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Sustainable warehouse evaluation with AHPSort traffic light visualization and post-optimal analysis method

Sustainable warehouse evaluation with AHPSort traffic light visualization and post-optimal analysis method
Sustainable warehouse evaluation with AHPSort traffic light visualization and post-optimal analysis method
Sustainable warehousing is essential for organizations to achieve overall supply chain sustainability. Warehousing facilities have the greatest potential for reducing socio-environmental impact. Yet, both research and practice have given relatively less attention to considering all aspects of sustainability in warehouses. In order to address this gap, this study proposes combining both input from professionals and from a literature survey of triple-bottom-line theory in order to develop a sustainable warehouse criteria framework, thus contributing to sustainable organizational warehouse evaluation. The method supporting the evaluation of this framework is based on the integration of a multicriteria AHPSort-traffic light visualization technique and novel post-optimal analysis. Furthermore, the authors deployed this framework and integrated methodology in an Indian manufacturing company to evaluate and classify seven of their warehouses for decision making. The traffic light visualization technique presents and conveys the results better than numbers. Finally, the new post-optimal analysis provides recommendations for cost efficient improvements. The findings of this study present valuable insights and guidelines for industrial managers and practitioners, especially those from the Indian manufacturing industry, for sustainable warehouse decision-making, and for improving their overall corporate sustainability performance.
0160-5682
Ishizaka, Alessio
1e2dac89-e340-4bd0-9b81-210e7fc8beab
Khan, Sharfuddin Ahmed
0d099816-dcae-4f73-8a82-6ebd590e1f1b
Kusi-Sarpong, Simonov
a7e68240-2b34-456e-9849-c01bd10c68f7
Naim, Iram
a884787f-a95e-4497-897c-b1e3b8fa1615
Ishizaka, Alessio
1e2dac89-e340-4bd0-9b81-210e7fc8beab
Khan, Sharfuddin Ahmed
0d099816-dcae-4f73-8a82-6ebd590e1f1b
Kusi-Sarpong, Simonov
a7e68240-2b34-456e-9849-c01bd10c68f7
Naim, Iram
a884787f-a95e-4497-897c-b1e3b8fa1615

Ishizaka, Alessio, Khan, Sharfuddin Ahmed, Kusi-Sarpong, Simonov and Naim, Iram (2020) Sustainable warehouse evaluation with AHPSort traffic light visualization and post-optimal analysis method. Journal of the Operational Research Society. (doi:10.1080/01605682.2020.1848361).

Record type: Article

Abstract

Sustainable warehousing is essential for organizations to achieve overall supply chain sustainability. Warehousing facilities have the greatest potential for reducing socio-environmental impact. Yet, both research and practice have given relatively less attention to considering all aspects of sustainability in warehouses. In order to address this gap, this study proposes combining both input from professionals and from a literature survey of triple-bottom-line theory in order to develop a sustainable warehouse criteria framework, thus contributing to sustainable organizational warehouse evaluation. The method supporting the evaluation of this framework is based on the integration of a multicriteria AHPSort-traffic light visualization technique and novel post-optimal analysis. Furthermore, the authors deployed this framework and integrated methodology in an Indian manufacturing company to evaluate and classify seven of their warehouses for decision making. The traffic light visualization technique presents and conveys the results better than numbers. Finally, the new post-optimal analysis provides recommendations for cost efficient improvements. The findings of this study present valuable insights and guidelines for industrial managers and practitioners, especially those from the Indian manufacturing industry, for sustainable warehouse decision-making, and for improving their overall corporate sustainability performance.

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Accepted/In Press date: 28 October 2020
e-pub ahead of print date: 11 December 2020

Identifiers

Local EPrints ID: 444925
URI: http://eprints.soton.ac.uk/id/eprint/444925
ISSN: 0160-5682
PURE UUID: 6e403ee4-a1d8-4a99-8feb-b3ec3d426ba7
ORCID for Simonov Kusi-Sarpong: ORCID iD orcid.org/0000-0003-1618-2518

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Date deposited: 12 Nov 2020 17:30
Last modified: 28 Apr 2022 04:36

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Contributors

Author: Alessio Ishizaka
Author: Sharfuddin Ahmed Khan
Author: Iram Naim

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