Enhancing Manufacturing Test Processes with Graph Analytics

Challenge

A leading motion control technology company, a global supplier of steering and drive line systems and software for automotive OEMs, faced significant challenges with the efficiency of their backdrive testing stations on a production line. Their primary objective was to leverage data analytics to refine these testing procedures dynamically. The company aimed to use data from preceding stations to adjust the operational parameters of backdrive machines in real-time, thereby reducing rejection rates and improving overall process efficiency. The ultimate goal was to implement an AI-driven solution that would enhance both efficiency and accuracy, leading to reduced total costs.

However, the company identified a critical gap in their strategy: the quality and sufficiency of the input data from their testing stations. The data collected was inadequate for a comprehensive understanding of the entire manufacturing process, including how configurations at earlier stages influenced the backdrive machine settings. This inadequacy posed a significant risk, as it could lead to a costly and time-consuming process of trial and error in machine learning modeling and implementation.

Solution

To address these challenges, the company partnered with GiQ, a data analytics platform that utilizes neural graph analytics and advanced AI capabilities. GiQ's rolewas pivotal in pinpointing the insufficiencies in the company's data collection processes and providing a robust framework for data-driven decision-making.

Implementation of GiQ Studio

GiQ Studio,with its advanced graph algorithms, was employed to analyze the data collected from various testing stations along the manufacturing line. This analysis enabled the company to understand the interdependencies and influences of different stages of the production process on the backdrive machine settings.

Data quality and insight

GiQ quickly identified that the initial plan to use the existing data for machine learning modeling was impractical due to its inadequacy. This insight was crucial as it prevented the company from investing in a flawed approach, saving significant time and resources.

GiQ's algorithms highlighted the need for more comprehensive and higher quality datato make accurate predictions and adjustments.

Dynamic parameter adjustment

Using the insights gained from GiQ, the company restructured its data collection process to ensure that all relevant parameters were accurately captured and analyzed. This new approach allowed for real-time adjustments to the backdrive machines' operational settings based on data from preceding stations. The AI-driven solution implemented through GiQ enabled a more precise and efficient testing process.

Outcomes

Increased efficiency

The implementation of GiQ's AI-driven solutions led to a substantial increase inthe efficiency of the backdrive testing stations. The dynamic adjustment of operational parameters based on high-quality data reduced the overall rejectionrates significantly.

Cost reduction

By avoidingthe trial and error approach and leveraging GiQ's advanced analytics, the company minimized unnecessary costs associated with the initial flawed strategy. The optimized testing process resulted in lower operational costs and improved resource utilization.

Enhanced quality

The AI-driven adjustments provided by GiQ ensured that the testing procedures were more accurate, leading to higher quality outputs and reducing the likelihood of errors. This precision was critical in maintaining the company's reputation for delivering reliable motion control solutions.