Abstract
This paper develops a data-driven framework for analyzing irregular circular economy cargo flows in seaports. A composite instability measure, the Intra-Port Irregular Circular Cargo Index (ICCI), is introduced to capture variability and irregular occurrence of cargo streams over time. The index is combined with cargo share to construct a decision-oriented classification matrix linking cargo behavior to management implications. The empirical analysis is based on cargo handling data for six Polish seaports over the period 2011-2022, covering 11,032 data cells. The results show that circular cargo flows are predominantly irregular and low in share, yet operationally demanding. The study proposes a methodology designed for integration into information systems, enabling continuous, real-time decision-making based on observed cargo flow dynamics. The proposed framework enables differentiation between stable, adaptive, and volatile cargo segments and supports more effective, data-driven decision-making in port management under conditions of uncertainty.
Paper Type
Short Paper
DOI
10.62036/ISD.2026.117
Data-Driven Analysis of Irregular Circular Supply Chain Cargo Handling in Seaports for Decision Support and Port Management
This paper develops a data-driven framework for analyzing irregular circular economy cargo flows in seaports. A composite instability measure, the Intra-Port Irregular Circular Cargo Index (ICCI), is introduced to capture variability and irregular occurrence of cargo streams over time. The index is combined with cargo share to construct a decision-oriented classification matrix linking cargo behavior to management implications. The empirical analysis is based on cargo handling data for six Polish seaports over the period 2011-2022, covering 11,032 data cells. The results show that circular cargo flows are predominantly irregular and low in share, yet operationally demanding. The study proposes a methodology designed for integration into information systems, enabling continuous, real-time decision-making based on observed cargo flow dynamics. The proposed framework enables differentiation between stable, adaptive, and volatile cargo segments and supports more effective, data-driven decision-making in port management under conditions of uncertainty.
Recommended Citation
Szaruga, E., Pluciński, M., Kotowska, I., Czermański, E. & Oniszczuk, A.(2026). Data-Driven Analysis of Irregular Circular Supply Chain Cargo Handling in Seaports for Decision Support and Port Management. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.117