possibly suggesting

improper regrouping.