Procurement teams can spend weeks extracting detailed supplier economics during an RFQ, only for much of that intelligence to disappear after the contract is awarded. New research shows the problem is less about access to cost data than what companies do with it afterward, leaving valuable supplier history disconnected from future negotiations and sourcing decisions.
RFQ Data Is Still Used Primarily for Negotiation
Supplier cost breakdowns can expose the economics behind a quote, including materials, labor, overhead, logistics, tooling, packaging, freight, tariffs and margin. More advanced teams can compare those inputs with commodity indices, engineering estimates, historical spend and should-cost models.
Research based on 100 procurement and supply chain professionals, approximately 80% of them in manufacturing, shows that companies make extensive use of this information during active sourcing.
Eighty-five percent use cost breakdowns for negotiation or price validation, while 65% use them for supplier or regional benchmarking. Half apply the information to design or engineering decisions, and roughly 45% to 50% use it for should-cost modeling.
Strategic use is considerably lower. Only 40% apply cost breakdowns to risks involving areas such as lower-tier suppliers, geography and tariffs, while 30% use the information for decisions including make-versus-buy and reshoring.
The gap widens after the award. Between 60% and 75% of organizations treat RFQs as one-time events once business has been placed. Detailed cost structures may remain in spreadsheets, PDFs or emails, while the ERP retains little more than the final unit price.
That can become costly when a supplier later requests an increase. Without the assumptions behind the original quote, procurement may know that the price changed without being able to quickly determine whether materials, labor, freight or another cost driver justifies the increase.
The research places 35% of organizations in the lowest cost-intelligence maturity phase, where RFQs mainly support documentation and quoting. Another 40% primarily use the information for reactive negotiation and purchase-price variance savings.
Only 20% have progressed to structured benchmarking and should-cost approaches, while 5% have integrated cost intelligence across systems. Predictive and collaborative capabilities were estimated at just 0% to 1%.
Advanced Purchasing Dynamics estimates from its experience with manufacturing procurement organizations that companies relying mainly on piece-price comparisons can face an 8% to 18% gap to economically supportable pricing. Consistent supplier cost breakdowns can narrow that estimated range to 6% to 13%, while more sophisticated cost models may reduce it further.
The figures are directional rather than guaranteed savings and vary with category, market conditions, supplier dynamics and data quality.
Fragmented Data Is Limiting Procurement’s Next Step
The largest obstacles are structural. Eighty percent of respondents cited fragmented information across spreadsheets and PDFs, 65% pointed to the absence of a central repository and 60% identified poor ERP or system integration.
The result is that supplier cost knowledge can remain attached to individual buyers rather than the organization. When employees change roles, historical understanding of supplier assumptions, previous negotiations and category economics can disappear with them.
That weakness also matters for AI. Seventy percent of respondents identified AI-driven cost analysis as the next frontier, including automated supplier comparisons, outlier detection and scenario analysis.
Those tools require consistent underlying information. Supplier submissions using different templates, definitions, currencies and cost structures are considerably harder to compare at scale, regardless of the analytical technology applied to them.
A stronger foundation starts with standardized cost-breakdown formats, a searchable repository and preservation of the underlying cost structure rather than only the final price. Linking that information with engineering and finance can extend its use into design-to-cost, budgeting and sourcing scenarios.
External indicators such as commodity prices, exchange rates, tariffs and freight markets can then be compared with historical supplier assumptions, creating a clearer basis for evaluating future price movements.
Every RFQ Should Make the Next One Smarter
The value of supplier transparency should not expire when the contract is signed. Preserving what suppliers said about materials, labor, freight and other cost drivers creates a historical baseline against which future quotes and price increases can be tested. That becomes particularly important as AI enters cost analysis: the advantage will come less from the model itself than from the depth, consistency and history of the procurement data it can interrogate.