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June 20, 2025 · The Bloomfield Team

Why Your Competitors Quote Faster Than You

A buyer at a defense subcontractor told us how she selects vendors for new programs. She sends the same RFQ package to four qualified shops on Monday. By Wednesday afternoon, two have responded with complete quotes including pricing, lead time, and process notes. The other two respond Friday. She has already built her evaluation matrix, presented it to the program manager, and narrowed the shortlist to the two fast responders before the slow ones arrive.

Your competitor is not quoting faster because they have more estimators. They quote faster because their estimator spends 80% of their time on analysis and 20% on information gathering. Your estimator spends those percentages in reverse.

The Information Gathering Tax

Map the actual steps your estimator takes to build a quote. We have done this exercise with dozens of shops and the pattern is remarkably consistent. The estimator reads the drawing. They understand the part within five minutes. Then they spend the next three to six hours collecting information scattered across six or seven systems.

Historical job data: locked inside the ERP in records with inconsistent descriptions, requiring manual search through dozens of old work orders to find a comparable part. Material pricing: buried in email threads with the last supplier quote, or in a spreadsheet on the purchasing manager's desktop. Machine availability: on a whiteboard, in the scheduler's head, or in a separate scheduling system that the estimator does not have access to. Quality history: in the memory of the machinist who ran the last similar job.

Each piece of information takes 10 to 30 minutes to locate. Multiply that by five or six data points per quote. Factor in interruptions, phone calls, and the three other quotes already in queue. The math produces a four to five day turnaround, and the estimator has worked efficiently the entire time. The system around them is the bottleneck.

What the Fast Shops Built

The shops that quote in under 48 hours have solved the information access problem. When their estimator opens an RFQ, the system has already identified the customer, pulled their order history and pricing trends, and surfaced the three to five most comparable past jobs with complete cost breakdowns.

Material pricing is current, pulled from the most recent supplier quotes on file. If the most recent quote is more than 30 days old, the system flags it for verification. Machine loading data is visible in the same interface where the estimator builds the quote. They can see whether the delivery window is feasible before they commit a number.

The estimator does exactly the same analytical work your estimator does. They evaluate the geometry, assess manufacturing complexity, apply judgment on pricing strategy and customer relationship. They do that work with complete information available from the start instead of spending hours assembling it from scattered sources.

For the full picture of how these tools work, see our guide to AI-powered quoting in manufacturing.

The Compounding Disadvantage

Quoting speed does more than affect individual bid outcomes. It compounds over time in ways that reshape the competitive dynamics of your market.

A shop that quotes in one day processes more RFQs per month with the same estimating team. More quotes at a higher win rate produces more jobs. More jobs produces more data on actual costs, cycle times, and quality outcomes. Better data produces more accurate quotes. More accurate quotes produce better margins and fewer cost overruns.

Meanwhile, the shop quoting in four days processes fewer RFQs, wins a smaller percentage, accumulates data more slowly, and makes less-informed pricing decisions on every subsequent quote. The gap widens with every passing quarter.

We covered the direct revenue impact of this dynamic in our piece on the hidden cost of slow quotes. The indirect impact, the compounding data advantage, is harder to measure and even more consequential over a three to five year horizon.

Three Things You Can Do This Month

Time your current process. Have your estimator log the actual time spent on each step for the next 10 quotes. Categorize time into analysis (evaluating the part, building the price, making decisions) and information gathering (finding past jobs, checking material costs, asking about machine availability). The ratio will tell you exactly how much room you have to improve.

Standardize your comparable job search. Build a spreadsheet or database of your last 200 quoted jobs with part description, material, key dimensions, quoted price, actual cost, and margin. This alone will cut 30 to 45 minutes from every quote that has a comparable precedent, and about 70% of quotes in a typical job shop have at least one.

Consolidate material pricing. Create a single source of truth for material costs. One spreadsheet, updated weekly by purchasing, with current pricing by alloy, form, and size from your primary suppliers. Eliminate the email search that happens on every quote.

These three changes require no technology investment. They can be implemented in two weeks. They will reduce average quote turnaround by one to two days, which at typical manufacturing win rates translates to 8 to 15% more jobs won per month from the same number of RFQs.

The Structural Solution

Manual process improvements will get you from five days to three. Getting from three days to same-day requires a system that connects your ERP data, quoting history, supplier pricing, and production schedule into a single tool that delivers context to the estimator automatically when an RFQ arrives.

That system is what AI makes possible in manufacturing today. Custom software built around your specific data, your workflow, and your team's way of working. The estimator still makes every decision. The tool eliminates the hours of detective work that precede those decisions.

Your competitors that quote in one day are not smarter. They built a better system. That system is buildable for any shop with five or more years of ERP data and a quoting process that someone has documented.