Stabilizing Battery Cell Production Through Jig Traceability

Background:

A leading automotive battery cell manufacturer supplying commercial passenger vehicle programs was experiencing recurring production disruptions on a highly automated serialized assembly line. Each battery cell traveled through the manufacturing process on an individual work jig, with more than 300 jigs circulating continuously throughout production.

While periodic recalibration was required to maintain process accuracy, the operation lacked the ability to identify calibration drift, track jig-specific performance, or associate defects to individual jigs. As a result, failures were addressed reactively through manual troubleshooting and off-line repair activities, creating throughput losses, increasing work-in-process inventory, and limiting root-cause visibility.

Crossover Solutions was engaged to establish a fact-based approach for identifying performance degradation, improving traceability, and restoring operational control.

Critical Issues:

• Reactive jig removal and repair disrupting production flow and reducing effective throughput
• Increasing work-in-process inventory driven by in-process repairs and rework
• No visibility into jig-specific failure modes, degradation trends, or performance history
• Root-cause analysis dependent on anecdotal observations rather than objective data
• Inability to identify recurring sources of process variation impacting quality and productivity

Our Approach:

XO established a data-driven operational control system that connected individual jig performance directly to production outcomes.

Rather than treating failures as isolated events, XO focused on creating complete traceability throughout the manufacturing process. By linking each jig to station-level performance data, the team created the first comprehensive view of how equipment condition influenced quality, throughput, and production stability.

The resulting system transformed a reactive maintenance environment into a predictive, fact-based operating model capable of identifying emerging issues before they impacted production.

Key Actions:

• Activated existing RFID infrastructure to establish jig-level traceability throughout the production process
• Installed station-level read heads to associate individual jigs with rejection events and process failures
• Integrated production data into a centralized SQL database for analysis and performance trending
• Developed analytical tools to identify recurring failure patterns and degradation trends
• Established performance monitoring at the individual jig level
• Designed a future-state automated calibration verification concept to support preventive process control

Results:

• Eliminated a significant source of hidden process variation by exposing jig-specific failure modes
• Improved first-time-through quality through earlier identification and correction of calibration-related defects
• Reduced unnecessary jig removals and reactive troubleshooting activities
• Established complete traceability and accountability across more than 300 production jigs
• Enabled data-driven maintenance decisions based on actual performance trends rather than anecdotal observations
• Created a scalable foundation for predictive maintenance and long-term process stability