Journal article
The effects of workforce heterogeneity on productivity in an experiential learning environment
International journal of production research, Vol.46(14), pp.3909-3929
07/15/2008
DOI: 10.1080/00207540600596981
Abstract
This paper investigates the effects of heterogeneity in various workforce learning/forgetting characteristics on productivity. An important aspect of this study is that worker heterogeneity is specifically modeled and examined by including the variance of key parameters as controlled experimental factors in addition to these parameters' means. Initially, we analytically investigate the impact of heterogeneity in each of the four parameters in the learning/forgetting model used in this study. The analytical results are subsequently verified and further investigated using computer simulation. Specifically, seven macro-level factors are investigated including the rate of turnover, the mean and variance of the latent initial expertise existing in the workforce for the tasks to be performed, the mean and variance of worker steady-state productivity levels, the variance of the rate of worker learning, and the variance of working forgetting. The simulation models were developed and informed by empirical data from industry. The results indicate significant effects for mean initial expertise, mean steady-state productivity, the variance of initial expertise, the variance of the rate of worker learning, and the variance of worker forgetting. Results have managerial implications in the comparison and selection of various workforce populations for production systems.
Details
- Title: Subtitle
- The effects of workforce heterogeneity on productivity in an experiential learning environment
- Creators
- David A Nembhard - Pennsylvania State UniversityScott M Shafer - Wake Forest University
- Resource Type
- Journal article
- Publication Details
- International journal of production research, Vol.46(14), pp.3909-3929
- Publisher
- Taylor & Francis Group
- DOI
- 10.1080/00207540600596981
- ISSN
- 0020-7543
- eISSN
- 1366-588X
- Language
- English
- Date published
- 07/15/2008
- Academic Unit
- Business Analytics; Industrial and Systems Engineering
- Record Identifier
- 9984187075802771
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