A Case Study With Avalign Technologies

Optimizing Overall Equipment Efficiency and Utilization

 

Avalign produces and supplies surgical implants, instruments, and delivery systems for the healthcare industry. It handles the entire development process in-house, from conception to launch. With facilities across the United States and Europe, the company’s vision is to improve patient outcomes by providing best-in-class medical equipment.

To achieve its goals, Avalign needed to increase throughput, reduce downtime, and improve equipment utilization and OEE by gaining complete control of its machine and operational data.

The Challenge

Like many manufacturers trying to get a grip on their data, Avalign used a manual tracking system. Operators felt that manual tracking was one of the worst aspects of the job, and management knew that manually collected data was unreliable and inaccurate.

The company’s biggest challenge was understanding why they were experiencing increased downtime. They struggled to completely understand their OEE with their existing processes and technology. Another part of the challenge was understanding bottlenecks. Without accurate data and analysis, their understanding and solutions were limited.

Avalign believed that with real-time, accurate machine data insights, management could better understand machine utilization against the quality and quantity of parts produced during cycles.

The Solution

Avalign rolled out MachineMetrics on 16 machines, including grinding machines and lathes. Over time, they seamlessly integrated the platform on 132 more machines across four facilities in the United States.

MachineMetrics' Digital IO approach was a game-changer for older machines and legacy assets. Legacy machines were up and running with MachineMetrics in less than an hour.

The visualization of machine data across lathe and grinding machines and the ability to act on insights led to increased machine utilization, OEE, and throughput. MachineMetrics' real-time analytics helped management uncover process inefficiencies and make better decisions.

Results

MachineMetrics helped Avalign discover the root cause of its machine under-utilization: a disproportionate amount of time attributed to machine tool setup. This lead Avalign to re-engineer existing tooling processes to ease operators' jobs and dramatically reduce setup times.

At the organizational level, MachineMetrics revealed that the company was understaffed. This led to Avalign hiring more operators and creating new schedules to optimize operational processes.

Avalign’s top five results from using MachineMetrics were reduced downtime, improved OEE, increased machine utilization, optimized tooling processes, and better access to data. In six months, throughput capacity increased by 9.9%.

Visibility of the production process enabled Avalign to identify improvement initiatives that impacted utilization. Real-time, actionable insights drove a dramatic downtime reduction and resulted in a savings of 14,000 hours on grinders alone compared to the previous year.

Monitoring the operations of lathe machines and grinders ensured they were working at optimal capacity within three shifts for five to six days a week. Pre-MachineMetrics, Avalign averaged an OEE of 25 to 30% with calculations done manually. Within five weeks of deploying MachineMetrics, OEE improved to 44%.

Since then, Avalign's OEE has been above the 60% mark, meaning a greater than 30% increase in equipment utilization, and it regularly hits the 80% OEE mark weekly.

Client:

Avalign Technologies

Industries:

Medical and Healthcare Industry

Equipment:

Lathes, Grinding Machines

The Metrics

Our impact on customers
40% Increase in OEE
$4.5MM Increase in Capacity Utilization
14K Hours Saved
9.9% Increase in Throughput

Download The Full Case Study

OEE Director Matt Townsend discusses the impact of deploying MachineMetrics across four of Avalign's facilities. Learn how Avalign Technologies was able to increase capacity utilization by $4.5mm.

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Avalign Case Study

Optimizing Overall Equipment Efficiency and Utilization
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