Chemicals and raw materials: connect market prices, contracts and forecasts

01

The problem starts before the invoice

A buyer sees market prices move, but supplier prices may follow a different path. Grade, freight, currency, fixed shares and index lags alter actual purchase cost. Production planning needs the market signal linked to contract rules and then to required volumes.

The question is not simply where to buy more cheaply. It is which information must come together before deciding, who can confirm the requirement and how to check the outcome after purchasing. Start with a manageable scope, retrieve existing records and make discrepancies discussable with the people who understand operations.

02

An application to develop with Kelqio

Track the relevant index, apply contract formulas and simulate future volumes. Compare central, upside and downside scenarios, then purchasing schedules including inventory and cash constraints.

The approach connects supplier information, catalogues, requests and orders. Each proposal should retain original references, the effective date of the terms used and the decision owner. Buyers can accept a suggestion, correct it or explain why it does not apply. That response becomes useful information for the next transaction.

Raw material forecasts

Compare the budget with three price scenarios while keeping volumes and assumptions visible.

What the monitoring could look like

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Raw material forecasts

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03

What makes a comparison useful

Technical descriptions are part of purchasing data. Unit, size, material, grade and drawing version can change the meaning of a price. Reference matching must preserve these attributes and previously approved equivalences. When information is missing, show two lines for review rather than merging them into a supposed saving based on different items.

Make switching costs visible before accepting alternatives. Depending on the case, these include trials, preparation, additional freight, qualification time or stock that becomes unusable. Including them allows procurement, production and quality teams to discuss the same trade-off rather than comparing unrelated estimates.

04

Bring the right data together

Material grades, units, indices, contracts, currency rules, stock and production plans.

First, check a few lines from end to end. Do references match across sources? Do amounts use the same unit and period? Are terms still valid? File imports can support this initial review. More automated connections follow once matching is understood and responsibilities are assigned.

Data freshness needs to stay visible. Old stock records, expired rate cards or unapproved documents can make a proposal unusable. Signal missing information and retain the last reliable state rather than presenting an apparently precise calculation based on incompatible inputs.

05

Turn a benchmark into a usable forecast

Map each material to a relevant source: grade, geography, currency and publication frequency. A global average may explain a trend without representing the purchased grade. Keep supplier prices, landed-cost components and contractual indices separate so their differences can be explained.

The forecast then combines period volumes and prices. Committed purchases retain known terms; remaining demand can be simulated under central, rising and falling price scenarios. A scenario entered by a buyer is not a model forecast. Show its date, assumptions and uncertain quantities, including the effect on products consuming the material.

When comparing immediate, phased or deferred purchases, include minimum quantities, storage duration, cash tied up, lead times and production needs. A supposedly lower price in three months does not justify waiting if production needs material earlier. Explain these constraints before presenting a choice for approval.

Evaluate forecasts against historical periods withheld from training. Compare errors with a simple baseline such as the last observed price to assess added value. Ranges should reflect uncertainty. Alerts support budget and purchasing reviews without promising the best buying date.

06

An example to explain the calculation

Consider a fictional example: a material costs €2/kg with a 30% fixed share. The remaining 70% follows an index moving from 100 to 110. The calculated price is 2 × (0.30 + 0.70 × 110/100), or €2.14/kg. A 10% index rise therefore produces a 7% contractual price increase.

For 10,000 kg to purchase in that period, the materials budget rises from €20,000 to €21,400. The €1,400 increase becomes visible in the forecast. Apply other agreed cost components, check committed volumes and distinguish this scenario from an actual market prediction.

07

Measure what actually changed

Delivered unit cost, forecast error and explained budget variance.

Agree the baseline before claiming a benefit. Define scope, eligible volumes, quality, time period and costs required to change the situation. An identified opportunity, an approved decision, an order and an invoiced result are separate steps. Tracking them independently prevents an estimate from becoming a promise.

Cash released from lower inventory and staff time saved create value, but are not automatically recurring cash savings of the same amount. If outcome-based fees are appropriate, agree their calculation base. Subscriptions or fixed fees may better suit risk prevention and avoided costs that are difficult to verify.

08

Conditions for a useful result

Forecasts are dated estimates, not guarantees. Measure historical error and distinguish benchmarks from delivered supplier prices.

Approval should match the consequence of the decision. A different pack size does not require the same review as a new critical reference. Teams need access to the explanation, documents and person who accepted the change. This traceability also supports disputes and rule corrections without removing history.

09

Start with a real case

Choose a category, site or contract where the problem can be observed. Gather sample orders, applicable terms and available outcome evidence. The first exercise should cover the whole chain: data, proposal, approval, action and measurement.

Then compare processing time, matching quality and verifiable results. Errors and rejected proposals matter as much as gains: they reveal missing information or constraints. Expand once owners have a method they understand and can use routinely.