Grafik Kendali Statistik sebagai Instrumen Pemantauan Efisiensi Industri Pelayaran
DOI:
https://doi.org/10.55606/isaintek.v8i2.356Keywords:
Emission, Statistical Control Chart, Energy Consumption, Green ShippingAbstract
The International Maritime Organization (IMO), as one of the key organizations contributing to the reduction of carbon emissions in the maritime sector, offers several options that maritime industry stakeholders can utilize to measure carbon emissions, one of which is calculating the Energy Efficiency Operational Indicator (EEOI). The statistical x-bar control chart, employed as a monitoring instrument for the EEOI quality parameter, indicates that one container ship trip recorded an EEOI value of 0.00104, which exceeded the upper control limit of 0.00098. The results of process capability analysis on the initial dataset sample revealed that the value of Cp < 1, indicating that energy efficiency performance was not satisfactory. Process control was subsequently applied by eliminating anomalous data, which demonstrated a potential reduction in fuel consumption and EEOI by approximately 25%, as reflected in the improvement of the Cp value. The potential for enhancing the ship’s operational capability, however, still requires further control measures to improve energy efficiency performance. The reduction in fuel consumption directly contributes to lower emission levels, thereby supporting the realization of a sustainable shipping industry.
References
Doty, Leonard A. 1996. Statistical Process Control Second Edition. New York: Industrial Press Inc.
Du, Wei, Yanjun Li, Guolei Zhang, Chunhui Wang, Baitong Zhu, and Jipan Qiao. 2022. “Energy Saving Method for Ship Weather Routing Optimization.” Ocean Engineering 258. doi:10.1016/j.oceaneng.2022.111771.
Hu, Zhihui, Yongxin Jin, Qinyou Hu, Sukanta Sen, Tianrui Zhou, and Mohd Tarmizi Osman. 2019. “Prediction of Fuel Consumption for Enroute Ship Based on Machine Learning.” IEEE Access 7:119497–505. doi:10.1109/ACCESS.2019.2933630.
International Maritime Organization (IMO). 2009. Second IMO GHG.
International Maritime Organization (IMO). 2021. Fourth IMO GHG Study 2020 Executive Summary.
Işıklı, Erkan, Nezir Aydın, Levent Bilgili, and Ali Toprak. 2020. “Estimating Fuel Consumption in Maritime Transport.” Journal of Cleaner Production 275. doi:10.1016/j.jclepro.2020.124142.
Megawati, Sintia, Aulia Siti Aisjah, and Sjarief Widjaja. 2023. “Prediction of Ship Fuel Consumption Due to the Effect of Weather Conditions.” Pp. 786–91 in 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding. Institute of Electrical and Electronics Engineers Inc.
MEPC.1/Circ.684. 2009. Guidelines for Voluntary Use of the Ship Energy Efficiency Operational Indicator (EEOI). London.
Molland, A. F., S. R. Turnock, and D. A. Hudson. 2011. Ship Resistance and Propulsion : Practical Estimation of Ship Propulsive Power. Cambridge University Press.
Montgomery, Douglas C. 2012. Design and Analysis of Experiments. 8th ed. John Wiley and Sons, Inc.
Montgomery, Douglas C., and John Wiley. 2009. Sixth Edition I Ntroduction to Statistical Quality Control. John Wiley and Sons, Inc.
Pangestu Gusti, Ayudhia, Aab Dinariyana, and M. Isa Irawan. 2018. A Study of Decision Making Process for the Most Efficient Steaming Speed of Ship.
Tran, Tien Anh. 2020. “The Novelty Numerical Simulation Method for Reducing the Fuel Oil Consumption and the Greenhouse Gas Emission in Shipping Transportation Industry.” Cogent Environmental Science 6(1). doi:10.1080/23311843.2020.1809072.
Yoo, Byunghyun, and Jinhwan Kim. 2018. “Ship Route Optimization Considering On-Time Arrival Probability under Environmental Uncertainty.” IEEE.
Zhao, Wei, Yan Wang, Zhanshuo Zhang, and Hongbo Wang. 2021. “Multicriteria Ship Route Planning Method Based on Improved Particle Swarm Optimization–Genetic Algorithm.” Journal of Marine Science and Engineering 9(4). doi:10.3390/jmse9040357.
