A Novel Method to Manage Production on Industry 4.0: Forecasting Overall Equipment Efficiency by Time Series with Topological Features

• URL: http://arxiv.org/abs/2507.02890v1
• Date: Fri, 20 Jun 2025 10:04:49 GMT
• Title: A Novel Method to Manage Production on Industry 4.0: Forecasting Overall Equipment Efficiency by Time Series with Topological Features
• Authors: Korkut Anapa, İsmail Güzel, Ceylan Yozgatlıgil,
• Abstract summary: Overall equipment efficiency (OEE) is a key manufacturing production, but its volatile nature complicates short-term forecasting.<n>This study presents a novel framework combining time series decomposition and topological data analysis to improve OEE prediction across various equipment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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