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Throughput forecasting of different types of cargo in the adriatic seaport Koper

Author

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  • Dejan Dragan
  • Abolfazl Keshavarzsaleh
  • Marko Intihar
  • Vlado Popović
  • Tomaž Kramberger

Abstract

An accurate forecasting system has manifested its role as an enabler in supply chains (SC), which makes the operation possible in a maximally synchronized manner. Its applications have gained the attention of scholars across various disciplines such as forecasting in market behavior analysis and tourism industry; material requirement planning in production; transport and logistics foresight in networks and facilities. Seaports, as specific SC members, are not an exception. Accurate forecasting is needed in almost all aspects of the ports’ operation to avoid financial losses related to inappropriate investments and planning. The paper addresses the forecasting of joint demand-supply cargo throughputs in the Adriatic Seaport Koper. The research presents a new forecasting approach, namely, DFA-ARIMAX (Dynamic Factor Analysis-ARIMAX modeling). External economic indicators were screened to obtain useful information using the DFA prior to directing the dynamic factors into the ARIMAX forecasting model. The principal component regression and Monte Carlo framework were included to identify indicators that are unique to the port. Findings revealed that a forecasting system by its enriched capabilities to predict the observed throughputs could be seen as Functional Decision Support System. The benchmarking shows that proposed models outperform competitive models. Practical implications are discussed in detail.

Suggested Citation

  • Dejan Dragan & Abolfazl Keshavarzsaleh & Marko Intihar & Vlado Popović & Tomaž Kramberger, 2021. "Throughput forecasting of different types of cargo in the adriatic seaport Koper," Maritime Policy & Management, Taylor & Francis Journals, vol. 48(1), pages 19-45, January.
  • Handle: RePEc:taf:marpmg:v:48:y:2021:i:1:p:19-45
    DOI: 10.1080/03088839.2020.1748242
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    Cited by:

    1. Shi-Yao She & Fang-Fang Yuan & Jun-Ke Li & Hong-Wei Dai, 2023. "Research on Material Demand Forecasting Algorithm Based on Multi-Dimensional Feature Fusion," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 14(1), pages 1-13, January.

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