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Explore Unit Theory and Cumulative Average Theory to estimate cost savings in production through learning curves. Learn equations, methods, and examples from the Canadian Frigate program. Analyze steepness, application, and benefits of each theory.
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Learning Curve Introduction • Learning Curves are used to estimate the time saving that is experienced when performing the same task multiple times • The greater the amount of labour involved in the task, the greater the saving experienced with each iteration
Learning Curve Introduction • Clearly manufacturing a ship or airline has a steeper learning curve (greater savings per iteration) than an automated process of making widgets (e.g. previous figure which is steep - ships) • Two predominant learning curve theories (though there are more) • Unit Theory • Cumulative Average Theory • Both will be explained using the Canadian Frigate program from the late 1980s and early 1990s
Unit Theory Learning Curve • The theory states that the cost decreases by a fixed percentage as the quantity produced doubles • The following table reflects an 80% learning curve which means the cost decreases by 20% for each doubling of production (multiply by .8) Note: Examples from Cost Estimation by Mislick and Nussbaum
Unit Theory Learning Curve • Unit Theory equation • Yx = A * xbwhere: • Yx = the cost of unit x • A = cost of the first unit • x = the unit number • s = the slope of the learning curve = 2b • Cost of 2x = (cost of x)*s • Substituting A*xb for cost & rearrange • s = A*(2x)b/A*xb = 2b • Using logarithms: b = ln(s)/ln(2)
Unit Theory Learning Curve • From the previous example: • A=100 • Slope of learning curve = 0.8 • 0.8 = 2b • Using logarithms: • ln(0.8) = b * ln(2) • b = ln(0.8)/ln(2) = -0.3219 • Therefore, cost unit x = 100 * x -0.3219 • For x = 4, cost = 64.0 • For x =3, cost = 70.2
Unit Theory Learning Curve • Canadian Frigate Example
Unit Theory Learning Curve • Yx = A * xb • To determine “b”, use logarithms, i.e. • ln(Yx) = ln(A) + b*ln(x) • “b” slope of this equation (learning slope = 2b) • Graph using Excel Scatter with Markers after converting your costs (hours in this case) and number of units using natural logs • Under Chart Tools, select Trendline– more trendline options • Then select display equation on chart and display R-squared value on chart
Unit Theory Learning Curve • From the chart equation, the slope is -0.4482 which = b • From before, if s = 2b, then s = 0.7330 or a 73.3% unit learning curve which is steeper than typical but reasonable given SJSL’s inexperience
Cumulative Average Theory • Subtly different from Unit Theory • Cumulative average theory is average cost of groups of units while unit theory is based on individual unit cost • Therefore, for cumulative average theory (using the previous 80% example): • The average cost of 2 units is 80% of 1 unit • The average cost of 4 units is 80% of 2 units • The average cost of 50 units is 80% of 25 units • Doubling your production reduces your average cost by your learning curve (80% for this example)
Cumulative Average Theory • Cumulative Average Theory equation • YN= A * Nbwhere: • YN= the cumulative average cost of N units • A = cost of the first unit • N = the cumulative number of units produced • As before: • s = the slope of the learning curve = 2b • Using logarithms: b = ln(s)/ln(2) • Cumulative Average Theory usually used for production lots where batches of units are made
Cumulative Average Theory • Canadian Frigate example again:
Cumulative Average Theory • From the chart equation, the slope is -0.2244 which = b • From before, if s = 2b, then s = 0.8560 or a 85.6% unit learning curve which is less steep than unit theory as expected
Unit vs Cumulative Average Theory • Cumulative average theory is a less steep curve such that unit learning is always below it • Cumulative average is less responsive to variation in costs from unit to unit since it is based on averages • Use cumulative if initial production is expected to have large cost variation unit to unit
Unit vs Cumulative Average Theory • For Frigate example, which learning curve is better? • Hard to tell visually, I used stats, went with unit • R2 for unit = 0.9562 vs cumulative = 0.9355 • P-value (not shown) unit=5.2x10-6 cum=2.0x10-5