Calendar

Managing Manufacturing Digital Transformation using AI/ML technologies (Virtual) 

January 15 - 6:00-7:30 pm 

VIRTUAL

The first 15-20 minutes will be focused on Chapter information / upcoming events, followed by our learning topic:

Managing Manufacturing Digital Transformation using AI/ML technologies (Virtual) 

In Rajeev Kalamadi’s presentation, attendees will learn best practices for managing the digital transformation of manufacturing processes using AI/ML technologies.  Specific Learning Objectives:  

1. Artificial Intelligence (AI) & Machine Learning (ML) technologies and understand manufacturing transformation use cases.  
2. Form and lead teams to solve business problems using AI/ML (Artificial Intelligence/Machine Learning) technology implementations and gain insights from analytics capabilities using Agile and Waterfall methodologies.  
3. Risk and Quality Management in AI/ML (Artificial Intelligence/Machine Learning) solution implementations 

 Rajeev will also provide an overview of AI/ML technologies such as IIoT, Computer Vision, Optimization, Large Language Models and their relevancy in a manufacturing landscape.  

About our Speaker | Rajeev Kalamadi, FBCS

Rajeev Kalamdani is a Data Science/Analytics Manager who is currently leading a global team of data scientists (located in Dearborn, Cologne, and Chennai) which delivers analytics solutions supporting Manufacturing Operations and Engineering. He has expertise in all aspects of manufacturing (engineering, launch, production, maintenance & quality) and his teams have deployed the following AI/ML solutions in a global enterprise setting:   

• Predictive Maintenance: AI/ML models to transition to condition- based maintenance using machine signal and sensor data with the objective of improving equipment up-time  

• Quality Monitoring & Improvement: Mine process and end of line inspection data to provide near real-time feedback to improve quality; Correlate machine signal data with sampled quality characteristics to migrate to 100% inspection using AI/ML models  
• Optimization Based Decision Support: Batch production schedule optimization driven by demand and subject to manufacturing constraints to minimize cost and inventory; Optimize sourcing decisions based on capacity and logistics constraints to minimize cost; Allocation of operators to assembly lines and features to machining lines to maximize utilization and minimize cost  
• Product Usage Feedback (Connected Vehicle Data) Utilization: Improve quality and reduce warranty through enhancing in-plant inspection strategies; Improve vehicle location efficiency based on GPS data  
• Labor Management Support: Minimize the time to shift startup by tracking present employees and identifying the most suitable replacements from available unassigned employees  
• Collaborating with the OT (Operational Technology) and IT (Information Technology) teams to plan and execute strategies for large scale data acquisition and ingestion into the data lake for development of analytics apps.  
• Leveraging the following analytics technology components to deliver solutions: Qlikview, Tableau, Alteryx, Python, Spark, Hadoop, Hive, SQL, Kafka, MQTT, Springboot, Angular, Pivotal Cloud Foundry and Data Robot 

Rajeev also has specific expertise in the use of Six Sigma & Lean Manufacturing and Big Data/IIoT analytics within the Manufacturing domain.