Abstract
Auto parts in the after-sales service flow across parts supplier, manufacturer, and transit store as well as service provider. The accurate and timely demand for auto parts plays a key role in the value-added process of the parts multiple-value chains. This paper presents a collaboration mechanism and evaluation method for auto parts multiple-value chains based on sales prediction. First, we study the composition of inner value chain of independent accounting unit, and discuss factors of value increment of the inner value chain. Moreover, dominated elements are selected as main factors for future value. Second, a colored petri nets model is created for each independent accounting unit all over the multiple-value-chains, of which value-added process maps to Transition, repository to Place and auto parts to Place’s Token. Such model is utilized to simulate the value-added process over the multiple-value chains. Finally, a collaboration mechanism for multiple-value chains is designed based on the prediction amount for auto parts. By means of randomly generating a sequence of custom’s arrival and Monte Carlo method, we use CPN Tools to simulate the value-added process over the multiple-value chains. Experiments show that the offered auto parts collaboration mechanism can fully utilize existed resources in the multiple-value chains, gets maximized rise in value, and further seek the fundamental path to value increment. The model implemented in this paper is able to provide some quantification references for the multiple-value chains.
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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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Sun, Y., Wu, C., Bo, W., Duan, L., Zhang, C. (2019). Sales-Forecast-Based Auto Parts Multiple-Value Chain Collaboration Mechanism and 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_34
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DOI: https://doi.org/10.1007/978-3-030-37429-7_34
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