FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386423087> ?p ?o ?g. }

• W4386423087 endingPage "9983" @default.
• W4386423087 startingPage "9983" @default.
• W4386423087 abstract "Deep learning has experienced an increased demand for its capabilities to categorize and optimize operations and provide higher-accuracy information. For this purpose, the implication of deep learning procedures has been described as a vital tool for the optimization of supply chain firms’ transportation operations, among others. Concerning the indexes of transportation operations of supply chain firms, it has been found that the contribution of big data analytics could be crucial to their optimization. Due to big data analytics’ variety and availability, supply chain firms should investigate their impact on their key transportation indexes in their effort to comprehend the variation of the referred indexes. The authors proceeded with the gathering of the required big data analytics from the most established supply chain firms’ websites, based on their (ROPA), revenue growth, and inventory turn values, and performed correlation and linear regression analyses to extract valuable insights for the next stages of the research. Then, these insights, in the form of statistical coefficients, were inserted into the development of a Hybrid Model (Agent-Based and System Dynamics modeling), with the application of the feedforward neural network (FNN) method for the estimation of specific agents’ behavioral analytical metrics, to produce accurate simulations of the selected key performance transportation indexes of supply chain firms. An increase in the number of website visitors to supply chain firms leads to a 60% enhancement of their key transportation performance indexes, mostly related to transportation expenditure. Moreover, it has been found that increased supply chain firms’ website visibility tends to decrease all of the selected transportation performance indexes (TPIs) by an average amount of 87.7%. The implications of the research outcomes highlight the role of increased website visibility and search engine ranking as a cost-efficient means for reducing specific transportation costs (Freight Expenditure, Inferred Rates, and Truckload Line Haul), thus achieving enhanced operational efficiency and transportation capacity." @default.
• W4386423087 created "2023-09-05" @default.
• W4386423087 creator A5023384878 @default.
• W4386423087 creator A5048905051 @default.
• W4386423087 creator A5049811978 @default.
• W4386423087 creator A5068775302 @default.
• W4386423087 date "2023-09-04" @default.
• W4386423087 modified "2023-09-30" @default.
• W4386423087 title "Engineering Supply Chain Transportation Indexes through Big Data Analytics and Deep Learning" @default.
• W4386423087 cites W1984020445 @default.
• W4386423087 cites W2024237844 @default.
• W4386423087 cites W2118023920 @default.
• W4386423087 cites W2354065779 @default.
• W4386423087 cites W2492270612 @default.
• W4386423087 cites W2505972499 @default.
• W4386423087 cites W2587345921 @default.
• W4386423087 cites W2767547957 @default.
• W4386423087 cites W2772434162 @default.
• W4386423087 cites W2898688472 @default.
• W4386423087 cites W2908216486 @default.
• W4386423087 cites W2923129012 @default.
• W4386423087 cites W2926425062 @default.
• W4386423087 cites W2939663913 @default.
• W4386423087 cites W2940493476 @default.
• W4386423087 cites W2946164520 @default.
• W4386423087 cites W2948646149 @default.
• W4386423087 cites W2965091566 @default.
• W4386423087 cites W2972154887 @default.
• W4386423087 cites W3000189571 @default.
• W4386423087 cites W3035200126 @default.
• W4386423087 cites W3044719873 @default.
• W4386423087 cites W3081491601 @default.
• W4386423087 cites W3091614728 @default.
• W4386423087 cites W3094605956 @default.
• W4386423087 cites W3112072273 @default.
• W4386423087 cites W3118482354 @default.
• W4386423087 cites W3135837256 @default.
• W4386423087 cites W3136509112 @default.
• W4386423087 cites W3142875706 @default.
• W4386423087 cites W3172464731 @default.
• W4386423087 cites W3175479149 @default.
• W4386423087 cites W3186690604 @default.
• W4386423087 cites W3187700419 @default.
• W4386423087 cites W3202602936 @default.
• W4386423087 cites W3207872601 @default.
• W4386423087 cites W4200155742 @default.
• W4386423087 cites W4200579548 @default.
• W4386423087 cites W4210413471 @default.
• W4386423087 cites W4210592510 @default.
• W4386423087 cites W4256520673 @default.
• W4386423087 cites W4312309353 @default.
• W4386423087 cites W4362720771 @default.
• W4386423087 cites W4367299008 @default.
• W4386423087 cites W4383906686 @default.
• W4386423087 doi "https://doi.org/10.3390/app13179983" @default.
• W4386423087 hasPublicationYear "2023" @default.
• W4386423087 type Work @default.
• W4386423087 citedByCount "0" @default.
• W4386423087 crossrefType "journal-article" @default.
• W4386423087 hasAuthorship W4386423087A5023384878 @default.
• W4386423087 hasAuthorship W4386423087A5048905051 @default.
• W4386423087 hasAuthorship W4386423087A5049811978 @default.
• W4386423087 hasAuthorship W4386423087A5068775302 @default.
• W4386423087 hasBestOaLocation W43864230871 @default.
• W4386423087 hasConcept C108713360 @default.
• W4386423087 hasConcept C121955636 @default.
• W4386423087 hasConcept C124101348 @default.
• W4386423087 hasConcept C127413603 @default.
• W4386423087 hasConcept C144133560 @default.
• W4386423087 hasConcept C162853370 @default.
• W4386423087 hasConcept C195487862 @default.
• W4386423087 hasConcept C2522767166 @default.
• W4386423087 hasConcept C26517878 @default.
• W4386423087 hasConcept C38652104 @default.
• W4386423087 hasConcept C41008148 @default.
• W4386423087 hasConcept C42475967 @default.
• W4386423087 hasConcept C44104985 @default.
• W4386423087 hasConcept C75684735 @default.
• W4386423087 hasConcept C79158427 @default.
• W4386423087 hasConceptScore W4386423087C108713360 @default.
• W4386423087 hasConceptScore W4386423087C121955636 @default.
• W4386423087 hasConceptScore W4386423087C124101348 @default.
• W4386423087 hasConceptScore W4386423087C127413603 @default.
• W4386423087 hasConceptScore W4386423087C144133560 @default.
• W4386423087 hasConceptScore W4386423087C162853370 @default.
• W4386423087 hasConceptScore W4386423087C195487862 @default.
• W4386423087 hasConceptScore W4386423087C2522767166 @default.
• W4386423087 hasConceptScore W4386423087C26517878 @default.
• W4386423087 hasConceptScore W4386423087C38652104 @default.
• W4386423087 hasConceptScore W4386423087C41008148 @default.
• W4386423087 hasConceptScore W4386423087C42475967 @default.
• W4386423087 hasConceptScore W4386423087C44104985 @default.
• W4386423087 hasConceptScore W4386423087C75684735 @default.
• W4386423087 hasConceptScore W4386423087C79158427 @default.
• W4386423087 hasIssue "17" @default.
• W4386423087 hasLocation W43864230871 @default.
• W4386423087 hasOpenAccess W4386423087 @default.
• W4386423087 hasPrimaryLocation W43864230871 @default.

• next