Fengjiao Wan. Jianghan University, Wuhan, China

Transcription

1 China-USA Business Review, June 2017, Vol. 16, No. 6, doi: / / D DAVID PUBLISHING Study on the Influence Factors of Logistics Demand in China Based on Grey Correlation Degree Model Fengjiao Wan Jianghan University, Wuhan, China In order to study on the relationship between the logistics demand in China and its related influencing factors, this paper finds out the key influence factors of affecting the logistics demand, and then provides decision for the development of logistics industry in china. This paper analyzes the concrete influence factors of logistics demand from the qualitative aspect, selects 15 indicators from infrastructure factors, economic factors, and information network influencing factors, collects the index data and freight turnover of in China, and then uses grey correlation model to study the grey correlation between logistics demand in China and its related influencing factors. Keywords: logistics demand, grey correlation degree, freight turnover Introduction The logistics industry is an important part of social and economic activity. Logistics demand is the important premise for the development of the logistics industry. Because China s logistics industry started relatively late, lack of the relevant data, this paper analyzes the influence factors of logistics demand from the qualitative aspect, and then gives the influencing index factors of logistics demand. On this basis, this paper uses grey relation analysis to study inner link between logistics demand and its related influence factors, in order to provide decision support for the development of logistics industry in China. In the literature, several researchers have studied logistics demand. There are also various contributions about grey correlation analysis in logistics field. Chen, Liu, and Hu (2010) analyzed the grey relation between comprehensive bonded zones and regional economic development, then demonstrated that the former had much bigger role than the latter. Zhang (2011) used grey correlation analysis method for logistics development influence factors. Gao and Zhang (2013) used grey correlation model to analyze the factors which influence the regional logistics development in Zhejiang province. Lian, Yang, and Xu (2014) selected 20 cities logistics development level index from the urban logistics demand, logistics supply, and logistics information construction, used grey correlation analysis method to filter and build index system, and then used factor analysis method to comprehend evaluation of regional urban logistics level. M. F. Zhang and L. Zhang (2015) Project Supported: Institute of Wuhan Studies funded projects (IWHS ); Hubei humanities and social science research base Manufacturing Industry Development Research Center on Wuhan City Circle funded projects. Fengjiao Wan, associate professor, School of Business, Manufacturing Industry Development Research Center on Wuhan City Circle of Jianghan University, Wuhan, China. Correspondence concerning this article should be addressed to Fengjiao Wan, No. 8 Triangle Lake Road, Wuhan Economic and Technological Development Zone, Wuhan, China.

2 280 STUDY ON THE INFLUENCE FACTORS OF LOGISTICS DEMAND IN CHINA used grey correlation model to study the relationship between regional logistics demand and regional economy development. Dou (2017) used the grey model correlation model to study grey correlation between the Belt and Road strategy of Gansu logistics demand and its influencing factors. Methodology According to the Iceberg theory, the logistics system is a grey system. It has obvious randomness and uncertainty. The relationship between influence factors of regional logistics and the related factors is always changing. It has obvious timeliness. Thus, analyzing logistics demand is a challenging task due to the uncertainty in the factors. Deng (1987) has put forward grey system theory. The grey system theory focuses on solving the small sample, poor information, and uncertain problem of fuzzy mathematics, probability, and statistics. Considering these characteristics of logistics demand and the advantages of grey system theory, grey correlation model is applied on the relationship between logistics and the related influence factors. Set X 0 X 0 1, X 0 2,, X 0 n for the behavior sequence of system characteristics. That is freight turnover indexes historical data of the logistics demand. In addition, X 1, X 2,, X n is N influence factors, the data columns that reflect the characteristics of the changes in various factors are respectively x 1 t, x 2 t,, x I t t 1, 2,, n. That is the influence factors historical data, which are related to freight turnover. The sequence of initial value is: X i ' X i n / X i 1 X i ' 1, X i ' 2,, X i ' n, i 1, 2,, I (1) To solve the absolute difference sequence between initial value of system characteristic behavior sequence and related factors initial value: i k X 0 ' k x i ' k, I i 1, i 2,, i n, i 1, 2,, I (2) To solve the maximum difference and minimum difference: M max i max k i k, m min i min k i k (3) According to the maximum and minimum difference it can solve the correlation coefficient: γ 0i k m εm / i k εm, ε 0, 1, k 1, 2,, n, i 1, 2,, I (4) According to calculated correlation coefficient between each index on each moment and freight turnover, the correlation index between the influencing factors and freight turnover is: γ 0i = 1/n I k=1 γ 0i (k); i 1,2,, I (5) ε generally takes 0.5. According to calculated correlation, it can determine the correlation between the different indicators and logistics demand (freight turnover). That can provide decision support to select index and analyze relationship between the mutual influencing factors and logistics demand. Grey Correlation Analysis of Logistics Demand Influence Factors The Index of Logistics Affecting Factors Commodity circulation depends on logistics. Therefore, this paper chooses the freight turnover as special factors to extract the dependent variable for research. Freight turnover: it is the sum of carrying quantity of the goods by various means of transport and its corresponding transportation distance in a certain period. It is an important index of transportation industry. Transport distance can calculate on billing distance. It extracts the freight related influencing factors from infrastructure factors, economic factors, and the information network factors respectively.

3 STUDY ON THE INFLUENCE FACTORS OF LOGISTICS DEMAND IN CHINA Infrastructure factors Infrastructure interconnectivity will boost the development of logistics industry. Logistics infrastructure includes transportation channel facilities and storage facilities. Logistics includes five transportations, which are water transportation, highway, railway, aviation, and pipeline. Therefore, in the grey relational analysis, it uses the road transportation mileage, miles of railway transportation, inland waterway transportation mileage, regular flight routes and pipeline (gas) mileage as well as the whole of society fixed assets investment of transportation, warehousing and postal service as a measure to analyze the infrastructure influence factors of logistics development. 2. Economic factors Under the rapid economic development, logistics as a derived part of the economy will certainly have a huge impact. At present, the national economic development strategy is the following three points: (1) Expanding domestic demand; (2) Expand exports; (3) The change of the economic structure. According to these effects, we extract the following factors as economic impact factors of logistics industry. (1) Gross domestic product (GDP), the total retail sales of social consumer goods; (2) The first, second, and third industry added value; Industry benefit is important factor as a measure of the economic structure change that will influence the development of logistics industry. (3) The total import and export of goods. 3. The information network factors Information network factor is an important part for the development of logistics industry; therefore, the information network factor includes: mobile phone users, post and telecommunications business volume and the number of Internet users. Grey Correlation Analysis In the process of grey correlation, the index data give a variable name as shown in Table 1, among them, the freight turnover variable name X 0. Table 1 The Index of Logistics and Its Related Influencing Factors Influence factors The first indicators Infrastructure factors Economic factors Information network factors The secondary indicators The variable name Highway transportation miles (thousands of kilometers) X1 Railway transportation miles (thousands of kilometers) X2 Inland waterway transport miles (kilometers) X3 Regular flight routes (thousands of kilometers) X4 Pipeline oil (gas) miles (thousands of kilometers) X5 Whole society fixed asset investment of transportation, warehousing and postal service (one hundred million yuan) X6 Gross domestic product (one hundred million yuan) X7 The added value of the first, second, and third industry (one hundred million yuan) X8, X9, X10 Total retail sales of social consumer goods (one hundred million yuan) X11 Total import and export of goods (one hundred million yuan) X12 Mobile phone users (thousands of families) X13 Business total of posts and telecommunications (one hundred million yuan) X14 Internet users (ten thousand people) X15

4 282 STUDY ON THE INFLUENCE FACTORS OF LOGISTICS DEMAND IN CHINA Specific data as shown in Table 2: Table 2 Historical Data of Logistics and Its Related Influencing Factors Year X 0 X1 X2 X3 X4 X5 X6 X , ,317, , , , ,637, , , , ,106, , , , ,280, , , , ,490, , , , ,765, , , , ,345, , , , ,461, , , , ,342, , , , ,113, , ,438.5 Year X8 X9 X10 X11 X12 X13 X14 X , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,974 46, , ,700 Source: 2016 national statistical yearbook. Due to the difference of units, this paper standardizes the above indicators, the results as shown in Table 3. Table 3 Standardized Data Year X 0 X 1 X 2 X 3 X 4 X 5 X 6 X

5 STUDY ON THE INFLUENCE FACTORS OF LOGISTICS DEMAND IN CHINA 283 Table 3 to be continued Year X8 X9 X10 X11 X12 X13 X14 X According to the formula (2), the absolute difference sequence between initial value of system characteristic behavior sequence and related factors initial value as shown in Table 4: Table 4 Absolute Difference Sequence Year X1 X2 X3 X4 X5 X6 X7 X Year X9 X10 X11 X12 X13 X14 X Among them, according to the formula (3), it can find the two maximum differences M = , two minimum differences of m = 0, ε = 0.5. Based on grey correlation formula (4), it can calculate the grey correlation coefficient matrix between freight volume and the related indicators is shown in Table 5:

6 284 STUDY ON THE INFLUENCE FACTORS OF LOGISTICS DEMAND IN CHINA Table 5 Grey Correlation Coefficient Matrix Year X1 X2 X3 X4 X5 X6 X7 X Year X9 X10 X11 X12 X13 X14 X According to the formula (5), the grey correlation degree is calculated, the results as shown in Table 6: Table 6 Grey Correlation Degree Between Freight Turnover X 0 and Various Influencing Factors Influence factors The variable name Correlation degree Highway transportation miles X Railway transportation miles X Inland waterway transport miles X Regular flight routes X Pipeline oil (gas) miles X Whole society fixed asset investment of transportation, warehousing and postal service X Gross domestic product X The added value of the first industry X The added value of the second industry X The added value of the third industry X Total retail sales of social consumer goods X Total import and export of goods X Mobile phone users X Business total of posts and telecommunications X Internet users X From the calculation result, grey correlation degree between freight turnover in the logistics demand and various influencing factors can be ordered by category from big to small:

7 STUDY ON THE INFLUENCE FACTORS OF LOGISTICS DEMAND IN CHINA 285 Infrastructure factors: γ 05 γ 02 γ 01 γ 03 γ 04 γ 06 ; Economy factors: γ 012 γ 09 γ 08 γ 07 γ 010 γ 011 ; Information network factors: γ 013 γ 015 γ 014. Conclusions This paper analyzes the influence factors on the domestic logistics demand based on grey correlation model, and can understand the influence extent of each index of domestic logistics demand. (1) There are many factors to influence logistics demand in China. They have strong correlation system. Although the paper has calculated the correlation of various factors, and sorted them by the influence degree of their influence, they have little difference, statistical error of the sample and imperfect will lead to the gap between calculation results and reality. In a certain extent, this paper also has reflected that the logistics demand development is influenced by these factors together. (2) In the infrastructure influence factors, the development of the five major modes of transportation has great impact on the logistics demand; one of the biggest impact factors is pipeline transportation. With the widely application of the pipeline in recent years, it has promoted the rapid development of logistics industry. In the current information age, the traditional mode of transportation has hindered the development of society. Urban traffic congestion, environmental pollution, and e-commerce logistics bottlenecks are also the primary problems in big cities. Therefore, the advantage of pipeline transportation has been highlighted. The second impact factor is the railway transportation. Therefore, in the future, China can continue to increase investment on infrastructure, and promote the development of logistics industry. (3) In economic factors, the grey correlation degree between freight turnover and the total import and export of the goods, the added value of the second industry, the added value of first industry and gross domestic product is at the top respectively. This suggests that the logistics demand has strong big impact on the international trade and industrial structure adjustment. The grey correlation degree between freight turnover and the added value of the third industry, total retail sales of social consumer goods is low; it means that logistics service socialization in the field of consumption and circulation is not high. In the future development, China can adjust the industrial structure, improve logistics service socialization, and increase the proportion of the third industry in the economic and so on. It can promote the continuous development of logistics industry. (4) In the information network factors, grey correlation degree between freight turnover and mobile phone users, internet users has a higher level. With the popularity of e-commerce, online shopping becomes a trend, these factors have a great impact on promoting logistics demand. Therefore, in order to promote the development of logistics, it needs to enhance consumption ability, perfect sales channels, construct information network platform, and expand the influence of logistics. References Chen, N., Liu, Y., & Hu, H. Q. (2010). Grey relational analysis between comprehensive bonded zone and regional economic development. Environmental Science and Information Application Technology (ESIAT), 2010 International Conference on IEEE, Dou, J. (2017). Study on the influencing factors of logistics demand in Gansu based on grey relational degree model In the background of the Belt and Road. Logistics Engineering and Management, (2), Gao, S., & Zhang, X. F. (2013). Research on the Influencing factors of regional logistics development in Zhejiang province based on gray correlation analysis. Journal of Modern Logistics, 35(9),

8 286 STUDY ON THE INFLUENCE FACTORS OF LOGISTICS DEMAND IN CHINA Deng, J. L. (1987). Basic method of gray system. Wuhan: Huazhong Institute of Technology Press. Lian, L., Yang, S., & Xu, Y. (2014). Research on regional urban logistics level comprehensive evaluation based on grey correlation degree and factors analysis. Journal of Shangdong Science, 27(6), Zhang, W. Y. (2011). Influence factors analysis of logistics development based on grey correlation analysis. Journal of Statistics and Decision, (23), Zhang, M. F., & Zhang, L. (2015). Study on relationship between regional logistics demand and regional economic development based on grey correlation analysis. Logistics Technology, 34(5),