Quality Check Analysis App

Quality Check Analysis – Power BI App Overview

In high-output manufacturing environments, managing quality failures proactively is critical to minimizing rework time and controlling production costs. The Quality Check Analysis Dashboard offers a clear, data-driven view of failure patterns, root causes, and resolution timelines—enabling operations and quality assurance teams to identify trends and prioritize improvements.

This Power BI app transforms fragmented QC logs into actionable insights, helping reduce recurring issues, optimize repair cycles, and lower the cost of non-conformance.

🔧 Technical Framework

The dashboard is powered by structured QC failure records, linked to plant locations, failure categories, and corrective action data.

Key Components:

Data Source: Excel or system exports detailing failure IDs, types, reasons, fix duration, and cost implications.

Data Preparation: Performed in Power Query to clean, standardize, and categorize failure reasons and resolution times.

Data Model Dimensions:

Location

QC Failure Type (e.g., Density, Packaging, Temperature Sensitivity)

Failure Reason

Time to Fix (Days)

Extra Cost

This structure supports scalable integration with MES, ERP, and CAPA systems.

🎛️ Interactive Filters

To support precise failure tracking, the dashboard includes:

Location Filter: Zoom in on plant-specific performance issues.

Failure Type Filter: Narrow the view to specific QC categories.

These filters help teams isolate critical issues and take location-specific or material-specific actions quickly.

📊 Dashboard Report Highlights

a. Pie Chart – QC Failure by Failure Type

Visual breakdown of the major types of quality failures across all locations (e.g., Density Failure, Raw Material Failure).

b. Column Chart – QC Failure by Failure Reason

Top reasons behind QC failures (e.g., Improper rolling, Overheated bitumen), along with their frequency—enabling pattern recognition and root cause analysis.

c. Column Chart – Average Time to Fix by Failure Type

Compares average resolution time across failure types, revealing which issues cause the most downtime.

d. Table – QC Failure Details

Detailed records of each failure including:

QC Failure ID

Location

Type and Reason of Failure

Days to Fix

Associated Extra Cost

A total extra cost figure at the bottom quantifies the operational impact of unresolved or recurring quality issues.

This dashboard enables a proactive quality control culture by making failure data visible, measurable, and actionable—reducing rework cycles, supporting root cause correction, and optimizing cost-efficiency in production.