Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era.

The present work aims to strengthen the core competitiveness of industrial enterprises in the supply chain environment, and enhance the efficiency of inventory management and the utilization rate of inventory resources. First, an analysis is performed on the supply and demand relationship between su...

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Autor principal: Hailan Ran
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Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/2a7897dba02b4c94b0b95670d845a5f0
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spelling oai:doaj.org-article:2a7897dba02b4c94b0b95670d845a5f02021-12-02T20:04:29ZConstruction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era.1932-620310.1371/journal.pone.0259284https://doaj.org/article/2a7897dba02b4c94b0b95670d845a5f02021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259284https://doaj.org/toc/1932-6203The present work aims to strengthen the core competitiveness of industrial enterprises in the supply chain environment, and enhance the efficiency of inventory management and the utilization rate of inventory resources. First, an analysis is performed on the supply and demand relationship between suppliers and manufacturers in the supply chain environment and the production mode of intelligent plant based on cloud manufacturing. It is found that the efficient management of spare parts inventory can effectively reduce costs and improve service levels. On this basis, different prediction methods are proposed for different data types of spare parts demand, which are all verified. Finally, the inventory management system based on cloud-edge collaborative computing is constructed, and the genetic algorithm is selected as a comparison to validate the performance of the system reported here. The experimental results indicate that prediction method based on weighted summation of eigenvalues and fitting proposed here has the smallest error and the best fitting effect in the demand prediction of machine spare parts, and the minimum error after fitting is only 2.2%. Besides, the spare parts demand prediction method can well complete the prediction in the face of three different types of time series of spare parts demand data, and the relative error of prediction is maintained at about 10%. This prediction system can meet the basic requirements of spare parts demand prediction and achieve higher prediction accuracy than the periodic prediction method. Moreover, the inventory management system based on cloud-edge collaborative computing has shorter processing time, higher efficiency, better stability, and better overall performance than genetic algorithm. The research results provide reference and ideas for the application of edge computing in inventory management, which have certain reference significance and application value.Hailan RanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0259284 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Hailan Ran
Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era.
description The present work aims to strengthen the core competitiveness of industrial enterprises in the supply chain environment, and enhance the efficiency of inventory management and the utilization rate of inventory resources. First, an analysis is performed on the supply and demand relationship between suppliers and manufacturers in the supply chain environment and the production mode of intelligent plant based on cloud manufacturing. It is found that the efficient management of spare parts inventory can effectively reduce costs and improve service levels. On this basis, different prediction methods are proposed for different data types of spare parts demand, which are all verified. Finally, the inventory management system based on cloud-edge collaborative computing is constructed, and the genetic algorithm is selected as a comparison to validate the performance of the system reported here. The experimental results indicate that prediction method based on weighted summation of eigenvalues and fitting proposed here has the smallest error and the best fitting effect in the demand prediction of machine spare parts, and the minimum error after fitting is only 2.2%. Besides, the spare parts demand prediction method can well complete the prediction in the face of three different types of time series of spare parts demand data, and the relative error of prediction is maintained at about 10%. This prediction system can meet the basic requirements of spare parts demand prediction and achieve higher prediction accuracy than the periodic prediction method. Moreover, the inventory management system based on cloud-edge collaborative computing has shorter processing time, higher efficiency, better stability, and better overall performance than genetic algorithm. The research results provide reference and ideas for the application of edge computing in inventory management, which have certain reference significance and application value.
format article
author Hailan Ran
author_facet Hailan Ran
author_sort Hailan Ran
title Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era.
title_short Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era.
title_full Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era.
title_fullStr Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era.
title_full_unstemmed Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era.
title_sort construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the internet of things era.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/2a7897dba02b4c94b0b95670d845a5f0
work_keys_str_mv AT hailanran constructionandoptimizationofinventorymanagementsystemviacloudedgecollaborativecomputinginsupplychainenvironmentintheinternetofthingsera
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