Logistics Demand Forecasting Research Articles

Leveraging Artificial Intelligence in Supply Chain Execution in E-commerce

Modern perspective of consumer satisfaction has changed due to the proliferation of online businesses. E-commerce has opened up a whole new world to the consumers. The consumers can now get what they want, when they want, delivered at their doorstep. While the ease of online shopping has increased consumerism and consumer demand, it poses significant challenges for the e-commerce players as the consumers are seldom willing to shell out money for availing the convenience of doorstep deliveries. Under such circumstances, the e-commerce players are left with little choices but to significantly enhance their supply chain efficiencies through the improvement of logistics and the delivery services they offer to their customers. Artificial Intelligence or AI, as it is popularly known, is altering the ways and means of these e-commerce businesses. It is changing the manner in which supply chains are managed. Through the use of smart algorithms, data analysis, and automation, it is possible for businesses to employ AI effectively for the optimization of their supply chain management involving inventory and logistics management, demand forecasting, and customer service. This article focuses on logistics management and the application of AI to optimize costs and efficiency in first mile, middle mile and last mile delivery.

Cold Chain Logistics Demand Forecasting for Fresh Agricultural Foods in Fujian Province, China

China's current cold chain infrastructure for fresh agricultural products faces numerous challenges, particularly within the context of Fujian Province. The cold chain logistics sector in the region is characterized by limited development and requires immediate improvements in its foundational supporting infrastructure. The establishment of a comprehensive cold chain logistics system tailored for fresh agricultural goods remains incomplete, resulting in inefficiencies within the supply network. An in-depth examination of the necessity for refrigerated transportation networks for fresh agricultural products through scientific inquiry reveals the potential for strategic investments in the industry. To address this gap, a study employing the GM (1,1) model is conducted to forecast the future demand for cold chain logistics in fresh agricultural items specifically within Fujian Province, China, over the next five years. The findings of the study indicate that by 2027, the demand for cold chain logistics services for fresh produce in Fujian Province is projected to reach 4765.6 million tons. These insights furnish valuable information for optimizing investment planning in cold chain logistics infrastructure and formulating pertinent legislative measures to stimulate industry growth. In summary, the integration of these findings into the context of Fujian Province underscores the significance of enhancing cold chain logistics capabilities to address existing challenges and capitalize on future opportunities within the region's fresh agricultural sector.

Logistics demand forecasting: a literature review

E-commerce has had significant growth in recent years, generating increased transportation demand and bringing challenges related to logistics resources and infrastructure planning. This paper develops a literature review on logistics demand forecasting, based on bibliometric analysis and systematic review of 48 selected papers. Results show that, when using a public sector point of view, there is a concentration of papers focused on long-term, high spatial aggregation models. The opposite happens when employing a private sector perspective, with more short-term and lower spatial aggregation. As a method, neural networks are gaining popularity. Additionally, mid-term logistics demand forecasting is little explored.

Novel grey forecasting model with bi-level structure for application to logistic demand

With the increasing demand for Chinese cold chain logistics, accurate forecasting becomes significantly important for decision-making and planning, but the data scarcity is still a challenging problem for modeling method. This paper proposes a novel grey prediction model with bi-level structure to improve the performance of cold chain logistics demand forecasting. The model adapts to the data characteristics and generates corresponding background series to adjust the parameters. A differential evolutionary algorithm is employed to merge the time response functions generated from the bi-level structure. The proposed model extends the applicability compared with existing similar models and can provide higher accuracy for data sequences with a high developing rate. The model outperforms the classic grey model and its extensions in terms of forecasting accuracy. The trend of Chinese cold chain logistics demand from 2021 to 2025 is studied with the proposed model. The computational results show that the proposed grey prediction model is significantly better than other models.

Overview of Logistics Demand Forecasting Methods

Accurate forecasting of logistics demand is of great theoretical significance and practical application value for the formulation of national policies and the satisfaction of actual demand in the logistics industry. From the modeling form, the existing logistics demand forecasting methods are divided into four categories: single traditional forecasting method, single intelligent forecasting method, combined forecasting method and mixed forecasting method. Among them, single traditional forecasting methods mainly include simple time series method, regression analysis, mathematical and statistical methods, etc.; single intelligent forecasting methods mainly involve gray forecasting method, neural network, support vector machine and their improved forms; combined forecasting methods are mainly summarized into three combined forms: linear combination of single forecasting results, nonlinear combination of single forecasting results, modified single forecasting results; mixed forecasting methods are mainly summarized into three hybrid forms: hybrid intelligent optimization algorithms with single prediction methods, hybrid data dimensionality reduction techniques with intelligent prediction methods, and hybrid data mining techniques with intelligent prediction methods. The four major types of forecasting methods are reviewed, and each forecasting model in the four major types of methods is evaluated in terms of modeling principles, advantages and disadvantages, and applicability, in order to find forecasting methods suitable for different logistics demand forecasting tasks for logistics demand researchers.

Demand Forecast of Railway Transportation Logistics Supply Chain Based on Machine Learning Model

The deep learning method based on long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) was constructed by researching the factors affecting railway transportation logistics. Moreover, a simulation study on Tianjin Station was conducted. The deep learning model suitable for the logistics demand forecasting of Tianjin Station was established, and the changing trend of logistics supply chain demand in Tianjin Station in the future was analyzed. Moreover, a strategy for railway construction and regional cooperation was proposed. In this study, three deep learning neural networks, namely LSTM, GRU, and Bi-LSTM, were used to construct a demand forecasting model for the logistics supply chain in Tianjin Station. Bi-LSTM, which has bidirectional storage performance and the highest prediction accuracy, is superior to the traditional neural network structure in terms of period and fluctuation.

A Novel Integrated Fuzzy-Rough MCDM Model for Assessment of Barriers Related to Smart Logistics Applications and Demand Forecasting Method in the COVID-19 Period

During the pandemic period, smart logistics applications have rapidly changed the way organizations do business in order to provide competitive products and services while still remaining flexible. Smart logistics applications and demand forecasting, which have an important place in ensuring customer satisfaction and increasing competitive advantage, came to the fore even more in this period. However, smart logistics applications are often bogged down by several barriers, and then there is the need to choose the most ideal demand forecasting method despite these barriers. The main purpose of this study is to assess the barriers to the smart logistics applications in companies that receive and provide logistics services with corporate identity in Ordu Province, and to choose the most ideal demand forecasting method during the COVID-19 period. This study has the characteristic of a roadmap that helps the construction of smart logistics transformation applications by detecting barriers related to smart logistics applications and determining the most ideal demand forecasting alternative in logistics sector. Fuzzy FUCOM (FUll COnsistency Method)-based interval rough EDAS (Evaluation based on Distance from Average Solution) methodology was used to weight the barriers and to rank and choose the most ideal demand forecasting method during COVID-19 period, respectively.

Logistics Demand Forecast of Fresh Food E-Commerce Based on Bi-LSTM Model

Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales data of a fresh food e-commerce enterprise as the logistics demand, analyzes the influence of time and meteorological factors on the demand, extracts the characteristic factors with greater influence, and proposes a logistics demand forecast scheme of fresh food e-commerce based on the Bi-LSTM model. The scheme is compared with other schemes based on the BP neural network and LSTM neural network models. The experimental results show that the Bi-LSTM model has good prediction performance on the problem of logistics demand prediction. This facilitates further research on some supply chain issues, such as business decision-making, inventory control, and logistics capacity planning.

Forecasting the Logistics Demand of Guangxi Beibu Gulf Port

Objective - The objective of this paper is to establish the forecasting models of port cargo throughput and container throughput in Guangxi Beibu Gulf Port in the next 5 years, and to put forward the countermeasures of port logistics development in Guangxi Beibu Gulf Port according to the forecast results. Methodology/Technique – The data of cargo throughput and container throughput of Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou in 2009-2020 are collected through the data of Guangxi Statistical Yearbook and Guangxi Statistical Bulletin. Based on 2019 and 2020, the forecasting models of cargo throughput and container throughput in Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou are establishe using a weighted moving average forecasting method. The cargo throughput and container throughput of Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou in 2020/2021-2025 are predicted. Findings – The forecast results show that by 2025, the cargo throughput of Guangxi Beibu Gulf Port is expected to exceed 400 million tons, and the container throughput is expected to exceed 10 million TEU. According to the fitting diagram of forecast results and actual data, it can be seen that the accuracy of the forecast results is very high. Novelty – It is innovative to select 2 base years in 2019 and 2020 to establish forecasting model. Based on the comparative analysis of the forecast results, this paper puts forward various measures to promote the development of port logistics of Guangxi Beibu Gulf port, such as strengthening the construction of port self-condition, strengthening the co-ordinated development of port and economic hinterland, speeding up the construction of port collection and distribution system, training and introducing all kinds of high-quality port logistics talents. Type of Paper: Empirical. JEL Classification: C53, R41. Keywords: Logistics Demand Forecast; Cargo Throughput Forecast; Container Throughput Forecast; Weighted Moving Average Forecasting Method; Guangxi Beibu Gulf Port Reference to this paper should be made as follows: Yue, N. (2021). Forecasting the Logistics Demand of Guangxi Beibu Gulf Port, GATR Global J. Bus. Soc. Sci. Review, 9(1): 73 – 89. https://doi.org/10.35609/gjbssr.2021.9.1(9)

Regional logistics demand forecasting: a BP neural network approach

With the rapid development of e-commerce, the backlog of distribution orders, insufficient logistics capacity and other issues are becoming more and more serious. It is very significant for e-commerce platforms and logistics enterprises to clarify the demand of logistics. To meet this need, a forecasting indicator system of Guangdong logistics demand was constructed from the perspective of e-commerce. The GM (1, 1) model and Back Propagation (BP) neural network model were used to simulate and forecast the logistics demand of Guangdong province from 2000 to 2019. The results show that the Guangdong logistics demand forecasting indicator system has good applicability. Compared with the GM (1, 1) model, the BP neural network model has smaller prediction error and more stable prediction results. Based on the results of the study, it is the recommendation of the authors that e-commerce platforms and logistics enterprises should pay attention to the prediction of regional logistics demand, choose scientific forecasting methods, and encourage the implementation of new distribution modes.

Forecasting the Logistics Demand of Guangxi Beibu Gulf Port

In February 2007, Guangxi Zhuang Autonomous Region People's Government integrated Fangcheng, Qinzhou and Beihai three coastal ports to establish Guangxi Beibu Gulf International Port Group Co., LTD. At this point, Guangxi Beibu Gulf Port integration of three, Guangxi Beibu Gulf Port ushered in a major development opportunity. Guangxi Beibu Gulf Port is one of the important ports in China's coastal cities open to the outside world. It is the meeting point of the three economic circles of South China, Southwest China and ASEAN, and also the most convenient land and sea passage between China and ASEAN countries. With the vigorous implementation of the "Belt and Road" initiative and the construction of new land and sea passages in western China, Guangxi Beibu Gulf Port has become an important gateway and connection point for the implementation of these strategies. Its strategic status keeps rising, and Guangxi Beibu Gulf Port has also developed rapidly. In recent years, how is the port logistics development of Guangxi Beibu Gulf Port? What are the changing rules and trends of its cargo throughput and container throughput? What measures can continuously and effectively promote the port logistics development of Guangxi Beibu Gulf Port? This paper will carry out research and analysis on these problems in order to better promote the healthy development of Guangxi Beibu Gulf Port. Keywords: Logistics demand forecast, Cargo throughput forecast, Container throughput forecast, Weighted moving average prediction method, Guangxi Beibu Gulf Port

NEW APPLICATOIN OF LOGISTICS PROCESSING

The report analyzes the possibility of using analytic hierarchy process in logistics demand forecasting. It proposes a method to estimate parameters of the individual values of the analysis.

Logistics Demand Forecast Model for Port Import and Export in Coastal Area

Yang, D., 2020. Logistics demand forecast model for port import and export in coastal area. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 678–681. Coconut Creek (Florida), ISSN 0749-0208.Port logistics has become one of the main logistics modes. In order to ensure the stability of its development, logistics demand forecasting model is often used to predict port logistics, but the forecasting error is large. Therefore, the coastal port import and export logistics demand forecasting model is designed. This paper analyzes the influencing factors of port import and export logistics demand, obtains the forecast index of port import and export logistics demand, and realizes port import and export logistics demand forecast by using BP neural network. So far, the coastal port import and export logistics demand forecasting model has been constructed. The experimental results show that the error between the prediction results of this model and the actual results is smaller, and the error between the prediction results of the original model and the actual results is larger. Thus, the coastal port import and export logistics demand forecasting model is more accurate, the use of better results.

Research on Logistics Demand Forecast in Southeast Asia

This article predicts Southeast Asia’s logistics needs from a Southeast Asian logistics development perspective. This is not only an important prerequisite for supporting Southeast Asia’s trade policy, but also promoting the development of Southeast Asia’s logistics industry, building logistics infrastructure and improving the level of logistics services. Due to differences in economic development levels, trade structures, infrastructure construction and logistics development levels of Southeast Asian countries. Therefore, considering the actual situation of Southeast Asian countries, this article selected 21 cities in Southeast Asia as the research object. Use L-OD logistics demand forecasting method to forecast logistics demand in Southeast Asia. Obtain the amount of logistics occurrence and attraction in 21 cities in Southeast Asia in the future. And construct a double constrained gravity model to predict logistics distribution in Southeast Asia. The forecast results provide scientific data support for future logistics development planning in Southeast Asia.

Logistics demand forecasting model based on improved neural network algorithm

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

Research on Regional Logistics Demand Forecast Based on Improved Support Vector Machine: A Case Study of Qingdao City under the New Free Trade Zone Strategy

Based on the analysis on the influencing factors of urban logistics demand, this paper, taking into account the logistics demand with non-linear and small sample modeling characteristics from the perspective of urban freight volume, introduces the ant colony algorithm into the modeling process to optimize the penalty parameter “c” and “g” parameter of Radial Basis Function in support vector machine, and has made a prediction to the logistics demand of Qingdao with the optimized support vector machine model. The experimental results show that the prediction results of the improved support vector machine can bring the prediction closer to the reality with their more accuracy, stronger stability and less error rate, thus providing a guarantee for the logistics demand forecast of Qingdao.

Research on Logistics Demand Forecast based on the Combination of Grey GM (1, 1) and BP Neural Network

The steady development of China’s economy has led to the rapid development of the logistics industry. Nowadays, the logistics efficiency in the world has been at a high position, but compared with advanced developed countries, logistics costs are still higher. Establishing an effective logistics demand forecasting model is of great significance to reduce logistics costs and optimize the layout. This paper establishes a combined model based on the research of grey GM (1, 1) and BP neural network considering the scope of application and error. Using the combined model to fit the freight volume data of China in the past 20 years, the results show that the combined model has less error and higher precision in the forecasting of logistics demand.

Research on Logistics Demand Forecast of Port Based on Combined Model

In order to improve the accuracy, practicability and prediction accuracy of the single prediction model, a logistics demand combination forecasting model based on cargo throughput is established. Based on the original statistical data of throughput, the two models of gray and exponential smoothing are established respectively. On this basis, the weighted assignment method of variance reciprocal is used to construct the combined forecasting model. According to the prediction results of different prediction models analyzed by the three evaluation indexes of average relative error, maximum fitting error and minimum fitting error, the three evaluation indexes of the combined prediction model are respectively 6.344%, 16.345% and 0.343%, which are smaller than the single item model. It indicates that the established combined forecasting model can effectively improve the accuracy of the throughput prediction model based on overcoming the shortcomings of the single-term throughput prediction model.

Research on the Demand Forecasting Method of Sichuan Social Logistics Based on Positive Weight Combination

The macro-social logistics demand forecast is of great strategic significance to optimize the national or regional economic structure, improve the investment environment and improve the overall competitiveness of regional economy. In this study, the total amount of social logistics in Sichuan province was selected to reflect the social logistics demand, the factors influencing the social logistics demand in Sichuan province were analyzed, and eight economic indicators were summarized. This study first USES the time series prediction model (including the time response model GM (1, 1)), an exponential smoothing model, causal relation model (including multidimensional prediction model GM (1, n) and BP neural network model), to build four methods combination model, weight given solution of linear programming each forecast model, the forecasting result of combination forecast model deviation is minimal. The posterior difference test was applied to the above five models to compare the prediction results of each prediction method.

A Method for Forecasting Regional Logistics Demand Based on BP Neural Network

Regional logistics demand forecast is an important part of the regional economy which can be developed well and fast, the regional economy is the decisive force to the regional logistics development, so it is feasible to predict the regional logistics demand by regional economic index. In this paper, we build the BP neural network model based on regional economic indices to forecast the regional logistics demand. The simulation results show that the model can reflect the nonlinear relationship between the regional logistics and regional economy, which causes the regional logistics demand forecast results are more reasonable.