Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?

TL;DR

This paper develops a causal machine learning model to optimize rework decisions in manufacturing, balancing yield improvements against costs, validated with real-world semiconductor data showing a 2-3% yield increase.

Contribution

It introduces a causal ML approach using DML techniques for rework policy optimization in manufacturing systems, addressing the challenge of delayed inspection and partial rework effects.

Findings

Achieved 2-3% yield improvement in LED manufacturing.

Demonstrated effectiveness of causal ML in manufacturing decision-making.

Validated model with real-world semiconductor data.

Abstract

In this paper, we present a data-driven model for estimating optimal rework policies in manufacturing systems. We consider a single production stage within a multistage, lot-based system that allows for optional rework steps. While the rework decision depends on an intermediate state of the lot and system, the final product inspection, and thus the assessment of the actual yield, is delayed until production is complete. Repair steps are applied uniformly to the lot, potentially improving some of the individual items while degrading others. The challenge is thus to balance potential yield improvement with the rework costs incurred. Given the inherently causal nature of this decision problem, we propose a causal model to estimate yield improvement. We apply methods from causal machine learning, in particular double/debiased machine learning (DML) techniques, to estimate conditional treatment effects from data and derive policies for rework decisions. We validate our decision model using real-world data from opto-electronic semiconductor manufacturing, achieving a yield improvement of 2 - 3% during the color-conversion process of white light-emitting diodes (LEDs).