• International Journal of Technology (IJTech)
  • Vol 13, No 3 (2022)

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

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

Title: Manufacturing Process Performance Measurement Model Using Categorical DEA Approach – a Case of Dry-Docking
Danijela Rabar, Denis Rabar, Duško Pavletic

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Cite this article as:
Rabar, D., Rabar, D., Pavletic, D., 2022. Manufacturing Process Performance Measurement Model Using Categorical DEA Approach – a Case of Dry-Docking. International Journal of Technology. Volume 13(3), pp. 484-495

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Danijela Rabar Juraj Dobrila University of Pula, Faculty of Economics and Tourism “Dr. Mijo Mirkovic”, Preradoviceva 1/1 52100 Pula, Croatia
Denis Rabar independent researcher, Nazorova 34, 52100 Pula, Croatia
Duško Pavletic University of Rijeka, Faculty of Engineering, Vukovarska 58, 51000 Rijeka, Croatia
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Abstract
Manufacturing Process Performance Measurement Model Using Categorical DEA Approach – a Case of Dry-Docking

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.

Data envelopment analysis; Dry-docking; Manufacturing; Performance measurement; Shipbuilding

Introduction

1.1. Dry-docking process

This paper describes the dry-docking performance measurement model based on Data Envelopment Analysis (DEA) methodology. DEA is a linear-programming-based, non-parametric, multi-criteria decision-making method. This method is applied to a population of thirty-four vessels under the final stage of construction (also called newbuildings), which are transferred by their own propulsion to the repair shipyard where they are lifted up from the water. During the vessel’s dry-docking, the underwater part is cleaned, checked, and recoated. Upon undocking, the vessel is ready for sea trials and a five-year  service period until the subsequent dry-docking. The dry-docking project is a work-intensive, cost-sensitive process carried out in a remote place linked with logistic challenges. Therefore, the necessity for a performance measurement model creation has appeared. The management staff involved in the newbuilding dry-docking is faced with a huge amount of technical and business data. Consequently, the performance targets are needed for proper managerial decision-making.
    The scope of the mainline of dry-docking activities is: i) vessel’s outer shell underwater part cleaning from fouling and grease by means of  high-pressure washing and solvents application, ii) steelwork such as launching the supporting structure removal, and shell plates welding/grinding, iii) spot grit blasting to remove damages, and coating system application, usually four touch-ups and one full coat, iv) check-ups of the main propulsion, steering, sea chests, side thrusters and  underwater sensors.
    The dry-docking period is also used to carry out the vessel’s systems check before the upcoming sea trials. This time frame could be used to complete works that were delayed in the previous time while the vessel remained berthed in the shipyard. The place of vessel dry-docking depends on the following factors: i) dry-docking place suitability based on vessel weight and overall dimensions, ii) dedicated dry-dock facility availability in a scheduled time window, iii) forecasted weather conditions.
    The DEA methodology allows the dry-docking to be described as a process determined by its inputs and outputs for performance measurement purposes. The categorical DEA model is going to be used in order to refine the process research and efficiency measurement.
    The performance measurement model is to be improved compared to the basic one formulated in the paper prepared by (Rabar et al., 2021).

1.2. Literature review

The literature review shows a limited number of papers dealing with the dry-docking practice.  The dry-docking practice has been comprehensively described in (House, 2015). Working activities usually carried out during the regular dry-dockings were described and categorized by (Butler, 2012). The dry-docking cost estimation model was developed by (Apostolidis et al., 2012). For the development of this model, data related to the vessels' age, size, and purpose were used by (Surjandari & Novita, 2013) and (Surjandari et al., 2015) using the Data Mining method and Numerical Ant-Colony Decision Tree algorithm, which considers the dry-docking time as shipyard productivity parameter as well as the vessel's service downtime impact related to dry-docking. The dry-docking data analysis model using linear regression for dry-docking duration, depending on the vessel's size and age, was made by (Dev & Saha, 2015). The improved multiple regression model dealing with the labor needed for dry-docking depending on the vessel size, deadweight, and age was introduced by (Dev & Saha, 2016). Further research improvement in the dry-docking process and labor was completed by (Dev & Saha, 2018). An analysis using the DEA studying twelve repair shipyards in China was published by (Yang & Wang, 2017). The Croatian shipbuilding industry analysis using DEA methodology, assessing the shipyard’s performance over time, identifying sources of inefficiency as well as propositions for increasing performance and altering decisions, was proposed by (Rabar, 2015). The United States East Coast repair shipyards capability estimation using DEA was made by (Mayo et al., 2020) to find the optimal repair solution for the ferry vessel fleet.
    The literature review revealed the useful samples of DEA methodology use, such as (Putri et al., 2016), where the comprehensive performance measurement on the industrial level was carried out. The DEA application in analysing two manufacturing processes was carried out by (Jain et al., 2011), allowing getting a deeper insight into manufacturing process efficiency measurement. A good opportunity for the use of DEA methodology in process optimization was noticed in (Gunawan et al., 2018) where the “ex post” principle in process analysis could be useful.  As referred by (Jandhana et al., 2018), the production function used is a good impulse for performance measurement in project manufacturing processes such as shipbuilding, and consequently, the dry-docking in this particular case.
    The literature review conclusion discloses a gap in research in relation to new building dry-docking performance measurement. The improvements of the existing papers could be achieved using the extended DEA models. This paper should at least partially fill the research gap by introducing the improved new building dry-docking efficiency measurement model using basic DEA model extensions. The DEA categorical model, which is not common in performance measurement research, has been used in this research in order to sort out the researched vessels by their technical characteristics, and it has resulted in the more precise efficiency measurement scores. The authors believe that the proposed methodology could be useful in various manufacturing performance measuring applications.

Conclusion

An improved dry-docking performance measurement model has been introduced in this paper. This model is based on DEA, and it could serve as a benchmarking tool for organization management. The studied dry-docking projects data were adjusted to DEA methodology by expressing the dry-docking process efficiency. The orientation was chosen according to the management strategy to reduce dry-docking overall costs. The dry-docking model uses the DEA advantage that no expert opinion is necessary for input and output weights determination, but the model relies on process knowledge while choosing inputs and outputs. During the research, several DEA model calculations have been carried out. The CCR and BCC models calculations and data set descriptive statistics have been completed, with the conclusion that a kind of distinction among the DMUs needs to be made because six DMUs have superior results due to their technical characteristics. This distinction has been carried out by the categorical DEA approach. During the research, the categorization criteria have been determined based on input and output data. The categories have been established for further calculations. The subsequent DEA calculations were performed with the categorical CCR model after concluding that the CCR model represents the data set in a proper way. The chosen new building dry-docking performance measurement model using the DEA methodology with extension to the categorical CCR model has resulted in the establishment of the efficient frontier that contains the efficient DMUs, giving the efficiency score to each particular DMU and detecting benchmarks determined by categories. The projections to the efficient frontier show the direction and intensity of required improvements of inputs and outputs representing each inefficient DMU. And finally, based on the used data, the inefficiency sources have been detected among the data used and sorted into three main groups: i) technical/technological issues, ii) planning and organization, iii) unfavourable weather conditions. The decision-making process follows the “ex-post” principle, making conclusions on already dry-docked vessels, and giving recommendations for future dry-docking projects. This vessel population could be augmented with the new dry-docked vessels and evaluated in repeated calculations using the same model. The introduced model could be used for performance measurement in other project manufacturing enterprises related to but not limited to shipbuilding, civil engineering, mining, project manufacturing, etc. Further research on this topic could lead to the usage of DEA slack-based-measure (SBM) models in combination with the categories in order to achieve higher rate of discrimination among the DMUs in the same category and probably a more sensitive detection of inefficiencies. The research path for ranking the efficient DMUs’ purpose could lead toward super-efficiency DEA models.

Acknowledgement

This paper is a result of the scientific project Accounting for the Future, Big Data and Economic Measurement, supported by the Faculty of Economics and Tourism “Dr. Mijo Mirkovi?”, Juraj Dobrila University of Pula. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the author and do not necessarily reflect the views of the Faculty of Economics and Tourism “Dr. Mijo Mirkovi?” Pula. This work has been supported by the University of Rijeka (contract no. uniri-tehnic-18-33) and the Croatian Science Foundation under the project IP-2018-01-3739.

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