Nonlinear Dynamics of Maritime Supply Chains

By volume, 90% of global supply chains are maritime. Those supply chains respond to subsystem dynamics reflecting exchange rates, trade policy, tax policy, and weather. The 2020 China pandemic inflicted its own shocks consistent with previous China pandemics.

Cuong et al., 2015 presents an interesting dynamical model of the three-stage maritime supply chain system that provide insights into supply chain stability and strategies for optimizing economic flow based on the equations of state describing the network dynamics.

Cuong et al,, 2020

x, y, z are sector-level state volume variables; a, b, c, and m are parameters characterizing transport risk (a), safety stocks and distortion coefficients (b, c), and customer satisfaction (m). The dynamical model describes the state variable variability over time time evolutions of state variables representing for volume demanded, including the shipment sent of retailer x, the inventory level of distributor y, quantity produced by manufacturer z.

All three equations constitute a general autonomous vector field defined by

This system is in equilibrium when this equation equals zero for each sector.

Cuong et al. demonstrate a number of interesting dynamics arising from coupled-information/commodity flows across three sectors.

Among the more interesting aspects of this paper is its exercising sliding mode controller to stabilize some of the harmonics.


Ngoc Cuong, T., Xu, X., Lee, S.-D., & You, S.-S. (2020). Dynamic analysis and management optimization for maritime supply chains using nonlinear control theory. Journal of International Maritime Safety Environmental Affairs and Shipping4(2), 48–55

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