Should-Cost Modeling, in the crypto procurement domain, is an analytical methodology used to estimate the fair, theoretical cost of a good, service, or digital asset. This approach decomposes a supplier’s offering into its fundamental cost components to determine what the cost ought to be, rather than simply accepting the quoted price.
Mechanism
This process involves a granular breakdown of expenses, including raw material costs, labor, operational overhead, intellectual property or technology development costs (e.g., for smart contract auditing), and a reasonable profit margin. Benchmarking against industry standards, utilizing market data, and potentially analyzing on-chain transaction data for certain crypto components, enables an objective and verifiable cost assessment.
Methodology
The strategic application of should-cost modeling empowers institutional buyers in the crypto space to conduct more effective negotiations with vendors and liquidity providers. By gaining a clear understanding of the true cost drivers, organizations can identify areas for cost reduction, challenge inflated pricing in RFQs for custom crypto options, and ensure equitable terms for complex technology or service contracts, thereby optimizing expenditure.
Maintaining competitive pricing in collaborative procurement is achieved by designing a system where transparent performance metrics and periodic, data-driven market testing validate the value of strategic partnerships.
Establishing a pre-AI RFP baseline is the systematic conversion of qualitative judgment and process outcomes into a quantitative, multi-faceted measurement framework.
Quantifying information leakage is the process of modeling and measuring the value lost when compromised data erodes an organization's negotiating power.
A firm quantifies information leakage by modeling the statistical correlation between information compromise events and deviations from a baseline procurement price.
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