Manufacturing Downtime Analysis — Power BI

This project examines manufacturing production data to identify the main causes of downtime and compare performance across products and production batches.

I used Power Query to clean and restructure production and downtime records, including preparing date and time fields and organizing downtime factors for analysis. I built a relational data model connecting production batches, products, and downtime information.

I developed DAX measures to calculate total downtime, downtime percentage, and actual versus target production time. I then created an interactive Power BI dashboard that allows users to compare batches and products, investigate downtime causes, and identify areas requiring further attention.

The analysis identified 1,388 total downtime minutes and an overall downtime rate of 36%. Machine-related issues accounted for 42.2% of total downtime. OR-600 had the highest product downtime rate, while Batch 422147 recorded the highest total downtime.

These findings provide a basis for prioritizing maintenance investigations and reviewing production processes associated with high downtime.

Tools and skills: Power BI, Power Query, DAX, data transformation, data modeling, performance analysis, and dashboard development.