Aging infrastructure and volatility in prices of raw materials, often place automakers in the tight spot to continuously look for radical ways to reduce operational costs. With powerful nexus of forces such as IoT, IIoT and machine learning, automakers stand at the brink to experience not just incremental but transformational benefits.

Paint application is touted to be one of the most complex and demanding activities. Any defects in the painting process can result in poor customer experience and impact the company brand. Join us for a webinar hosted by Anita Rajasekaran, Principal Growth Hacker, DataRPM featuring Abhishek Tandon, Business Insights Manager, DataRPM where he will discuss how automakers can improve their output quality, reduce defects and improve the operational efficiencies by applying analytics in the right way across the paint shop.

By attending this webinar, you will learn

a. How predictive maintenance can result in improvement in quality, reduction in unit cost and improved production efficiencies on paint assembly lines

b. How OEMs can use sensor data to identify erratic machine behavior which cause inconsistent production output?

c. Use Case - How a prominent UK Automotive achieved 75% reduction in rework by identifying the indicators of defects in the paint shop

REGISTER NOW 

Host and Speaker
Principal Growth Hacker
DataRPM
Speaker: Abhishek Tandon
Business Insights Manager
DataRPM
Head of Strategy, King Content
Thank You For Registration
Related Resources
About Us
DataRPM provides an award winning cognitive data science platform on cloud or on premises for enterprises to build data products for Recommendations & Predictions. DataRPM prides it self in delivering the fastest and scalable automated data science platform which guarantees a return on investment for their customers and turns everyone in the organization into citizen data scientists. Enterprises across the globe are using DataRPM to digitally transform their businesses in the core areas of Product Recommendations, Content Recommendations, Predictive Maintenance and Churn Predictions.