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

TL;DR

This paper presents a novel approach combining process mining and recurrent neural networks to reconstruct and predict business process workloads, aiding management decisions and resource planning.

Contribution

It introduces a data-driven method that integrates process mining with neural networks for workload prediction based on manufacturing process logs.

Findings

Achieves 19% MAPE in one-week workload forecast

Demonstrates effective workload reconstruction from process logs

Supports medium-term resource planning and decision-making

Abstract

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.