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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