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Unified Quality Dashboards

Analytics & Dashboards

Research-backed case study: single source of truth, ETL, and measurable impact on reporting time.

Executive Summary

Quality and production data lived in multiple spreadsheets and legacy systems. There was no single place to see incoming quality, in-process quality, and customer quality in one view. This project built a single source of truth using SQL for extraction, transformation, and loading (ETL), and Power BI for dashboards that supported daily standups and trend analysis. The result was approximately 60% less time spent on manual reporting, same-day visibility for management, and drill-down capability from plant to part level.

~60% less time on manual reporting
Same-day visibility for management
Drill-down from plant to part level

Context & Problem

Incoming inspection results, in-process checks, and customer returns or complaints were tracked in different tools: Excel workbooks, legacy databases, and sometimes paper or local files. Consolidating a “quality picture” for leadership required manual pulls, copy-paste, and reconciliation-often taking hours and prone to version and timing issues. Decisions were delayed because data was stale or inconsistent across sources.

Goals were to: (1) create one place where quality metrics could be viewed together; (2) reduce the time spent on repetitive reporting so that quality engineers could focus on analysis and improvement; (3) enable trend analysis and drill-down for root cause; and (4) support daily or weekly standups with up-to-date, trustworthy numbers.

Methodology & Frameworks

The approach followed data-engineering and BI best practices:

ETL (Extract, Transform, Load) - Data was extracted from source systems (databases, spreadsheets, exports), transformed (cleaned, standardized, joined) using SQL and Power Query (M), and loaded into a structured schema that Power BI could consume. This ensured one definition of “incoming defect rate,” “in-process yield,” and “customer PPM,” for example.
Star schema / dimensional model - Facts (e.g., inspection results, defect counts) and dimensions (time, plant, part, supplier) were modeled so that dashboards could slice by any dimension without duplicating logic. DAX measures were used for KPIs (e.g., defect rate, trend comparisons).
Incremental refresh and refresh schedules - Where possible, data was refreshed on a schedule (e.g., daily or after shift) so that reports reflected current state without requiring manual exports.

Implementation

Source systems were mapped and access was secured. SQL scripts (and, where needed, Power Query) were developed to pull and transform data into staging and then into a clean schema. Power BI data models were built with appropriate relationships and DAX measures. Dashboards were designed for different audiences: (1) executive summary (high-level KPIs and trends), (2) plant/line view (incoming, in-process, customer by location), and (3) drill-down (e.g., by part number, defect type, time period) for engineers and quality leads.

Training and change management focused on “one source of truth”: stakeholders were directed to use the dashboards for standups and reporting instead of ad-hoc Excel. Over time, redundant manual reports were retired, and ownership for data quality at the source was clarified so that the ETL pipeline had reliable inputs.

Outcomes

~60% less time on manual reporting-engineers could run the same (or better) views in minutes instead of hours.
Same-day visibility for management, enabling faster decisions and alignment in standups.
Drill-down from plant to part level for root cause and trend analysis without leaving the BI tool.

Lessons Learned

(1) Agreeing on definitions (e.g., “defect,” “lot,” “PPM”) up front avoided rework. (2) Involving the people who had been doing manual reports in the design increased adoption. (3) Starting with a few critical metrics and then expanding kept the project manageable. (4) Data quality at the source is the bottleneck-ETL can only be as good as the inputs. (5) Refreshing on a schedule and documenting it set the expectation that “the dashboard is the report.”