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

Philip Woodall, Vaggelis Giannikas, Wenrong Lu, Duncan McFarlane

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationICIQ 2016: 21st International Conference on Information Quality
Place of PublicationCiudad Real, Spain
Pages1 - 12
Number of pages12
Publication statusPublished - 22 Jun 2016
Event21st Conference on Information Quality - Cuidad Rel, Spain
Duration: 22 Jun 201623 Jun 2016

Conference

Conference21st Conference on Information Quality
Abbreviated titleICIQ 2016
CountrySpain
CityCuidad Rel
Period22/06/1623/06/16

Fingerprint Dive into the research topics of 'Data State Tracking: labelling good quality data to improve warehouse operations'. Together they form a unique fingerprint.

  • Cite this

    Woodall, P., Giannikas, V., Lu, W., & McFarlane, D. (2016). Data State Tracking: labelling good quality data to improve warehouse operations. In ICIQ 2016: 21st International Conference on Information Quality (pp. 1 - 12). [Paper 13].