Nowadays, industry is being forced to produce smaller and more
diverse batches, increasing the complexity of internal supply chains. Data has
become a valuable asset, supporting the development of intelligent automation
solutions. Decision support systems, which leverage data, require the
automation pyramid to be more flexible, as information needs to be exchanged
simultaneously and in real-time with all automation layers. This paper proposes
a framework for intelligent automation to deal with current challenges in acquisition and management of data in industrial settings, towards feeding
decision support systems. It frames the topic within the scope of internal supply
chains, addressing the framework impact on work practices within the
organisation. Two real industrial implementation cases are examined, in the
wood and chemical industries. Results help practitioners address the most
impactful challenges affecting the performance of internal supply chains, by
developing systems which are faster, more flexible, efficient and with improved
quality.This work was supported by FCT, through IDMEC, under LAETA, project
UIDB/50022/2020
