Sustainability and robust decision-support strategy for multi-echelon supply chain system against disruptions

Due to the higher nonlinearity and parametric sensitivity of multi-echelon supply chains, minor changes may result in a completely different evolution of supply chain capabilities. Realizing resilient and sustainable development goals for enterprise logistics requires great insights into management schemes in a volatile market. This paper deals with dynamic characteristics and resilience issues of managing multi-echelon supply chain systems against disruptions. Discrete-time nonlinear systems with time delay are extensively evaluated via computation of Lyapunov exponent, bifurcation diagram, permutation entropy, and Poincare map. For time-series evolution, forecasting and predictability are assessed by the Hurst exponent. An echo state network (ESN) will be utilized to predict the future states of the supply chain dynamics. Based on a decision-support strategy with an ESN model, a resilience strategy concerning disturbing factors is implemented to ensure a sustainable supply chain network. Numerical simulations are demonstrated to validate dynamical behaviors and verify the efficiency of control theory with machine learning, guaranteeing the resilience and sustainability of a supply chain against market volatility. The test results demonstrate how machine learning algorithms can be integrated with management schemes. Finally, the presented methodology can ensure more supply chain visibility and traceability under highly uncertain markets.

Automated summary

This paper addresses the dynamic characteristics and resilience issues of managing multi-echelon supply chain systems against disruptions. It evaluates discrete-time nonlinear systems with time delay using Lyapunov exponent, bifurcation diagram, permutation entropy, and Poincare map. An echo state network (ESN) is used to predict the future states of the supply chain dynamics and implement a resilience strategy. Numerical simulations demonstrate the integration of machine learning algorithms with management schemes, ensuring the resilience and sustainability of the supply chain against market volatility.