1. Introduction and Objectives
As workers repeat complex assembly procedures, cognitive familiarity and muscle memory reduce the time required to complete subsequent units. Failing to account for this learning effect leads to over-budgeting labor costs for new product introductions.
 
2. Wright’s Cumulative Average Time Model
Wright’s law states that each time the cumulative volume of units produced doubles, the cumulative average time required per unit decreases by a fixed learning percentage.
  • Mathematical Equation:
    \(Y=aX^{b}\)
    • Where:
      • Y = Cumulative average time per unit
      • a = Time required to produce the very first unit
      • X = Cumulative number of units produced (expressed in doubling steps)
      • b = Learning index calculation constant
        (log (Learning %) / log(2))

3. Computational Progression Framework
If a product has an 80% learning rate, doubling production triggers a predictable step-down in average manufacturing time:
  • Unit 1: Baseline Time (100% of initial effort)
  • Unit 2 (First Double): Average time drops to 80% of baseline
  • Unit 4 (Second Double): Average time drops to 64% of baseline (80% × 80%)
  • Unit 8 (Third Double): Average time drops to 51.2% of baseline (64% × 80%)

4. Strategic Limitations
The learning effect does not continue indefinitely. Production eventually reaches a steady state known as the Steady State Plateau, where physical machine cycles and human speed limits prevent further optimization.
 
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