Newbuilding dry-docking is a part of the shipbuilding manufacturing process, common for vessels built on slipways. The subject of this research is steel-built vessels intended for non-restricted sea-going navigation. Based on former experience, the necessity of the dry-docking projects measurement has been noted as a managerial tool for performance estimation and project comparison. The dry-docking project is a complex task which includes the first self-propelled sea passage and the transfer of the manufacturing process to a remote place. The dry-docking result is a surveyed and coated vessel ready for sea trials and five-year service until the next dry-docking. This paper deals with a model which enables process measurement using the analytic hierarchy process (AHP) method for qualitative data related to the dry-docking places and data envelopment analysis (DEA) for quantitative data related to the vessels’ technical and cost data. The modelled data are collected from the completed dry-dockings, and the twenty-nine studied vessels represent the decision-making units (DMU) used in two-step process measurement calculations. The obtained results can distinguish the efficient DMUs, which create an efficient frontier as benchmarks or “the best practice units” in the given DMU set. For the non-efficient DMUs, the efficiency score and rate of improvements needed to reach the efficient frontier will be calculated, and the sources of inefficiency will be recognized.
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 drydockings, 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-basedmeasure model, respecting the limitation of data homogeneity.
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