255 resultados para Kommissorder-picking


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In consequence of rapidly changing market demands companies are permanently encouraged to review their own processes and structures and to modify them. Being one of these developments, order-picking is involved as part of an intra-logistics system. But to take appropriate actions, system performance and system costs have to be measured permanently. Concerning this the use of performance measurement-systems as further development of traditional systems of key figures is suitable. In this paper various performance measurement-systems are compared and their suitability for an implementation in order-picking systems is estimated. On the basis of the result of the evaluation a first concept of a performance measurement-system for order-picking will be developed by using typical key figures that are mentioned in academic literature. Finally, hints for a necessary detailed implementation and evaluation in practice will be given.

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Person-to-stock order picking is highly flexible and requires minimal investment costs in comparison to automated picking solutions. For these reasons, tradi-tional picking is widespread in distribution and production logistics. Due to its typically large proportion of manual activities, picking causes the highest operative personnel costs of all intralogistics process. The required personnel capacity in picking varies short- and mid-term due to capacity requirement fluctuations. These dynamics are often balanced by employing minimal permanent staff and using seasonal help when needed. The resulting high personnel fluctuation necessitates the frequent training of new pickers, which, in combination with in-creasingly complex work contents, highlights the im-portance of learning processes in picking. In industrial settings, learning is often quantified based on diminishing processing time and cost requirements with increasing experience. The best-known industrial learning curve models include those from Wright, de Jong, Baloff and Crossman, which are typically applied to the learning effects of an entire work crew rather than of individuals. These models have been validated in largely static work environments with homogeneous work contents. Little is known of learning effects in picking systems. Here, work contents are heterogeneous and individual work strategies vary among employees. A mix of temporary and steady employees with varying degrees of experience necessitates the observation of individual learning curves. In this paper, the individual picking performance development of temporary employees is analyzed and compared to that of steady employees in the same working environment.

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Ink on linen. Location, type of plantings. At lower right: number 7 in pencil. Signed. 61 cm. x 36 cm. Scale: 1"=10' [from photographic copy by Lance Burgharrdt]

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Yellow and black ink on linen. Location, type of plantings. At lower right: Number 6 in pencil. Signed. 61 cm. x 28 cm. Scale: 1"=10' [from photographic copy by Lance Burgharrdt]

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Saline Valley Farms was an experiment in cooperative farming and living begun in 1932 by Harold S. Gray.

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Saline Valley Farms was an experiment in cooperative farming and living begun in 1932 by Harold S. Gray.

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Saline Valley Farms was an experiment in cooperative farming and living begun in 1932 by Harold S. Gray.

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Saline Valley Farms was an experiment in cooperative farming and living begun in 1932 by Harold S. Gray.

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Saline Valley Farms was an experiment in cooperative farming and living begun in 1932 by Harold S. Gray.

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Mode of access: Internet.