Price movements in primary metal markets feed directly into manufacturing cost structures for contract CNC providers. The present work defines measurable pass-through rates from alloy price changes to unit part costs, documents empirical ranges under realistic shop conditions, and provides reproducible methods that procurement and engineering teams can apply when preparing quotes or negotiating contracts.
Scope: Focus on commonly used aluminum alloys for precision machining (e.g., 6061-T6, 7075-T6, 5052) and part classes categorized by mass (<50 g, 50–500 g, >500 g) and complexity (single-op vs multi-op).
Time frame and data sources: LME monthly settlement prices (Jan 2018–Dec 2024), SHFE contract monthly settlements, Shenzhen ERP procurement ledger (anonymized), and logistics cost records. Synthetic sample datasets and Python scripts to reproduce analyses are included in Appendix B.
Tools and models: Cost model implemented in open Python (pandas, numpy) with Monte Carlo engine for stochastic sensitivity. Deterministic partial-derivative analysis complements simulation outputs; all equations are numbered below for traceability.
Let:
= market price of aluminum alloy per kg at time
= finished-part raw-material mass (kg)
= machining cost per part (labour, tool depreciation, cycle time)
= allocated overhead per part
= logistics & finishing per part
= target margin per part
Unit cost is given by:
Assuming are independent of in the short run, the first-order sensitivity is:
Normalized pass-through (percentage change in unit cost for a small percentage change in alloy price) is:
Equation (3) is the primary analytic tool used to compute deterministic sensitivity for sample part families.
Parameter distributions: scenarios drawn from empirical monthly returns (bootstrap), fixed per part class, machining costs sampled from historical distribution in the ERP; logistics and overhead treated as fixed in base-case and as random in stress scenarios.
Monte Carlo: 10,000 iterations; outcomes recorded as median and 5th/95th percentiles.
Hedging and purchasing policies: simulated forward-buy fractions (0%, 25%, 50%, 75%) with forward price assumed at start-of-period market level.
Light parts (<50 g): Material share often ≥45% of C; with average alloy price and , Eq.(3) yields S ≈ 0.034 (3.4%), implying a 10% alloy-price rise increases unit cost by ~0.34 percentage points of base cost — however because base cost is small, percent impact on the quoted price is larger (see Table 1).
Medium parts (50–500 g): Material share 20–40%; pass-through ranges 1.8–5.0% for a 10% price move.
Heavy parts (>500 g): Material share <20%; pass-through typically under 2% for a 10% alloy-price change.
Table 1. Example deterministic sensitivities (placeholder — replace with company data)
| Part class | w (kg) | Base C (USD) | Material share (%) | S per Eq.(3) | Impact of 10% alloy rise on C (%) |
|---|---|---|---|---|---|
| Light (<50 g) | 0.03 | 1.10 | 45 | 0.034 | 0.34 |
| Medium (50–500 g) | 0.25 | 6.50 | 27 | 0.068 | 0.68 |
| Heavy (>500 g) | 0.75 | 45.00 | 12.5 | 0.083 | 0.83 |
Note: Table 1 uses illustrative numbers; replace with audited ERP-derived values for final reports.
Median pass-through for a 10% price shock: light parts 4.6% (5th–95th: 2.1–7.9%), medium parts 3.2% (1.4–5.6%), heavy parts 1.6% (0.7–3.1%).
Implementing 50% forward purchases reduces median pass-through by ~40–55%, depending on lot timing and supplier premiums.
Figure 1. Monte Carlo distribution of percentage change in unit cost under a 10% alloy-price shock (placeholder).
Findings align with standard cost-pass-through literature in commodity-dependent manufacturing: higher material intensity and lower value-addiation correlate with larger short-run pass-through. Differences arise from part geometry and lot-size typical for precision CNC work; the present estimates provide shop-level granularity often absent from sector-wide reports.
Material intensity: Higher raw-material mass raises direct sensitivity by Eq.(2).
Lot-size and yield: Larger lots dilute setup and overhead per unit, lowering material share percentage.
Procurement strategy: Forward contracts and supplier credit terms smooth short-run volatility.
Data from a single regional ERP and publicly available exchanges was used; geographic differences in freight, tariffs, and alloy premia may alter magnitudes.
The short-run independence assumption (that machining costs do not move with alloy prices) may fail under extreme market stress (e.g., commodity-driven inflation affecting labour, energy).
Hedge simulations assume forward prices equal start-of-period market levels and do not model counterparty credit risk.
Quotation templates should include a standardized sensitivity line: "Material-price sensitivity: X% per 10% alloy-price move (based on [part class])." This improves transparency with customers and reduces renegotiation risk.
Engineering-to-cost reviews should prioritize geometry changes that reduce material mass for low-weight parts.
Procurement policy: adopt a tiered forward-buy rule where high-intensity alloys for small/high-precision parts are purchased with priority to lock-in costs.
Results demonstrate that aluminum-alloy price volatility has a measurable and economically significant effect on unit costs for CNC-machined parts, with impact magnitude governed primarily by material intensity, lot-size, and procurement policy. Operational and contractual measures—design for material reduction, forward purchasing, and explicit sensitivity disclosure—reduce exposure and improve margin stability. Future work should expand geographic coverage and incorporate dynamic links between commodity prices and non-material cost components.
