Using Explainable Artificial Intelligence to Improve Process Quality: Evidence from Semiconductor Manufacturing

• 成果类型: Article
• 署名作者: Senoner, Julian; Netland, Torbjorn; Feuerriegel, Stefan
• 署名单位: Swiss Federal Institutes of Technology Domain; ETH Zurich
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.4190
• 发表日期: 2022
• 页码: 5704-5723
• 关键词: manufacturing; QUALITY MANAGEMENT; Artificial intelligence; SHAP value method

摘要

We develop a data-driven decision model to improve process quality in manufacturing. A challenge for traditional methods in quality management is to handle high-dimensional and nonlinear manufacturing data. We address this challenge by adapting explainable artificial intelligence to the context of quality management. Specifically, we propose the use of nonlinear modeling with Shapley additive explanations to infer how a set of production parameters and the process quality of a manufacturing system are related. Thereby, we contribute a measure of process importance based on which manufacturers can prioritize processes for quality improvement. Grounded in quality management theory, our decision model selects improvement actions that target the sources of quality variation. The decisionmodel is validated in a real-world application at a leadingmanufacturer of high-power semiconductors. Seeking to improve production yield, we apply our decision model to select improvement actions for a transistor chip product. We then conduct a field experiment to confirm the effectiveness of the improvement actions. Compared with the average yield in our sample, the experiment returns a reduction in yield loss of 21.7%. Furthermore, we report on results from a postexperimental rollout of the decision model, which also resulted in significant yield improvements. We demonstrate the operational value of explainable artificial intelligence by showing that critical drivers of process quality can go undiscovered by the use of traditionalmethods.