Journal of Quality Engineering and Management

Abstract Determining the Factors Influencing the Prediction of Helicopter Rotor Failures

Purpose: The purpose of this paper is to investigate and identify the variables that influence the occurrence of helicopter accidents caused by different types of rotor failures. These crucial factors include flight conditions, maintenance conditions, and helicopter configuration. With this approach, accidents can be investigated more effectively and flight safety can be significantly improved.

Methodology: By analyzing 135 rotor faults accident from a comprehensive dataset containing 5652 helicopter-related accidents, eight classes of rotor faults were identified. Based on expert surveys and a review of studies in the field of helicopter accidents, nine features were proposed as crucial factors to such accidents. The significance of these factors was assessed using five feature selection methods. The input features included maximum takeoff weight, flight hours since the last inspection, type of last inspection, engine power, flight hours, altitude, wind speed, wind direction, and flight phase. Five well-known feature selection techniques—Correlation Matrix, Extreme Gradient Boosting (XGBoost), Mutual Information, Deep Learning, and Neural Network—were employed to identify the most essential factors.

Findings: "Maximum weight", "helicopter engine power", "flight phase" and "flight hours" were identified as variables with the highest degree of importance in predicting faults class of helicopter rotor, which also have a strong and acceptable justification in flight mechanics.

Originality/Value: The distinction of the present study from similar works lies in the inclusion of a broader range of variables, such as flight conditions and helicopter configuration, in contrast to previous studies that considered only a limited set of variables. By prioritizing these variables, the findings pave the way for proactive measures to prevent rotor faults, aiming to enhance prediction accuracy, reliability, and flight safety.

Abstract Purpose: This paper examines how blockchain technology can improve reliability and operational performance in hospital pharmaceutical supply chains with a focus on inventory variability and responsiveness to demand.
Methodology: A system dynamics model of a three-echelon chain (manufacturer–distributor–hospital) is developed. Two information-sharing scenarios are compared: a traditional setting with centralized, delayed information and a blockchain setting with real-time, decentralized data sharing.
Findings: Results indicate that blockchain adoption enhances behavioral stability, reduces the persistence of hospital backlog, and shortens mean delivery lead time. Specifically, mean lead time decreases by ~15.1% and mean hospital backlog decreases by ~15.8% (both statistically significant). However, the difference in mean hospital inventory is not significant; stability improves, with inventory SD decreasing by ~21.5% and lead-time SD decreasing by ~10%. Taken together, these effects strengthen service reliability and overall supply-chain performance.
Originality/Value: By integrating blockchain-based decentralized data sharing with system dynamics modeling in the hospital pharmaceutical context, this study provides quantitative evidence of how transparency supports quality-oriented supply chain management.