Improving the Productivity of a Production Line through Explainable Artificial Intelligence for Data Analysis

Abstract

Improving the performance of industrial production systems through data analysis is a recurrent topic in both research and industry. However, most existing data-driven approaches in manufacturing focus on engineer level decision support, such as predictive maintenance or strategic resource planning and pay little attention to operator-centred decision support. At the same time, production data exhibit specific characteristics that make their analysis challenging: high-dimensionality, often heterogeneous, and naturally structured as time series and point processes. These gathered properties require dedicated modelling strategies insufficiently addressed in the literature.An additional and increasingly recognised challenge is the adoption of digital decision-support tools on the shop floor. Operators and line adjusters need to trust the models’ outputs in order to integrate them into their daily practices. This calls for the design of explainable artificial intelligence approaches made for production contexts and for the needs of human operators.The proposed approach is developed in a real factory, to support operators decisions based on the state of the production system.. We detail the decision model building process from dimensional reduction to combination with field knowledge.Our goal is to build models that learn to recognise production situations similar to past ones that have already led to stops, so as to suggest relevant corrective actions to implement. A key aspect of our approach is the continuous confrontation of the modelling choices with field experience, in order to provide interpretable reasons for the recommendations.