  Recent advances in the interconnectedness and digitization of industrial
machines, known as Industry 4.0, pave the way for new analytical techniques.
Indeed, the availability and the richness of production-related data enables
new data-driven methods. In this paper, we propose a process mining approach
augmented with artificial intelligence that (1) reconstructs the historical
workload of a company and (2) predicts the workload using neural networks. Our
method relies on logs, representing the history of business processes related
to manufacturing. These logs are used to quantify the supply and demand and are
fed into a recurrent neural network model to predict customer orders. The
corresponding activities to fulfill these orders are then sampled from history
with a replay mechanism, based on criteria such as trace frequency and
activities similarity. An evaluation and illustration of the method is
performed on the administrative processes of Heraeus Materials SA. The workload
prediction on a one-year test set achieves an MAPE score of 19% for a one-week
forecast. The case study suggests a reasonable accuracy and confirms that a
good understanding of the historical workload combined to articulated
predictions are of great help for supporting management decisions and can
decrease costs with better resources planning on a medium-term level.
