The effects of unequal relearning rates on estimating forgetting parameters associated with performance curves

Hewitt, Darlene; Sprague, Kelli; Yearout, Robert; Lisnerski, Donald; Sparks, Catherine

doi:10.1016/0169-8141(92)90035-x

Cited by 26 publications

(5 citation statements)

References 3 publications

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“…In contrast, forgetting for the procedural task is a function of the learning prior to interruption and the elapsed time of the interruption. Hewitt et al (1992) essentially supported the findings of Bailey. More recently, Shafer et al (2001) captured worker forgetting as a function of task tenure, which, in the shop environment studied, affects the worker's amount learned prior to interruption and the interruption interval.…”

Section: Learning and Forgetting Modelsupporting

confidence: 84%

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Performance implications of assembly work teams

McCreery

Krajewski

Leong

et al. 2004

J of Ops Management

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This paper explores the role of selected workforce management practices in developing mix, volume, and product flexibility. Using a model of a manually-paced assembly area, we examine three workforce management practices-the configuration of work teams, the extent of cross training, and the deployment of workers-that have the potential to enhance the level of manufacturing flexibility. We examine the effects of these practices at the level of the individual operation and individual worker, with the goal of maximizing overall system performance in a variety of manufacturing environments. Our results indicate that the value of workforce flexibility is contingent upon characteristics of the operating environment. Environments having high levels of product variety call for the use of a larger number of parallel work teams, while environments with highly complex tasks tend to require a smaller number of parallel teams. Further, the value of cross training and worker task sharing is diminished as work tasks become more complex, due to learning and forgetting effects on the workforce. The overall implication is that more worker flexibility does not always yield improved system performance.

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Section: Learning and Forgetting Modelsupporting

confidence: 84%

“…Finally, the fourth parameter, speed of forgetting, has values based on the controlled experiments of Bailey (1989) and Hewitt et al (1992) that measured the intervals at which complete forgetting occurs, for both simple and complex tasks that use motor and cognitive skills.…”

Section: Learning and Forgetting Modelmentioning

confidence: 99%

Performance implications of assembly work teams

McCreery

Krajewski

Leong

et al. 2004

J of Ops Management

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show abstract

“…The works of Carlson and Rowe (1976), Globerson and Levin (1987), Elmaghraby (1990), and Jaber and Bonney (1996) fall in the first group with some of them were tested against experimental or empirical data. The works of Bailey (1989), Globerson et al (1989), andHewitt et al (1992) are from the second group, where forgetting was modelled using data collected from laboratory experiments performed by students as surrogates of workers in manufacturing environments. Nembhard (2000) and Nembhard and Uzumeri (2000a) cautioned against using these models as they use single rather than multiple breaks when modelling forgetting.…”

Section: Forgetting Modelsmentioning

confidence: 99%

The effect of learning, forgetting, fatigue, and recovery on the performance of dual-resource constrained (DRC) systems

Sadeghigivi¹

2021

Preprint

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Dual-Resource Constrained (DRC) systems consist of two resources: workers and machines (stations). DRCs have become common in manufacturing and service firms that emphasise flexibility, where workers perform different tasks. Although having a flexible workforce is beneficial, it comes at a cost. When workers alternate between different jobs the productivity of the system is affected. On one hand the system becomes more responsive to changes (internal/external), and on the other hand worker productivity and system throughput deteriorate because of the loss of knowledge and workers’ fatigue. This subjects workers to conflicting phenomena. When workers are performing a task they are learning but also accumulating fatigue, which may result in error or injury. When transferred to another task, or on a break, workers may forget what they have learnt but at the same time recover from fatigue, either fully or partially. In particular, forgetting and fatigue are interesting to be considered as they directly affect the quality of products. This research investigates the effects of workers’ learning-forgetting and fatigue-recovery on DRC systems. First, it modifies a known learning-forgetting model by accounting for fatigue and recovery. Second, it assumes that the quality of a production process may deteriorate and generate defective items that require rework. Third, a human error model is developed that considers human learning-forgetting and fatigue-recovery in producing defective items. Fourth, a comprehensive model is developed that integrates learning, forgetting, fatigue, and recovery into a DRC system with quality consideration. This model is investigated for different transfer and flexibility policies. Numerical results provide insights and guidelines that may help operations managers with decisions on how to improve a system’s performance and throughput, while considering worker welfare. Results indicate that it is important to consider workers capabilities and limitations when designing manufacturing systems. They also suggest that ignoring human restrictions and abilities results in unrealistic production planning and erroneous cost estimation.

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“…Individuals performing the complicated tasks involved with AMT operation will forget them at a faster rate during interruptions such as job rotations. The opposite is true for simple tasks (Hewitt et al, 1992). Based on these claims, one might argue that job complexity and specialization are natural complements.…”

Section: Job Factorsmentioning

confidence: 99%

Strategies to foster labor flexibility

Karuppan

2004

International Journal of Productivity and Performance Management

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This empirical study identifies individual, organizational, and job factors of the range-number element of labor flexibility. Tenure and emotional stability are found to increase a worker's flexibility. Emphases on quality, speed, and flexibility (time) also have positive influences on labor flexibility. Finally, task complexity, joint responsibility for decision making, and automation require workers to expand their skill repertoire and therefore enhance their flexibility. This study focuses on a sound measurement of labor flexibility and proposes strategies to cultivate this capability.

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The effects of unequal relearning rates on estimating forgetting parameters associated with performance curves

Cited by 26 publications

References 3 publications

Performance implications of assembly work teams

Performance implications of assembly work teams

The effect of learning, forgetting, fatigue, and recovery on the performance of dual-resource constrained (DRC) systems

Strategies to foster labor flexibility

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