Should-Cost Modeling: How BOM Integration Changes the Game
What Is Should-Cost Modeling?
Should-cost modeling is the practice of estimating what a product or service should cost based on a detailed analysis of its component parts: raw materials, labor, manufacturing overhead, logistics, and supplier margin.
Unlike price benchmarking (which compares what others pay), should-cost analysis builds a cost estimate from the ground up. It gives procurement teams a fact-based target price to use in negotiations, rather than relying on historical pricing or supplier-provided cost breakdowns.
The Traditional Approach
Manual, consultant-dependent, and stuck in spreadsheets.
Manual Spreadsheets
Cost models built in Excel with manually sourced data. Formulas break, assumptions go stale, and version control is nonexistent.
Consultant-Dependent
Many organizations rely on external consultants to build should-cost models at $500-2,000 per model. Expensive and not scalable.
Point-in-Time Analysis
Traditional models are snapshots. They reflect market conditions at the time of creation but become outdated within weeks as commodity prices shift.
No Real-Time Market Data
Material cost assumptions are based on last-known prices or industry reports. By the time you negotiate, the data may already be wrong.
BOM Integration
How integrating Bill of Materials data transforms should-cost analysis.
Automatic Material Cost Lookup
Upload a BOM and ProcureLabs automatically maps each line item to current market prices. No more manually researching commodity costs across multiple sources.
Commodity Index Tracking
Each material in your BOM is linked to the relevant commodity index. When steel, copper, or resin prices move, your should-cost model updates automatically.
Supplier Quote Comparison
Compare supplier quotes line-by-line against should-cost baselines. Instantly see which line items are overpriced and by how much.
Make-vs-Buy Analysis
For each BOM component, model the cost of manufacturing in-house vs. sourcing externally. Factor in labor, overhead, tooling, and logistics.
AI-Enhanced Should-Cost
Four capabilities that make should-cost modeling proactive, not reactive.
Real-Time Market Data
Automatically pulls live commodity prices, foreign exchange rates, and energy costs. Your should-cost models stay current without manual data entry or stale spreadsheet assumptions.
Supplier Benchmarking
Compare supplier quotes against peer benchmarks across your vendor base. Identify which suppliers are priced competitively and which have room for negotiation.
Scenario Modeling
Run what-if analysis on material substitution, supplier changes, volume shifts, and geographic sourcing. Understand cost impact before you commit.
Historical Trend Analysis
Track cost evolution over time across materials, suppliers, and categories. Identify trends, seasonality, and anomalies that inform your negotiation strategy.
Negotiation Leverage
How should-cost data strengthens your negotiating position.
Data-Backed Targets
Walk into every negotiation with a fact-based target price built from real market data. No more guessing or accepting supplier-stated costs at face value.
Line-Item Transparency
Break down every quote into its component costs: materials, labor, overhead, logistics, and margin. Challenge specific line items, not just the total.
Total Cost of Ownership
Go beyond unit price. Factor in quality costs, logistics, inventory carrying costs, and risk premiums to understand the true cost of each sourcing decision.
Build Your First Should-Cost Model
Start your free trial and see AI-enhanced should-cost modeling in action.