Penerapan Data Driven Decision Making dalam Perspektif Pemilik dan Operator Kapal

Authors

  • Marsellinus Bachtiar Wahju Universitas Katolik Indonesia Atma Jaya

DOI:

https://doi.org/10.25170/cylinder.v10i2.6018

Keywords:

Maritime Industry, Decision making, Ship management

Abstract

The maritime industry’s crucial role in the global economy is vital, serving as the lifeline for the distribution of commercial goods worldwide. Data-Driven Decision Making (DDDM) in the maritime sector has the potential to be a game changer. By utilizing data obtained from sensors, communication tools, and integrated systems, operators can optimize fleets, improve fuel efficiency, and enhance maintenance scheduling. From the perspective of ship owners/operators, decisions regarding scheduling, operations, docking, and crew management are prioritized to ensure company profitability. Current business practices necessitate exploring the benefits of using DDDM in decision-making for ship owners/operators. The use of Big Data and DDDM approaches offers advantages such as cost efficiency and improved fleet performance. Data collection and analysis in the maritime sector include various methods, such as Automatic Identification Systems (AIS), weather data, and onboard sensor equipment. Implementing DDDM presents several challenges from the perspective of ship owners/operators, including data quality and availability, system integration, high implementation costs, regulatory compliance, and resistance to change.

References

E. Brynjolfsson, L. M. Hitt, , & H. Kim, Strength in numbers: How does data-driven decision-making affect firm performance? (SSRN Scholarly Paper ID 1819486.2011 doi : https://doi.org/10.2139/ssrn.1819486

Munim, Z. H., Dushenko, M., Jimenez, V. J., Shakil, M. H., & Imset, M. “Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions. Maritime Policy & Management, Vol 47(5), pages 577-597, 2020.

Durlik, I., et al. Navigating the Sea of Data: A Comprehensive Review on Data Analysis in Maritime IoT Applications. Applied Sciences, 13(17), 9742, 2023.

S. Aslam, H. Herodotou, E. Garro, A. Martínez-Romero, M. A. Burgos, A. Cassera, A., G. Papas, & M. P. Michaelides, IoT for the Maritime Industry: Challenges and Emerging Applications. Proceedings of the 18th Conference on Computer Science and Intelligence Systems, pages 855–858, 2023.

Á. Szukits, & P. Móricz,. Towards data-driven decision making: the role of analytical culture and centralization efforts. Review of Managerial Science, 18(10), pages 2849–2887, 2023.

T.Moi, A. Cibicik, T. Rølvåg, “Digital twin based condition monitoring of a knuckle boom crane: An experimental study”, Engineering Failure Analysis. Vol. 112, 2020, doi : https://doi.org/10.1016/j.engfailanal.2020.104517. ISSN 1350-6307

Steve Saxon, Benjamin Weber, Qiao Xie, and Apostolos Zampelas, “After a few boom years, the global shipping industry faces a potential downcycle—but increased digitization can help companies prepare for volatility”, McKinsey & Company, 2022 [Online] https://www.mckinsey.com/industries/trav

el-logistics-and-infrastructure/our-insights/how-to-transform-your-shipping-company

R. Buijsse, M. Willemsen, & C. Snijders, Data-Driven Decision-Making. In Data Science for Entrepreneurship pp. 239-277, 2023.

Published

2024-10-29

How to Cite

Wahju, M. B. (2024). Penerapan Data Driven Decision Making dalam Perspektif Pemilik dan Operator Kapal. Cylinder : Jurnal Ilmiah Teknik Mesin, 10(2), 70–76. https://doi.org/10.25170/cylinder.v10i2.6018

Issue

Section

Articles
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