Causally Learning an Optimal Rework Policy
This work addresses a specific optimization problem in manufacturing for improving production efficiency, but it is incremental as it applies an existing causal method to a new domain.
The paper tackled the problem of determining when to apply a rework step in opto-electronic semiconductor manufacturing to optimize yield, using double/debiased machine learning to estimate the conditional treatment effect and derive policies for rework.
In manufacturing, rework refers to an optional step of a production process which aims to eliminate errors or remedy products that do not meet the desired quality standards. Reworking a production lot involves repeating a previous production stage with adjustments to ensure that the final product meets the required specifications. While offering the chance to improve the yield and thus increase the revenue of a production lot, a rework step also incurs additional costs. Additionally, the rework of parts that already meet the target specifications may damage them and decrease the yield. In this paper, we apply double/debiased machine learning (DML) to estimate the conditional treatment effect of a rework step during the color conversion process in opto-electronic semiconductor manufacturing on the final product yield. We utilize the implementation DoubleML to develop policies for the rework of components and estimate their value empirically. From our causal machine learning analysis we derive implications for the coating of monochromatic LEDs with conversion layers.