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Overtime schedules for full-time service workers

Listed author(s):
  • Easton, F. F.
  • Rossin, D. F.
Registered author(s):

    Part-time employees help service organizations extend their operating hours and provide extra capacity for peak demand periods. While this strategy tends to increase the number of employees needed to staff the system, part-timers usually earn less per week than full-timers because they don't work as many hours. However, escalating per capita labor expenses have increased the effective hourly wages for part-timers, threatening one of their key advantages. According to government statistics, service sector employees now obtain more labor from overtime work than they do from part-timers. Although the benefits of part-time scheduling policies are well understood, comparatively little research has focused on overtime scheduling policies. Typically, we think of overtime as a means of buffering service systems against supply and demand uncertainty. In this study, however, we demonstrate that scheduled overtime provides many of the same operational advantages of part-time scheduling policies. We evaluate the effects of alternative overtime staffing and scheduling policies on important performance measures such as total labor expense, labor utilization, and workforce size. Compared with standard (40 hours per week) employee schedules, we find that even small amounts of premium-pay overtime work provide significant savings. We also find that the ideal workforce size and proportion of overtime work for a given scheduling policy seem to be relatively insensitive to changes in per capita labor expenses. This means that employers may need much more aggressive overtime scheduling policies to mitigate the effects of rising per capita labor expenses.

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    Article provided by Elsevier in its journal Omega.

    Volume (Year): 25 (1997)
    Issue (Month): 3 (June)
    Pages: 285-299

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    Handle: RePEc:eee:jomega:v:25:y:1997:i:3:p:285-299
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    1. Fred F. Easton & Donald F. Rossin, 1991. "Sufficient Working Subsets for the Tour Scheduling Problem," Management Science, INFORMS, vol. 37(11), pages 1441-1451, November.
    2. Vincent A. Mabert & Charles A. Watts, 1982. "A Simulation Analysis of Tour-Shift Construction Procedures," Management Science, INFORMS, vol. 28(5), pages 520-532, May.
    3. Larry P. Ritzman & LeRoy J. Krajewski & Michael J. Showalter, 1976. "The Disaggregation of Aggregate Manpower Plans," Management Science, INFORMS, vol. 22(11), pages 1204-1214, July.
    4. Ahmad I. Z. Jarrah & Jonathan F. Bard & Anura H. deSilva, 1994. "Solving Large-Scale Tour Scheduling Problems," Management Science, INFORMS, vol. 40(9), pages 1124-1144, September.
    5. Edward P. C. Kao & Maurice Queyranne, 1985. "Budgeting Costs of Nursing in a Hospital," Management Science, INFORMS, vol. 31(5), pages 608-621, May.
    6. Willie B. Henderson & William L. Berry, 1976. "Heuristic Methods for Telephone Operator Shift Scheduling: An Experimental Analysis," Management Science, INFORMS, vol. 22(12), pages 1372-1380, August.
    7. Stephen E. Bechtold & Larry W. Jacobs, 1990. "Implicit Modeling of Flexible Break Assignments in Optimal Shift Scheduling," Management Science, INFORMS, vol. 36(11), pages 1339-1351, November.
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