基于消费者偏好的A公司国产新能源汽车区域订单预测
Order Prediction for Company A’s Domestic New Energy Vehicles Based on Consumer Preferences

随着新能源汽车行业竞争的加剧与消费者偏好的快速变化，传统的基于历史数据的订单预测方法已经无法满足企业精确决策的需求。现有研究主要忽视了消费者情感的动态变化，导致预测准确性有偏差。如何结合消费者情感的实时变化进行订单预测，已成为提高企业竞争力的重要课题。本文提出了一种基于消费者情感的订单预测模型，以A公司为例，通过情感分析量化消费者评论中的情感特征，并将其与历史销量结合，构建考虑消费者情感的预测模型。该模型通过生成消费者偏好指数(Behavioral Index, BI)，充分反映消费者情感在订单预测中的重要作用，并根据这一信息优化区域订单预测的精度。实证研究结果表明，考虑消费者偏好后的订单预测模型提高了预测准确性。特别是西南和华南地区的预测精度达到93%以上，其他大区的预测精度也均超过90%。这一结果表明，消费者情感分析在订单预测中具有重要价值，能够为企业的生产和供应链管理提供更科学的决策支持。
With the intensifying competition in the new energy vehicle industry and the rapid changes in consumer preferences, traditional order prediction methods based on historical data can no longer meet the precise decision-making needs of enterprises. Existing research mainly overlooks the dynamic changes in consumer sentiment, leading to deviations in prediction accuracy. How to integrate real-time changes in consumer sentiment for order prediction has become a crucial issue for improving enterprise competitiveness. This paper proposes an order prediction model based on consumer sentiment, taking Company A as an example. It quantifies the emotional characteristics in consumer reviews through sentiment analysis and combines these with historical sales data to construct a prediction model that considers consumer sentiment. The model generates a Consumer Behavioral Index (BI), which fully reflects the significant role of consumer sentiment in order prediction and optimizes the accuracy of regional order forecasts based on this information. Empirical research results show that the order prediction model considering consumer preferences improves prediction accuracy. Specifically, the prediction accuracy for the Southwest and South China regions reaches over 93%, with other major regions also exceeding 90%. These results indicate that sentiment analysis of consumers plays a crucial role in order prediction and provides more scientific decision-making support for enterprise production and supply chain management.