Case study
BI & Analytics Platform to Improve ADY% and Reduce Scrap in Telecom Manufacturing
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.
Table of contents
Business objective
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
50% Runtime Reduction (8 Hours to 4 Hours)
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.
More results
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