There are two common applications of the learning curve:
- analysis of production performance and projection of future costs projections
- identification of performance enhancement opportunities achievable through investment in learning or automation
Several learning curve algorithms have been developed for projecting future learning curve effects based upon measured historic data. Their common objective has been to measure and then simulate the historic doubling of production effect so that future cost performance can be estimated.Performance enhancement opportunities
One of the most challenging issues facing organizational management is not only to determine the learning effect and project its impact into the future, based upon historic performance, but is also to analyse the source of performance gains within the process of learning so as to answer the questions:
- where are there learning opportunities to improve performance?
- what aspects of learning can and should be automated?
This is an important aspect of decision analysis applied to whole enterprise strategic planning (see Enterprise models & strategies
) as a way to identify opportunities to secure gains in productivity, process yield, low reject levels and reduced unit costs.