  The implementation of Artificial Intelligence (AI) systems in the
manufacturing domain enables higher production efficiency, outstanding
performance, and safer operations, leveraging powerful tools such as deep
learning and reinforcement learning techniques. Despite the high accuracy of
these models, they are mostly considered black boxes: they are unintelligible
to the human. Opaqueness affects trust in the system, a factor that is critical
in the context of decision-making. We present an overview of Explainable
Artificial Intelligence (XAI) techniques as a means of boosting the
transparency of models. We analyze different metrics to evaluate these
techniques and describe several application scenarios in the manufacturing
domain.
