Data State Tracking: labelling good quality data to improve warehouse operations

Philip Woodall; Vaggelis Giannikas; Wenrong Lu; Duncan McFarlane

Data State Tracking: labelling good quality data to improve warehouse operations

Philip Woodall, Vaggelis Giannikas, Wenrong Lu, Duncan McFarlane

University of Cambridge

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

Abstract

Warehouse Management Systems (WMSs) record where products are stored in a physical warehouse; however, when pickers misplace products the WMS becomes misaligned with the real situation. Due to the fact that the WMS is not aware of these misalignments, it can lead pickers to empty shelves or shelves with the wrong and unexpected products. This paper proposes an extension to a WMS that tracks the state of the quality of the data and modifies picking reports based on this state to help pickers either avoid or discover these misalignments, depending on how it is configured. Using an experimental simulation to evaluate the approach, this paper shows how the solution can outperform a standalone WMS by approximately 1% when trying to avoid misalignments, and by approximately 32% when attempting to discover the misalignments.

Original language	English
Title of host publication	ICIQ 2016: 21st International Conference on Information Quality
Place of Publication	Ciudad Real, Spain
Pages	1 - 12
Number of pages	12
Publication status	Published - 22 Jun 2016
Event	21st Conference on Information Quality - Cuidad Rel, Spain Duration: 22 Jun 2016 → 23 Jun 2016

Conference

Conference	21st Conference on Information Quality
Abbreviated title	ICIQ 2016
Country/Territory	Spain
City	Cuidad Rel
Period	22/06/16 → 23/06/16

Cite this

Data State Tracking: labelling good quality data to improve warehouse operations. / Woodall, Philip; Giannikas, Vaggelis; Lu, Wenrong et al.
ICIQ 2016: 21st International Conference on Information Quality. Ciudad Real, Spain, 2016. p. 1 - 12 Paper 13.

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

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abstract = "Warehouse Management Systems (WMSs) record where products are stored in a physical warehouse; however, when pickers misplace products the WMS becomes misaligned with the real situation. Due to the fact that the WMS is not aware of these misalignments, it can lead pickers to empty shelves or shelves with the wrong and unexpected products. This paper proposes an extension to a WMS that tracks the state of the quality of the data and modifies picking reports based on this state to help pickers either avoid or discover these misalignments, depending on how it is configured. Using an experimental simulation to evaluate the approach, this paper shows how the solution can outperform a standalone WMS by approximately 1% when trying to avoid misalignments, and by approximately 32% when attempting to discover the misalignments. ",

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AU - Lu, Wenrong

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N2 - Warehouse Management Systems (WMSs) record where products are stored in a physical warehouse; however, when pickers misplace products the WMS becomes misaligned with the real situation. Due to the fact that the WMS is not aware of these misalignments, it can lead pickers to empty shelves or shelves with the wrong and unexpected products. This paper proposes an extension to a WMS that tracks the state of the quality of the data and modifies picking reports based on this state to help pickers either avoid or discover these misalignments, depending on how it is configured. Using an experimental simulation to evaluate the approach, this paper shows how the solution can outperform a standalone WMS by approximately 1% when trying to avoid misalignments, and by approximately 32% when attempting to discover the misalignments.

AB - Warehouse Management Systems (WMSs) record where products are stored in a physical warehouse; however, when pickers misplace products the WMS becomes misaligned with the real situation. Due to the fact that the WMS is not aware of these misalignments, it can lead pickers to empty shelves or shelves with the wrong and unexpected products. This paper proposes an extension to a WMS that tracks the state of the quality of the data and modifies picking reports based on this state to help pickers either avoid or discover these misalignments, depending on how it is configured. Using an experimental simulation to evaluate the approach, this paper shows how the solution can outperform a standalone WMS by approximately 1% when trying to avoid misalignments, and by approximately 32% when attempting to discover the misalignments.

M3 - Chapter in a published conference proceeding

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BT - ICIQ 2016: 21st International Conference on Information Quality

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T2 - 21st Conference on Information Quality

Y2 - 22 June 2016 through 23 June 2016

ER -

Data State Tracking: labelling good quality data to improve warehouse operations

Abstract

Conference

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