| Resumo: | 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.
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