The parts were ready. The smaller jobs were still waiting.
An illustrative manufacturing use case showing how Power BI can help separate material delays from production scheduling delays.
Custom equipment manufacturing | Power BI + SQL Server + SharePoint
This is a representative scenario. Project names, dates, and figures are synthetic and do not identify a client.
The problem
When orders shipped late, long-lead materials were often the first explanation. Some purchased parts did take months to arrive, so purchasing received much of the attention.
But that didn’t explain every late order.
Smaller jobs could have their drawings released and materials available, yet still wait for a production slot. Larger orders took priority, and those smaller jobs slipped.
The solution
Bring planned and actual milestone dates into one Power BI report, from order entry through shipment.
For each job, the report shows when drawings were released, when materials were ready, when production started, and when the order shipped. Filter by project size to see whether smaller jobs follow a different pattern from larger orders.
The key comparison is between being ready for production and actually starting. That helps the team distinguish a material problem from a scheduling problem and decide where to investigate.
In this example, actual dates come from an ERP database on SQL Server. Planned dates and additional milestones come from SharePoint lists or a planning spreadsheet.
Explore the example
Use the filters to compare small, medium, and large projects. Start with Small and look at Materials Ready, Production Start, and Ship.
Average milestone variance (days from plan)
Green = ahead of plan. Red = behind plan. The report shows where dates moved, not why a scheduling decision was made.
| Project | Order Entry | Drawings Released | Materials Ready | Production Start | Production Complete | Ship |
|---|---|---|---|---|---|---|
| P0001 | 0 | -1 | 2 | 18 | 21 | 22 |
| P0002 | 1 | 3 | 11 | 5 | 7 | 8 |
| P0003 | -1 | 1 | -2 | 2 | 3 | 4 |
| P0004 | 0 | 2 | 4 | 10 | 12 | 13 |
| P0005 | 1 | 0 | -1 | 24 | 26 | 29 |
| P0006 | -1 | 1 | 3 | 9 | 11 | 12 |
| P0007 | 0 | -2 | 1 | 3 | 4 | 5 |
| P0008 | 1 | 2 | 15 | 7 | 8 | 10 |
| P0009 | 0 | 2 | 3 | 21 | 24 | 26 |
| P0010 | -1 | 0 | -3 | 1 | 2 | 2 |
| P0011 | 0 | 3 | 6 | 12 | 15 | 16 |
| P0012 | 1 | -1 | 2 | 8 | 10 | 11 |
| P0013 | 0 | 1 | 4 | 11 | 13 | 15 |
| P0014 | -1 | 1 | 0 | 16 | 19 | 21 |
| P0015 | 0 | 2 | 9 | 10 | 13 | 14 |
| P0016 | 1 | 0 | -2 | 7 | 9 | 10 |
| P0017 | 0 | -2 | 1 | 27 | 30 | 32 |
| P0018 | -1 | 1 | 4 | 3 | 5 | 6 |
| P0019 | 1 | 2 | 4 | 19 | 22 | 24 |
| P0020 | 0 | 1 | -2 | 23 | 25 | 28 |
| P0021 | 0 | 3 | 18 | 8 | 10 | 12 |
| P0022 | -1 | 0 | 2 | 9 | 12 | 13 |
| P0023 | 1 | -1 | 3 | 20 | 23 | 25 |
| P0024 | 0 | 2 | 5 | 13 | 15 | 17 |
What to notice
In the example data, materials for most small jobs are ready close to plan. The more consistent delays appear at Production Start and continue through shipment.
Larger jobs generally start closer to their planned dates.
That pattern points to a scheduling gap worth reviewing. It does not prove why a job was deprioritized. A scheduling review would still need to confirm available capacity, job dependencies, promised dates, and the rules used to sequence work.
The practical value is knowing where to ask the next question. The manufacturer can review how smaller jobs are prioritized and give customers delivery dates that better reflect the production schedule.