Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: http://hdl.handle.net/10419/228944
Autoren: 
Janmontree, Jettarat
Zadek, Hartmut
Datum: 
2020
Quellenangabe: 
[Editor:] Jahn, Carlos [Editor:] Kersten, Wolfgang [Editor:] Ringle, Christian M. [Title:] Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 30 [ISBN:] 978-3-7531-2347-9 [Year:] 2020 [Pages:] 3-31
Zusammenfassung: 
Purpose: This research aims to develop a framework of sustainability performance measurement and to propose sustainability impact criteria that can be used to measure complex sustainability impacts in the manufacturing industry. Methodology: Fuzzy-Analytical Hierarchy Process (Fuzzy-AHP) and the Delphi method were used to calculate the weights of sustainability impact criteria. Then, the impact pathway of a life cycle assessment was constructed to illustrate the inter-relationship between each impact criterion. A proposed framework of sustainability performance measurement is presented along with the suggested sustainability impact criteria. Findings: Based on the Delphi method and Fuzzy-AHP, the environmental aspect is the area that has received the highestconcern (49.4%). The important endpoint impact criteria of the environmental aspect consist of Effect on global climate, Ecosystem quality, Animal biodiversity, and Resource management. Originality: The biggest challenge of sustainable development that is yet to be answered is how to measure sustainability performance. The environmental aspect is the area that has received the most attention while the economic and social aspects are still under-represented. To fill the gap, this research proposes a framework of sustainability performance measurement that considers all interrelationships between each sustainability aspect.
Schlagwörter: 
Logistics
Industry 4.0
Supply Chain Management
Sustainability
City Logistics
Maritime Logistics
Data Science
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
https://creativecommons.org/licenses/by-sa/4.0/
Dokumentart: 
Conference Paper

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