Effect of Connected and Autonomous Vehicles on Supply Chain Performance

Abstract

This paper proposes a simulation framework that quantitatively assesses the direct and indirect effects of connected and autonomous vehicles (CAVs) on a supply chain system by varying the levels of CAV market penetration and driverless truck adoption. To quantify CAV effects on transportation network, this paper first collects secondary data and adopts simulation parameters and equations from existing literature. The results from transportation analysis are then incorporated into supply chain analysis to evaluate how CAVs would change supply chain performance measured by total travel time, greenhouse gas emissions, and supply chain cost. As the performance of supply chain systems involving perishable or semi-perishable products is highly sensitive to CAV market penetration rate and driverless truck adoption rate mainly because of reduced travel time, this paper uses fresh potato supply chain systems as an illustrative example. The case study results indicate that CAVs can greatly improve supply chain performance directly and indirectly by decreasing total travel time and supply chain costs, whereas emissions are reduced primarily through the adoption of driverless trucks in the supply chain system. The effect of CAVs on supply chain performance becomes even greater when commodities travel longer distances. This study will allow supply chain managers (and grocery delivery companies) to better understand how supply chain design and operation could be transformed and reoptimized in response to the introduction of CAV technologies. The research outcomes would help them better utilize the opportunities and address possible challenges that may arise as a result of CAVs.