Investigating Efficiency Utilizing Data Envelopment Analysis: Case Study of Shipyards

Mayo, Glenda; Shoghli, Omidreza; Morgan, Tyler R.

doi:10.1061/(asce)is.1943-555x.0000541

Cited by 10 publications

(9 citation statements)

References 15 publications

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“…At the same time, the implementation of floating offshore wind platforms in shipyards was addressed by Castro-Santos et al 51,52 Energy efficiency decisions and the selection of main engines in a sustainable shipyard supply chain were addressed by Xie et al 53 and Krishnan et al 54 examined optimal cargo handling in a shipyard. Meanwhile, the utilization of data for maintenance and repair activities in shipyards was investigated by Mayo et al 55 and Praharis et al 56 studied the improvement of energy efficiency with respect to the material supply chain through the development of modeling. Given the extensive literature review, the authors have identified the following research gaps:…”

Section: Of Operational Co 2 Emissionsmentioning

confidence: 99%

“…140 In alignment with that operation of the shipyards such as Resource Management (Demand response management), 141 Project Management, 142 Lean Approach, 143,144 Production Planning and Strategy, for example, Integrated Hull Construction, Outfitting, and Painting (IHOP), 37 and Optimizing shipyard design 145 was chosen as another column in the EnMF because it can potentially influence productivity, efficiency, and manpower requirements in shipyards. 55 Moreover, policy and regulation discipline including Environmental regulations (International, Regional, National, and Local), 146 Life Cycle orientation, 147 Green Products, 148 Innovation, sustainability, and competitiveness, 149 Energy Management System (ISO 50001), 150 Environment Management System (ISO 14001), 151 Cyber security, 152 Circular Economy, 153 and Voluntarily agreement 154 is one of the most important factors to assist the shipping industry in achieving its ambitious goal for a substantial and swift reduction for carbon emissions reduction and the sociotechnical transition within the sector. 155 Finally, economic factors and analysis including Financial Source, 156 Cost of energy, 157 Levelized cost of energy, 52 Lifecycle cost, 48 Societal cost, 158 Economic competitiveness, 31 Cost saving, 157 Incentive 159 due to its important role in decision making and competitiveness within the shipyard and the maritime cluster 160 were chosen as the fifth discipline in the EnMF.…”

Section: Concept Of Enmfmentioning

confidence: 99%

See 1 more Smart Citation

Energy‐related clean and green framework for shipbuilding community towards zero‐emissions: A strategic analysis from concept to case study

Vakili

Ölçer

Schönborn

et al. 2022

Intl J of Energy Research

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Shipping is looking to progressively develop into a zero-emissions industry, in agreement with pledges made under the Initial IMO Strategy on reduction of greenhouse gas emissions from ships, and its vision to phasing out greenhouse gas emissions as soon as possible during this century. International regulations currently focus on the design and operational phases of the industry, which carries the largest life-cycle climate impact of a ship. As the level of emissions from the operational phase is reduced, the inclusion of emissions from shipyards during construction, maintenance, and disposal becomes increasingly important. The main objective of this study is to improve energy efficiency and reduce air emissions in the shipbuilding industry through the development of an energy-management framework. A systematic literature review was used to define criteria for clean and green shipyards and to develop a novel, holistic, systematic, and transdisciplinary framework for energy management. The energy-management framework is aimed at creating a decision-support mechanism for complex situations, using a multiple criteria decision-making approach. The energy-management framework was applied to case studies of a Bangladeshi and an Italian shipyard. The potential implementation of the framework in yards of different sizes, types and geographical location demonstrates that it has the potential to improve energy management and thereby energy efficiency while increasing productivity and profitability in shipyards.

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Section: Of Operational Co 2 Emissionsmentioning

confidence: 99%

Section: Concept Of Enmfmentioning

confidence: 99%

Energy‐related clean and green framework for shipbuilding community towards zero‐emissions: A strategic analysis from concept to case study

Vakili

Ölçer

Schönborn

et al. 2022

Intl J of Energy Research

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“…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.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Rabar¹,

Rabar²,

Pavletic³

2022

IJTech

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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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“…Following the development of KPS, to successfully promote the spread of smart factories, the Smart Manufacturing Innovation Planning Division of the Bureau of SMEs developed a diagnostic tool that can present plans for smart factory construction, with the goal of objectively diagnosing and assessing the manufacturing industry's smart maturity level [25] (see Table A3). This diagnostic tool follows ISO 9001:2015 (management system), IEC 62264 (manufacturing operation system), ISO 22400, and SCOR (KPI); and was developed for certifying factory operation systems and designed to be linked with in-house enterprise certification systems, reflecting the culture and characteristics of the Korean manufacturing industry (Samsung Electronics, Hyundai Motor Company, POSCO, etc.).…”

Section: Appendix B Korea Production System and Smart Factory Assessmentioning

confidence: 99%

“…Their studies extended beyond a qualitative analysis; through DEA, they expressed the relative levels of the most advanced shipyards and those lagging behind them, thereby presenting directions for improvement and quantitative improvement levels. A shipyard efficiency analysis using DEA was also introduced [25]. Turnover/cost values were derived by combining various shipyard production indicators to compare the actual competitiveness of shipyards [26].…”

Section: Introductionmentioning

confidence: 99%

Assessment Framework of Smart Shipyard Maturity Level via Data Envelopment Analysis

et al. 2021

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The fourth industrial revolution (“Industry 4.0”) has caused an escalating need for smart technologies in manufacturing industries. Companies are examining various cutting-edge technologies to realize smart manufacturing and construct smart factories and are devoting efforts to improve their maturity level. However, productivity improvement is rarely achieved because of the large variety of new technologies and their wide range of applications; thus, elaborately setting improvement goals and plans are seldom accomplished. Fortunately, many researchers have presented guidelines for diagnosing the smartness maturity level and systematic directions to improve it, for the eventual improvement of productivity. However, most research has focused on mass production industries wherein the overall smartness maturity level is already high (e.g., high-level automation). These studies thus have limited applicability to the shipbuilding industry, which is basically a built-to-order industry. In this study, through a technical demand survey of the shipbuilding industry and an investigation of existing smart manufacturing and smart factories, the keywords of connectivity, automation, and intelligence were derived and based on these keywords, we developed a new diagnostic framework for smart shipyard maturity level assessment. The framework was applied to eight shipyards in South Korea to diagnose their smartness maturity level, and a data envelopment analysis (DEA) was performed to confirm the usefulness of the diagnosis results. By comparing the DEA models, the results with the smart level as an input represents the actual efficiency of shipyards better than the results of conventional models.

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Investigating Efficiency Utilizing Data Envelopment Analysis: Case Study of Shipyards

Cited by 10 publications

References 15 publications

Energy‐related clean and green framework for shipbuilding community towards zero‐emissions: A strategic analysis from concept to case study

Energy‐related clean and green framework for shipbuilding community towards zero‐emissions: A strategic analysis from concept to case study

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

Assessment Framework of Smart Shipyard Maturity Level via Data Envelopment Analysis

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