Beaulieu Group: Boosting Extrusion Throughput by 21%

Beaulieu International Group (BIG), a global leader in flooring solutions and technical textiles, operates complex manufacturing processes that demand high energy efficiency and production throughput. One of its key challenges is the extrusion process, which involves high-powered equipment responsible for producing polymers and fibers. With increasing pressure for sustainable manufacturing and rising energy costs, Beaulieu aimed to explore how AI and data-driven insights could optimise energy consumption and production efficiency.

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Need

Challenge

Efficient extrusion requires a balance between speed, temperature, and pressure to optimise material flow, energy consumption, and final product quality. However, the extrusion process at Beaulieu presented unique challenges:

Aging equipment: the extruder has two motors that have been in service for over 25 years, potentially leading to inefficiencies
Balancing throughput and energy efficiency: increasing throughput should not come at the cost of excessive energy consumption
Understanding the effect of speed on efficiency: operators needed quantifiable insights on how motor speed, power, and throughput interact
Data-driven decision making: Beaulieu expressed the desire to gain insights from the extensive amount of available machine and energy data

To address these challenges, Beaulieu partnered with Faktion to conduct a data-driven analysis of the extrusion process, uncovering critical insights that could pave the way for throughput and energy optimisation initiatives in the near future.

The solution

Solution

We conducted an extensive data analysis on Beaulieu’s extrusion process, focusing on energy consumption, throughput, and process efficiency. Instead of deploying AU models immediately, the approach centred around extracting valuable insights from existing machine data, which would serve as the foundation for future monitoring, simulation, and AI-driven recommendations for process optimisation.

The analysis explored several key factors:

Energy consumption patterns: understanding where inefficiencies occur, particularly in relation to aging motors and varying extrusion speeds
Material dosage optimisation: evaluating how raw material dosage impacts both throughput and energy usage
Process steering & machine performance: identifying inefficiencies in machine settings and control strategies
Throughput vs efficiency trade-offs: assessing the relationship between motor speed, torque, and production efficiency

Approach

Our analysis followed a structured methodology to extract meaningful insights from Beaulieu’s production data:

Data exploration & anomaly analysis: our team examined 65 days of extruder data, including motor speed, torque, pressure, and energy usage. Unexpected findings were validated against the data to ensure accuracy.
Foundation for AI-powered monitoring & simulation: with the analysis completed, we laid the groundwork for a future real-time monitoring and simulation tool, which will provide operators with AI-driven recommendations for energy-efficient process adjustments.

Outcome

The analysis provided Beaulieu with critical insights that will drive future optimisation efforts:

Heating element issues: some heating elements were running at 100% power without achieving expected temperature increases, suggesting possible inefficiencies
Pump downtime & flow loss: machine downtimes led to significant throughput loss
Different extruder responses: identical extruders exhibited different pressure responses to interventions, indicating further opportunities for tuning
Efficiency drops at high speeds: contrary to expectations, running the extruder at higher speeds reduced efficiency by 21%, highlighting the need for an optimised speed-to-power ratio

These findings helped operators reassess process assumptions and prioritise key inefficiencies, providing an actionable optimisation focus. With the foundations in place, Beaulieu and Faktion are now preparing for the next phase of this project:

Developing a monitoring & simulation tool for continuous tracking of energy consumption, speed, and throughput
AI-driven optimisation recommendations based on the monitoring & simulation tool
Scaling to other Beaulieu plants

By leveraging AI & data analytics, Beaulieu International Group is setting the stage for a future of optimised, energy-efficient manufacturing processes - ensuring both sustainability and competitiveness in a demanding industry.

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