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

Despite their vital role in the global rural economy, and as a major source of employment for women in the developing world, artisanal supply chains continue to be plagued by low productivity and high poverty levels. Identifying effective and implementable solutions to improve artisan productivity is a challenging task due to high fragmentation in the upstream parts of the supply chain. This paper presents research conducted in close collaboration with one of the leading exporters of handmade rugs in India. Leveraging insights from the field visits and detailed supply chain data, we provide robust empirical evidence that frequent supervisor visits can play a crucial role in improving artisans’ productivity. Our results from Instrumental Variables analysis indicate that a one-day decrease in the average number of days between supervisor visits to remote weavers can increase weaving rates by 8.5%. We also find that (i) visits to looms with difficult-to-weave rugs and (ii) visits that are consistently scheduled are associated with a more substantial positive impact on weavers’ productivity. To capitalize on these insights, we propose an optimization framework for scheduling supervisor visits in the supply chain. Finally, we work with our collaborator to implement this scheduling framework for a set of treatment looms for 25 consecutive weeks. A difference-in-differences analysis using data from almost 6,000 supervisor visits across 200 looms during the implementation suggests that the implementation is associated with a statistically significant 16.7% increase in the weaving rate for treatment looms, highlighting the practical relevance of the work.

Funding: Financial support from the Institute for Outlier Research in Business at the University of Southern California (USC) Marshall School of Business, the Lloyd Greif Center for Entrepreneurial Studies at the USC Marshall School of Business, and the New York University Stern Center for Sustainable Business Research Grant Program is gratefully acknowledged.

Supplemental Material: All supplemental materials, including the code, data, and files required to reproduce the results, are available at https://doi.org/10.1287/opre.2024.1009.