Causally Learning an Optimal Rework Policy

Oliver Schacht, Sven Klaassen, Philipp Schwarz, Martin Spindler, Daniel Grunbaum, Sebastian Imhof
Proceedings of The KDD'23 Workshop on Causal Discovery, Prediction and Decision, PMLR 218:3-24, 2023.

Abstract

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 optoelectronic 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v218-schacht23a, title = {Causally Learning an Optimal Rework Policy }, author = {Schacht, Oliver and Klaassen, Sven and Schwarz, Philipp and Spindler, Martin and Grunbaum, Daniel and Imhof, Sebastian}, booktitle = {Proceedings of The KDD'23 Workshop on Causal Discovery, Prediction and Decision}, pages = {3--24}, year = {2023}, editor = {Le, Thuc and Li, Jiuyong and Ness, Robert and Triantafillou, Sofia and Shimizu, Shohei and Cui, Peng and Kuang, Kun and Pei, Jian and Wang, Fei and Prosperi, Mattia}, volume = {218}, series = {Proceedings of Machine Learning Research}, month = {07 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v218/schacht23a/schacht23a.pdf}, url = {https://proceedings.mlr.press/v218/schacht23a.html}, abstract = { 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 optoelectronic 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.} }
Endnote
%0 Conference Paper %T Causally Learning an Optimal Rework Policy %A Oliver Schacht %A Sven Klaassen %A Philipp Schwarz %A Martin Spindler %A Daniel Grunbaum %A Sebastian Imhof %B Proceedings of The KDD'23 Workshop on Causal Discovery, Prediction and Decision %C Proceedings of Machine Learning Research %D 2023 %E Thuc Le %E Jiuyong Li %E Robert Ness %E Sofia Triantafillou %E Shohei Shimizu %E Peng Cui %E Kun Kuang %E Jian Pei %E Fei Wang %E Mattia Prosperi %F pmlr-v218-schacht23a %I PMLR %P 3--24 %U https://proceedings.mlr.press/v218/schacht23a.html %V 218 %X 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 optoelectronic 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.
APA
Schacht, O., Klaassen, S., Schwarz, P., Spindler, M., Grunbaum, D. & Imhof, S.. (2023). Causally Learning an Optimal Rework Policy . Proceedings of The KDD'23 Workshop on Causal Discovery, Prediction and Decision, in Proceedings of Machine Learning Research 218:3-24 Available from https://proceedings.mlr.press/v218/schacht23a.html.

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