Keysight > Case Studies > Accelerating Root Cause Analysis in Automotive Manufacturing with PathWave Manufacturing Analytics

Accelerating Root Cause Analysis in Automotive Manufacturing with PathWave Manufacturing Analytics

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 Accelerating Root Cause Analysis in Automotive Manufacturing with PathWave Manufacturing Analytics - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Real Time Analytics
  • Functional Applications - Manufacturing Execution Systems (MES)
Applicable Industries
  • Automotive
  • Equipment & Machinery
Applicable Functions
  • Maintenance
Use Cases
  • Additive Manufacturing
  • Root Cause Analysis & Diagnosis
Services
  • Testing & Certification
The Customer

PathWave Manufacturing Analytics

About The Customer
The customer in this case study is a global automotive component manufacturer with decades of experience in continuous improvements to their manufacturing lines. They strive to provide the best quality for their customers and have set high standards for their production process, with functional test yields less than 90% being unacceptable. They had established real-time monitoring displays above each tester to monitor performance and had developed their own in-house analytical tool for data analytics. However, they were facing challenges in identifying and resolving issues that were causing low production yields, leading them to seek a more efficient solution.
The Challenge
A global automotive component manufacturer was facing a significant challenge in maintaining high First Pass Yield (FPY) rates in their production lines. Despite having real-time monitoring displays and an in-house analytical tool, the company was struggling with functional test failures that led to FPY rates dropping below 90%. The company spent approximately six months trying to identify the root cause of the issue, which turned out to be a faulty fixture in the functional test systems. This lengthy process involved extensive manual data aggregation and transformation, and required the production line to be shut down for troubleshooting. Even after identifying the issue, the company had to perform monthly maintenance on all fixtures in the functional tester lines to mitigate the FPY loss.
The Solution
The company turned to PathWave Manufacturing Analytics (PMA) to expedite the root cause analysis process. PMA's features, including fixture comparisons, ranking of top failing tests, and top alert statistics, were used to analyze the problematic period that had previously taken six months to investigate. The PMA platform automated the analysis of the historical data, and within five minutes, a Keysight Field Engineer was able to identify the problematic fixture and the reason for its issues. This process demonstrated PMA's ability to drastically reduce investigation time. The 'Compare by Fixture' feature allowed the team to quickly identify the unstable measurements from the faulty fixture. The PMA platform also includes a customizable report generation tool, which enabled the team to generate and share reports based on their preferences, further facilitating the troubleshooting process.
Operational Impact
  • The use of PathWave Manufacturing Analytics (PMA) resulted in significant operational improvements for the automotive component manufacturer. The software's ability to quickly identify and isolate the root cause of functional test failures allowed the company to drastically reduce downtime and get their production line back on track more quickly. The 'Compare by Fixture' feature provided clear visibility into the performance of each fixture, enabling the team to focus on resolving equipment or product issues rather than spending time collecting and analyzing data. The customizable report generation tool also facilitated communication and tracking of issues, making the root cause and corrective action process almost effortless. The continued use of PMA contributed to further improvements in the company's production lines.
Quantitative Benefit
  • Reduced root cause analysis time from 6 months to 5 minutes
  • Identified the problematic fixture causing low FPY rates in real-time
  • Automated data analysis, eliminating the need for manual data aggregation and transformation

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