论文标题
业务流程的工作负载预测 - 一种基于过程挖掘和复发性神经网络的方法
Workload Prediction of Business Processes -- An Approach Based on Process Mining and Recurrent Neural Networks
论文作者
论文摘要
工业机器的互连和数字化的最新进展（称为工业4.0）为新的分析技术铺平了道路。实际上，与生产相关数据的可用性和丰富性可实现新的数据驱动方法。在本文中，我们提出了一种以人工智能增强的过程挖掘方法，该方法（1）重建公司的历史工作量，（2）使用神经网络预测工作量。我们的方法取决于日志，代表与制造业相关的业务流程历史。这些日志用于量化供求，并将其送入复发性神经网络模型以预测客户订单。然后，根据痕量频率和活动相似性等标准，通过历史机制将相应的实现这些订单的活动从历史上取样。该方法的评估和说明是在Heraeus Materials SA的管理过程中进行的。为期一年的测试集中的工作量预测在一周的预测中获得了19％的MAPE得分。案例研究表明，准确性合理，并确认对历史工作量的良好理解与表达的预测相结合对支持管理决策有很大的帮助，并且可以通过在中期水平上进行更好的资源计划来降低成本。
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.
