An Integrated Lean Service and Machine Learning Approach to Improve On-Time Delivery in a SME Restaurant

Arroyo-Valladares P.; Cardalda-Angeles A.; Chavez-Ugaz R.; Carlos Quiroz-Flores J. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2026) · DOI: 10.18687/laccei2026.1.1.1914

Resumen

Food service establishments operate in environments where demand variability, ingredient availability, and delivery speed affect operational performance. In this context, a diagnostic assessment conducted in a pizza restaurant identified inefficiencies such as inventory inaccuracy because of insufficient supplies, inefficient mise in place due to long search times, and unproductive time related to process control and layout design. To address these main issues, this study proposes an integrated model that applies a Random Forest algorithm to forecast weekly demand and synchronizes purchasing and production using an EOQ-MRP system supported by Kanban as a visual control mechanism, in addition, standardized work and workstation redesign are implemented to reduce preparation time. The proposed model improves demand forecasting and enables better inventory planning and coordination between purchasing and preparation activities. Hybrid validation shows an increase in process compliance from 79% to 97% after implementation. These findings indicate that integrating Machine Learning forecasting with Lean Service tools process synchronization between all the processes and stabilizes ingredient supply, enhancing operational performance in foodservice environments. These results open the path for future research integrating predictive analytics with Lean Service methodologies to address operational variability in dynamic foodservice settings.

Instituciones: Universidad de Lima