The vital task of improving the Responsiveness of the automotive supply chains is to forecast the demand and analyze the vehicle's most influential attributes. The purpose of this paper is to develop a model to forecast the demand and analyzing the vehicle attributes using a combined approach of big data analytics and fuzzy decision-making trial and evaluation laboratory (DEMATEL) technique. The forecasting process includes the sentiment analysis of product review and creating a predictive model using an artificial neural network algorithm. The most influential attributes of the vehicle were extracted from online customer reviews and these attributes were analyzed using the Fuzzy DEMATEL method. A newly introduced vehicle in the Mid- SUV segment of the Indian automotive sector has been chosen as a case to illustrate the developed model. The forecasted demand shows an accuracy of 95.5% and the price of the vehicle and safety features are identified as attributes with higher prominence value.

改善汽车供应链的响应能力的关键任务是预测需求并分析汽车最有影响力的属性。本文的目的是使用大数据分析和模糊决策试验与评估实验室（DEMATEL）技术相结合的方法，开发一种预测需求并分析车辆属性的模型。预测过程包括产品评论的情绪分析，并使用人工神经网络算法创建预测模型。从在线客户评论中提取了车辆最有影响力的属性，并使用模糊DEMATEL方法对这些属性进行了分析。已选择印度汽车行业中型SUV领域的新车作为案例来说明开发的模型。