Juan Serpa, Assistant Professor in Operations Management was recently awarded a 2017 FRQSC New Academics Grant for his project "Estimation de la relation entre la qualité du produit et la proximité de la chaîne d'approvisement".
This project will study the effect of supply chain proximity on product quality with data from the auto industry. To this end, we merge four independent data sources, collecting
(i) auto part failure rates,
(ii) upstream component factory locations,
(iii) downstream assembly plant locations, and
(iv) product-level links connecting the upstream and downstream factories.
Combining these data sets yields one of the most detailed supply chain samples ever created, detailing the flow of 27,807 products through 529 supplier factories and 175 assembly plants. To consistently estimate this relationship, we need to create a new estimator that allows us to eliminate the impact of selection biases in an assembly line. This estimator takes advantage of B-spline functions to provide a fully generalizable selection-correction estimator and, as such, it advances the estimation techniques in network econometrics.