An automotive manufacturer was struggling with inefficient line balancing in their sub-assembly operations. Traditional time studies and analysis processes significantly delayed improvement opportunities. The manufacturer needed to optimize their workforce allocation while enhancing efficiency. The engineering team sought a solution that could accelerate their analysis process and provide data-driven insights.
The existing time study and line balancing required engineers to spend 2-3 weeks collecting data and analyzing it for each line balancing initiative. This long timeline created a significant bottleneck in their Operational Excellence strategy, delaying process improvements and cost optimization efforts. Another major challenge was inefficient resource allocation across the sub-assembly line. The line was operating with six operators, but uneven workflow distribution meant some stations had excess capacity while others created bottlenecks. The lengthy analysis process also hindered their ability to respond quickly to production changes.
The company implemented Kaizen Copilot to address the challenges. The implementation process was simple and straightforward. A Lean Engineer mounted a smartphone above each of the six stations, capturing 10-15 minutes of assembly video from each location. The system automatically analyzed the videos, breaking down cycles into steps without requiring detailed manual annotation.
Kaizen Copilot uses AI and computer vision to analyze the workflow and provide automatic line balancing recommendations. The setup was completed in just 2 hours. The software also recommended a new Standard Operating Procedure (SOP), which was further validated and implemented by the company. This data-driven approach helped optimize workforce allocation across all stations.
The implementation of Kaizen Copilot delivered immediate impact, reducing the analysis time from 2-3 weeks to just 2 hours. The system identified opportunities to reduce operator headcount from 6 to 4 workers while maintaining productivity. The new line balance was successfully implemented within a week, demonstrating significant improvements in operational efficiency.
A leading automotive parts manufacturer recognized the importance of efficient line balancing and process optimization to enhance operational excellence in their sub-assembly operations.
Automotive Parts Manufacturing
Optimize workforce allocation through automated line balancing.
Brandin and his team help our customers with setting up the Pathfinder platform and getting up-to-speed on all the features and functionalities, in addition to assisting them with standardizing their processes where needed.
Brandin is a seasoned manufacturing leader, who has spent more than a decade designing and setting up assembly lines in both the US and China, optimizing them, and working with human operators to maximize productivity and quality.
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Andrey co-founded and successfully exited a startup to immigrate to the US. He spent his next eight years building the core data science infrastructure for Amazon’s supply chain planning team, and the CV/ML infrastructure for Microsoft’s HoloLens organization.
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