Business Objective / Goal
To minimize scrap (initially at 21%) and enhance ADY% (production efficiency initially at 79%) using a consolidated BI & analytics platform with integrated ML models, real-time reporting, and on-demand data availability.
Solutions & Implementation
- Built a scalable analytics platform capable of deploying, refreshing, and maintaining machine learning models within 4 hours.
- Implemented collaborative BI dashboards and reports to validate business KPIs using Power BI and PowerPivot.
- Integrated the BI platform with SAP and other enterprise systems for seamless data flow.
- Enabled real-time data availability for accurate decision-making across production operations.
- Established a dedicated Dev/Support team for platform management and continuous enhancement.
Major Technologies Used
- Azure Machine Learning – For model training and deployment
- Azure Cloud + Azure SQL Server – For data hosting and processing
- Power BI, Power Query, PowerPivot – For dashboarding and report visualization
- Python – For scripting and ML algorithm execution
- Visual Studio Team Services – For versioning and collaboration
Business Outcomes
- 200% Improvement in Model Execution Performance Reduced execution time from 8 hours to 4 hours through optimized BI and ML model integration.
- Enhanced ADY% and Scrap Reduction Analytics-driven optimization led to improved production efficiency and scrap minimization.
- Integrated Reporting with Real-Time Dashboards Enabled collaborative BI dashboards and reports across departments with dynamic KPIs.
- Always-On Platform Support 24x7 platform management allowed continuous improvement cycles and new development flexibility.
