A Data-Driven Approach to Improve Artisans' Productivity in Distributed Supply Chains.

This article investigates the impact of frequent, predictable supervisor visits on artisan productivity within distributed supply chains, focusing on Jaipur Rugs, a major Indian exporter employing rural women weavers. Empirical analysis using loom-level data shows that reducing the average interval between supervisor visits by one day increases weaving rates by 8.5%, with greater effects for complex rugs and when visits follow consistent schedules. Building on these findings, the authors develop and implement an optimization framework for scheduling supervisor visits, which in a 25-week field trial involving about 6,000 visits across 200 looms, resulted in a statistically significant 16.7% increase in weaving speed for treatment looms. The study highlights the potential of data-driven supervision to enhance productivity and earnings in artisanal and similar smallholder supply chains in resource-constrained settings, while noting operational challenges and the need for further research on causal mechanisms and scalability.