1. Performance Improvement  Learning (Improvement of Performance) =  Reduced Production Time and/or Reduced Errors  Learning = Individual + Organizational  (Manufacturing Progress)

2. Learning Curves  Theory  As people gain experience performing a task,  the time it takes to complete the task decreases.  Specifically,  as the accumulated number of units doubles,  the time required to produce a unit declines  at a constant rate.  Note: Cognitive Learning > Manual Learning

3. Learning Curve Equations  Hyperbolic Y x = KX N  Logarithmic log Y x = log K + N log X  Y x = Cumulative Average Time to Produce X Units  K = Time Required to Produce the First Unit  X = Cumulative Total Number of Units Produced  N = Exponent of the Learning Curve Slope ( tan  )

4. Synonyms  Learning Curve  Manufacturing Progress Function  Cost / Quantity Relationship  Product Acceleration Curve  Improvement Curve  Performance Curve  Experience Curve  Efficiency Curve

5. Typical Productivity Learning Curve

6. 80 % Learning Curve

7. Learning Curve  Slope LC %  - 0.514 70  - 0.415 75Assembling  - 0.322 80  - 0.234 85Welding  - 0.152 90  - 0.074 95Machining

8. Notes  LC % = 2 N where N = tan   New Production Time (Cost) = LC % x Old  As total number of production units increases and/  or average production time per unit decreases,   tan  decreases which implies LC % increases.  Shallow learning curves (higher LC %) means  relatively limited cost savings.

10. Examples     N=tan  LC %  5  -0.087 94 %  10  -0.176 88  30 -0.577 67  45 -1.000 50  60 -1.730 30

11. Learning Curve Solutions Click for Solutions Generator

12. Food for Thought  Was it Agatha Christie’s Inspector Poirot or maybe  Dr. Watson’s comment to Sherlock Holmes  “we often go more quickly by going more slowly”.  Maxim for Computer-Aided Statistical Analysis  Garbage In = Gospel Out