  New technologies such as Machine Learning (ML) gave great potential for
evaluating industry workflows and automatically generating key performance
indicators (KPIs). However, despite established standards for measuring the
efficiency of industrial machinery, there is no precise equivalent for workers'
productivity, which would be highly desirable given the lack of a skilled
workforce for the next generation of industry workflows. Therefore, an ML
solution combining data from manufacturing processes and workers' performance
for that goal is required. Additionally, in recent times intense effort has
been devoted to explainable ML approaches that can automatically explain their
decisions to a human operator, thus increasing their trustworthiness. We
propose to apply explainable ML solutions to differentiate between expert and
inexpert workers in industrial workflows, which we validate at a quality
assessment industrial workstation. Regarding the methodology used, input data
are captured by a manufacturing machine and stored in a NoSQL database. Data
are processed to engineer features used in automatic classification and to
compute workers' KPIs to predict their level of expertise (with all
classification metrics exceeding 90 %). These KPIs, and the relevant features
in the decisions are textually explained by natural language expansion on an
explainability dashboard. These automatic explanations made it possible to
infer knowledge from expert workers for inexpert workers. The latter
illustrates the interest of research in self-explainable ML for automatically
generating insights to improve productivity in industrial workflows.
