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October 13, 2025 · The Bloomfield Team

The 6 Types of Data Every Manufacturer Has But Doesn't Use

Every manufacturer we have worked with has the same reaction when we map their data sources. They know where the data lives. They know it could be useful. They have never had a system that made it usable in the moment a decision actually needs to happen. Here are the six categories of data sitting in your operation right now, what each one contains, and what becomes possible when they are connected.

1. ERP Transaction Data

Your ERP holds years of job records, purchase orders, shipping confirmations, and accounting transactions. In most shops, this data serves one purpose: looking up a specific job number when someone asks a question. The other 99% of its value goes untouched.

ERP transaction data, when structured and analyzed, reveals patterns that no single person can see by scanning records. Which job types have the widest gap between quoted and actual costs. Which customers generate the highest margins. Which operations consistently run over estimated time. The answers are in your JobBOSS, Epicor, or Global Shop Solutions database right now. They have been accumulating for years. The question is whether you have a way to extract them. For a deeper look at this, see our piece on why your ERP data is more valuable than you think.

2. Quoting History

Estimators build quotes from a combination of current costs and past experience. The past experience part usually lives in memory, supplemented by occasional searches through old quotes stored in spreadsheets, PDFs, or ERP records with inconsistent description fields.

A structured quoting database that captures every quote with part description, material, tolerances, operations, quoted price, win/loss outcome, and actual job cost creates a feedback loop that gets more accurate with every cycle. An estimator working from a database of 5,000 historical quotes with outcome data can build prices faster and with more confidence than one working from memory and scattered files.

3. Quality and Inspection Records

First article inspections, in-process checks, final inspections, customer complaints, NCRs, CAPAs. Most shops maintain these records because their quality system requires it. Few shops analyze them as a dataset.

Quality data, when connected to job records and machine data, reveals failure patterns that spot inspections miss. A specific part geometry that fails 15% of the time on Machine A but only 2% on Machine B. A vendor's material that produces dimensional variation at a rate three times the industry average. A specific customer's tolerances that trigger rework on 22% of orders. Each of these patterns is actionable. Each of them is invisible without aggregation.

4. Email and Communication History

Customer communications, supplier quotes, engineering clarifications, delivery negotiations. The average manufacturing front office sends and receives 200 to 400 emails per day. Buried in that volume are material price confirmations, customer specifications, delivery promises, and verbal agreements that affect how work gets quoted, scheduled, and billed.

This data is the hardest to structure because it was never meant to be structured. But it contains information that directly affects operational decisions. The supplier who confirmed a material lead time of eight weeks that actually came in at twelve. The customer who verbally agreed to a tolerance relaxation that reduced machining time by 30%. The engineering change that arrived by email after the job was already in production. Making this information findable and connected to the relevant jobs is one of the highest-value applications of modern AI tools.

5. Machine and Production Data

Modern CNC machines generate data on spindle load, cycle time, tool wear, and program execution. Older machines still produce cycle count and runtime data through basic monitoring. Even shops without machine monitoring have production data in the form of time tickets, job travelers with operator notes, and handwritten logs.

This data tells you what actually happened on the floor, as opposed to what was planned. Actual cycle times vs. estimated cycle times. Actual setup durations vs. standard setup times. Machine utilization rates by shift, by operator, by job type. The gap between what the ERP says a job should take and what the floor data says it actually took is where margin either grows or erodes.

6. Tribal Knowledge

The hardest data type to capture and the most valuable. Tribal knowledge is the accumulated experience of your team: the machinist who knows that a particular fixture needs 0.003 inches of preload to hold tolerance on long parts, the estimator who knows that Customer X always negotiates 8% off the first price, the quality manager who knows that a specific material supplier's certs should be verified because they have had three discrepancies in the past two years.

This knowledge drives daily decisions across every function. It is also the most fragile data source in your operation because it exists only in the minds of the people who hold it. When they leave, the knowledge leaves with them. Capturing tribal knowledge in a structured, searchable format is one of the most important data projects a manufacturer can undertake.

What Happens When the Six Connect

Each data type on its own answers a narrow set of questions. ERP data tells you what happened on a job. Quoting history tells you what you estimated. Quality data tells you what failed. Machine data tells you how long things took. Email tells you what was communicated. Tribal knowledge tells you why things work the way they do.

Connected, these six sources form a complete picture of your operation. An estimator opening a new RFQ sees the three most similar past jobs, their actual costs, any quality issues, the customer's price sensitivity from past negotiations, and the setup notes from the machinist who ran the last one. That context, assembled automatically from six separate data sources, is what turns a three-day quoting process into a 90-minute one.

The technology to connect these data sources and deliver them in a usable format exists today. The data has been accumulating in your operation for years. The only question is when you start putting it to work.