Study on Mining Big Users Data in The Development of Hubei Auto-Parts Enterprise

Study on Mining Big Users Data in The Development of Hubei Auto-Parts Enterprise

Rengan WeiJianguo Zhen Lili Bao 

Donghua University, Shanghai, China

Hubei University of Automotive Technology, Shiyan, China

Wuhan University, Wuhan, China

Corresponding Author Email: 
wrg863@163.com
Page: 
1-6
|
DOI: 
http://dx.doi.org/10.18280/mmep.020401
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

It’s a low-input and low-risk green transformation mode to mining big users data in the midst of transformation, encouraging the development path of auto-parts enterprise to transit from technology leading to management leading. To study the technological Innovation in Hubei auto-parts enterprise, some methods are carried out, such as data collection, comprehensive analysis, expert questionnaire and empirical research. Based on the analysis of Hubei auto-parts enterprise development, a whole set of strategy is proposed to promote the development path transition of Hubei auto-parts enterprise and ultimately achieve leapfrog development.

Keywords: 

Auto parts, Technological Innovation, Big users data, Green Development.

1. Introduction
2. The Current Corporate Development Status of Hubei Auto-Parts Industry
3. The Importance and Necessity of Hubei Automobile Parts Industry to Promote the Big User Data Application
4. The Main Ideas and Countermeasures of Auto Parts Big User Data Applications
5. Conclusion
Acknowledgement

This work is supported by National Natural Science Foundation of China (Grant. No. 70971020), Universities in Hubei province outstanding young scientific and technological innovation team project (T201411), Hubei province Soft Science Research Program (2012GDA01301), and The Humanities and Social Sciences project of Hubei provincial department of education (2014y452).

  References

[1] Wen Shia, Jennifer Shang, “Optimal design of the auto parts supply chain for JIT operations: sequential bifurcation factor screening and multi-response surface methodology,” European Journal of Operational Research, Volume 236, Issue 2, 16 July 2014, Pages 664–676.

[2] N.Kim, R.K.Srivastava, “Managing intra-organizational diffusion of technological innovations,” Industrial Marketing Management, 27 (3) (1998), pp. 229–246.

[3] D.Laney, 3D Data Management: Controlling Data Volume, Velocity, and Variety Technical Report (2001). URL: http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf (accessed on August 2015).

[4] Anand, G., “Dynamic capabilities through continuous improvement infrastructure,” J. Operations Management, 2009, 27(6):444-461.

[5] Ittner, C.D., Larcker, D.F., “The performance effects of process management techniques,” Management Science, 2009, 43(4): 522~534.

[6] Wong M.L., “The role of culture in implementing lean production system,” Advances in Production Management System, Taiwan: SPRINGER, 2007.

[7] Li Shunxi, Wang Aimin, “An evaluation of chinese automobile manufacturers using the analytic hierarchy process and catastrophe theory”, AISS: Advances in Information Sciences and Service Sciences, Vol. 4, No. 1, pp. 28-36, 2012.

[8] XIONG Jian-qiang, HUANG Ju-hua, LIAO Qun, “The study on automobile engine noise based on sound intensity method”, AISS: Advances in Information Sciences and Service Sciences, Vol. 4, No. 1, pp. 147-153, 2012.

[9] Sunghan Lim, “Advanced road management system using IT technology”, IJEI: International Journal of Engineering and Industries, Vol. 2, No. 1, pp. 84-90, 2011.

[10] Rohit Deshpandé, A.Parasuraman, “Linking corporateculture to strategic planning,” Technovation, Volume 23, Issue 12, December 2003, Pages 917-928.

[11] Victor Newman, Kazem Chaharbaghi, “The corporate culture myth,” Long Range Planning, Vol.31, No.4, pp514-522:1998.