To be competitive under the paradigm of Industry 4.0 (I4.0), companies must develop a Production Planning and Control (PPC) able to respond to disturbances. Notably, unexpected machine breaks leading to corrective maintenance actions drastically delay operations as they inhibit the machine production, affecting the production schedule. To create an adaptive PPC, data from the shop floor must be collected and analyzed. However, these data are often unstructured, as they come from either sensors or humans, which makes their use difficult. Notably, human based operations such as maintenance activities often produce textual data. Hence, the objective of this paper is twofold: firstly, it aims to propose a framework for a model capable of estimating the production inhibition time based on textual data. Secondly, it settles the basis for a Dynamic Production Schedule (DPS) model considering these estimations. To achieve this, an approach using Text Mining (TM) and Machine Learning (ML) techniques is proposed