Manufacturing Process Performance Measurement Model Using Categorical DEA Approach – a Case of Dry-Docking
Published at : 01 Jul 2022
Volume : IJtech
Vol 13, No 3 (2022)
DOI : https://doi.org/10.14716/ijtech.v13i3.5457
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
| 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 |
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
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.
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.
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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