Overview
I led the development of a machine‑sensor analytics dashboard that unified multiple data sources into a single source of truth for machine‑level performance. The first version (2024) gave my department its first real visibility into why defects were happening and how to prevent them, enabling faster and more consistent troubleshooting. In 2025, after the plant upgraded its control systems, I rebuilt the entire solution with richer sensor data, a new database structure, and automated refreshes, transforming it into a scalable analytics tool adopted across multiple sites.
Problem
Before this project, defect analysis was slow reactive, and inconsistent. Data lived across:
MES logs
SAP quality results
Excel files
Legacy sensor programs
There were no visuals showing which machines, styles, or colors were driving issues. I relied on manual spreadsheets, and operators had no real‑time visibility into machine behavior.
This created:
Delayed root cause analysis
Repeated failures
Inconsistent troubleshooting
Limited ability to identify trends
The department needed a unified, reliable analytics system.
Aproach
In 2024, I independently initiated the first version of the dashboard after realizing that the department lacked a clear way to analyze defects. This was not part of my formal responsibilities, I saw the opportunity and built the solution.
A Power BI dashboard showing machine‑level KPIs, defect trends, and failure patterns
SQL logic joining MES logs, SAP quality data, Excel files, and legacy databases
The first unified view of machine performance for engineering analysis
Built the SQL model and dashboard end‑to‑end
Created KPIs for defect rate, downtime, and failure patterns
Trained engineers on how to use the dashboard for investigations
MES + SAP + Excel → SQL → Power BI (manual refresh)
Limited sensor visibility
Inconsistent data structures
First iteration of drilldowns by machine, shift, and time window
2024 Dashboards:
In 2025, the plant migrated to Adaptive, a new HMI/interface with a completely different data structure. This created an opportunity to rebuild the system from the ground up.
I partnered directly with the engineering team responsible for the new platform to design the new database and unlock sensor data we never had before.
A new SQL model aligned to the updated data structure
A centralized database consolidating all machine data
Automated Power BI dashboards with deeper machine insights
Operator‑friendly pages for real‑time decision‑making
Co‑designed the new database structure with platform engineers
Rebuilt all SQL logic, DAX, and Power Query transformations
Created second‑generation dashboards
Trained both engineers and operators on the new system
New Database → SQL → Power BI (automated refresh)
Clean timestamps
Richer sensor tags
Faster refresh cycles
More accurate KPIs and trend analysis
2025 Dashboards:
Results
~50% reduction in machine‑driven defects
More than $300K in annualized savings enabled by faster root‑cause analysis
Recognized with a 4th Quarter Milliken Performance System award for enabling data‑driven defect reduction
Replicated at additional sites due to its scalable design
Improved engineering analysis and operator decision‑making
📌 Add Visual Here:
Before vs. After defect chart (blur internal identifiers)
Key Skills Demonstrated
SQL data modeling
Power BI development
Multi‑system data integration
KPI design & visualization
Root cause analysis support
Automated reporting pipelines
Cross‑functional collaboration
Operator‑focused design