Dynamic Optimization and Multi-Regional Performance Validation of Automotive Sales Strategies in the United States
DOI:
https://doi.org/10.70393/616a6e73.333934ARK:
https://n2t.net/ark:/40704/AJNS.v3n1a01Disciplines:
Computer ScienceSubjects:
Data ScienceReferences:
17Keywords:
Dynamic Sales Optimization, Organizational Learning, Performance Governance, Automotive Market AdaptationAbstract
In the context of global automotive market restructuring, this study proposes a comprehensive framework for dynamic sales optimization and performance governance to enhance adaptability and resilience in multi-regional automotive markets. Drawing on the U.S. automotive sector and China’s rapidly expanding new energy vehicle (NEV) exports as empirical contexts, the paper integrates theories of market orientation, organizational learning, and dynamic management to construct a continuous feedback loop of sensing–learning–adjusting–validating. The proposed framework emphasizes real-time responsiveness, cross-regional performance validation, and feedback-driven learning as key mechanisms for sustaining competitiveness under policy volatility, technological transition, and regional heterogeneity. By aligning strategic adaptability with data-driven decision-making and human-centered agility, the study provides both theoretical insights and practical guidance for automotive manufacturers, insurers, and related service organizations seeking to achieve sustainable, region-specific performance in a dynamically evolving global market.
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[1] Okonkwo, K. (2024). Using artificial intelligence (AI) to manage buyer persona in e-commerce based on Kotler & Keller's 2016 model of consumer behaviour: Studying consumer behaviour in e-commerce through archival research based on secondary data in the form of relevant publications [Unpublished manuscript].
[2] Valenti, A., Srinivasan, S., Yildirim, G., & Pauwels, K. (2024). Direct mail to prospects and email to current customers? Modeling and field-testing multichannel marketing. Journal of the Academy of Marketing Science, 52(3), 815-834.
[3] Siddiqui, Z. A., & Haroon, M. (2024). Ranking of components for reliability estimation of CBSS: An application of entropy weight fuzzy comprehensive evaluation model. International Journal of System Assurance Engineering and Management, 15(6), 2438-2452.
[4] Do, Q. H. (2024). Evaluating lecturer performance in Vietnam: An application of fuzzy AHP and fuzzy TOPSIS methods. Heliyon, 10(11).
[5] Chachra, A., Kumar, A., & Ram, M. (2024). A Markovian approach to reliability estimation of a series-parallel system with Fermatean fuzzy sets. Computers & Industrial Engineering, 190, 110081.
[6] Zhao, Y., Wang, T., Zhang, C., Hamat, B., & Pang, L. L. L. (2024). Research on the application of AHP-FAST-FBS in the design of home entrance disinfection devices in the post-pandemic era. Scientific Reports, 14(1), 20550.
[7] Xu, I. (2025). Computer vision-enabled inventory management system: A cloud-native solution for retail cost reduction [Unpublished manuscript].
[8] Yang, J., Wu, Y., Yuan, Y., Xue, H., Bourouis, S., Abdel-Salam, M., & Por, L. Y. (2025). Llm-ae-mp: Web attack detection using a large language model with an autoencoder and a multilayer perceptron. Expert Systems with Applications, 274, 126982.
[9] Yuan, Y., & Xue, H. (2025). Multimodal information integration and retrieval framework based on graph neural networks. In Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network (pp. 135-139).
[10] Amoako, G. K., Acquah, I. S. K., Abubakari, A., & Gabrah, A. Y. B. (2025). The role of effective channel management on brand identity in a B2B setting from an emerging market perspective. Journal of Business-to-Business Marketing, 1-28.
[11] Reinartz, W. J., & Kumar, V. (2003). The impact of customer relationship characteristics on profitable lifetime duration. Journal of Marketing, 67(1), 77-99.
[12] Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96.
[13] Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121.
[14] Li, Z., Ji, Q., & Ling, X. (2025). A comprehensive review of multi-agent reinforcement learning in video games. Authorea Preprints.
[15] Zhang, Z., Wang, J., & Li, Z. (2025). AnnCoder: A multi-agent-based code generation and optimization model [Unpublished manuscript].
[16] Hu, R., Jian, X., Zhao, H., & Wang, J. (2025). Design and realization of computer vision-assisted human rehabilitation training system [Unpublished manuscript].
[17] Lu, J., Zhao, H., Zhai, H., Yang, X., & Han, S. (2025). DeepSPG: Exploring deep semantic prior guidance for low-light image enhancement with multimodal learning. In Proceedings of the 2025 International Conference on Multimedia Retrieval (pp. 935-943).
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