Simulation of Supply Chain Performance in the Period of Implicit Uncertainty using Cellular Automata

Abstract

Managing a distribution planning problem in an integrated supply chain environment is daunting. These challenges are aggravated when there are multiple stakeholders involved. The proposed simulation model provides an environment to gauge the existing adversities in the distribution plan of a two-stage supply chain (SC) network. In addition to the underlined issues, the model captures the influence of decisions from neighboring firms in a periodical decision-making plan. A cellular automaton (CA) based approach is implemented to present the complete analysis and impact of endogenous and exogenous situations affecting the decision-making. The decision environment involves two states of selecting an efficient supply chain strategy (ESC) and responsive supply chain strategy (RSC) based on the implicit uncertainty and performance of Moore-based neighboring cells. The study contributes to the scant literature on the application of CA-based evolutionary decisions in the SC context. The simulation model characterizes the neighboring firm's influences in strategic decision-making and the implicit uncertainty in supply and demand. The modeling framework is tested with a significantly larger set, and the results are graphically presented to provide further clarity.