The warranty data plays a crucial role in the improvement of the manufactured product. Association rule analysis is an efficient methodology for eliciting useful information from warranty data by defining the relationships between production data and failure data within warranty period. We extract association rules from warranty data of heavy duty diesel engine in order to find significant patterns of failures along with manufacturing information. We also used Weibull regression to identify influential factors that affect the variation in mean time between failures which are identified from extracted association rules. The results from the empirical study for manufacturing firm "D" provide information as to the areas in which improvements should be made. Moreover, the result for specific failure is able to suggest a solution to overcome short-sighted improvement representatively. This study expects to contribute quality improvement to manufacturing industry which is under coarse warranty data with enormous unrevealed information to draw meaningful information with ease.
Bibliographical noteFunding Information:
Corresponding author׳s work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIP) ( 2013R1A2A1A09004699 ).
© 2014 Elsevier Ltd.
All Science Journal Classification (ASJC) codes
- Safety, Risk, Reliability and Quality
- Industrial and Manufacturing Engineering