STEP 01
Map the real exposure
Identify relevant commodities, currency pairs, countries and forecast horizons; distinguish market benchmarks from contracted input costs.
A CONSENSUS USE CASE
Put market assumptions on a consistent footing.
Energy producers, commodity-intensive manufacturers and trading or procurement teams face price volatility that can affect capital spending, operating costs and financing needs. Professional consensus forecasts can provide an independent planning baseline for covered commodities, currencies and wider economic indicators. They inform scenario discussion rather than predicting short-term market moves or prescribing a hedge.
THE QUESTION
“How do we test our commodity price and currency assumptions before committing capital or budgets?”
Energy & Commodity BusinessesCONSENSUS ECONOMICS
FORECASTS RELEVANT TO YOUR WORK
Commodity forecasts for energy and procurement teams
Match the information to your decision, then choose an appropriate publication or data format.
01 / FORECASTS
Covered benchmarks such as Brent crude oil and copper, plus currencies and inflation.
02 / EVIDENCE
Professional commodity price expectations, survey revisions and available ranges.
03 / PRACTICAL OUTPUT
A price exposure and FX sensitivity register tied to the operating plan.
04 / ACCESS
Energy, metals and agriculture forecast publications and associated data.
The examples below use illustrative figures. Available data and usage rights depend on the service.
01 / THE SITUATION
Here’s the decision.
The operating plan
An international manufacturer is reviewing the sensitivity of next year’s costs to commodity prices.
The procurement budget assumes lower input prices, while treasury expects a less favourable currency path. Senior management wants a common external benchmark, clarity about the spread of professional expectations and an explanation of which assumptions could materially affect margins. The team must still model contract prices, basis risk and operational exposures itself.
THE OBJECTIVE
Build a documented external reference and test the company-specific price and currency sensitivities before approving the plan.
02 / THE WORKFLOWComparing commodity price expectations
Comparing commodity price expectations
with operating assumptions.
Separate commodity benchmark forecasts from company-specific realised costs, compare the house budget to outside expectations, then test exposures and document decisions.
STEP 02
Establish a comparable baseline
Compare the internal price and foreign exchange assumptions with covered professional forecasts using consistent units and dates.
STEP 03
Challenge the assumptions
Review forecast revisions and dispersion, then model the effect of internally defined price and currency sensitivities.
STEP 04
Translate into decisions
Show margins, cash flow, project sensitivities and mitigation options alongside source and assumption limitations.
03 / A PRACTICAL EXAMPLEA worked example.
A worked example.
The numbers in context.
Which input-price assumptions could change the budget?
A manufacturer compares its internal commodity and foreign exchange inputs with illustrative outside benchmarks. Every number here is synthetic.
WORKING EXAMPLEIllustrative example
Swipe sideways to view the complete example table
| Planning driver | Internal budget | Professional benchmark |
|---|---|---|
| Brent crude (synthetic USD/bbl) | $104 | $112 |
| Copper (synthetic index points) | 84 | 78 |
| USD/local currency | 1.18 | 1.14 |
THE PRACTICAL DELIVERABLE
A sensitivity register linking each economic input, dated benchmark and company exposure to a named planning owner.
SUITABLE STARTING POINTExplore forecast publications
Review commodity and FX forecast availability and confirm definitions before modelling.
These example values are illustrative, not survey data. Explore an actual sample forecast publication
GO FURTHER / APPLY THE FULL DISTRIBUTION
The range behind
The range behind
the average.
Use commodity price and currency forecasts as outside references for budget sensitivities. Survey highs and lows show professional disagreement; they are not a recommended hedge or an expected trading range.
FORECAST DISTRIBUTIONSynthetic Brent crude oil price in USD/bbl, same forecast horizon
Consensus mean$112
Low$88
High$145
Standard deviation$15
BEHIND THE AVERAGE
Illustrative professional forecasts of $98 and $132 demonstrate divergence around the mean. The figures do not incorporate the company’s contracts or actual purchase costs.
LOW · $88HIGH · $145
Consensus mean Selected individual forecasts
A PRACTICAL SCENARIO OR RESEARCH TEST
Illustrative exampleSeparate a price benchmark from operating exposure
Swipe sideways to review the scenario table
| Budget lens | Illustrative reference | Decision question |
|---|---|---|
| External reference | $112/bbl | How does it compare with the synthetic $104/bbl budget input? |
| Lower-price sensitivity | $88/bbl | What would change in cost of goods and cash flow, including FX? |
| Higher-price sensitivity | $145/bbl | Which operational protections and capital-spend decisions merit stress testing? |
All figures are synthetic. Forecaster lows and highs are not probability intervals or self-contained price paths. Contract terms, freight, product specifications, basis risk, currency exposures and hedges must be modelled separately.
OTHER APPLICATIONS FOR THIS AUDIENCE
Further applications.
Procurement planning
Prioritise the commodity assumptions that matter most for purchase budgets.
USE THE DATA
Compare survey forecasts with company-specific volumes and contracts.
OUTPUT
A price and exposure sensitivity table.
Capital investment analysis
Test project returns against alternative input and output price assumptions.
USE THE DATA
Record external vintages and internally designed scenarios.
OUTPUT
A capital-project macro assumptions pack.
Treasury and FX coordination
Keep the FX and commodity reference dates aligned in financial plans.
USE THE DATA
Document FX assumptions and potential joint sensitivities rather than assuming unrelated market moves.
OUTPUT
A treasury and procurement review memo.
04 / WHAT YOU TAKE AWAYA practical result.
A practical result.
Ready to review.
Build an output that can be explained, challenged and updated as forecasts change.
How the consensus is calculatedEXAMPLE DELIVERABLE↗
Illustrative commodities deliverableA price-exposure sensitivity register
A practical planning table linking economic and commodity forecast assumptions to operating exposure, owner and sensitivity.
WHAT IT CONTAINS
Adapt the example to your own data and decision process.✓Commodity and FX benchmarks with survey dates
✓Internal assumption differences and sensitivity ranges
✓Operational exposure, mitigation owner and review trigger
05 / THE CONSENSUS ADVANTAGEWhy the data
Why the data
matter here.
Match the forecast inputs to the decision. The average, the distribution and the historical record serve different purposes.
RELEVANT FORECAST INPUTS
01
Selected commodity price expectations by market where covered
02
Foreign exchange and inflation forecasts
03
Professional forecast revisions and ranges where available
WHY CONSENSUS ECONOMICS
Comparable outside reference for volatile inputs.
Professional commodity, inflation and FX expectations provide reference points for budget and capital spending discussions. Survey revisions and disagreement help prioritise the sensitivities worth testing. Forecasts are not spot quotes, hedge instructions, or probability-weighted price distributions.
1,000+professional forecasters1989first survey
COMMON QUESTIONS
Practical questions, clear answers.
Are the Brent and copper figures in the worked example actual forecasts?
No. All numerical values in the worked example and scenario lab are synthetic illustrations. Use the linked commodity coverage pages for information about actual services.
How can commodity forecasts support procurement?
Compare professional price expectations with your contracted grades, delivery terms, currency exposures and purchasing assumptions.