Manufacturing Process Performance Measurement Model using Categorical DEA Approach – a Case of Dry-Docking

Abstract

This paper describes the improved performance measuring model for vessel dry-docking. Dry-docking represents the operation where the vessel is put out of the water to clean and coat the vessels, and equipment check. This model deals with data collected from thirty-four completed dry-dockings, all supported by the Data Envelopment Analysis (DEA) methodology. To solve the limits appearing from extreme values for some vessels, an extension in the form of the categorical model was introduced. By the categorical model implementation, a more precise efficiency measurement was enabled. The performance calculation results contain the efficiency scores for all vessels and target improvements for the inefficient vessels. Inefficiency sources were detected using the DEA methodology, and the proposed solutions are based on process knowledge and data set. This model also introduced and set the parameters for category division and revealed the benchmarks among the studied vessels. The model introduced can be used for efficiency measurement of similar vessels, or as a prediction-based model by introducing vessels with hypothetic data. This model could also be utilized for similar manufacturing processes which can be found in civil engineering, project manufacturing, or transportation. Further research could be conducted based on the slack-based-measure model, respecting the limitation of data homogeneity.