Abstract
During the automobile pre-sale service, understanding customer preferences and segmenting customer purchasing power can provide a foundation for automobile dealers’ vehicle allocation strategy to manufacturers. It can improve the service ability of dealers and increase marketing revenue. This paper comes up with a verification method based on customer segmentation for multi-value-chain collaborative mechanism. First, we studied the process and inner accounting mechanism of the automobile marketing value chain and production value chain. We can explore the value-added of customer segmentation in each value chain and examine the extent to which customer segmentation results affect the value-added. Then, we construct a colored Petri net model for the value chain of each accounting unit. The sales service is mapped to transition. The sales resource management department is mapped to the place. The sales resource is mapped to the Token in the place. And we simulate the value-added process of the automobile marketing value chain and production value chain. Finally, we offer differentiated sales service processes for different categories of customers based on customer segmentation and design a multi-value-chain collaborative mechanism. On the CPN Tools simulation platform, we simulate the value-added quantitative effects in multi-value-chain collaborative state. We verified the correctness of the collaborative mechanism. In the simulation experiment, we monitored the number of automobile sales, the number of backlogs. The simulation result shows that the multi-value-chain collaborative mechanism based on customer segmentation designed in this paper can effectively increase the number of automobile sales by dealers and manufacturers. It provides a quantifiable basis for the optimization solutions of multi-value-chain.
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References
Viegas, J.L., Vieira, S.M., Melicio, R., Mendesjoao, V.M.F., Sousa, M.C.: Classification of new electricity customers based on surveys and smart metering data. Energy (09), 123–125 (2016)
Parsell, R.D., Wang, J., Kapoor, C.: CUSTOMERSEGMENTATION: WO/2014/099928 (2014)
Murata, T.: Petri nets: properties, analysis and applications. Proc. IEEE 77(4), 541–580 (1989)
Yuan, C.Y.: Principle and Application of Petri Nets. Publishing House of Electronics Industry, Beijing (2005). (in Chinese)
Zhou, M., Wu, N.: System modeling and control with resource-oriented Petri nets. Computer Science, Engineering & Technology (2010)
Bao, Z., Zhao, Y., Zhao, Y.: Segmentation of Baidu takeaway customer based on RFA model and cluster analysis. Comput. Sci. 45(S2), 436–438 (2018)
Yu, Q., Cai, L., Tan, X.: Airport emergency rescue model establishment and performance analysis using colored Petri Nets and CPN tools. Int. J. Aerosp. Eng. 2018 (2018). Article ID 2858375, 8 pages
Jiang, S., Cai, S., Olle Olle, G., Qin, Z.: Durable product review mining for customer segmentation. Kybernetes 44(1), 12–17 (2015)
Yang, Z., Zhang, J., Chen, X.: The pricing strategy for the dual channel supply chain with pre-sale service. In: 2018 15th International Conference on Service Systems and Service Management (ICSSSM) (2018)
Xin, Z., Minghai, Y., Mingdi, J.: Study on designing modern auto service marketing mode based on value chain theory. In: The 3rd International Conference on Information Sciences and Interaction Sciences, 23–25 June 2010
Arciniegas, D., Herrera, M.A., Táutiva, K.: Automation process modeling of a electric cars production line through Petri nets and GRAFCET. In: 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC), 18–20 October 2017
Chen, Z.-Y., Fan, Z.-P., Sun, M.: A multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendations. Eur. J. Oper. Res. 11, 34–37 (2016)
Wei, H.: Empirical study on Chinese car enterprise upgrading based on global value chain. In: 2011 International Conference on Product Innovation Management (ICPIM 2011) (2011)
Nauhria, Y., Kulkarni, M.S., Pandey, S.: Development of strategic value chain framework for indian car manufacturing industry. Glob. J. Flex. Syst. Manage. 19(1), 21–40 (2018)
Yuxin, Y., Hongyan, Z.: The research of auto-adaptive production strategy based on supply chain coordination. In: 2010 3rd International Conference on Information Management, Innovation Management and Industrial Engineering, 26–28 November 2010
Acknowledgment
The authors would like to thank the anonymous referees for their valuable comments and helpful suggestions. This paper is supported by The National Key Research and Development Program of China (2017YFB1400902).
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Duan, L., Bo, W., Wu, C., Ning, H., Zhang, C. (2019). Customer Classification-Based Pre-sale Multi-value-chain Collaborative Mechanism Verification. In: Milošević, D., Tang, Y., Zu, Q. (eds) Human Centered Computing. HCC 2019. Lecture Notes in Computer Science(), vol 11956. Springer, Cham. https://doi.org/10.1007/978-3-030-37429-7_9
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DOI: https://doi.org/10.1007/978-3-030-37429-7_9
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