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


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

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A short abstract: 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, particularly in light of the highly fragmented nature of the upstream supply chain, where artisans often work from their individual homes. This study presents research conducted in close collaboration with one of the leading exporters of handmade rugs in India, aimed at addressing this challenge. Leveraging insights from field visits and analysis of 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 decrease weaving times by 13.1%-14.1%, which can lead to a 15-17% increase in monthly income for weavers. Our analysis also suggests that this impact is heterogeneous, with visits to more complex and difficult-to-weave rugs, and visits that are more consistent, leading to maximum productivity gains for the weavers. To capitalize on these insights, we propose a novel predict-then-optimize framework for optimizing supervisor visits in the supply chain. This research offers valuable insights for other distributed supply chains in resource constrained settings that share similar characteristics and highlight how supply chain considerations can play a critical role in improving the productivity of workforce in geographically isolated regions. (Joint work with Divya Singhvi (NYU) and Xinyu Zhang (NYU)) 

About the Speaker:
Dr. Somya Singhvi is an assistant professor of Data Sciences and Operations at University of Southern California’s Marshall School of Business. Somya received his Ph.D. in Operations Research from MIT, focusing on improving the design of digital agri-platforms and markets. Somya's research is driven by a desire to create social impact using a combination of field-based and data-driven research methods. He is particularly interested in developing actionable insights for supply chains and digital platforms in resource-constrained settings. His research has spanned a range of application areas, including agricultural, artisanal and healthcare supply chains, as well as ed-tech and charity donation platforms. Somya has received a number of recognitions for his work, including the George B. Dantzig Dissertation Award, MSOM Responsible Research Award, Public Sector Operations Best Paper Award, Doing Good With Good OR Award.  

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