Should Cost Analysis
Should-cost analysis is a procurement and sourcing method that estimates the reasonable expected cost of a product or service by building a bottom-up cost model from its cost drivers—typically materials, labor, overhead, logistics, and margin—instead of relying on the supplier’s quoted price alone. In manufacturing practice, it validates quotes, sets target prices, and identifies negotiation gaps by comparing the modeled should cost against actual supplier pricing.
On the shop floor, should-cost analysis turns every purchased part into a transparent equation. For an engineered component, the team breaks the drawing into a bill of materials, maps each machining or assembly step, applies cycle times, machine rates, yield losses, tooling amortization, and then layers on packaging, freight, and a reasonable supplier margin. Each input is benchmarked against ERP data, market indices, and historical invoices. Buyers use the resulting should-cost baseline to challenge inflated quotes, catch hidden scrap assumptions, duplicate freight charges, or over-engineered specifications, and set realistic target prices for negotiation. In warehousing and inventory control, the analysis separates true product cost from receiving, storage, and handling cost, which helps rationalize SKUs, compare total cost across suppliers, and avoid confusing low unit price with cheap total cost.
Is should-cost analysis the same as cost breakdown analysis?
Yes, many sources treat them as the same or closely related terms; both use a bottom-up decomposition of cost drivers to estimate a fair or expected price.
How does should-cost analysis differ from historical spend analysis?
Historical spend analysis looks backward at what was paid; should-cost analysis builds an independent forward-looking estimate of what an item should cost under efficient conditions.
What data improves should-cost accuracy the most?
Current market material indices, verified cycle times, machine rates, scrap/yield data, logistics costs, and recent supplier or third-party benchmarks improve fidelity the most.