A population of learners: A new way to measure organizational learning

Uzumeri, Mustafa; Nembhard, David A.

doi:10.1016/s0272-6963(97)00017-x

Cited by 68 publications

(24 citation statements)

References 11 publications

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“…Table 7 Field studies industry type. Adler and Clark (1991) Electronic equipment manufacturing Bevis et al (1970) Fessia et al (2007 Macher and Mowery (2003) Nembhard and Osothsilp (2001) Towill (1977) Almgren (1999 Automotive parts manufacturing Foster and Adam (1996) Franceschini and Galetto (2004) Hinze and Olbina (2009) Construction Jarkas and Horner (2011) Bevis et al (1970) Tobacco manufacture Chambers and Johnston (2000) Aviation service Lapré (2011) Household products manufacturer warehouse Huntley (2003) Software service Junginger et al (2006) Energy generation Levy (1965) Commercial printing Nembhard and Uzumeri (2000) Textile manufacturing Towill (1990) Electrical inspection Towill et al (1989) Machine-building Uzumeri and Nembhard (1998) Manufacturing…”

Section: Resultsmentioning

confidence: 99%

Production economics and the learning curve: A meta-analysis

Grosse

Glock

Müller

2015

International Journal of Production Economics

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Section: Resultsmentioning

confidence: 99%

Production economics and the learning curve: A meta-analysis

Grosse

Glock

Müller

2015

International Journal of Production Economics

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“…Argote (1993) provides one of the best reviews of different aspects of group and organizational learning -a review that includes reference to the social psychology literature. Some authors have focussed on seeking learning curves for individual workers (Uzumeri and Nembhard 1998;Nemdhard and Uzumeri 2000).…”

Section: Personal and Corporate Learningmentioning

confidence: 99%

Learning in disrupted projects: on the nature of corporate and personal learning

Howick

Eden

2007

International Journal of Production Research

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The majority of large, complex projects suffer disruptions. These can have unexpected and significant impacts on a project, resulting in excessive time and cost overruns. Disrupted projects therefore require careful management in order to minimize the impact of a disruption. One element of a project for which disruptions will have a particular impact is on any anticipated learning gains. Loss of learning from disruptions can often be very significant. This lost learning may be from individual workers (personal) or by the organization (corporate). An understanding of the impact of a particular disruption on learning is required to enable effective management of that disruption. This paper argues that improved management of learning in disrupted projects may come from the disaggregation of personal and corporate learning from the typically used aggregated learning curve presumptions. The literature is reviewed to explore whether a method of disaggregation can be determined from existing propositions about the behaviour of learning curves. The explorations demonstrate that the disaggregation of a learning curve is complex. The contribution of this paper derives from developing an understanding of the role of asymptotes in constructing learning curves, and the nature of the interaction between personal and corporate learning. These two aspects are argued to be crucial aspects in managing the impact of disruptions on learning. The main motivation for this work is to provide managers with an understanding of the disaggregation process, in order that they can think through the impact of a disruption on learning in their own projects

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“…The method we propose relates to the approach of Uzumeri and Nembhard (1998) with respect to its main objective, i.e. optimize the assignment of tasks to teams.…”

Section: Introductionmentioning

confidence: 99%

“…When r50, the LC decreases representing declining performance usually related to worker's fatigue. Some of the profiles described above are exemplified in figure 1 (Uzumeri and Nembhard 1998). Uzumeri and Nembhard (1998) and Nembhard and Uzumeri (2000a) modelled performance data from a population of workers exposed to new tasks using the hyperbolic model.…”

Section: Introductionmentioning

confidence: 99%

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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A population of learners: A new way to measure organizational learning

Cited by 68 publications

References 11 publications

Production economics and the learning curve: A meta-analysis

Production economics and the learning curve: A meta-analysis

Learning in disrupted projects: on the nature of corporate and personal learning

Learning curve modelling of work assignment in mass customized assembly lines

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