Learning curve theory

In this lesson

1. 0:00 Learning curve theory introduction
2. 1:55 Worked example: Lux lounge chair production
3. 5:27 Algebraic approach using the formula
4. 7:06 Finding individual unit production time
5. 9:35 Factors affecting learning rate changes
6. 11:13 Simple linear regression and forecasting

About this lesson

Learning curve theory explains how efficiency improves as workers repeat tasks, provided certain conditions are met: the work involves significant manual content, tasks are repetitive, production is in early stages, and the workforce is stable and motivated. The learning rate is expressed as a percentage—for example, a 75% learning rate means that each time cumulative output doubles, the average time per unit falls to 75% of the previous level. Understanding when and how to apply this theory is essential for Performance Management and cost forecasting across CIMA and ACCA qualifications. You'll work through practical examples using both tabular and algebraic approaches. The tabular method tracks cumulative time and average time per unit as output doubles. The algebraic formula—Y equals A times X to the power of B—allows you to calculate precise timings for individual units, even when doubling doesn't occur. This is particularly useful in exam scenarios where you need to isolate the cost or time for a specific unit of production. The lesson also explores why actual learning rates differ from expected rates, covering factors like staff turnover, design changes, and machine breakdowns. You'll practise identifying which conditions support learning curve application and distinguish between forecasting methods like interpolation and extrapolation, reinforcing how learning curve analysis fits within broader cost and performance management decision-making.

Duration: 14m