Transformation of Grocery Shopping and Delivery: Perspective from an Instacart Delivery Shopper

Abstract

This study investigates the economic viability of independent workers in platform-based last-mile grocery delivery systems, focusing specifically on Instacart as a representative service supply chain. While prior research has examined operational efficiency, platform-level matching, scheduling, and service performance, less attention has been given to worker-level net income viability within the operational design and cost structures set by platforms. To address this gap, we develop a hybrid simulation framework integrating discrete-event simulation (DES) and system dynamics (SD). The DES model captures order processing, shopping, and delivery operations, while the SD model represents the accumulation of earnings and costs over time. Drawing on Expected Utility Theory and Distributive Justice Theory, we model worker decision-making under uncertainty and evaluate income fairness relative to effort and cost exposure. Using empirically grounded parameters, we analyze how batch structure, store characteristics, fuel costs, and tipping behavior affect hourly net income. Results reveal substantial earnings variability and highlight trade-offs between throughput efficiency and income stability. This study contributes by introducing a human-centered perspective on last-mile delivery, developing a hybrid simulation approach for platform-based supply chains, and providing insights for platform design and compensation policies.