20% Reduction in Rework, Weld Defect Analytics for Automotive OEM

Client

The client is the North America subsidiary of a global manufacturing conglomerate of world-class automobiles, motorcycles, and power equipment.

Business Need

To predict the probability of a bad weld and prompt corrective action to improve quality and reduce rework/scrap work for a leading Automotive Equipment Manufacturer. 

Challenges

• The parameters essential to determine the quality were identified as resistance, current, voltage, phase angle, and temperature. 
• Next, statistical modelling techniques were used to find hidden patterns in the factors that led to the occurrence of bad welds.
•  Confounding algorithms were used to prioritize the factors by importance and severity of impact. Machine learning algorithms were used to predict the use of robots, which could have caused a bad weld, given the order of priority of factors. This approach helped reduce the sampling rate, thus improving the manufacturing process. 
• Persona-based dashboards with streamlined & historic data summary of readings for current and past understanding of data, helped the client understand the situation better.

LTM Solution

•  Created a common Manufacturing Quality Information System to standardize processes, procedures, terms, acronyms, and calculations. 
• Provided ability to enter defects on manufacturing line using an image or text.
• Enabled tracking of vehicle inventory during repairs.
• Enabled integration with manpower and process work instruction data to trace defects caused due to flaws in process instructions or line associate skill.

Business Benefits

• 20% reduction in sampling checks for cars in assembly line with proactive governance, reducing the manual inspection and rework time. 
• 20% reduction in manual labour for inspection of selected car and weld.
• 33% improved visibility to action-driven, persona-based dashboards for routine inspection and historical analysis for root cause detection.