Wright Law Learning Curve Cost Doubling Lookup
Estimate unit cost from production doublings and a caller-supplied constant percent learning rate.
Description
Learning curves express a hypothetical unit-cost reduction per doubling of cumulative production.
Learning curves express a hypothetical unit-cost reduction per doubling of cumulative production. The chosen rate is an assumption, not an observed future trend.1
The returned values include Estimated cost reduction percent, Estimated end unit cost, Production doublings. The How section below explains the calculation, input rules and interpretation.
When to use Wright Law Learning Curve Cost Doubling Lookup
Use Wright Law Learning Curve Cost Doubling Lookup when you need to evaluate the supplied records under the calculation and evidence rules below. No rate fitting, supply constraint or guarantee of learning is included.
Define the question, evidence and scenario assumptions first. Supply the chosen score scales, time horizons or reference selection explicitly. Interpret generated text as a discussion template and screen labels as the stated rules.
How to use Wright Law Learning Curve Cost Doubling Lookup and understand its results
- Define the question, evidence and scenario assumptions first. Supply the chosen score scales, time horizons or reference selection explicitly. Interpret generated text as a discussion template and screen labels as the stated rules.
- Enter endVolume, learningRatePercent, startUnitCost, startVolume using the field table and calculation rules, then run the tool.
- Read costReductionPercent, estimatedEndUnitCost, productionDoublings. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| endVolume | End production volume | number; minimum: 0 | Required |
| learningRatePercent | Learning rate percent (%) | number; minimum: 0; maximum: 100 | Required |
| startUnitCost | Start unit cost | number; minimum: 0 | Required |
| startVolume | Start production volume | number; minimum: 0 | Required |
Require positive finite costs and volumes and rate≥0 but strictly below 100%. Return finite doublings, end cost and 100×(1−endCost/startCost) reduction. Declining volume gives negative doublings and can increase estimated cost.
doublings=log2(end/start); endCost=startCost×(1−rate/100)^doublings Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| costReductionPercent | Estimated cost reduction percent (%) | number |
| estimatedEndUnitCost | Estimated end unit cost | number; minimum: 0 |
| productionDoublings | Production doublings | number |
{
"input": {
"endVolume": 800,
"learningRatePercent": 20,
"startUnitCost": 100,
"startVolume": 100
},
"output": {
"costReductionPercent": 48.79999999999999,
"estimatedEndUnitCost": 51.20000000000001,
"productionDoublings": 3
}
}Limitations and assumptions
No rate fitting, supply constraint or guarantee of learning is included.
Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Missing fields without a declared default, extra fields, wrong value types and violations of the calculation rules are input errors.
Alternative or Complementary methods
Use Moore Law Transistor Density Endgame Note Lookup: Feature-size bands offer a rough process-comparison context. A node label alone cannot determine density, cost, power or performance. For a complementary task, use Kurzweil Law Accelerating Returns Lookup: Accelerating returns can be screened by comparing early and late annualized log growth in supplied milestones. Growth and acceleration are distinct.
References
Similar or alternative tools
- Moore Law Transistor Density Endgame Note Lookup
Feature-size bands offer a rough process-comparison context. A node label alone cannot determine density, cost, power or performance.
- Kurzweil Law Accelerating Returns Lookup
Accelerating returns can be screened by comparing early and late annualized log growth in supplied milestones. Growth and acceleration are distinct.