A Novel Method to Manage Production on Industry 4.0: Forecasting Overall Equipment Efficiency by Time Series with Topological Features
This work addresses short-term forecasting challenges for production managers in Industry 4.0, but it is incremental as it builds on existing methods with hybrid techniques.
The study tackled the problem of forecasting volatile overall equipment efficiency in manufacturing by proposing a framework that combines time series decomposition with topological data analysis, resulting in significantly lower error metrics compared to conventional and transformer-based models.
Purpose: Overall equipment efficiency (OEE) is a key manufacturing KPI, but its volatile nature complicates short-term forecasting. This study presents a novel framework combining time series decomposition and topological data analysis to improve OEE prediction across various equipment, such as hydraulic press systems. Methods: The approach begins by decomposing hourly OEE data into trend, seasonal, and residual components. The residual, capturing short-term variability, is modeled using a seasonal ARIMA with exogenous variables (SARIMAX). These exogenous features include statistical descriptors and topological summaries from related time series. To manage the high-dimensional input space, we propose a hybrid feature selection strategy using recursive feature elimination based on statistically significant SARIMAX predictors, coupled with BIC-guided particle swarm optimization. The framework is evaluated on real-world datasets from multiple production systems. Results: The proposed model consistently outperforms conventional time series models and advanced transformer-based approaches, achieving significantly lower mean absolute error and mean absolute percentage error. Conclusion: Integrating classical forecasting with topological data analysis enhances OEE prediction accuracy, enabling proactive maintenance and informed production decisions in complex manufacturing environments.