An Integrated Approach to Level of Repair Optimization

In this paper an integrated approach to iteratively performing level of repair analysis (LORA) is presented. Performing LORA requires a significant amount of product data; data that is typically maintained in a Logistics Product Data (LPD) database. LPD standards such as SAE GEIA-STD-0007 (GEIA-STD-0007) and ASD/AIA S3000L (S3000L) simplifies data interchange between the LPD and LORA analytics tools and optimizers. To efficiently solve the level of repair optimization problem, it is shown how existing optimization methods can be extended to better support iterative LORA evaluations. In the case study, a data pipeline is developed that takes logistics product data in a standardized format and builds and executes a level of repair optimization. It is observed that most data elements needed in the optimization model are found in the standards, but it is necessary to supplement with data specific for LORAproblems. It is discussed how the proposed approach can be expanded to feed back the result of the LORA into the LPD, e.g., which components are repaired, and which components are reordered.