A combined big data analytics and Fuzzy DEMATEL technique to improve the responsiveness of automotive supply chains
Journal of Ambient Intelligence and Humanized Computing, ISSN: 1868-5145, Vol: 12, Issue: 7, Page: 7949-7963
2021
- 16Citations
- 74Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
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.
Bibliographic Details
Springer Science and Business Media LLC
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