A methodology for improving enterprise performance by analysing worker capabilities via simulation

Kernan, Brian Patrick; Lynch, Andrew; Sheahan, Con

doi:10.1080/00207543.2010.527387

Cited by 2 publications

(1 citation statement)

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“…Specifically, they look at finding out the impact of the workers' qualifications on throughput and production time for different machines. Similarly, in the paper on the simulation of worker capabilities (Kernan et al, 2011a), the authors look at upgrading workers' skill-sets to reduce the bottleneck effect of certain skills and increase throughput. However, the work in these two papers aim at determining which worker to best upgrade with which skill, as opposed to simply using worker selection heuristics to improve the status quo, without upgrading existing workers.…”

Section: Introductionmentioning

confidence: 98%

An investigation into heuristics for alternative worker selection in discrete event simulation

Kernan

Sheahan

2013

Journal of Simulation

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In this paper, we analyse four different heuristics for qualified worker selection for machines in discrete event simulation. Conventional simulators simply select a capable worker randomly or from the top-of-the-stack (TOS) of candidates that are qualified to operate a machine, without considering the impact of removing that worker from the current available qualification pool (qPool). To investigate the efficacy of this approach, we compare these random and TOS approaches with two other worker selection rules: least number of qualifications (LENQ), and a heuristic that selects a worker with the lowest impact factor on the qualification pool (LIMP). LIMP ranks workers based on their contribution to the qPool and the constrainedness of each of their qualifications. We apply LENQ to a simulation model of a real company, and compared with the Random heuristic we observe a 44% reduction in the qualification resource constraint metric (RCM q ) and a 2% reduction in the total lateness in sales-order satisfaction. For the LIMP heuristic, the RCM q reduction is 77%. However, LIMP yields no significant improvement in sales-order lateness over the simpler LENQ approach. The LENQ and LIMP heuristics also have the benefit of more closely modelling what happens in reality, as they are based on intuition that would be used in practice, rather than using a random or simple TOS approach followed in conventional simulation.

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Section: Introductionmentioning

confidence: 98%