Smart, Sustainable Manufacturing: Using IoT and Lean to Reduce Scrap in a Lima, Peru Pizza Producer

Torres Z.; Gamonal P.; Hurtado A. · Advances in Transdisciplinary Engineering (2026) · DOI: 10.3233/atde260725

Resumen

This study addresses product waste in the family-size Hawaiian pizza line of an industrial food plant by proposing and validating an integrated improvement model that combines Poka-Yoke 4.0, digital Andon, and Predictive Maintenance 4.0 within a Lean 4.0 and Green Value Stream Mapping (GVSM) framework supported by an Internet of Things (IoTx) Edge-Gateway-Cloud architecture. The intervention begins with a GVSM-based diagnosis to identify bottlenecks and loss drivers, followed by the instrumentation of critical equipment with smart sensors, interoperable cloud-connected Andon boards, and condition-monitoring devices that feed machine-learning models for predictive maintenance. Validation is performed through discrete-event simulations calibrated with historical production and failure data. Results show that, compared to the baseline, product waste is reduced by 19%, production increases by 31.06%, and the environmental footprint decrease from 6.9 to 5.0 t CO2-eq. This study provides applied evidence, in a pizza production context, of the convergence of Green-Lean-IoT with predictive maintenance, highlighting its potential to minimize waste and improve environmental performance in the food industry.

Instituciones: Universidad Peruana de Ciencias Aplicadas