Should-cost modelling is a technique that builds up the expected price of a product or service from its constituent cost drivers — materials, labour, overhead, and profit — to establish a target price before supplier negotiations.
Should-cost analysis gives buyers an independent view of what a product or service ought to cost based on the inputs required to produce it. Rather than accepting a supplier's quoted price as a starting point, a buyer using should-cost modelling can enter negotiations knowing the theoretical minimum and maximum cost and can challenge supplier pricing with data.
For manufactured goods, should-cost models typically include raw material costs (often tied to commodity indices), labour costs at the country of manufacture, overhead allocation, tooling amortisation, and an expected profit margin. For services, models focus on labour rates, utilisation, and overhead. The output is a credible target price range that sets the floor for negotiations.
ProcureLabs' should-cost module combines internal spend data with external market intelligence and commodity pricing to build should-cost models for direct and indirect categories. Pala can generate should-cost comparisons on demand, supporting category managers heading into supplier negotiations.
Value leakage is the gap between the savings a procurement team negotiates and the savings that actually reach the bottom line, typically caused by contract non-compliance, maverick spending, or billing errors.
Tail spend refers to the large number of low-value, low-frequency purchases that collectively represent a significant portion of total spend but receive little procurement attention.
Contract intelligence is the use of AI to extract, analyse, and monitor commercial obligations from supplier contracts so that procurement teams can enforce terms and capture negotiated value.
Procurement intelligence is the use of data analytics and AI to give procurement teams real-time visibility into spend patterns, supplier risks, market conditions, and savings opportunities across the full procurement lifecycle.
Pala, the ProcureLabs AI copilot, surfaces insights about should-cost modelling and helps your team act on them — no data science skills required.