Putting the learning curve in context

Morrison, J. Bradley

doi:10.1016/j.jbusres.2007.11.009

Cited by 36 publications

(36 citation statements)

References 32 publications

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“…Learning curve is considered in the model to improve bene ts and organizational productivity in reward systems [4]. Newly hired labors are less e cient than the experienced ones, but they can improve their productivity by repeating their tasks [28,29]. Aryanezhad et al [1] x x x x x x 1 Ra ei and Ghodsi [2] x x x x x x x 1 Ra ei et al [4] x x x x x x 1 Paydar et al [7] x x x x x x 2 Saeidi et al [9] x x x x 2 Saxena and Jain [16] x x x x x x x 1 Wang et al [19] x x x x 1 Defersha and Chen [22] x x x x x x x 1 Cao et al [23] x x x x 1 Egilmez et al [24] x x 2 Sudhakara Pandian and Mahapatra [25] x 1 Chung et al [26] x x x x 1 Arikan and G ung or [27] x x x x x x 2 This paper x x x x x x x x x x x 1 Note.…”

Section: Proposed Modelmentioning

confidence: 99%

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A bi-objective mathematical model for dynamic cell formation problem considering learning eect, human issues, and worker assignment

et al. 2016

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Abstract. One of the important aspects neglected in the literature related to cell formation problem is human issues. In this study, a bi-objective mathematical model is developed in which human issues and dynamic cell formation are taken into consideration simultaneously. The rst objective function deals with costs associated with machines and human issues. The costs of human issues relate to salary, hiring, ring, reward/penalty policy, and worker assignment. The second objective function takes into account labor utilization as a criterion for reward/penalty policy. Since the available time in di erent real conditions is not constant, we include learning e ect to consider the real workers time. The nature of dynamic cell formation problem is NP-hard, and thus a Linear Programming embedded Genetic Algorithm (LP-GA) is employed to solve the model. In order to improve the performance of the applied GA, its parameters are tuned by means of Central Composite Design (CCD) method. Moreover, to validate the LP-GA, some test problems are solved and the results are compared with those obtained from an exact method and GA. The computational results show that the near optimal solutions yielded by LP-GA are better than GA in large-sized problems.

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Section: Proposed Modelmentioning

confidence: 99%

“…Constraint (28) enforce workload balance among cells. Labor utilization is modeled by inequality Constraint (29). Constraints (30) and (31) indicate the applied reward or penalty policy, which depends on labor utilization value.…”

Section: Mathematical Model Assumptionsmentioning

confidence: 99%

A bi-objective mathematical model for dynamic cell formation problem considering learning eect, human issues, and worker assignment

et al. 2016

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“…Knowledge can be learned by units, such as an individual, a group, or a company and the relationships between units in an organization affect learning because knowledge is transferred between units (Argote et al 2003). Although the management literature has shown a variety of learning curves (e.g., Argote 1993;Wiersma 2007;Morrison 2008), the most classic learning curve captures the mechanism of "learning-by-doing" (Adler and Clark 1991) or experience-based learning. Experience provides learning units with the opportunity to learn through repetition and trial-and-error learning.…”

Section: Learning Curves and Errors In Xbrl Filingmentioning

confidence: 99%

XBRL Mandate: Thousands of Filing Errors and So What?

Vasarhelyi

Zheng

2013

Journal of Information Systems

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Acknowledgement: We are grateful for the exchanges with Cliff Binstock, the CEO of XBRL Cloud, who helped us understand the data of XBRL filing errors his company compiled and we used in our study. provider, more than 4000 filing errors have been identified. We examine the overall changing pattern of the errors to understand whether the vast number of errors may hamper the transition to the interactive data reporting. Using a sample of 4532 filings that contain 4260 errors, we document significant learning curves exhibited by the SEC XBRL filing environment, by the filers, and by the XBRL software vendors. Specifically, we find that the number of errors per filing is significantly decreasing as more quarters pass, when a company files more times, and when a higher version of software is used, suggesting that the SEC, the company filers, and the technology community all learn from their experiences and therefore the future filings are improved. Our findings provide evidence to encourage the regulatory body, the filers, and the XBRL technology supporting community to embrace the new disclosure requirement in financial reporting. The significantly decreased number of errors also helps address the information users' concerns regarding the data quality of XBRL filings.

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“…Este enfoque, aunque interesante, tiene como objetivo la misma modelización del aprendizaje en lugar de su aplicación a la estimación de tiempos, de ahí su complejidad. Por ello no se ha incluido en el enfoque seguido al desarrollar la metodología [95].…”

Section: Curvas De Aprendizajeunclassified

“…Se ha implementado un modelo ampliamente aceptado [91]- [95], que establece que el tiempo de finalización se ve disminuido según un parámetro denominado coeficiente de aprendizaje, al duplicar el número de veces que se ha realizado esa operación. En la sección 5.5.6 se explica el modelo definido de manera detallada.…”

Section: Variación Del Número De Ciclos Transcurridos (Influencia Delunclassified

Metodología para la generación y evaluación multicriterio de soluciones conceptuales de diseño de líneas de montaje final de producto en el sector aeronáutico

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Putting the learning curve in context

Cited by 36 publications

References 32 publications

A bi-objective mathematical model for dynamic cell formation problem considering learning eect, human issues, and worker assignment

A bi-objective mathematical model for dynamic cell formation problem considering learning eect, human issues, and worker assignment

XBRL Mandate: Thousands of Filing Errors and So What?

Metodología para la generación y evaluación multicriterio de soluciones conceptuales de diseño de líneas de montaje final de producto en el sector aeronáutico

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