e-Commerce and Supply Chain Management in China
Read this article. The authors provide a broad view of decisions on supply chain management concerning e-commerce in China, considering several factors, including consumer access to the internet and telecommunication infrastructure for a given location. Two case studies are included to evaluate the models included in the article. Pay particular attention to the intersection of IT and non-IT considerations for firm supply-chain management strategies.
E-commerce enterprise development in the prominent regions of China
According
to Digital analytics firm eMarketer, an improving
export business, higher employment and wages as well as rising
e-commerce and cross-border sales across Asia-Pacific, North America and
Western Europe were all factors in driving retail spending. Thus, the
location of omnichannel shoppers is determined by multi-criteria that
support online and offline trade development. The factors for such
decisions are mainly related to the economic benefits of both
enterprises and mentioned in earlier research (Panova and Hilmola,
2018): (1) National economic; (2) Industrial; (3) Logistical; (4)
Economical; and (5) Regulative.
Many sub-factors influence the
location decision. Their relevance depends on the scope of the location
problems (whether it is viewed from international, national, statewide
or communitywide level). From the
international level, factors such as the access to the natural
resources, provision of labour forces, foreign exchange rate, business
climate, components of the multiplier effect, inflation level, duties
and taxes are essential. If the scope of location problem is narrower,
then the factors like property tax incentive, government regulations,
transport tariffs, the selling price of goods, and business climate on
local basis play a role.
Authors stress that the development of technical infrastructure and
regulatory frameworks stimulate e-commerce growth.
The research
of critical e-commerce factors in France by Colla and Lapoule
revealed that an integrated multichannel approach for online business
requires more differentiated logistical system and improved customer
relations marketing. The number of internet users in less developed
provinces was considered as a constraint for e-commerce growth. Meanwhile, Internet usage is beginning to increase outside
of the wealthy and educated urban elite as more people go online in
China. The overall trend of the expected growth of middle- and
upper-income categories to some 190 million by 2020 from 17 million in
2010 makes the whole market attractive for foreign direct investments.
Unlike a few decades ago when low-cost production attracted companies to
enter the market, nowadays, the consumption forecast opens up new
opportunities due to triple growth of Chinese economy during 2000 to
2010. So as to identify the most attractive region
for the investors in China, 15 factors were chosen (Fig. 2) for the
general choice algorithm.
Fig. 2 Factors taken into general choice algorithm
![](https://learn.saylor.org/pluginfile.php/5619169/mod_book/chapter/50157/2.jpeg)
In
support of e-commerce enterprise location in China, governmental
regulations trigger the business activity at local level in many
regions. In particular, the government of China develops comprehensive
cross-border e-commerce zones. These zones are designated exclusively
for the development of cross-border e-commerce industry, as they target
preferential tax policies and streamlined customs clearance procedures. Such zones have been developed in three stages,
starting from zone Hangzhou, home to the e-commerce giant Alibaba that
was established in 2015 (first phase). The latest, third phase, which
was announced recently, in July 2018 foresees the creation of the
additional 22 zones). Thus, according to Acolink.com, in China, 35 cross-border e-commerce zones will be developed.
Their location in regard to 22 Chinese provinces, as well as to five
Autonomous Regions (Inner Mongolia, Guangxi, Tibet, Ningxia, and
Xinjiang) and four Chinese municipalities (Beijing, Shanghai, Tianjin,
and Chongqing), which are under the direct administration of central
government, has been summarized in Table 1. Additionally, at this table,
the "E-commerce development plan" in Xinjiang is depicted. According to
Hongfei, this plan was designed to facilitate exports to the
Middle East and East Asia. Hongfei also noted that Guangxi
Province promoted cooperation between China and Vietnam through
E-commerce trade and cooperation. Heilongjiang Province listed the
Suifenhe border economic cooperation zone as a base of cross-border
e-commerce development and focused on opening cross-border trade with
Russia. Recent news also mentioned about the creation of free trade zone
in Luoyang in addition to already established zone in Zhengzhou.
Table 1 Provinces of China with cross-border comprehensive e-commerce zones (number of zones in each province)
Province | Administrative center | Number of Zones (*E-commerce development plan) | Row average values from normalized matrix |
---|---|---|---|
Anhui | Hefei | 1 | 0.027 |
Fujian | Fuzhou | 1 | 0.027 |
Gansu | Lanzhou | 1 | 0.027 |
Guangdong | Guangzhou | 4 | 0.108 |
Guizhou | Guiyang | 1 | 0.027 |
Hainan | Haikou | 1 | 0.027 |
Hebei | Shijiazhuang | 1 | 0.027 |
Heilongjiang | Harbin | 1 | 0.027 |
Henan | Zhengzhou | 1 | 0.054 |
Hubei | Wuhan | 1 | 0.027 |
Hunan | Changsha | 1 | 0.027 |
Jiangsu | Nanjing | 3 | 0.081 |
Jiangxi | Nanchang | 1 | 0.027 |
Jilin | Changchun | 1 | 0.027 |
Liaoning | Shenyang | 2 | 0.054 |
Qinghai | Xining | 0 | 0.000 |
Shanxi | Taiyuan | 0 | 0.000 |
Shandong | Jinan | 2 | 0.054 |
Shaanxi | Xi’an | 1 | 0.027 |
Sichuan | Chengdu | 1 | 0.027 |
Yunnan | Kunming | 1 | 0.027 |
Zhejiang | Hangzhou | 3 | 0.081 |
Beijing | Beijing | 1 | 0.027 |
Shanghai | Shanghai | 1 | 0.027 |
Tianjin | Tianjin | 1 | 0.027 |
Chongqing | Chongqing | 1 | 0.027 |
Inner Mongolia | Hohhot | 1 | 0.027 |
Guangxi | Nanning | 1 | 0.027 |
Tibet | Lhasa | 0 | 0.000 |
Ningxia | Yinchuan | 0 | 0.000 |
Xinjiang | Urumchi | 1 | 0.027 |
The availability and closeness to these zones is essential in terms of logistics processes. Specifically, the bonded warehouse model can be used for the delivery of the products to final clients from abroad. Investors may set up a warehouse within their respective e-commerce zone. Goods will then be transported and stored temporarily within the warehouse under the Customs supervision, before they are delivered to domestic customers. In this case, the delivery time can be reduced to 5 days, compared to the direct shipping model that requires 7–30 days for delivery. The reason for long deliveries is that under the direct sale model, foreign manufacturers maintain warehouses in their home countries and send goods to customers after they have made orders online that involves a relatively more complicated customs clearance procedure. As can be seen from Table 1 and after a provided pair-wise comparison, the most preferred regions are Guangdong, Jiangsu, and Zhejiang while Qinghai and Shanxi regions are less prioritized by the criterion of e-commerce zones availability.
Apart from the regulative preferences, it is essential to take into account the potential basic conditions for the developing of the online retail trade. For these reasons, total sales and purchases value of retail trade, as well as a number of corporate enterprises of retail trade and stock of retail trade across Chinese provinces have been analyzed by the pair-wise comparison. The total purchases value refer to the total value of purchases of commodities by enterprises (establishments) from other establishments or individuals (including direct import from abroad) for the purpose of re-selling, either with or without further processing of the commodities purchased. Sales value of retail trade refers to the value of commodities sold by the establishments to other establishments and individuals. The commodities include: (1) commodities sold to urban and rural residents and social groups for their consumption; (2) commodities sold to establishments in all industries for their production and operation, including commodities sold to wholesale and retail establishments for re-selling, with or without further processing; and (3) commodities for direct export to abroad.
Province | Preference vectors for the criteria | |||
---|---|---|---|---|
Number of corporate enterprises of retail trade (Unit) | Sales value (Yuan) | Purchase value (Yuan) | Stock Value (Yuan) | |
Beijing | 0.020 | 0.063 | 0.069 | 0.057 |
Tianjin | 0.012 | 0.025 | 0.023 | 0.015 |
Hebei | 0.026 | 0.026 | 0.027 | 0.028 |
Shanxi | 0.021 | 0.000 | 0.016 | 0.022 |
Inner Mongolia | 0.014 | 0.014 | 0.013 | 0.012 |
Liaoning | 0.025 | 0.027 | 0.026 | 0.025 |
Jilin | 0.018 | 0.016 | 0.015 | 0.013 |
Heilongjiang | 0.013 | 0.014 | 0.013 | 0.013 |
Shanghai | 0.019 | 0.057 | 0.053 | 0.086 |
Jiangsu | 0.087 | 0.093 | 0.092 | 0.092 |
Zhejiang | 0.057 | 0.066 | 0.064 | 0.069 |
Anhui | 0.047 | 0.033 | 0.033 | 0.031 |
Fujian | 0.057 | 0.038 | 0.036 | 0.030 |
Jiangxi | 0.024 | 0.016 | 0.016 | 0.034 |
Shandong | 0.088 | 0.084 | 0.087 | 0.062 |
Henan | 0.080 | 0.046 | 0.045 | 0.038 |
Hubei | 0.052 | 0.060 | 0.062 | 0.058 |
Hunan | 0.048 | 0.037 | 0.036 | 0.047 |
Guangdong | 0.086 | 0.101 | 0.095 | 0.105 |
Guangxi | 0.020 | 0.014 | 0.014 | 0.019 |
Hainan | 0.002 | 0.004 | 0.004 | 0.005 |
Chongqing | 0.035 | 0.031 | 0.029 | 0.021 |
Sichuan | 0.046 | 0.047 | 0.048 | 0.035 |
Guizhou | 0.021 | 0.014 | 0.013 | 0.013 |
Yunnan | 0.023 | 0.020 | 0.020 | 0.018 |
Shaanxi | 0.034 | 0.028 | 0.028 | 0.027 |
Gansu | 0.011 | 0.010 | 0.010 | 0.008 |
Qinghai | 0.002 | 0.002 | 0.002 | 0.002 |
Tibet | 0.001 | 0.001 | 0.001 | 0.001 |
Ningxia | 0.003 | 0.003 | 0.003 | 0.003 |
Xinjiang | 0.009 | 0.008 | 0.008 | 0.011 |
- Note: In parenthesis, the units are for the initial values of data processed in pair-wise comparison
- Entries in boldface are largest values to identify highest performing regions
Province | Preference vectors for the criteria | ||
---|---|---|---|
Number of postal offices (unita) | Number of outlets for express services (unita) | Length of postal routes (kma) | |
Beijing | 0.033 | 0.004 | 0.091 |
Tianjin | 0.009 | 0.011 | 0.021 |
Hebei | 0.030 | 0.003 | 0.023 |
Shanxi | 0.028 | 0.003 | 0.015 |
Inner Mongolia | 0.019 | 0.021 | 0.040 |
Liaoning | 0.027 | 0.032 | 0.042 |
Jilin | 0.017 | 0.025 | 0.019 |
Heilongjiang | 0.022 | 0.026 | 0.023 |
Shanghai | 0.035 | 0.047 | 0.036 |
Jiangsu | 0.060 | 0.066 | 0.031 |
Zhejiang | 0.055 | 0.071 | 0.105 |
Anhui | 0.035 | 0.042 | 0.009 |
Fujian | 0.034 | 0.039 | 0.035 |
Jiangxi | 0.030 | 0.026 | 0.014 |
Shandong | 0.047 | 0.052 | 0.067 |
Henan | 0.050 | 0.045 | 0.023 |
Hubei | 0.055 | 0.055 | 0.027 |
Hunan | 0.037 | 0.042 | 0.015 |
Guangdong | 0.100 | 0.132 | 0.068 |
Guangxi | 0.028 | 0.029 | 0.038 |
Hainan | 0.008 | 0.009 | 0.014 |
Chongqing | 0.023 | 0.022 | 0.021 |
Sichuan | 0.076 | 0.071 | 0.041 |
Guizhou | 0.028 | 0.029 | 0.015 |
Yunnan | 0.029 | 0.029 | 0.042 |
Shaanxi | 0.030 | 0.026 | 0.032 |
Gansu | 0.021 | 0.024 | 0.029 |
Qinghai | 0.005 | 0.004 | 0.014 |
Tibet | 0.004 | 0.005 | 0.009 |
Ningxia | 0.006 | 0.004 | 0.008 |
Xinjiang | 0.018 | 0.006 | 0.031 |
- Notea: In parenthesis the units are for the initial values of data processed in pair-wise comparison
- Entries in boldface are largest values to identify highest performing regions
Province | Preference vectors for the criteria | ||
---|---|---|---|
Broadband subscribers port of Internet | Number of websites | Number of Internet users | |
Beijing | 0.016 | 0.155 | 0.024 |
Tianjin | 0.010 | 0.013 | 0.014 |
Hebei | 0.054 | 0.032 | 0.055 |
Shanxi | 0.025 | 0.014 | 0.028 |
Inner Mongolia | 0.014 | 0.004 | 0.018 |
Liaoning | 0.033 | 0.030 | 0.038 |
Jilin | 0.015 | 0.007 | 0.020 |
Heilongjiang | 0.019 | 0.010 | 0.026 |
Shanghai | 0.021 | 0.025 | 0.025 |
Jiangsu | 0.090 | 0.065 | 0.063 |
Zhejiang | 0.073 | 0.085 | 0.051 |
Anhui | 0.036 | 0.017 | 0.038 |
Fujian | 0.039 | 0.073 | 0.037 |
Jiangxi | 0.028 | 0.009 | 0.028 |
Shandong | 0.080 | 0.069 | 0.072 |
Henan | 0.059 | 0.051 | 0.057 |
Hubei | 0.038 | 0.026 | 0.042 |
Hunan | 0.036 | 0.018 | 0.042 |
Guangdong | 0.094 | 0.185 | 0.112 |
Guangxi | 0.027 | 0.011 | 0.031 |
Hainan | 0.006 | 0.005 | 0.007 |
Chongqing | 0.024 | 0.013 | 0.022 |
Sichuan | 0.062 | 0.050 | 0.050 |
Guizhou | 0.015 | 0.004 | 0.021 |
Yunnan | 0.022 | 0.006 | 0.026 |
Shaanxi | 0.027 | 0.015 | 0.028 |
Gansu | 0.013 | 0.003 | 0.015 |
Qinghai | 0.003 | 0.001 | 0.004 |
Tibet | 0.001 | 0.000 | 0.002 |
Ningxia | 0.004 | 0.002 | 0.005 |
Xinjiang | 0.016 | 0.003 | 0.000 |
- Entries in boldface are largest values to identify highest performing regions
Province | Wages level (Yuan) | Unemployment rate (%) | Labour productivity (Yuan/person) | Preference vectors for the criteria | ||
---|---|---|---|---|---|---|
Wages level (Yuan) | Unemployment rate (%) | Labour productivity (Yuan/person) | ||||
Beijing | 119928 | 1.4 | 324301.7 | 0.017 | 0.071 | 0.024 |
Tianjin | 86305 | 3.5 | 625275.8 | 0.024 | 0.028 | 0.047 |
Hebei | 55334 | 3.7 | 501398.5 | 0.037 | 0.027 | 0.038 |
Shanxi | 53705 | 3.5 | 303110.2 | 0.038 | 0.028 | 0.023 |
Inner Mongolia | 61067 | 3.7 | 618200.1 | 0.034 | 0.027 | 0.047 |
Liaoning | 56015 | 3.8 | 396989.6 | 0.037 | 0.026 | 0.030 |
Jilin | 56098 | 3.5 | 458693.2 | 0.037 | 0.028 | 0.035 |
Heilongjiang | 52435 | 4.2 | 362136.4 | 0.039 | 0.024 | 0.027 |
Shanghai | 119935 | 4.1 | 448861.9 | 0.017 | 0.024 | 0.034 |
Jiangsu | 71574 | 3 | 516852.2 | 0.029 | 0.033 | 0.039 |
Zhejiang | 73326 | 2.9 | 445368.4 | 0.028 | 0.034 | 0.034 |
Anhui | 59102 | 3.2 | 472055.3 | 0.035 | 0.031 | 0.036 |
Fujian | 61973 | 3.9 | 430760.9 | 0.033 | 0.026 | 0.033 |
Jiangxi | 56136 | 3.4 | 392368.5 | 0.037 | 0.029 | 0.030 |
Shandong | 62539 | 3.5 | 559660.5 | 0.033 | 0.028 | 0.042 |
Henan | 49505 | 3 | 353468.5 | 0.042 | 0.033 | 0.027 |
Hubei | 59831 | 2.4 | 454114.7 | 0.035 | 0.041 | 0.034 |
Hunan | 58241 | 4.2 | 555081.2 | 0.035 | 0.024 | 0.042 |
Guangdong | 72326 | 2.5 | 413037.1 | 0.029 | 0.040 | 0.031 |
Guangxi | 57878 | 2.9 | 456355.2 | 0.036 | 0.034 | 0.034 |
Hainan | 61663 | 2.4 | 400593 | 0.033 | 0.041 | 0.030 |
Chongqing | 65545 | 3.7 | 429679.1 | 0.032 | 0.027 | 0.032 |
Sichuan | 63926 | 4.2 | 418200.4 | 0.032 | 0.024 | 0.032 |
Guizhou | 66279 | 3.2 | 379307.2 | 0.031 | 0.031 | 0.029 |
Yunnan | 60450 | 3.6 | 352962.4 | 0.034 | 0.028 | 0.027 |
Shaanxi | 59637 | 3.3 | 379350.2 | 0.035 | 0.030 | 0.029 |
Gansu | 57575 | 2.2 | 275918.5 | 0.036 | 0.045 | 0.021 |
Qinghai | 66589 | 3.1 | 407749.2 | 0.031 | 0.032 | 0.031 |
Tibet | 103232 | 2.9 | 365411.0 | 0.020 | 0.038 | 0.028 |
Ningxia | 65570 | 3.9 | 448237.4 | 0.031 | 0.026 | 0.034 |
Xinjiang | 63739 | 2.5 | 301101.5 | 0.032 | 0.040 | 0.023 |
- Entries in boldface are largest values to identify highest performing regions
Province | Wages level (Yuan) | Unemployment rate (%) | Labour productivity (Yuan/person) | Preference vectors for the criteria | ||
---|---|---|---|---|---|---|
Wages level (Yuan) | Unemployment rate (%) | Labour productivity (Yuan/person) | ||||
Beijing | 119928 | 1.4 | 324301.7 | 0.017 | 0.071 | 0.024 |
Tianjin | 86305 | 3.5 | 625275.8 | 0.024 | 0.028 | 0.047 |
Hebei | 55334 | 3.7 | 501398.5 | 0.037 | 0.027 | 0.038 |
Shanxi | 53705 | 3.5 | 303110.2 | 0.038 | 0.028 | 0.023 |
Inner Mongolia | 61067 | 3.7 | 618200.1 | 0.034 | 0.027 | 0.047 |
Liaoning | 56015 | 3.8 | 396989.6 | 0.037 | 0.026 | 0.030 |
Jilin | 56098 | 3.5 | 458693.2 | 0.037 | 0.028 | 0.035 |
Heilongjiang | 52435 | 4.2 | 362136.4 | 0.039 | 0.024 | 0.027 |
Shanghai | 119935 | 4.1 | 448861.9 | 0.017 | 0.024 | 0.034 |
Jiangsu | 71574 | 3 | 516852.2 | 0.029 | 0.033 | 0.039 |
Zhejiang | 73326 | 2.9 | 445368.4 | 0.028 | 0.034 | 0.034 |
Anhui | 59102 | 3.2 | 472055.3 | 0.035 | 0.031 | 0.036 |
Fujian | 61973 | 3.9 | 430760.9 | 0.033 | 0.026 | 0.033 |
Jiangxi | 56136 | 3.4 | 392368.5 | 0.037 | 0.029 | 0.030 |
Shandong | 62539 | 3.5 | 559660.5 | 0.033 | 0.028 | 0.042 |
Henan | 49505 | 3 | 353468.5 | 0.042 | 0.033 | 0.027 |
Hubei | 59831 | 2.4 | 454114.7 | 0.035 | 0.041 | 0.034 |
Hunan | 58241 | 4.2 | 555081.2 | 0.035 | 0.024 | 0.042 |
Guangdong | 72326 | 2.5 | 413037.1 | 0.029 | 0.040 | 0.031 |
Guangxi | 57878 | 2.9 | 456355.2 | 0.036 | 0.034 | 0.034 |
Hainan | 61663 | 2.4 | 400593 | 0.033 | 0.041 | 0.030 |
Chongqing | 65545 | 3.7 | 429679.1 | 0.032 | 0.027 | 0.032 |
Sichuan | 63926 | 4.2 | 418200.4 | 0.032 | 0.024 | 0.032 |
Guizhou | 66279 | 3.2 | 379307.2 | 0.031 | 0.031 | 0.029 |
Yunnan | 60450 | 3.6 | 352962.4 | 0.034 | 0.028 | 0.027 |
Shaanxi | 59637 | 3.3 | 379350.2 | 0.035 | 0.030 | 0.029 |
Gansu | 57575 | 2.2 | 275918.5 | 0.036 | 0.045 | 0.021 |
Qinghai | 66589 | 3.1 | 407749.2 | 0.031 | 0.032 | 0.031 |
Tibet | 103232 | 2.9 | 365411.0 | 0.020 | 0.038 | 0.028 |
Ningxia | 65570 | 3.9 | 448237.4 | 0.031 | 0.026 | 0.034 |
Xinjiang | 63739 | 2.5 | 301101.5 | 0.032 | 0.040 | 0.023 |
- Entries in boldface are largest values to identify highest performing regions
Based on the general choice algorithm (GCA) we tried to locate some e-commerce companies in the list of ten preferable locations to conduct interview to understand their supply chain network and their customer distribution. Eventually, two e-commerce companies located in Shanghai were willing to take part in this research.