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Stinson M (2014). Learning Curves of Temporary Workers in Manual Order Picking Activities. Logistics Journal : Proceedings, Vol. 2014. (urn:nbn:de:0009-14-40574)

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%0 Journal Article
%T Learning Curves of Temporary Workers in Manual Order Picking Activities
%A Stinson, Matthew
%J Logistics Journal : Proceedings
%D 2014
%V 2014
%N 01
%@ 2192-9084
%F stinson2014
%X 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.
%L 620
%K learning curves
%K manual performance evaluation
%K order picking
%R 10.2195/lj_Proc_stinson_en_201411_01
%U http://nbn-resolving.de/urn:nbn:de:0009-14-40574
%U http://dx.doi.org/10.2195/lj_Proc_stinson_en_201411_01
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@Article{stinson2014,
  author = 	"Stinson, Matthew",
  title = 	"Learning Curves of Temporary Workers in Manual Order Picking Activities",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2014",
  volume = 	"2014",
  number = 	"01",
  keywords = 	"learning curves; manual performance evaluation; order picking",
  abstract = 	"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.",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_Proc_stinson_en_201411_01",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-40574"
}
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RIS

TY  - JOUR
AU  - Stinson, Matthew
PY  - 2014
DA  - 2014//
TI  - Learning Curves of Temporary Workers in Manual Order Picking Activities
JO  - Logistics Journal : Proceedings
VL  - 2014
IS  - 01
KW  - learning curves
KW  - manual performance evaluation
KW  - order picking
AB  - 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.
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-40574
DO  - 10.2195/lj_Proc_stinson_en_201411_01
ID  - stinson2014
ER  -
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Wordbib

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<b:Comments>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.</b:Comments>
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ISI

PT Journal
AU Stinson, M
TI Learning Curves of Temporary Workers in Manual Order Picking Activities
SO Logistics Journal : Proceedings
PY 2014
VL 2014
IS 01
DI 10.2195/lj_Proc_stinson_en_201411_01
DE learning curves; manual performance evaluation; order picking
AB 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.
ER
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Mods

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  <abstract>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.</abstract>
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Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2014, Iss. 01
Title

Learning Curves of Temporary Workers in Manual Order Picking Activities (eng)

Lernkurven von Zeitarbeitern in manuellen Kommissioniertätigkeiten (ger)
Author Matthew Stinson
Language eng
Abstract 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. Die Person-zur-Ware-Kommissionierung weist eine hohe Flexibilität auf und ist mit geringen Investiti-onskosten verbunden in Vergleich zu entsprechenden automatisierten Lösungen. Auf diese Eigenschaften ist die Verbreitung traditioneller Kommissioniersysteme in der Distributions- sowie in der Produktionslogistik zurückzuführen. Aufgrund ihres großen Anteils an manuellen Tätigkeiten verursacht die Kommissionierung die höchsten operativen Personalkosten aller intralogistischen Prozesse. Die häufige Anpassung der Personalkapazität eines Kommissioniersystems als Reaktion auf kurz- und mittelfristig schwankende Kapazitätsanforderungen bedingt einen häufigen Personalwechsel. Die ent-sprechend oft anfallende Einarbeitung neuer Mitarbei-ter in Kombination mit einer steigenden Komplexität der Arbeitsinhalte betont die Bedeutung von Lernpro-zessen in der Kommissionierung. In industriellen Anwendungen werden Lerneffekte i. d. R. anhand von Zeit- und Kosteneinsparungen, die sich in Abhängigkeit mit der Erfahrung ergeben, quantifi-ziert. Zu den bekanntesten industriellen Lernkurven-modellen zählen u. a. die Modelle von Wright, de Jong, Baloff und Crossman. Diese Modelle werden typischer-weise zur Beschreibung von Lerneffekten einer Beleg-schaft statt Individuen herangezogen. Die Modelle wurden jeweils in hauptsächlich statischen Arbeitsumgebungen mit homogenen Arbeitsinhalten. Über Lerneffekte in der Kommissionierung ist bis dato wenig bekannt. Hier sind die Arbeitsinhalte heterogen und die Arbeitsstrategien der einzelnen Mitarbeiter un-terschiedlich. Eine Mischung aus kurz- und langfristig eingestellten Kommissionierern mit unterschiedlichen Erfahrungsgraden bedingt die Betrachtung individueller Lernkurven. In diesem Beitrag wird die individuelle Leistungsent-wicklung von Zeitarbeitern in der Kommissionierung analysiert. Diese wird mit der Leistungsentwicklung fest angestellter Kommissionierer im selben Kommissioniersystem verglichen.
Subject learning curves, manual performance evaluation, order picking
DDC 620
Rights fDPPL
URN: urn:nbn:de:0009-14-40574
DOI https://doi.org/10.2195/lj_Proc_stinson_en_201411_01