Real-Time Machine Learning Integration with MES for Waste Reduction, Quality Control, and Production Optimization in Flexible Packaging at Transcontinental

This project focuses on the real-time integration of machine learning (ML) models with the Manufacturing Execution System (MES) at Transcontinental (TC) to enhance quality control, reduce production waste, and optimize operations in flexible packaging manufacturing. TC’s current MES infrastructure ensures traceability and workflow coordination but lacks predictive analytics capabilities, leading to a reactive approach where quality issues and process inefficiencies are often addressed only after causing significant material loss, downtime, and customer dissatisfaction.

To address this gap, the project introduces a strategic initiative aimed at transforming live production data into actionable insights through ML-driven predictive modeling. The solution will enable early detection of process anomalies, suggest real-time corrective actions, and evolve continuously with new data patterns. Historical and live data from critical processes such as extrusion, printing, and lamination will be leveraged to train, validate, and deploy ML models directly into the MES environment, enhancing decision-making at the operational level. By embedding intelligence into MES workflows, the project aims to minimize scrap rates, improve first-pass yield, reduce energy usage, and increase overall equipment effectiveness (OEE).

Expected outcomes include significant reductions in waste and energy consumption, enhanced process stability, and improved responsiveness on the shop floor. This initiative positions TC at the forefront of Industry 4.0 by integrating AI into core manufacturing operations, supporting long-term innovation, sustainability, and digital transformation in the flexible packaging sector.

Faculty Supervisor:

Amin Chaabane;Rim Larbi

Student:

Partner:

Transcontinental

Discipline:

Engineering

Sector:

Manufacturing

University:

École de technologie supérieure

Program: