← Back to Field Notes

September 17, 2025 · The Bloomfield Team

The Manufacturer's Guide to Data-Driven Decisions

A $14 million job shop in Indiana made a $320,000 capital expenditure decision last year based on the production manager's gut feeling that they needed another vertical machining center. The machine arrived, was installed, and ran at 38% utilization for the first six months. The actual bottleneck was the horizontal boring mill two cells over, which was running at 94% and creating a queue that backed up the entire second-shift schedule.

The data to make the right call existed in their ERP. Machine utilization logs, job routing records, and scheduling data all pointed to the boring mill. Nobody pulled the reports.

The Data You Already Have

Most manufacturers are data-rich and insight-poor. A typical operation running an ERP system for five years has accumulated thousands of job records containing actual cycle times, setup times, material costs, labor hours, quality outcomes, and delivery performance. That data sits in database tables that were designed for transaction processing, and very few shops use it for decision-making.

The gap between available data and data used for decisions is where most operational improvement opportunity lives.

Four Decisions Data Should Drive

1. Pricing and Quoting

Your ERP contains the actual cost of every job you have run. Quoted price versus actual cost. Estimated setup time versus actual setup time. Estimated cycle time versus actual cycle time. That history is the most accurate pricing tool available to any estimator, and in most shops, the estimator cannot access it in a usable format during the quoting process.

Pulling quoted-versus-actual data for your top 20 part families reveals which jobs consistently run above estimate and which run below. That analysis takes one afternoon and directly changes how you price the next 50 quotes.

2. Capacity Planning

Machine utilization data answers the question every shop owner asks: do I need more capacity, or am I not using what I have? The answer requires looking at utilization by machine, by shift, and by job type. A machine that shows 70% overall utilization might be at 95% during first shift and 40% during second, which is a scheduling problem rather than a capacity problem.

3. Delivery Performance

On-time delivery is the metric customers care about most. Tracking OTD by customer, by job type, and by work center reveals where delays originate. Most shops that struggle with delivery find that 60 to 70% of late jobs trace to the same two or three bottleneck operations, and fixing those specific bottlenecks improves the overall number faster than any across-the-board initiative. For a deeper analysis framework, see why your OTD number might be lying to you.

4. Quality Cost Allocation

Quality costs, scrap, rework, inspection time, customer complaints, and warranty claims are real numbers with real impact on margins. Tracking them by root cause category reveals whether the quality problem is a machine capability issue, a process issue, or an information issue. The fix for each is different, and the data tells you which fix applies.

Getting Started Without a Data Team

Most manufacturers under $50 million in revenue do not have a data analyst on staff. That is fine. The first step is not building dashboards. The first step is pulling one report per week from your ERP and discussing it in a meeting that matters.

Start with quoted-versus-actual cost variance on your last 20 completed jobs. Pull it into a spreadsheet. Sort by variance. The jobs at the top of the list, where actual cost exceeded the quote by the largest margin, are your highest-priority pricing fixes. The jobs at the bottom, where you came in well under quote, are opportunities to price more competitively on similar work and win more volume.

One report. One meeting. One set of actions. That is the foundation of data-driven decision making in manufacturing. Build from there.

The tools to make this data accessible in real time, organized around the decisions your team makes every day, exist today. The shops that connect their ERP data, their job records, and their floor metrics into a system designed for operational decisions gain an advantage that compounds with every quarter of data they accumulate.