Executive Summary
Traditional should-cost modeling has long been procurement’s gold standard for gaining leverage in high-value negotiations. But at enterprise scale, accuracy alone is not enough.
Manual models can take weeks to build and quickly lose relevance as market conditions change. SME availability creates another constraint. Regional differences can also affect model accuracy across global supply chains.
The bigger issue is coverage.
Organizations may achieve 95% precision while covering only 5% of total spend. A moderately accurate model covering 70% to 80% of the portfolio can create far greater organizational impact. The neglected middle tier holds significant untapped savings opportunities yet remains difficult to model manually at scale.
The path forward requires should-cost modeling that can scale with the enterprise.
The paper explores the shift toward autonomous, predictive should-cost modeling, using AI, automation, and live market data to expand spend portfolio coverage while maintaining cost visibility.
It also examines the structural changes required to build an enterprise cost capability, including centralized expertise, data infrastructure, governance, and adoption across procurement workflows.
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Frequently asked questions
Agentic should-cost modeling automation for SKUs puts multiple AI agents to work at once. They pull BOMs from your ERP and use NLP to match each item to its cost drivers. Models are built and refreshed in parallel, so you can cover thousands of SKUs without a matching rise in SME effort.
The middle tier holds too many items for manual analysis to justify the effort, so it often goes unmodeled. Automated should-cost modeling covers the full tier and shows where supplier prices sit above should-cost. You can then reduce middle-tier spend costs by targeting the largest gaps.
A highly detailed should-cost model that requires three weeks to build can become obsolete within six to eight weeks. Continuous cost modeling for market volatility feeds up-to-date market indices into each model and refreshes its baseline assumptions. Your numbers stay current between sourcing events.
Price benchmarking compares a quote with other prices but can’t show what the item should cost. Continuous should-cost intelligence in supplier negotiations breaks each price into its cost elements and keeps them current. Talks then center on specific cost drivers, which supports more collaborative discussions and stronger supplier accountability.