Model value chains and link benchmarks and indices to uncover how each cost driver impacts your final product — enabling fair, data-backed negotiations.
Every price conversation starts with a data-backed model — the cost drivers, their weights, and where they're headed.
Time-lag analysis models how feedstock and energy shifts propagate to your final product — days or weeks before the supplier invoice.
Every proposed price increase is tested against the model — with facts, not opinions.
Suppliers justify price changes with the same numbers you see — one shared language.
The AI-based Cost Model automates parts of the value-chain construction and correlation analysis.
Category knowledge sits in the model — visible to the team, portable across suppliers.
Five capabilities that turn a supplier's price into a defensible number.
Break a final item into feedstocks, transportation, energy and other drivers — as deep as the category needs.
Weights, units and adjustments per driver — one transparent formula behind the final price.
See how each factor contributes to the final price — quantified, not intuited.
Model how cost changes propagate through the value chain over time — days or weeks ahead of the invoice.
Link cost drivers to live commodity indices and PINPOOLS benchmarks — no manual data pulls.
Define the product. Link its drivers. Let the model do the math.
Create the final item — set name, owner, unit, currency. Choose calculation type (absolute or relative). All models live in one list, with Should Cost, Volume and Spend visible at a glance.
Add each driver as a child item. Pick from four data sources — PINPOOLS Benchmarks, Commodity Indices, Your own indices, or a Custom Item. Set quantity per kg, apply an optional formula adjustment, and choose region/country for AI data.
Chain feedstocks, energy and other blocks into a tree. Switch between Tree, Framework, and Split views. Add CO₂ per driver with the AI estimate.
Read historical price structure as a stacked bar over months, and current cost structure as a share pie. See which driver moved the total — and by how much.
Track forecast accuracy per driver with the MAPE view. Test every proposed supplier price against the model — accept, challenge, or counter with facts.
Customers use the should-cost model to build complete cost structures for their core materials. By linking relevant feedstock indices, transportation costs, and energy data, they visualize how each factor contributes to final product pricing. This transparency enables data-driven negotiations, ensuring suppliers justify price changes with facts.
The model is often used alongside benchmarking and forecasting to anticipate market movements and build resilient sourcing strategies. For many organizations, it replaces time-consuming spreadsheet analysis and secures cost stability by grounding discussions in objective data. It has also become a key knowledge tool, capturing category expertise that remains available even when personnel change.
“We replaced twelve Excel files with one PINPOOLS model. Category knowledge that used to live in one buyer's head is now visible to the whole team — and every price discussion starts with data.”
Any material or product where you know (or can infer) the cost structure — raw materials, packaging, freight, energy-intensive products, and similar.
No. Indices and benchmarks can be linked directly, and the AI-based Cost Model automates correlation analysis and time-lag effects.
Cost models sit alongside RFX and Series Requests, so proposed prices can be validated against the model before a buyer accepts or challenges them.
Yes — customers report that models capture category expertise, so knowledge stays with the organization even when personnel change.
Yes. Cost Model + Forecasting is a common combination for "what-if" scenario simulation and proactive risk management.
See a should-cost model built on your data in a 30-minute demo.
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