Optimizing Shipping Operations through Real-Time Monitoring and Control
A Decision Support System for Container Stripping Processes
:
https://doi.org/10.9744/jti.25.1.43-52
Keywords:
Shipping logistics, data mining, container stripping, tardiness, Google Data Studio, performance dashboardAbstract
The shipping industry plays a vital role in the global economy, with container shipping being one of the critical components. Shipping companies outline the time for customer stripping days in its contracts. The availability of the containers depends on the stripping days. The stripping days’ tardiness will hinder the availability of the containers. Therefore, it is fundamental for shipping companies to monitor both the actual condition and the contract condition of stripping days to estimate container availability and prompt customers to expedite the unloading process. However, there has yet to be a tool for monitoring the actual and the contract conditions. In this study, we used the recorded container stripping data to analyze container stripping days, tardiness, and other important parameters that indicate the performance and reliability of stripping containers. These data were post-processed and analyzed using data mining methods, and the resulting information was visualized using a dashboard to facilitate quick and effortless monitoring the dashboard in this study depicts post-processed data on container stripping days and tardiness for each port of discharge, cargo, customer, and other parameters. The dashboard was constructed using Google Data Studio. As a result, the dashboard is expected to help companies monitor, control, and analyze customers with high tardiness, allowing companies to act and ensure that the number of available containers after stripping meets demand at a given time.
References
The Economic Commission for Latin America, “The shipping cycle in the international container market: Which will be the actual shipping cycle in the future of shipping, the traditional or a new one?” [Online]. Available: https://www.cepal.org/en/notes/shipping-cycle-international-container-market-which-will-be-actual-shipping-cycle-future, Apr. 05, 2017.
International chamber of shipping, “International chamber of shipping: Annual review,” [Online]. Available: https://www.ics-shipping.org/wp-content/uploads/2019/06/ics-annual-review-2019-min.pdf, 2019.
A. Grzelakowski, “Global container shipping market development and Its impact on mega logistics system,” TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, vol. 13, no. 3, pp. 529–535, 2019, doi: 10.12716/1001.13.03.06.
J. Karmelić, Č. Dundović, and I. Kolanović, “Empty container logistics,” PROMET - Traffic&Transportation, vol. 24, no. 3, pp. 223–230, Jan. 2012, doi: 10.7307/ptt.v24i3.315.
C. Martius, L. Kretschmann, M. Zacharias, C. Jahn, and O. John, “Forecasting worldwide empty container availability with machine learning techniques,” Journal of Shipping and Trade, vol. 7, no. 1, p. 19, Dec. 2022, doi: 10.1186/s41072-022-00120-x.
H. Gençer, and M. H. Demir, “Optimization of empty container repositioning in liner shipping,” Business and Management Horizons, vol. 8, no. 1, p. 1, Jan. 2020, doi: 10.5296/bmh.v8i1.16327.
A. Budipriyanto, M. D. Novianti, and T. Susanto, “Empty container reposition using max-min review system: Simulation approach,” IOP Conference Series: Materials Science and Engineering, vol. 1072, no. 1, pp. 012048, Feb. 2021, doi: 10.1088/1757-899X/1072/1/012048.
M. Chen et al., “Data, information, and knowledge in visualization,” IEEE Computer Graphics and Applications, vol. 29, no. 1, pp. 12–19, Jan. 2009, doi: 10.1109/MCG.2009.6.
Port Technology International, “Hapag-Lloyd to improve vessel transparence following covid congestion and Suez obstruction,” [Online]. Available: https://www.porttechnology.org/news/hapag-lloyd-to-improve-vessel-transparency-following-covid-congestion-and-suez-obstruction/, Jun. 04, 2021.
J. Oktaviani, S. Halim, and Felecia, “Dashboard design of shipping container reparation,” in presented in International Conference on Global Optimization and Its Applications 2022 (ICoGOIA 2022), Melaka, Nov. 2022.
GDS, “Google Data Studio,” [Online]. Available: https://datastudio.withgoogle.com, 2023.
R-Software, “R-Project,” [Online]. Available: r-project.org, 2022.
H. Cahyono, and I. N. Pujawan, “Analysis of empty containers supplies in the shipping network: Case study at national shipping company,” IPTEK Journal of Proceedings Series, vol. 0, no. 5, p. 441, Dec. 2019, doi: 10.12962/j23546026.y2019i5.6390.
J. Enström, A. Eriksson, L. Eliasson, A. Larsson, and L. Olsson, “Wood chip supply from forest to port of loading- A simulation study,” Biomass Bioenergy, vol. 152, pp. 106182, Sep. 2021, doi: 10.1016/j.biombioe.2021.106182.
J.-G. Kim, H.-B. Jun, J.-Y. Bang, J.-H. Shin, and S.-H. Choi, “Minimizing tardiness penalty costs in job shop scheduling under maximum allowable tardiness,” Processes, vol. 8, no. 11, pp. 1398, Nov. 2020, doi: 10.3390/pr8111398.
X. Shu and Y. Ye, “Knowledge discovery: Methods from data mining and machine learning,” Social Science Research, vol. 110, pp. 102817, Feb. 2023, doi: 10.1016/j.ssresearch.2022.102817.
C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data,” Front Energy Research, vol. 9, Mar. 2021, doi: 10.3389/fenrg.2021.652801.
F. Coenen, “Data mining: Past, present and future,” The Knowledge Engineering Review, vol. 26, no. 1, pp. 25–29, Feb. 2011, doi: 10.1017/S0269888910000378.
A. Kassambara, Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning, 1st ed. Statistical Tools for High-Throughput Data Analysis, 2017.
Y. Lei, D. Yu, Z. Bin, and Y. Yang, “Interactive K-Means clustering method based on user behavior for different analysis target in medicine,” Computational and Mathematical Methods in Medicine, vol. 2017, pp. 1–9, 2017, doi: 10.1155/2017/4915828.
J. A. Hartigan, and M. A. Wong, “Algorithm AS 136: A K-means clustering algorithm” Applied Statistics, vol. 28, no. 1, pp. 100, 1979, doi: 10.2307/2346830.
M. Telgarsky and A. Vattani, “Hartigan’s method: K-means clustering without Voronoi” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), Chia Laguna Resort, Sardinia Italy, 2010, pp. 820–827.
C. Plaisant, “The challenge of information visualization evaluation,” in Proceedings of the working conference on Advanced visual interfaces, New York, NY, USA: ACM, May 2004, pp. 109–116. doi: 10.1145/989863.989880.
R. Toasa, M. Maximiano, C. Reis, and D. Guevara, “Data visualization techniques for real-time information- A custom and dynamic dashboard for analyzing surveys’ results,” in 2018 13th Iberian Conference on Infor¬mation Systems and Technologies (CISTI), IEEE, Jun. 2018, pp. 1–7. doi: 10.23919/CISTI.2018.8398641.
R. Brath, and M. Peters, “Why dashboard design is important,” [Online]. Available: http://cs.furman.edu/~pbatchelor/csc105/articles/TUN_DM_ONLINE.pdf, Oct. 04, 2004.
A. Y. Yap, “Creating business analytics dashboard designs using visualization methodologies: Case methods for innovative analytics pedagogy,” Information Systems Education Journal, vol. 18, no. 5, pp. 25–33, 2020.
Support Google, “Upload csv files to Looker Studio,” [Online] https://support.google.com/looker-studio/answer/7333350?hl=en#zippy=%2Cin-this-article, 2022.
D. Usman, and F. S. Stores, “On some data pre-processing techniques for K-means clustering algorithm,” Journal of Physics: Conference Series, vol. 1489, no. 1, pp. 012029, Mar. 2020, doi: 10.1088/1742-6596/1489/1/012029.
A. Makles, “Stata tip 110: How to get the optimal K-means cluster solution,” The Stata Journal: Promoting Communications on Statistics and Stata, vol. 12, no. 2, pp. 347–351, Jun. 2012, doi: 10.1177/1536867X1201 200213.
M. Ahmed, R. Seraj, and S. M. S. Islam, “The K-means algorithm: A comprehensive survey and performance evaluation,” Electronics (Basel), vol. 9, no. 8, pp. 1295, Aug. 2020, doi: 10.3390/electronics9081295.
S. Xu, X. Qiao, L. Zhu, Y. Zhang, C. Xue, and L. Li, “Reviews on determining the number of clusters,” Applied Mathematics & Information Sciences, vol. 10, no. 4, pp. 1493–1512, Jul. 2016, doi: 10.18576/amis/100428.
Downloads
Published
How to Cite
Issue
Section
License
Articles published in the Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri will be Open-Access articles distributed under the terms and conditions of the Creative Commons Attribution License (CC BY).
This work is licensed under a Creative Commons Attribution License (CC BY).
