A Power BI dashboard for monitoring and analyzing manufacturing line calls, defect categories, model-wise trends, line-call status, and response time.
The Line Call Monitoring Dashboard provides an interactive view of production quality and line-call data.
The dashboard was designed around a manufacturing quality-monitoring workflow, with a focus on identifying trends, comparing models, analyzing defect categories, and monitoring line-call status.
The portfolio version uses a synthetic, non-confidential dataset to recreate the analytical workflow without exposing company data.
The dashboard tracks the following key performance indicators:
- Total Line Calls
- Closed Line Calls
- Average Response Time
- Total Defect Punches
Tracks the number of line calls across months to identify changes and fluctuations in line-call volume over time.
Visualizes the distribution of line calls across different statuses:
- Closed
- Initial Investigation
- Final Submission
Compares line-call volume across different vehicle models and helps identify models associated with higher numbers of line calls.
Analyzes line calls across defect categories and highlights categories contributing to the highest line-call volume.
The dashboard includes interactive slicers for:
- Raising Department
- Division
- Financial Year
- Model
These filters allow users to analyze the dashboard from different operational perspectives.
The project uses a synthetic dataset containing manufacturing line-call information.
- Control Number
- Date
- Financial Year
- Raising Department
- Division
- Model
- Part Family
- Reporting Shop
- Defect Category
- Defect Action
- Line Call Status
- Defect Punch Number
- Response Time
Note: The original internship data was confidential and is not included in this repository. The dataset provided here is synthetic/non-confidential and is used only to demonstrate the dashboard structure and analytical workflow.
The dashboard uses DAX measures for KPI calculations, including:
DAX Total Line Calls = COUNTROWS(LineCalls)
Total Defect Punches = SUM(LineCalls[Defect Punch Number])
Additional measures were used for metrics such as: Closed Line Calls Average Response Time Defect Punch Rate
LineCalls │ │ Many-to-One ▼ DateTable
The DateTable contains fields such as: -Date -Year -Month -Month Number -Month Year -Quarter The month sorting column ensures that monthly trends appear in chronological order rather than alphabetical order.
Power BI DAX Power Query Microsoft Excel Data Visualization Data Analysis
The data was prepared before building the dashboard. The main steps included: -Checking the data types of columns -Converting the Date column into the correct date format -Creating a separate DateTable -Creating calculated columns required for time analysis -Creating DAX measures for the dashboard KPIs -Creating relationships between the data table and DateTable -Sorting Month Year chronologically -Building visuals based on the prepared data
-Clone or download this repository. -Open Line-Call-Monitoring-Dashboard.pbix using Power BI Desktop. -If Power BI asks for the data source, select the included synthetic_line_call_data.xlsx file. -Refresh the data if required. -Use the filters and visuals to explore the dashboard.
Using the dashboard, the data can be analyzed from different perspectives, such as: -Changes in line call volume over time -Models with higher line call counts -Defect categories with higher line call volumes -Current line call status -Average response time -Number of defect punches
These views make it easier to identify areas that may need further quality or production analysis.
- The original data used during my internship was company-confidential and is not included in this repository.
- The dataset included here is synthetic and has been used only to recreate the dashboard structure and demonstrate the analysis for portfolio purposes.
- No confidential company files, reports, or original datasets are included in this repository.
Muskan Yadav Computer Science & Engineering | AI/DL GitHub: @muskanydv11