Explainable Machine Learning for Lead Time Prediction : A Case Study on Explainability Methods and Benefits in the Pharmaceutical Industry

Abstract: Artificial Intelligence (AI) has proven to be highly suitable for a wide range of problems in manufacturing environments, including the prediction of lead times. Most of these solutions are based on ”black-box” algorithms, which hinder practitioners to understand the prediction process. Explainable Artificial Intelligence (XAI) provides numerous tools and methods to counteract this problem. There is however a need to qualify the methods with human-centered studies in manufacturing environments, since explainabilityis context-specific. The purpose of this mixed-method case study is to examine the explainability of regression models for lead time prediction in quality control laboratories at a biopharmaceutical production site in Sweden. This entails the research questions of which methods can increase the explainability of lead time prediction, what type of explanation is required to enable explainability and what are the benefits of explaining regression models in this context. This is why relevant literature in the field of XAI and AI-based lead time prediction is reviewed. An explainable lead time prediction modelis developed and a Delphi study is carried out to gauge the importance of different explanation types and to identify explainability-related benefits. The results show a transparency-performance trade-off and highlight eight benefits that are mapped to the model’s life cycle. These findings provide new insights into the explainability requirements and benefits in quality control processes and support practitioners in steering their implementation efforts.