The concept of learning curve or experience curve or progress curve or improvement curve originated from proactive and learning by doing. It shows the phenomenon, where a decreasing amount of labour is required to accomplish a repetitive task.
Workers take lesser and lesser time to perform their work as they learn, making the work process faster due to improvement in their skills.
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The increase in efficiency of the work are on account of smooth supply and processing of raw materials, use of better and specialised tools, adaptability to work etc.
It was discovered in 19th century by German psychologist Hermann Ebbinghaus and first quantified at Wright Patterson Air Force Base in United States in 1936.
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Learning curves are frequently used by engineers and managers in cost analysis, cost estimation and profitability studies. These curves are useful to measure the number of hours required to produce a particular product before actual production so as to establish a selling price, a delivery date or a bid to contract.
They reduce manufacturing losses and help in improving efficiency of the operator, as output rises. Learning curves also help in stabilizing design resulting in fewer design changes. Cost reduction and value analysis program are also possible with these curves.
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The learning curve is a graphical expression of the learning or experience function. It measures the input requirement as a function of the units of output produced cumulative output is plotted on the X-axis and the number of labour hours needed to produce each unit of output on the Y-axis (Fig. 9.16). Fewer the labour hours needed, lower will be the AC and MC of production. The learning curve is based on the following equation
L = A + BN-ß
Here,
‘L’ is labour input per unit of output
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‘N’ is cumulative output
‘A’ ‘B’ and ß are constants
A > 0, B > 0 and 0 < β< i.
On the basis of the relationship given by the learning curve equation, we have following four cases.
(i) If N = 1, L = A + B
Thus, the labour input required to produce the first lot is A + B
(ii) If ß = 0, 1 = A + B
So, irrespective of the number of lots produced (N), labour input required to produce per lot (unit of output) remains the same at A + B. Here, there is no learning effect
(iii) If ß > 0, as the value of ‘N’ rise, labour becomes close to constant ‘A’. Thus ‘A’ shows the minimum labour input per unit of output after all the learning has taken place.
(iv) If ß = 1, as ‘N’ approaches infinity, L = A. Thus, the larger the value of ß, greater is the learning effect, since ß is learning effect parameter.