Learning curve theory
Learning curve theory is a principle that indicates the time required to complete a task decreases with each repetition of that task, following a predictable pattern known as a curve. Initially described by German psychologist Hermann Ebbinghaus in 1885, this theory highlights how both memory and skill improve with practice. Ebbinghaus's work on the forgetting curve led to insights about the efficiency gained through repeated learning. The theory was further articulated by American engineer Theodore Wright in 1936, who observed that productivity in manufacturing, particularly in airplane production, improved significantly each time the production quantity doubled.
As tasks are performed repeatedly, individuals become more adept, resulting in reduced time and cost associated with the completion of those tasks. The learning curve typically ranges from 60 to 95 percent, with a lower percentage indicating a greater reduction in completion time with increased experience. Despite its strengths, critics note that variations in worker motivation, equipment issues, and employee turnover can affect the outcomes predicted by the learning curve. Nevertheless, it remains a crucial tool for cost estimation and efficiency evaluation in various industries, such as construction and manufacturing, helping organizations optimize their processes and anticipate staffing needs.
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Learning curve theory
Learning curve theory is a concept used in many fields that states that it takes less time to do a task each time it is completed. However, the time gained will decrease gradually over time and the overall decrease in the time it takes will happen in a predictable pattern known as a curve. Related to this concept is the idea that the cost related to the task, such as the cost to manufacture an item, will be reduced according to the same curve. This theory is used in a number of industries to estimate costs and anticipate staffing needs for projects.
Background
The principles that became part of the learning curve theory were first discovered by German psychologist Hermann Ebbinghaus in 1885. Ebbinghaus was interested in how the memory worked and conducted a number of experiments to determine how people learn and memorize. He developed what became known as Ebbinghaus's forgetting curve, which said that people forget things that they have learned at a steady and predictable pattern after they have learned it. Ebbinghaus also noted that the time it takes to relearn something is shorter each time relearning occurs, and that this also occurs in a predictable way. The pattern of this relearning is the learning curve.
In 1936, American aeronautics engineer Theodore Paul "T.P." Wright wrote an article for the Journal of the Aeronautical Sciences in which he proposed what has become known as Wright's law, also known as the learning curve theory or the experience curve. While studying ways to improve the efficiency of making airplanes, Wright noticed that every time manufacturers doubled the number of planes they made, the amount of labor it took to do this decreased between 10 and 15 percent.
His 1936 article "Factors Affecting the Cost of Airplanes" laid out his theory that experience made it easier and cheaper to complete the tasks related to completing an airplane. While several factors were reflected in the cost reduction, including the fact that the same machinery was required whether a company made two planes or two hundred, one main factor was that the people operating the machinery were able to complete the task faster and more efficiently with practice. This became the learning curve theory.
Overview
Ebbinghaus and Wright both noticed that it becomes easier to do something with each attempt, a concept sometimes expressed in the maxim "practice makes perfect." Any action that is performed repetitively over time not only becomes easier but also can be done faster because both the brain and the body learn the steps and motions needed to complete it and need to expend less conscious effort to do so. The worker learns all the steps needed to complete the task, learns where all the necessary tools and supplies are in the workstation, and may even learn some ways to be more efficient, such as reorganizing the work space or using a different tool to perform a task quicker.
However, this improvement only occurs up to a point; it does not become possible to do something so well that it can be done in literally no time. This rate of decreased time to complete a task is predictable and can be observed and plotted on a curve. The learning or experience curve may be known in some industries or individual companies as a cost curve, efficiency curve, or production curve, but each refers to this same concept.
Experts use several basic graphs to calculate the learning curve. To gather the needed information, they first determine how long it takes to fully complete each task and then count how many times the task can be completed in a certain period; for example, how many total units can be made by the production line during an eight-hour shift. From this, one can determine the total number that can be completed in a single shift and the average amount of time it takes to complete each task.
Each of these facts are compiled on graphs, which according to the theory will show an increase in the number of tasks being completed and reflect a decrease in the time it takes any single task to be finished. Additional statistics, such as per-worker averages or team averages, can also be compiled. By gathering this information repeatedly over multiple shifts or multiple weeks and plotting them on graphs, it is possible to determine the overall rate of decrease in the cumulative time it takes to complete the task.
The learning curve generally ranges between 60 and 95 percent, depending on the industry. The amount of improvement is reflected by subtracting the curve from 100 percent. For example, a 70 percent curve means that each time the production of a unit or completion of a task doubles in quantity, there will be a 30 percent decrease in the amount of time it takes to complete the task. If a company is manufacturing cars, for instance, and it starts by producing one hundred cars a month with an 85 percent curve, it has improved its efficiency by 15 percent over when it began. When it increases to two hundred cars per month, it can expect to see another 15 percent increase in efficiency, according to the learning curve theory.
Critics point out that there can be anomalies in the theory. For instance, a team of very motivated workers may improve more quickly than a team with more lackluster work ethics, and equipment problems can interfere with improvement. They also note that employee bases are rarely stable, and as new employees are hired and those with more experience leave, the curve is affected. As a result, the curve is not viewed as a stand-alone factor in predicting improvement, but is seen as part of an overall approach to reviewing increased efficiency and production.
Despite some shortcomings, the learning curve remains a valuable tool for predicting costs in a number of industries. For example, the construction industry frequently uses it to predict the costs of new construction, especially when building multiple units of a particular model. It is often frequently used in many factory settings to both predict costs and identify areas where performance improvements are needed.
Bibliography
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