Predicting and Analysing E-Logistics Demand in Urban and Rural Areas: An Empirical Approach on Historical Data of China

Huang, Lijuan; Xie, Guojie; Li, Dahao; Zou, Chunfang

doi:10.23940/ijpe.18.07.p19.15501559

Cited by 4 publications

(2 citation statements)

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“…Huang et al [89] compared a backpropagation (BP) neural network and a linear regression analysis for forecasting of e-logistics demand in urban and rural areas in China using data from 1997 to 2015. By comparing mean absolute error (MAE) and the average relative errors of backpropagation neural network and linear regression, they showed that backpropagation neural networks could reach higher accuracy (reflecting lower differences between predicted and actual data).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

2020

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Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and accordingly highlights avenues for future research.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

2020

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“…The ubiquity in internet usage has made positioning and tracking identification and information communication technologies a major part of logistics enterprises [8]. Information infrastructure has become an essential tool in the development process [77,78,79]. For example, in the "Chang-Zhu-Tan" two-oriented social construction area, the use of internet has become a standard infrastructure in its development and urbanization.…”

Section: ) Findingsmentioning

confidence: 99%

Crowd-Sourcing a Way to Sustainable Urban Logistics: What Factors Influence Enterprises’ Willingness to Implement Crowd Logistics?

et al. 2020

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The sharing economy has brought new opportunities to the logistics industry, which has created an emerging trend known as crowd logistics. Implementing this mode offers a basis for more sustainable urban logistics, but there is limited research on what leads enterprises to adopt crowd logistics. Based on the technology-organization-environment (TOE) theoretical model, this paper developed a model to study the influencing factors of enterprises' willingness to implement crowd logistics. The data were collected through questionnaire surveys, SPSS and AMOS were used for data analysis. The empirical results showed that the relative advantage, absorptive capacity, market environment, and external motivations have significant positive impact on the company's willingness to implement crowd logistics, while complexity and resources have no significant impact. Crowd logistics offers an important route to more sustainable urban logistics. Logistics enterprises should take measured steps when implementing crowd logistics, improve their absorptive capacity, and take necessary precautions towards minimizing the risks of crowd logistics.

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Efficiency Improvement Mechanism of Smart Logistics Technology in Rural E-commerce Logistics and Distribution

Yang

2024

Applied Mathematics and Nonlinear Sciences

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This paper addresses the problems existing in the current two-way logistics and distribution model of rural e-commerce and proposes the study of rural e-commerce distribution efficiency with the technical support of intelligent logistics. Based on the knowledge of multi-objective optimization theory, the maximization of distribution efficiency of logistics enterprises and the maximization of demand coverage as the objective function, while the constraints are vehicle load, service time, path constraints, and finally, complete the construction of the multi-objective optimization model of rural e-commerce logistics and distribution efficiency. According to the NSGA-II algorithm with low computational complexity, fast operation speed, and good convergence, the NSGA-II algorithm is used to solve the constructed model, and then the optimal solution of rural e-commerce logistics distribution efficiency is derived. With regional logistics as the research subject, a multi-objective optimization model for rural e-commerce logistics distribution efficiency supported by intelligent logistics technology is simulated and analyzed. The data show that compared with other algorithms, the NSGAII algorithm has a smoother learning curve oscillation, while the final result of convergence (-1.473), the objective function is smaller (20.42), and the optimal total transportation time is smaller (609.22s). Finally, in order to solve the current rural e-commerce logistics and distribution problems in part, from the improvement of rural e-commerce logistics and distribution system, strengthen the rural logistics infrastructure construction of the two aspects of rural e-commerce logistics and distribution efficiency improvement strategy.

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Predicting and Analysing E-Logistics Demand in Urban and Rural Areas: An Empirical Approach on Historical Data of China

Cited by 4 publications

References 12 publications

Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

Crowd-Sourcing a Way to Sustainable Urban Logistics: What Factors Influence Enterprises’ Willingness to Implement Crowd Logistics?

Efficiency Improvement Mechanism of Smart Logistics Technology in Rural E-commerce Logistics and Distribution

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