Underquoting vs Overquoting: Finding Balance

Every digital manufacturer knows the feeling: you send a quote, win the job, and halfway through production realize you’re losing money. Or you quote conservatively, never hear back, and watch capacity sit idle. Both scenarios drain profitability, yet most shops oscillate between them without a system to stop the cycle.

The problem isn’t bad math — it’s inconsistent quoting logic. When estimators rely on tribal knowledge, spreadsheets, or gut feel, the same job gets priced differently depending on who’s quoting, how busy the shop is, or whether the last job lost money. The fix is a repeatable framework that accounts for every cost driver, every time.

Why Underquoting Happens

Underquoting usually stems from missing costs, not miscalculated ones. Common blind spots include:

• Setup and programming time for complex geometries
• Post-processing steps like support removal, heat treatment, or surface finishing
• Material waste from nesting inefficiency or minimum order quantities
• Quality inspection and first-article reporting
• Project management overhead for multi-process jobs

Shops also underquote when they use historical pricing without adjusting for current material costs, machine wear, or labor rates. A quote based on last year’s aluminum price and a fully depreciated machine doesn’t reflect today’s reality.

Why Overquoting Happens

Overquoting is often a fear response. After getting burned on a few jobs, estimators add blanket buffers — 20% here, 15% there — without tying them to specific risks. The result: quotes that are technically accurate but commercially uncompetitive.

Other overquoting drivers include:

• Quoting worst-case cycle times instead of realistic ones
• Double-counting overhead already covered in machine hour rates
• Pricing for peak capacity when the shop has slack
• Ignoring process optimizations (better nesting, faster toolpaths) that reduce actual cost

Customers notice. RFQs that used to go to three shops now go to five because conversion rates dropped.

Build a Cost Model You Trust

The middle ground starts with a machine hour rate that reflects true cost. Calculate it from the bottom up:

1. Depreciation or lease cost per hour
2. Maintenance and consumables (tooling, filters, gas)
3. Energy consumption at current utility rates
4. Operator labor burdened rate
5. Facility overhead allocation (rent, insurance, software)

Do this for every machine. A 5-axis mill and an SLS printer have wildly different cost structures. Using a single shop rate distorts every quote.

Next, codify your process recipes. For each manufacturing method, define the standard steps and their time drivers: setup, run, post-process, inspect, package. Attach the correct machine hour rate to each step. This turns quoting into assembly rather than invention.

Separate Risk from Cost

Buffers belong in a risk layer, not the cost layer. Identify specific risks per job — new material, tight tolerance, aggressive lead time, customer with change-order history — and price each one deliberately. A 10% risk adder for a first-time Inconel job is defensible. A 10% adder because “things go wrong” is not.

Track which risks actually materialize. Over time, your risk library becomes data, not guesswork.

Use Quote Templates, Not Blank Sheets

Templates enforce consistency. A sheet metal template should prompt for material grade, thickness, bend count, finish, hardware, and packaging — every time. A 3D printing template should require orientation, support strategy, post-process steps, and inspection criteria.

When estimators fill in structured fields instead of typing free-form notes, missing costs drop dramatically. The template becomes your institutional memory.

Close the Loop With Actuals

You can’t improve what you don’t measure. After every job, compare quoted hours and costs to actuals. Flag variances >10%. Root-cause the top three each month. Feed findings back into your machine rates, process recipes, and risk library.

This feedback loop is where quoting accuracy compounds. Shops that do it consistently move from ±25% variance to ±5% within a few quarters.

Speed Without Sacrifice

The objection is always time: “We can’t do this for every RFQ.” You don’t have to. Standard jobs run through templates in minutes. Complex jobs get the full treatment. The key is having the infrastructure so the choice is deliberate, not forced by tool limitations.

Solvi’s quoting engine is built for this exact workflow — customizable process recipes, machine-specific rates, risk layers, and template-driven speed. Shops using it report quoting times dropping from hours to minutes while margins hold or improve. See how it works at https://www.solvi.io.

Conclusion

Underquoting and overquoting are two sides of the same coin: an unstructured quoting process. The solution isn’t quoting higher or lower — it’s quoting systematically. Build your cost model, separate risk, template your workflows, and close the loop with actuals. Your margins and your win rate will both improve.