Print Email Facebook Twitter Identifying strategic maintenance capacity for accidental damage occurrence in aircraft operations Title Identifying strategic maintenance capacity for accidental damage occurrence in aircraft operations Author Narayanan, Prasobh (Student TU Delft) Verhagen, W.J.C. (TU Delft Air Transport & Operations) Dhanisetty, V.S.V. (TU Delft Air Transport & Operations) Date 2019 Abstract Airline operators face accidental damages on their fleet of aircraft as part of operational practice. Individual occurrences are hard to predict; consequently, the approach towards repairing accidental damage is reactive in aircraft maintenance practice. However, by aggregating occurrence data and predicting future occurrence rates, it is possible to predict future long-term (strategic) demand for maintenance capacity. In this paper, a novel approach for integration of reliability modelling and inventory control is presented. Here, the concept of a base stock policy has been translated to the maintenance slot capacity problem to determine long-term cost-optimal capacity. Demand has been modelled using a superposed Non-homogeneous Poisson Process (NHPP). A case study has been performed on damage data from a fleet of Boeing 777 aircraft. The results prove the feasibility of adopting an integrated approach towards strategic capacity identification, using real-life data to predict future damage occurrence and associated maintenance slot requirements. Subject aircraft maintenanceinventory controlstochastic processstrategic capacity identification To reference this document use: http://resolver.tudelft.nl/uuid:2ba23185-5a96-408a-914f-a71f7751053f DOI https://doi.org/10.1080/23270012.2019.1570364 ISSN 2327-0012 Source Journal of Management Analytics, 6 (1), 30-48 Part of collection Institutional Repository Document type journal article Rights © 2019 Prasobh Narayanan, W.J.C. Verhagen, V.S.V. Dhanisetty Files PDF Identifying_strategic_mai ... ons_1_.pdf 1.3 MB Close viewer /islandora/object/uuid:2ba23185-5a96-408a-914f-a71f7751053f/datastream/OBJ/view