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Management and worker factors
Published in Lincoln H. Forbes, Syed M. Ahmed, Lean Project Delivery and Integrated Practices in Modern Construction, 2020
Lincoln H. Forbes, Syed M. Ahmed
A learning curve is the phenomenon demonstrated by the progressive reduction in the time taken by an individual, or by a team to perform a task or a set of tasks on a repetitious basis. The individuals performing the task or project become more proficient with each repetition; the observed improvement serves as a motivator and a learning tool resulting in successively shorter performance times. The learning curve is represented by an equation of the form Tn= T1⋅n(−a) where Tn is the time for the nth cycle, T1 is the time for the first cycle, N is the number of cycles and a is the constant representing the learning rate. This equation produces a hyperbolic curve. (Readers, please note that this generalized formula is not effective in solving problems. Instead, the methods shown below should be applied.)
Cost estimating techniques
Published in John Vail Farr, Isaac Faber, Engineering Economics of Life Cycle Cost Analysis, 2018
Learning curves, and expressions such as “experience curve,” “cost improvement curve,” “progress curve,” “progress function,” “startup curve,” “improvement curve,” and “efficiency curve” are often used interchangeably and are based on the concept that resources required to produce each additional unit decline as the total number of units produced increases (NASA, 2015). The term “learning curve” is used when referring to an individual's or organization's performance as knowledge or insight is gained. The learning curve concept is used primarily for repetitive and labor-intensive tasks and finds most of its utility in manufacturing. In most practical applications, learning curve analysis is used to predict the cost of making the nth unit given the time and cost of making the first unit. This CER's data is gathered under the design and implementation phase of the life cycle costing process. It uses historical data, by definition, to calculate a cumulative average cost for producing an item. Consistency in improvement converts to a constant percentage reduction in time required over successively doubled quantities of units produced. This constant percentage by which the costs of the doubled quantities decrease is called the rate of learning (Federal Aviation Administration, n.d.).
Quantity-based Learning Curves
Published in Alan R. Jones, Learning, Unlearning and Re-learning Curves, 2018
Typically, the activity could be the manufacture or assembly of a recurring product, or the performance of some other recurring manual activity. The main premise that supports the existence of a Learning Curve is that there must be some opportunity for knowledge or experience to be retained or passed on for the benefit of future repetitions of the task. This does not necessarily imply that the frequency of the repetitions has to be continuous, or any set pattern. If an organisation chooses to subcontract each instance of a repetitive activity to a different independent organisation, then a Learning Curve would not be appropriate. (Clearly, this is largely a hypothetical example only as it is difficult to comprehend why any organisation would do such a thing except as part of some expensive benchmarking exercise for future preferred supplier selection process. Surely, we must doubt that scenario.)
Can China’s offshore wind power achieve grid parity in time?
Published in International Journal of Green Energy, 2021
Chenxi Xiang, Fei Chen, Fan Wen, Feng Song
The most commonly employed method to investigate the future evolution of the cost of renewable energy is the learning curve model. Its basic assumption is that the unit cost of a new technology decreases along with the increase in the cumulative installed capacity and the accumulation of experience with the technology, which is called the learning-by-doing effect (Arrow 1962; Berglund and Söderholm 2006). The learning curve model measures the learning effect using the learning rate, which is usually expressed as a percentage and indicates the cost reduction level for each doubling of the cumulative experience or production. Grübler, Nebojša, and Victor (1999) focused on energy technologies and used the learning curve model to describe the improvement in cost and performance due to cumulative experience and investment. Among different energy sources, wind power, with its fast growth in recent years, has received considerable attention. Many studies have evaluated the technology improvements of onshore wind power in various regions across the world as well as in China (Söderholm and Klaassen 2007; Tang and Popp 2016; Wiser and Bolinger 2011).
Skills development of mechanized softwood sawtimber cut-to-length harvester operators on the Highveld of South Africa
Published in International Journal of Forest Engineering, 2020
Roland Wenhold, Pierre Ackerman, Simon Ackerman, Kayla Gagliardi
A learning curve is the improvement of performance as an operator becomes more experienced on a machine over time (Björheden 2000; Purfürst 2010; Purfürst and Erler 2011). It is well known that productivity levels and influencing variables (such as the type of machine, tree species, slope, terrain roughness, and tree size) can be measured. For this reason, a learning curve is defined as the relation between productivity and time per work cycle (Purfürst 2010). It is assumed that the more time an operator spends on an activity, either on a simulator or in-field working on a machine, the more familiar they will become with the controls and the environment. This will eventually lead to an enhancement of performance as their skills increase over time (Purfürst 2010). According to Björheden (2000), Ranta (2009), Purfürst (2010), and Gellerstedt (2013), there are two phases involved in the learning period of operators. The first phase is characterized by a rapid learning period in the beginning because there is much to learn at this stage in terms of controls, movement, planning, technique, etc. In the second phase, the learning slows down, but still constantly increases at a decreasing rate. This phase, leading to full productivity, can last up to five years for an average harvester operator.
Effects of feedback type and modality on motor skill learning and retention
Published in Behaviour & Information Technology, 2020
Biwen Zhu, David Kaber, Maryam Zahabi, Wenqi Ma
Task learning was assessed in terms of (1) learning percentage and (2) performance scores during training and retention periods, respectively. Learning curve analysis provides a structured, mathematical approach to predict how task performance improves over time (Wright 1936). The learning percentage was calculated with the following standard learning curve analysis equations, where is the performance score at the cycle.