AI in Procurement: From Data to Value
Since 2020, extreme price volatility has reshaped global procurement. Raw materials, energy, logistics, and labor costs have moved in sharp waves, but price adjustments have not followed symmetrically. Suppliers are quick to reference rising markets when justifying increases, yet declining indices rarely trigger equivalent reductions. Without transparency on how market movements translate into product-level costs, organizations often fail to detect structural cost gaps.
What used to be a discussion about isolated increases has become a debate about price development. And yet, many negotiations still fall into the same trap: suppliers selectively reference individual market indices and time windows that support their narrative. Capable buyers respond by reviewing broader index developments — but often lack transparency on how strongly a specific cost driver actually influences the product price, what a fair adjustment would look like, and how other cost components have evolved in parallel.
The result is an asymmetry of information: both sides bring data, but neither side shares a product-level view of total cost development. The conversation quickly turns into “story versus story” rather than a fact-based discussion about fair price evolution.
What’s missing is transparency at product level: a shared, fact-based understanding of how market movements actually translate into the cost of a specific article or SKU.
This is exactly the problem the Price Development Challenger (PDC) was designed to solve.
As one procurement leader from a global industrial manufacturer summarized it: “With extreme volatility, transparency becomes essential. If I can simulate fair price development at scale, I know where to focus my negotiations and where the real potential lies.”
With extreme volatility, transparency becomes essential. If I can simulate fair price development at scale, I know where to focus my negotiations and where the real potential lies.
Price Development Challenger is a GenAI-enabled, index-driven cost breakdown capability for direct materials. It translates market movements into a quantified cost development at article level and produces negotiation-ready insights that procurement teams can directly use in supplier discussions.
Importantly, PDC is not designed to replace full cost engineering. Instead, it offers a pragmatic “should-cost light” approach – ideal for situations where traditional cost models would be too slow, too expensive, or simply not scalable, such as in annual price rounds or broad portfolio reviews.
In practice, this means a specifically trained AI model decomposes an article into cost categories such as raw materials, labor, energy, and overhead, assigns realistic cost shares, and links each driver to relevant indices. The resulting view shows how the product’s cost should have developed based on market data, and how this compares to the supplier’s pricing. Beyond a point-in-time comparison, it also quantifies the cumulative cost gap that has accrued since the last negotiation, highlighting where market developments and negotiated prices have diverged over time. This turns historical volatility into a transparent basis for forward-looking negotiation.
In the consumable-item negotiation mentioned earlier, the breakdown revealed a cost structure of roughly 49% raw material, 10% labor and packaging, 10% energy, and 31% overhead. When index-based cost development was compared to the supplier’s price request, clear inconsistencies emerged.
The outcome was a 6.3% price decrease in the annual negotiation, achieved not through confrontation, but through fact-based discussion about fair price development.
The Price Development Challenger is not a standalone analytics exercise. It is embedded into end-to-end procurement and cost transformation programs.
PDC is typically used to identify and prioritize negotiation opportunities early on, prepare fact-based negotiation strategies, and support supplier discussions with credible, market-backed insights. Combined with category strategy, negotiation execution, and capability building, it enables clients to move from reactive price defense to systematic price development management.
In an environment defined by volatility, the ability to simulate fair price development – quickly, consistently, and at scale – is a decisive advantage.
Principal Data & Insights
Senior Consultant Data & Insights
Consultant
