Assessing the factors impacting shipping container dwell time: a multi-port optimization study
Abstract
Ocean transportation is the most preferred mode of transportation that represents a significant role in the global trade. Ocean transportation comprises around 80% of the aggregate worldwide cargo volume. This research paper focused on evaluating the factors that influence the dwell time of the shipping containers. Dwell time is one of the important port performance parameters which evaluates the time spent by the container in a port. In this research, the data from the fourteen major ports was collected and analysed across the variables, such as cycle, size, mode, status, delivery and tracking technology for evaluating the variation in container dwell time. OLS regression method (Ordinary least squares) along with independent sample T test was adopted for the analysis of 2.8 million container data entries utilizing python for big data analysis and SPSS. For the top three ports with lowest RMSE (Root mean square error), Port A – 15.6 %, Port G – 15.7 % and Port L – 15.86 %, a qualitative study was performed to identify the reasons for the variation in dwell time. The major reasons identified included free days period, trans-shipment port, high rail frequency, industrial hubs in the vicinity of the ports for lower dwell time. A qualitative research framework was presented as the research outcomes and reasons for variations in a multiport study.
Keyword : dwell time, port performance, optimization, container, shipping, ocean port
How to Cite
Saini, M., & Lerher, T. (2024). Assessing the factors impacting shipping container dwell time: a multi-port optimization study. Business: Theory and Practice, 25(1), 51–60. https://doi.org/10.3846/btp.2024.19205
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Aminatou, M., Jaqi, Y., & Okyere, S. (2018). Evaluating the impact of long cargo dwell time on port performance: An evaluation model of Douala International Terminal in Cameroon. Archives of Transport, 46(2), 7–20. https://doi.org/10.5604/01.3001.0012.2098
Ayutia, Y., Sirait, D. P., Maligasach, S. S., Ramadhani, R., Hamdi, A., & Krisnawati, S. (2023). Managing the congestion for delivering and receiving truck container at the Tanjung Priok terminal by analyzing the congestion at Koja container terminal. KnE Social Sciences, 8(9), 818–826. https://doi.org/10.18502/kss.v8i9.13395
Brooks, M. R. (2006). Issues in measuring port devolution program performance: A managerial perspective. Research in Transportation Economics, 17, 599–629. https://doi.org/10.1016/S0739-8859(06)17025-0
Brooks, M. R., & Schellinck, T. (2013). Measuring port effectiveness in user service delivery: What really determines users’ evaluations of port service delivery? Research in Transportation Business & Management, 8, 87–96. https://doi.org/10.1016/j.rtbm.2013.04.001
Budiyanto, M. A., Zaki, M. I., & Suhendar, S. B. (2023). Operational effect on the increase of quay cranes to reduce dwelling time at the container terminal. E3S Web of Conferences, 405, Article 04025. https://doi.org/10.1051/e3sconf/202340504025
Chu, C., & Huang, W. (2005). Determining container terminal capacity on the basis of an adopted yard handling system. Transport Reviews, 25(2), 181–199. https://doi.org/10.1080/0144164042000244608
Cooke, J. (2022). 2022 update the worst major ports for congestion. https://www.project44.com/blog/2022-update-the-worst-major-ports-for-congestion
De Armas Jacomino, L., Medina-Pérez, M. A., Monroy, R., Valdes-Ramirez, D., Morell-Pérez, C., & Bello, R. (2021). Dwell time estimation of import containers as an ordinal regression problem. Applied Sciences, 11(20), Article 9380. https://doi.org/10.3390/app11209380
Deniz, E., Tuncel, G., Yalçınkaya, Ö, & Esmer, S. (2021). Simulation of multi-crane single and dual cycling strategies in a container terminal. International Journal of Simulation Modelling, 20(3), 465–476. https://doi.org/10.2507/IJSIMM20-3-559
Elbert, R., & Wu, H. (2023). Dynamic container routing problem on a rail-based hub-and-spoke network. In The Interdisciplinary Conference on Production, Logistics and Traffic (pp. 131–146). Springer. https://doi.org/10.1007/978-3-031-28236-2_9
Fruth, M., & Teuteberg, F. (2017). Digitization in maritime logistics – What is there and what is missing? Cogent Business & Management, 4(1), Article 1411066. https://doi.org/10.1080/23311975.2017.1411066
Hoffmann, J., & Hoffmann, J. (2021). Bigger ships and fewer companies – Two sides of the same coin. UNCTAD Transport and Trade Facilitation Newsletter N˚89 – First Quarter 2021.
Huang, S. Y., Hsu, W., Chen, C., Ye, R., & Nautiyal, S. (2008). Capacity analysis of container terminals using simulation techniques. International Journal of Computer Applications in Technology, 32(4), 246–253. https://doi.org/10.1504/IJCAT.2008.021379
Irannezhad, E., Prato, C., & Hickman, M. (2019). A joint hybrid model of the choices of container terminals and of dwell time. Transportation Research Part E: Logistics and Transportation Review, 121, 119–133. https://doi.org/10.1016/j.tre.2017.12.005
Kourounioti, I., Polydoropoulou, A., & Tsiklidis, C. (2015). Development of a methodological framework for the dwell time of containers in marine terminals – first results. In Proceeding of 4th ICTR (International Congress on Transportation Research). ICTR.
Maldonado, S., González-Ramírez, R. G., Quijada, F., & Ramírez-Nafarrate, A. (2019). Analytics meets port logistics: A decision support system for container stacking operations. Decision Support Systems, 121, 84–93. https://doi.org/10.1016/j.dss.2019.04.006
Marlow, P. B., & Casaca, A. C. P. (2003). Measuring lean ports performance. International Journal of Transport Management, 1(4), 189–202. https://doi.org/10.1016/j.ijtm.2003.12.002
Merckx, F. (2005, 23–25 June). The issue of dwell time charges to optimize container terminal capacity. In The Proceedings IAME 2005 Annual Conference. Limassol, Cyprus.
Moini, N., Boile, M., Theofanis, S., & Laventhal, W. (2012). Estimating the determinant factors of container dwell times at seaports. Maritime Economics & Logistics, 14, 162–177. https://doi.org/10.1057/mel.2012.3
Muñuzuri, J., Onieva, L., Cortés, P., & Guadix, J. (2020). Using IoT data and applications to improve port-based intermodal supply chains. Computers & Industrial Engineering, 139, Article 105668. https://doi.org/10.1016/j.cie.2019.01.042
Nicoll, J., & Nicholson, D. (2007). Container tracking system – dwell time and transit time management at the Port of Halifax. Port of Halifax.
Nooramin, A. S., Ahouei, V. R., & Sayareh, J. (2011). A Six sigma framework for marine container terminals. International Journal of Lean Six Sigma, 2(3), 241–253. https://doi.org/10.1108/20401461111157196
Owusu-Oware, E., Effah, J., Adam, I. O., & Amankwah-Sarfo, F. (2023). Actualizing the affordances of seaport smart container terminal system in a developing country. Journal of Information Technology Case and Application Research, 25(4), 339–367. https://doi.org/10.1080/15228053.2023.2250238
Refas, S., & Cantens, T. (2011). Why does cargo spend weeks in African ports. The Case of Douala, Cameroon. The World Bank. https://doi.org/10.1596/1813-9450-5565
Rodrigue, J. P. (2022). Generations of containerships. http://transportgeography.org/generations-of-containerships-update/
Rodrigue, J., & Notteboom, T. (2009). The terminalization of supply chains: Reassessing the role of terminals in port/hinterland logistical relationships. Maritime Policy & Management, 36(2), 165–183. https://doi.org/10.1080/03088830902861086
Saini, M., Efimova, A., & Chromjaková, F. (2021). Value stream mapping of ocean import containers: A process cycle efficiency perspective. Acta Logistica, 8(4), 393–405. https://doi.org/10.22306/al.v8i4.245
Saldaña, J. (2021). The coding manual for qualitative researchers. Sage.
Strauss, A., & Corbin, J. (1990). Basics of qualitative research. Sage Publications.
Tongzon, J. L. (1995). Determinants of port performance and efficiency. Transportation Research Part A: Policy and Practice, 29(3), 245–252. https://doi.org/10.1016/0965-8564(94)00032-6
UNCTAD. (2018). Review of maritime transport. https://unctad.org/system/files/official-document/rmt2018_en.pdf
Whelan, S. (2021, November 18). India’s container market is cooling, but “kinks” in supply chains persist. The Loadstar. https://theloadstar.com/indias-container-market-is-cooling-but-kinks-in-supply-chains-persist/
Woo, S., Pettit, S., & Beresford, A. K. (2011). Port evolution and performance in changing logistics environments. Maritime Economics & Logistics, 13, 250–277. https://doi.org/10.1057/mel.2011.12
Yang, S. Y., & Tan, C. (2022). Blockchain-based collaborative management of job shop supply chain. International Journal of Simulation Modelling, 21, 364–374. https://doi.org/10.2507/IJSIMM21-2-CO10
Yildiz, T. (2017). An empirical analysis оn logistics performance and the global competitiveness. Business: Theory and Practice, 18(1), 1–13. https://doi.org/10.3846/btp.2017.001
Yoon, J., Kim, S., Jo, J., & Park, J. (2023). A comparative study of machine learning models for predicting vessel dwell time estimation at a terminal in the Busan new port. Journal of Marine Science and Engineering, 11(10), Article 1846. https://doi.org/10.3390/jmse11101846
Zhao, W., & Goodchild, A. V. (2010). The impact of truck arrival information on container terminal rehandling. Transportation Research Part E: Logistics and Transportation Review, 46(3), 327–343. https://doi.org/10.1016/j.tre.2009.11.007
Zuidwijk, R. A., & Veenstra, A. W. (2015). The value of information in container transport. Transportation Science, 49(3), 675–685. https://doi.org/10.1287/trsc.2014.0518
Ayutia, Y., Sirait, D. P., Maligasach, S. S., Ramadhani, R., Hamdi, A., & Krisnawati, S. (2023). Managing the congestion for delivering and receiving truck container at the Tanjung Priok terminal by analyzing the congestion at Koja container terminal. KnE Social Sciences, 8(9), 818–826. https://doi.org/10.18502/kss.v8i9.13395
Brooks, M. R. (2006). Issues in measuring port devolution program performance: A managerial perspective. Research in Transportation Economics, 17, 599–629. https://doi.org/10.1016/S0739-8859(06)17025-0
Brooks, M. R., & Schellinck, T. (2013). Measuring port effectiveness in user service delivery: What really determines users’ evaluations of port service delivery? Research in Transportation Business & Management, 8, 87–96. https://doi.org/10.1016/j.rtbm.2013.04.001
Budiyanto, M. A., Zaki, M. I., & Suhendar, S. B. (2023). Operational effect on the increase of quay cranes to reduce dwelling time at the container terminal. E3S Web of Conferences, 405, Article 04025. https://doi.org/10.1051/e3sconf/202340504025
Chu, C., & Huang, W. (2005). Determining container terminal capacity on the basis of an adopted yard handling system. Transport Reviews, 25(2), 181–199. https://doi.org/10.1080/0144164042000244608
Cooke, J. (2022). 2022 update the worst major ports for congestion. https://www.project44.com/blog/2022-update-the-worst-major-ports-for-congestion
De Armas Jacomino, L., Medina-Pérez, M. A., Monroy, R., Valdes-Ramirez, D., Morell-Pérez, C., & Bello, R. (2021). Dwell time estimation of import containers as an ordinal regression problem. Applied Sciences, 11(20), Article 9380. https://doi.org/10.3390/app11209380
Deniz, E., Tuncel, G., Yalçınkaya, Ö, & Esmer, S. (2021). Simulation of multi-crane single and dual cycling strategies in a container terminal. International Journal of Simulation Modelling, 20(3), 465–476. https://doi.org/10.2507/IJSIMM20-3-559
Elbert, R., & Wu, H. (2023). Dynamic container routing problem on a rail-based hub-and-spoke network. In The Interdisciplinary Conference on Production, Logistics and Traffic (pp. 131–146). Springer. https://doi.org/10.1007/978-3-031-28236-2_9
Fruth, M., & Teuteberg, F. (2017). Digitization in maritime logistics – What is there and what is missing? Cogent Business & Management, 4(1), Article 1411066. https://doi.org/10.1080/23311975.2017.1411066
Hoffmann, J., & Hoffmann, J. (2021). Bigger ships and fewer companies – Two sides of the same coin. UNCTAD Transport and Trade Facilitation Newsletter N˚89 – First Quarter 2021.
Huang, S. Y., Hsu, W., Chen, C., Ye, R., & Nautiyal, S. (2008). Capacity analysis of container terminals using simulation techniques. International Journal of Computer Applications in Technology, 32(4), 246–253. https://doi.org/10.1504/IJCAT.2008.021379
Irannezhad, E., Prato, C., & Hickman, M. (2019). A joint hybrid model of the choices of container terminals and of dwell time. Transportation Research Part E: Logistics and Transportation Review, 121, 119–133. https://doi.org/10.1016/j.tre.2017.12.005
Kourounioti, I., Polydoropoulou, A., & Tsiklidis, C. (2015). Development of a methodological framework for the dwell time of containers in marine terminals – first results. In Proceeding of 4th ICTR (International Congress on Transportation Research). ICTR.
Maldonado, S., González-Ramírez, R. G., Quijada, F., & Ramírez-Nafarrate, A. (2019). Analytics meets port logistics: A decision support system for container stacking operations. Decision Support Systems, 121, 84–93. https://doi.org/10.1016/j.dss.2019.04.006
Marlow, P. B., & Casaca, A. C. P. (2003). Measuring lean ports performance. International Journal of Transport Management, 1(4), 189–202. https://doi.org/10.1016/j.ijtm.2003.12.002
Merckx, F. (2005, 23–25 June). The issue of dwell time charges to optimize container terminal capacity. In The Proceedings IAME 2005 Annual Conference. Limassol, Cyprus.
Moini, N., Boile, M., Theofanis, S., & Laventhal, W. (2012). Estimating the determinant factors of container dwell times at seaports. Maritime Economics & Logistics, 14, 162–177. https://doi.org/10.1057/mel.2012.3
Muñuzuri, J., Onieva, L., Cortés, P., & Guadix, J. (2020). Using IoT data and applications to improve port-based intermodal supply chains. Computers & Industrial Engineering, 139, Article 105668. https://doi.org/10.1016/j.cie.2019.01.042
Nicoll, J., & Nicholson, D. (2007). Container tracking system – dwell time and transit time management at the Port of Halifax. Port of Halifax.
Nooramin, A. S., Ahouei, V. R., & Sayareh, J. (2011). A Six sigma framework for marine container terminals. International Journal of Lean Six Sigma, 2(3), 241–253. https://doi.org/10.1108/20401461111157196
Owusu-Oware, E., Effah, J., Adam, I. O., & Amankwah-Sarfo, F. (2023). Actualizing the affordances of seaport smart container terminal system in a developing country. Journal of Information Technology Case and Application Research, 25(4), 339–367. https://doi.org/10.1080/15228053.2023.2250238
Refas, S., & Cantens, T. (2011). Why does cargo spend weeks in African ports. The Case of Douala, Cameroon. The World Bank. https://doi.org/10.1596/1813-9450-5565
Rodrigue, J. P. (2022). Generations of containerships. http://transportgeography.org/generations-of-containerships-update/
Rodrigue, J., & Notteboom, T. (2009). The terminalization of supply chains: Reassessing the role of terminals in port/hinterland logistical relationships. Maritime Policy & Management, 36(2), 165–183. https://doi.org/10.1080/03088830902861086
Saini, M., Efimova, A., & Chromjaková, F. (2021). Value stream mapping of ocean import containers: A process cycle efficiency perspective. Acta Logistica, 8(4), 393–405. https://doi.org/10.22306/al.v8i4.245
Saldaña, J. (2021). The coding manual for qualitative researchers. Sage.
Strauss, A., & Corbin, J. (1990). Basics of qualitative research. Sage Publications.
Tongzon, J. L. (1995). Determinants of port performance and efficiency. Transportation Research Part A: Policy and Practice, 29(3), 245–252. https://doi.org/10.1016/0965-8564(94)00032-6
UNCTAD. (2018). Review of maritime transport. https://unctad.org/system/files/official-document/rmt2018_en.pdf
Whelan, S. (2021, November 18). India’s container market is cooling, but “kinks” in supply chains persist. The Loadstar. https://theloadstar.com/indias-container-market-is-cooling-but-kinks-in-supply-chains-persist/
Woo, S., Pettit, S., & Beresford, A. K. (2011). Port evolution and performance in changing logistics environments. Maritime Economics & Logistics, 13, 250–277. https://doi.org/10.1057/mel.2011.12
Yang, S. Y., & Tan, C. (2022). Blockchain-based collaborative management of job shop supply chain. International Journal of Simulation Modelling, 21, 364–374. https://doi.org/10.2507/IJSIMM21-2-CO10
Yildiz, T. (2017). An empirical analysis оn logistics performance and the global competitiveness. Business: Theory and Practice, 18(1), 1–13. https://doi.org/10.3846/btp.2017.001
Yoon, J., Kim, S., Jo, J., & Park, J. (2023). A comparative study of machine learning models for predicting vessel dwell time estimation at a terminal in the Busan new port. Journal of Marine Science and Engineering, 11(10), Article 1846. https://doi.org/10.3390/jmse11101846
Zhao, W., & Goodchild, A. V. (2010). The impact of truck arrival information on container terminal rehandling. Transportation Research Part E: Logistics and Transportation Review, 46(3), 327–343. https://doi.org/10.1016/j.tre.2009.11.007
Zuidwijk, R. A., & Veenstra, A. W. (2015). The value of information in container transport. Transportation Science, 49(3), 675–685. https://doi.org/10.1287/trsc.2014.0518