Modeling blockchain adoption in supply chain through system dynamics

The adoption of blockchain technology (BCT) in supply chains presents a complex interplay of costs and benefits. While higher adoption rates can lead to significant reductions in costs, they also impose higher ongoing costs related to infrastructure, energy consumption, and technical maintenance. Through the utilization of system dynamics (SD) and regression methods, this study explores the intricate relationship between adoption rates, associated costs, and failure rates of blockchain implementation. Afterwards, a mathematical model is designed to optimize the adoption rate in order to reduce costs. The functions obtained from regression are integrated into this model, leading to the determination of the adoption rate. The results indicate that increasing the adoption rate may not always be profitable and could potentially increase costs. In this study, an adoption rate of 0.567 is obtained, suggesting that at this adoption rate, costs reach their minimum level, and that at higher or lower rates, costs increase.