Quoting Variable Lot Sizes Efficiently

Every shop knows the drill: a customer sends an RFQ for 5 prototypes, then asks for pricing at 50, 200, and 1,000 units — all in the same email. You could build four separate quotes. Or you could build one model that scales.

Variable lot sizing is where quoting either becomes a competitive advantage or a time sink. The difference comes down to whether your pricing logic lives in a spreadsheet or in a system that understands your actual cost structure.

Why Lot-Size Quoting Breaks Down

Most shops price by intuition: “This setup takes two hours, material is $X, I’ll add 30% margin.” That works for one-offs. It fails when the same request spans prototype, bridge, and production volumes because the cost drivers shift:

• Setup amortization — a $200 setup is $40/part at qty 5, $0.20/part at qty 1,000
• Material breaks — volume discounts kick in at different thresholds per supplier
• Process changes — 3D printing makes sense at 50 units; injection molding wins at 5,000
• Labor efficiency — batch processing, nesting gains, and reduced changeovers only appear at scale

If you’re recalculating manually for each tier, you’re not quoting — you’re doing arithmetic. And arithmetic doesn’t win bids.

Build a Cost Model, Not a Calculator

The fix is separating your cost structure from the quote output. Your cost structure includes:

• Fixed costs per job (setup, programming, first-article inspection)
• Variable costs per unit (material, machine time, labor, consumables)
• Step-function costs (tooling, fixturing, mold amortization)
• Volume discounts from suppliers (material, outside processing)

Once those are defined as rules — not numbers — you can apply any quantity and get an accurate price. The quote becomes a function: Price = f(quantity, process, material, margin).

This is exactly what Solvi‘s quoting engine does. You define your process parameters once — setup times, rates, material costs, discount tiers — and the system calculates accurate prices across any quantity range instantly.

Handle Process Transitions Cleanly

The hardest part of variable lot quoting isn’t math — it’s process selection. A part quoted for CNC at qty 10 might switch to casting at qty 500. Your model needs to know:

• Which processes are viable at which volumes
• The crossover points where one process becomes cheaper than another
• Any non-recurring engineering (NRE) costs for each process (tooling, molds, fixtures)

Build this as a decision tree. When quantity crosses a threshold, the engine evaluates the next process automatically. The customer sees a clean price curve; you see the optimal process at every volume.

Show Customers the Full Curve

Don’t just send a single price. Send a quantity-break table that shows:

• Unit price at each tier
• Total price at each tier
• Lead time at each tier (it changes too)
• Process used at each tier

This does two things: it builds trust through transparency, and it anchors the conversation around the volume you want to run. If the sweet spot is 250 units, make that tier look obvious.

Automate the Re-Quote

Customers change quantities. Constantly. “Actually, can you price 75 instead of 50?”

If your model is rule-based, the re-quote takes seconds. Change the quantity input, regenerate, send. No formula auditing, no “did I update the setup row?” anxiety.

Solvi’s instant quoting handles this natively — the same engine that built the original quote recalculates with the new quantity, preserving all your process logic and margin rules.

Track What Actually Happened

After the job ships, compare actuals to your quote:

• Did setup take 2 hours or 3?
• Was material yield 92% or 87%?
• Did the 500-unit run actually use the process you quoted?

Feed those deltas back into your cost model. Over time, your quantity-break curves get sharper, your margins improve, and you stop leaving money on the table at every volume tier.

Stop Quoting the Same Part Twice

Variable lot sizes aren’t a special case — they’re the norm for digital manufacturers. The shops that win are the ones who treat quantity as a variable in a model, not a trigger for a new spreadsheet.

Define your cost rules once. Let the engine do the math. Spend your time on the exceptions that actually need engineering judgment.

Solvi helps digital manufacturers build rule-based quoting engines that scale from prototype to production without rework. See how it works for your shop.