Viable Intertwined Supply Network: Modelling and Dynamic Analysis Using Artificial Neural Networks

The viability of intertwined supply networks (ISNs) has recently been studied as a critical topic in operations management. Modeling the viability of ISNs is considered a promising tool to meet the demands of extraordinary events, such as the Russo-Ukrainian War and the COVID-19 pandemic. To enhance the viability of ISNs, ISN structures must be modeled and the behavioral dynamics of interactions between firms in a network in a changing environment should be analyzed. In this study, a trophic chain-based dynamic formulation of ISN viability is presented and a solution methodology for dynamic analysis of the ISN viability model is designed. The dynamic model of ISN is represented by a system of nonlinear differential equations and described in terms of three dynamic values: suppliers X(τ), focal firms Y(τ), and market demand Z(τ). Stochastic numerical simulations are performed by conducting a dynamical analysis of the ISN model using a scaled conjugate gradient neural network. Two numerical cases are investigated to evaluate the performance of the proposed approach. The results indicate that the dynamical mode can effectively analyze the ISN structures and help researchers and practitioners ensure the survival of supply chains during extraordinary events.