Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/317358 
Erscheinungsjahr: 
2019
Quellenangabe: 
[Journal:] Journal of Business Economics and Management (JBEM) [ISSN:] 2029-4433 [Volume:] 20 [Issue:] 5 [Year:] 2019 [Pages:] 897-919
Verlag: 
Vilnius Gediminas Technical University, Vilnius
Zusammenfassung: 
The rapid development of e-commerce in China has played a critical role in the development of the national economy and ongoing modernization. The plant industry is unique among industries that employ e-commerce sales models because its products exhibit special characteristics such as high death and damage rates. Therefore, its e-commerce and logistical requirements are stricter than in other industries and, as a result, excess warehouse storage can be extremely difficult for e-commerce-based plant shops to manage. Numerous studies have indicated the need to identify a product's most up-to-date market conditions, as well as the type, function, and size of warehouses. Therefore, based on a case study, this study proposes an optimization plan for solving excess warehouse storage in e-commerce-based plant shops. First, sales volume data of the case company, Enterprise A, were analyzed to predict future sales. Then, entropy and the technique for order preference by similarity to an ideal solution were used to construct the decision-making model. Finally, a cloud warehouse-based optimization plan was proposed to solve excess warehouse storage in e-commerce-based plant shops. This plan can serve as a reference for decision-makers or executives in e-commerce-based plant shops when handling excess warehouse storage.
Schlagwörter: 
plant industry
excess warehouse storage
cloud warehouse
exponential smoothing
multiple criteria decision making (MCDM)
JEL: 
L81
M10
C22
C44
C53
D07
Persistent Identifier der Erstveröffentlichung: 
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Dokumentart: 
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