Data Studio manufacturing dashboards for plant and operations heads

What does our manufacturing dashboard show the plant team?

Output, losses and their causes, machine by machine. The manufacturing dashboard we built in Data Studio for a manufacturing unit starts with units produced against units lost and overall plant production, then moves to the machine level.

At machine level it shows units lost per machine and productivity by machine, so the worst performers are obvious. Operator availability sits alongside, because a machine standing idle for want of a trained operator is a different problem from one waiting on a spare part. A causes-of-loss chart and a maintenance cost view complete the picture.

Where does production data live in a typical plant?

Across more systems than most people expect. Production orders, receipts and stock movements sit in the ERP, often SAP Business One, Tally or NetSuite for growing manufacturers. Machine counts and stoppages come from an MES if you have one, from PLC or controller logs exported as CSV, or from shift sheets typed up by supervisors.

Maintenance work orders and spare parts may be in a separate CMMS or simply in a spreadsheet kept by the maintenance head. Attendance and shift rosters, needed for operator availability, often live in HR or payroll software.

We bring these into BigQuery or a set of governed Google Sheets, with a machine master table that gives every machine one ID across systems. Without that table, the ERP's work center, the MES's asset tag and the maintenance log's nickname for the same press never line up.

• ERP: production orders, good receipts, scrap postings, cost centers.
• MES or machine logs: counts, cycle times, stoppage start and end times.
• Maintenance: work orders, parts used, labour hours, vendor invoices.
• Shift and attendance: rostered versus present operators by line.

Can a Data Studio report calculate OEE?

Yes, if the inputs exist, and we are careful to say when they do not. OEE multiplies three rates: availability (run time over planned production time), performance (ideal cycle time times total count, over run time) and quality (good count over total count). Each needs data many plants do not yet capture reliably.

If your machine logs have stoppage times but no ideal cycle time, we show availability and quality and leave performance out rather than guessing. We build each component as its own calculated field so supervisors can see which one is dragging the score down, and we never present a single OEE figure without its parts.

How should downtime and unit loss causes be classified?

Into a short, agreed list that operators can actually choose from. Free-text reasons like stuck, jam, waiting and no material are common in shift sheets and machine logs, and they make cause charts useless.

We map existing reasons into categories such as breakdown, changeover, material shortage, quality reject and operator unavailable, using a CASE statement or a mapping sheet the plant can edit. Unmapped reasons stay in their own bucket on the report so the list gets tidier each month. A Pareto chart of units lost by cause then points to the two or three issues worth a kaizen. Where a loss happens matters too, so each cause can be split by machine, shift and product, which often shows that one changeover on one line explains most of a category.

How do you track maintenance cost per machine?

By joining maintenance work orders and parts consumption to the machine master, then setting cost next to the losses each machine causes. A machine with high maintenance spend and low losses may be well looked after; one with high spend and high losses may be due for replacement.

Cost comes from the ERP or the maintenance log, split into parts, labour and outside service. We show it by month and by machine, with a filter for planned versus breakdown maintenance where your work orders record it. A rising share of breakdown work against planned work is an early sign that preventive schedules are slipping, and it is easy to see once both types sit on one chart.

Why work with an agency rather than build it in-house?

Mostly for the data plumbing. Charts are the quick part; mapping machine IDs, reason codes and ERP exports is where in-house projects stall. As a data studio agency with experience from top consulting firms, we have built this pattern before and can usually reuse the model.

We also set up scheduled delivery so a PDF of yesterday's production lands in the plant head's inbox before the morning meeting, and we keep blends under Data Studio's five-source limit by joining upstream. Plants with an old Looker Studio report keep it; the product was simply renamed back to Data Studio.