English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recent advances in the interconnectedness and digitization of industrialmachines, known as Industry 4.0, pave the way for new analytical techniques.Indeed, the availability and the richness of production-related data enablesnew data-driven methods. In this paper, we propose a process mining approachaugmented with artificial intelligence that (1) reconstructs the historicalworkload of a company and (2) predicts the workload using neural networks. Ourmethod relies on logs, representing the history of business processes relatedto manufacturing. These logs are used to quantify the supply and demand and arefed into a recurrent neural network model to predict customer orders. Thecorresponding activities to fulfill these orders are then sampled from historywith a replay mechanism, based on criteria such as trace frequency andactivities similarity. An evaluation and illustration of the method isperformed on the administrative processes of Heraeus Materials SA. The workloadprediction on a one-year test set achieves an MAPE score of 19% for a one-weekforecast. The case study suggests a reasonable accuracy and confirms that agood understanding of the historical workload combined to articulatedpredictions are of great help for supporting management decisions and candecrease costs with better resources planning on a medium-term level.

Workload Prediction of Business Processes -- An Approach Based on Process Mining and Recurrent Neural Networks