Applying Resiliency in Predicting Demand for the Automotive Supply Chain - Akhlaghpour, Ali; Heidari, Mohamad Reza; Pourghader Chobar, Adel (International journal of industrial engineering and operational research, 2023)
The automotive industry is known for its volatility, and disruptions such as natural disasters, economic downturns, and supply chain disruptions can have a significant impact on demand. Accurately predicting demand is crucial for the success of the automotive supply chain. In this paper, we propose applying resiliency in predicting demand for the automotive supply chain. By using advanced algorithms and machine learning techniques, we can analyze historical data, market trends, and other relevant factors to make accurate predictions about future demand. We discuss the importance of building resiliency into demand forecasting models and how it can help minimize the impact of disruptions on the supply chain. We also provide examples of how resiliency has been applied in other industries and how it can be adapted for the automotive industry. Overall, we believe that applying resiliency in predicting demand for the automotive supply chain is a strong strategy for ensuring long-term success and growth in the industry.