Balancing flex and non-flex labor to reliably meet on-demand capacity

The 21st century workforce is increasingly characterized by more flexible labor models, particularly in e-commerce and supply chain operations. While previous research has focused mostly on last-mile, on-the-road settings, we focus on under-the-roof (UTR) environments, which present unique challenges due to their complex, varied tasks requiring training and experience. Our study addresses the need to better understand how a blended UTR workforce balances factors like structural efficiency and labor flexibility in complex logistics management. We present an optimization framework for determining an effective workforce composition of flexible and non-flexible associates in UTR environments. We validate insights from the optimization framework through an empirical study that increased flexible staffing at Amazon delivery stations. Our analysis includes measuring differences in productivity learning curves and examining impact on efficiency and the associate experience. Key findings reveal that while flexible associates take longer to achieve full proficiency, especially for complex tasks, these effects diminish over time. Importantly, a blended workforce improves structural staffing efficiency and makes it easier to accommodate demand shocks. We estimate that the upper bound of the efficiency improvement to be around 4%. Our research highlights the benefits of strategic workforce planning in the face of increasing demand volatility and a need for operational agility.