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Journal Article

Citation

Boonstra-Hörwein K, Punzengruber D, Gartner J. Appl. Ergon. 2011; 42(2): 233-237.

Affiliation

XIMES GmbH, Hollandstrasse 12, 1020 Vienna, Austria.

Copyright

(Copyright © 2011, Elsevier Publishing)

DOI

10.1016/j.apergo.2010.06.008

PMID

20708728

Abstract

The ergonomic quality of shift schedules can be improved by reducing time periods with understaffing (resulting in work-pressure, poor quality, etc.) and evening, night and/or weekend work. Improving the quality of forecasts regarding future workforce requirements as well as the optimization of work processes by moving as much work as possible to more suitable time zones are two approaches to this. We introduce and propose Temporal Profile Optimization (TPO) as a systematic approach to question the demand as well as its translation to workforce planning. Temporal profiles describe the number of employees needed over time (e.g. for different days of the week, times of day, for different calendar days) as well as the shift-times and staffing levels planned to meet this workforce demand. With Temporal Profile Forecasts we introduce a forecasting method that is based on time-stamped historical data and methodologically supplements traditional time series models like SARIMA in many ways. With Temporal Profile Reengineering we use systematic and often participatory methods from business process reengineering to identify moveable work and streamline the load lines by (re-)distributing movable work such that shifts and schedules are improved. The approach is illustrated along two business cases. Using TP-Forecasts for air traffic controllers increased forecasting accuracy whereby a different shift design was possible resulting in 3-4% less shift work. In a warehouse of an Austrian freight carrier a TP-Forecast together with TP-Reengineering helped to rearrange work processes such that the resulting workforce requirements curve had a more even form. This allowed for shorter shifts than before (thereby decreasing overtime). Experiences made so far stress the potential of Temporal Profile Optimization.


Language: en

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