Industry 4.0: Meeting the Challenges of Demand Sensing in the Automotive Industry
Shikha Singh, Baljeet Yadav, Roshan Batheri
Abstract
The automotive industry is a crucial contributor to the economy of countries worldwide. Demand forecasting is integral to demand management since it directly impacts automotive businesses' strategy, revenues, and supply chain participants. Genuine demand for automotive vehicles is contingent on economic factors, weather conditions, market analytics, social events, online and offline traffic, and other factors. This article discusses the challenges in forecasting demand, the shortcomings of traditional forecasting methods, and the role of Industry 4.0 technologies in sensing market demands. In order to predict short-term and long-term demand for automotive sales, an artificial intelligence (AI) and machine learning (ML) model is proposed that utilizes various automotive demand signals and vehicle sales data. In addition to the significant demand signals, online social listening inputs and offline consumer traffic through test drives were also considered in the proposed model.