Abstract
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.
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Sathyan, R., Parthiban, P., Dhanalakshmi, R. et al. A combined big data analytics and Fuzzy DEMATEL technique to improve the responsiveness of automotive supply chains. J Ambient Intell Human Comput 12, 7949–7963 (2021). https://doi.org/10.1007/s12652-020-02524-8
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DOI: https://doi.org/10.1007/s12652-020-02524-8