In Industry 4.0 manufacturing environments, forecasting Overall Equipment Efficiency (OEE) is critical for data-driven operational control and predictive maintenance. However, the highly volatile and nonlinear nature of OEE time series--particularly in complex production lines and hydraulic press systems--limits the effectiveness of forecasting. This study proposes a novel informational framework that leverages Topological Data Analysis (TDA) to transform raw OEE data into structured engineering knowledge for production management. The framework models hourly OEE data from production lines and systems using persistent homology to extract large-scale topological features that characterize intrinsic operational behaviors. These features are integrated into a SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors) architecture, where TDA components serve as exogenous variables to capture latent temporal structures. Experimental results demonstrate forecasting accuracy improvements of at least 17% over standard seasonal benchmarks, with Heat Kernel-based features consistently identified as the most effective predictors. The proposed framework was deployed in a Global Lighthouse Network manufacturing facility, providing a new strategic layer for production management and achieving a 7.4% improvement in total OEE. This research contributes a formal methodology for embedding topological signatures into classical stochastic models to enhance decision-making in knowledge-intensive production systems.

翻译：在工业4.0制造环境中，预测整体设备效率（OEE）对于数据驱动的运营控制与预测性维护至关重要。然而，OEE时间序列的高度波动性和非线性特性——特别是在复杂生产线和液压机系统中——限制了预测方法的有效性。本研究提出了一种新颖的信息处理框架，利用拓扑数据分析（TDA）将原始OEE数据转化为可用于生产管理的结构化工程知识。该框架通过持续同调方法对生产线和系统的每小时OEE数据进行建模，提取能够表征内在运行行为的大规模拓扑特征。这些特征被整合到SARIMAX（带外生回归量的季节性自回归积分滑动平均）架构中，其中TDA组件作为外生变量以捕捉潜在的时序结构。实验结果表明，相较于标准季节性基准模型，该框架的预测精度至少提升17%，且基于热核的特征被一致证明为最有效的预测因子。所提出的框架已在全球灯塔网络制造工厂中部署，为生产管理提供了新的战略层级，并实现了总OEE 7.4%的提升。本研究贡献了一种将拓扑特征嵌入经典随机模型的规范化方法论，以增强知识密集型生产系统中的决策能力。