Every manufacturer we talk to has the same problem: the data exists, but it doesn’t work. It’s locked in an ERP that was configured a decade ago, exported to spreadsheets that live in someone’s My Documents folder, or summarized in a weekly report that nobody reads past the first page.
The data isn’t the problem. The problem is that nobody built the model.
What the data actually contains
A standard ERP production export — work orders, operations, quantities, timestamps — contains everything you need to answer the questions that matter:
- Which machines are your constraint?
- Which shifts are consistently underperforming?
- What percentage of your downtime is unplanned versus scheduled?
- Where are you losing throughput between planned and actual?
None of those questions require a data warehouse, a BI consultant, or a $200K implementation. They require a clean, consistent model and someone willing to define what “downtime” means for your floor.
The mapping problem
Here’s what usually breaks it: every ERP exports differently, and every floor runs differently. “Downtime” in one plant is a work order type; in another it’s a reason code; in another it’s inferred from idle time between operations.
Before you can answer any of the questions above, you need a mapping layer — a place where your ERP’s native codes get translated into a consistent taxonomy. Unclassified codes stay visible so operators know what needs attention. Tagged codes flow into the metrics. Nothing gets silently dropped.
That’s the foundation. Once it’s in place, the analysis is straightforward.
What changes when the model exists
When you have a clean operational model, the daily conversation on your floor changes. Instead of “I think Line 3 had a rough week,” you have “Line 3 was at 71% throughput efficiency Tuesday through Thursday — here’s the constraint.”
That’s not a small shift. It’s the difference between managing by intuition and managing by signal.
The data was always there. It just needed a model.