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A Manpower Allocation Model for Service Jobs

A Manpower Allocation Model for Service Jobs

Isaac Balaila, Issachar Gilad
Copyright: © 2012 |Volume: 3 |Issue: 2 |Pages: 22
ISSN: 1947-959X|EISSN: 1947-9603|EISBN13: 9781466614024|DOI: 10.4018/jssmet.2012040102
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MLA

Balaila, Isaac, and Issachar Gilad. "A Manpower Allocation Model for Service Jobs." IJSSMET vol.3, no.2 2012: pp.13-34. http://doi.org/10.4018/jssmet.2012040102

APA

Balaila, I. & Gilad, I. (2012). A Manpower Allocation Model for Service Jobs. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 3(2), 13-34. http://doi.org/10.4018/jssmet.2012040102

Chicago

Balaila, Isaac, and Issachar Gilad. "A Manpower Allocation Model for Service Jobs," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) 3, no.2: 13-34. http://doi.org/10.4018/jssmet.2012040102

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Abstract

While the work measurement concept has evolved from the manufacturing world, it has not been fully adopted yet to the global shift into the service sector. Certain factors create inherent difficulties in determining time standards for manpower allocation in service jobs: (a) wide variation in Time Between Arrivals and Service Performance Time and (b) the difficulty of assessing the damage done to the organization by long customer Waiting Times (WT) for service. This difficulty makes it hard to calculate the Break-Even Point (BEP) between raising worker output, which minimizes labor costs but increases customer WT, and improving service quality by lowering customer WT. The model proposed overcomes most of the difficulties by taking a multi-domain approach to the problem: 1) The model deploys a series of indicators for a correlation between output and WT. The indicator values are affected by service level of urgency and the initial number of service workers and 2) Cost-Benefit – finding the best BEP by comparing the operational cost of an additional worker with the economical benefit caused by the decrease in WT at the margin. Thus, the model finds the best balance between worker output and service quality.

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