Integrated Green-Oriented Model Based on Forecasting Techniques, TPM 4.0, and Poka-Yoke to Reduce Newspaper Returns in the Editorial Industry

Huachhuaco C.; Guerrero Y.; Hurtado A. · Advances in Transdisciplinary Engineering (2026) · DOI: 10.3233/atde260753

Resumen

This research proposes a green-oriented model designed to reduce returns in the editorial sector, which are primarily caused by inaccurate forecasts, equipment failures, and human errors in the dispatch area. These factors increase logistical costs and intensify environmental impacts due to paper waste and emissions associated with reverse logistics. The study is developed as an applied case within an editorial company, utilizing historical data on print volume, returns, and mechanical failures. The model integrates demand forecasting through machine learning, autonomous maintenance supported by IoT sensors (TPM 4.0), and a digital Poka-Yoke system using QR codes. The integration of these tools was validated through simulation in Simio under three scenarios: pessimistic, moderate, and optimistic. The optimistic scenario demonstrated the best performance, reducing the Mean Absolute Percentage Error (MAPE) from 7.16% to 3%. Additionally, the Mean Time Between Failures (MTBF) increased from 24.11 to 70 hours. The Mean Time to Repair (MTTR) decreased from 2.68 to 0.60 hours, and operational availability rose from 90.02% to 98.95%. Order allocation errors were reduced from 13.36% to 4.08%. These improvements represent significant savings in paper and resources. The implemented tools introduce automation into processes that were previously manual, reducing both workload and the margin of human error. The proposed model constitutes an original contribution that is replicable in paper-intensive industries, fostering efficiency, and sustainability.

Instituciones: Universidad Peruana de Ciencias Aplicadas