Abstract The ability to predict the remaining cycle-time in industrial environments is of major concern among production managers. An accurate prediction would enable managers to handle undesired situations with more control, thereby preventing future losses. However, making such predictions is no trivial task: there are many methods available to cope with this problem, including a recent research stream in process mining. Process mining provides tools for automated discovery of process models from event logs, and eventually, extend those models in driving predictions. In general, predictive models in process mining generally deals with business processes, and not directly with the industrial environment, which contains a full prism of particularities. In this paper we propose a hybrid predictive model based on transition-systems and statistical regression which is “product-oriented”, tailored to better predict online cycle-times on industrial environments. We propose a weight for each method, optimized by a linear programming model. We tested our new approach on an artificially created log that emulates an industrial environment, and on a real manufacture log. Results showed that our approach provides better accuracy measures for both test instances.

中文翻译：

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使用过程挖掘的制造系统剩余时间预测的扩展模型

摘要 预测工业环境中剩余周期时间的能力是生产经理的主要关注点。准确的预测将使管理人员能够以更多的控制权处理不良情况，从而防止未来的损失。然而，做出这样的预测并非易事：有许多方法可以解决这个问题，包括最近在过程挖掘方面的研究流。流程挖掘提供了从事件日志中自动发现流程模型的工具，并最终在驾驶预测中扩展这些模型。一般来说，流程挖掘中的预测模型通常处理业务流程，而不是直接处理工业环境，工业环境包含完整的特殊性。在本文中，我们提出了一种基于转换系统和统计回归的混合预测模型，该模型是“面向产品的”，旨在更好地预测工业环境中的在线周期时间。我们为每种方法提出一个权重，通过线性规划模型进行优化。我们在模拟工业环境的人工创建的日志和真实的制造日志上测试了我们的新方法。结果表明，我们的方法为两个测试实例提供了更好的准确性度量。