Statistical attributes of the power learning curve model

Globerson, Shlomo; Gold, Darina

doi:10.1080/002075497195669

Cited by 29 publications

(15 citation statements)

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“…In spite of the numerous practical applications of LC reported in the literature (Chambers and Johnston 2000, Pramongkit et al 2000, Franceschini and Galetto 2002 and the accumulated knowledge on the effect of human variability on manual tasks (Globerson and Gold 1997, Nembhard and Uzumeri 2000a, b, Nembhard and Osothsilp 2002, Smunt and Watts 2003, a major research gap still remains in the LC literature; namely, the use of LCs to guide the assignment of products to workers according to product characteristics and batch sizes. The current literature seems to focus more on the effect of learning when determining the optimum production lot-size for a new product Bonney 1999, 2001), and on the relationship among improvement performance in manufacturing, quality management and learning (Wang andLee 2001, Vits andGelders 2002).…”

Section: Introductionmentioning

confidence: 98%

Learning curve modelling of work assignment in mass customized assembly lines

Anzanello¹,

Fogliatto²

2007

International Journal of Production Research

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In customized markets, a large variety of product models is demanded by customers. That requires fast set-up of production resources to comply with specifications of the next model to be produced. Such compliance, however, may cause considerable production and quality losses owing to workers' poor performance during the initial production runs of a new model. Therefore, modelling workers' learning upon exposure to each product model may help production managers to define the best assignment scheme for models and workers, such that losses in the initial stages of production are minimized. The current paper presents a method that uses learning curves to guide the best assignment of product models to teams of workers. Product models are first clustered into families based on their similarities, aiming at a smaller data collection to generate the learning curves. Allocation of product families to teams is then carried out, based on the analysis of their corresponding learning curves. Two courses of action are proposed for this, depending on whether the production batch will lead to longer or shorter production runs. The proposed methodology is illustrated in a case study from the shoe manufacturing industry.

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Section: Introductionmentioning

confidence: 98%

Learning curve modelling of work assignment in mass customized assembly lines

Anzanello¹,

Fogliatto²

2007

International Journal of Production Research

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“…Most of the learning models treat learning as a deterministic phenomenon. Globerson and Gold (1997) addressed learning curve's stochastic nature and derived the expressions of variance and probability density function of the performance. This paper presents an innovative approach to a P control chart that incorporates learning dynamics (Globerson and Gold 1997) in the fraction of nonconforming.…”

Section: Introductionmentioning

confidence: 99%

Design the fraction of nonconforming control chart with learning curve consideration

Yang

2012

2012 International Conference on Machine Learning and Cybernetics

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The fraction of nonconforming control scheme with learning effect consideration is presented. The learning curve (LC) is used to describe and forecast the progress in reducing the fraction of nonconforming (p). The reduction rate of p may be large during the initial stage of the quality improvement program, and influences the construction of p control chart.Based on average run lengths, the simulation is used to compare the shift-detecting performance of the Shewhart-p chart and modified-p chart, with-and without-consideration of LC in the order of production progress. Via simulation, The p charts with consideration of LC are more efficient than that without consideration of LC. The modified-p chart with consideration of LC performs best.

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“…(7) In this model K is added to the model of Log -Linear while K is a constant steady state performance of workers. Referring to the equation: (8) On the Plateau model, the condition steady state concluded after learning was completed or when the limitations of the machine development or learning on workers [1,17,18]. (9) The constant B, presented the availability of an equivalent unit at the commencement of the experiment.…”

Section: Learning Curvementioning

confidence: 99%

“…According to Blancett [7], Globerson and Gold [8], log linear learning curve is the most common learning curve that have been used to predict the production level in repetitive operation. This model described the mostly manual-based operations with acceptable precision [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation Learning and Forgetting Curve to Scheduling in Garment Industry

Badri

Deros

Syahri

et al. 2016

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. The learning curve shows the relationship between time and the cumulative number of units produced which using the mathematical description on the performance of workers in performing repetitive works. The problems of this study is level differences in the labors performance before and after the break which affects the company's production scheduling. The study was conducted in the garment industry, which the aims is to predict the company production scheduling using the learning curve and forgetting curve. By implementing the learning curve and forgetting curve, this paper contributes in improving the labors performance that is in line with the increase in maximum output 3 hours productive before the break are 15 unit product with learning curve percentage in the company is 93.24%. Meanwhile, the forgetting curve improving maximum output 3 hours productive after the break are 11 unit product with the percentage of forgetting curve in the company is 92.96%. Then, the obtained 26 units product on the productive hours one working day is used as the basic for production scheduling.

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Statistical attributes of the power learning curve model

Cited by 29 publications

References 0 publications

Learning curve modelling of work assignment in mass customized assembly lines

Learning curve modelling of work assignment in mass customized assembly lines

Design the fraction of nonconforming control chart with learning curve consideration

Implementation Learning and Forgetting Curve to Scheduling in Garment Industry

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