Applying Resiliency in Predicting Demand for the Automotive Supply Chain
- سال انتشار: 1402
- محل انتشار: مجله بین المللی مهندسی صنایع و تحقیقات عملیاتی، دوره: 5، شماره: 3
- کد COI اختصاصی: JR_BGS-5-3_004
- زبان مقاله: انگلیسی
- تعداد مشاهده: 76
نویسندگان
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Department of Management, Technical and Vocational University(TVu), Tehran, Iran
Department of Industrial engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran
چکیده
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.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.کلیدواژه ها
Resiliency, Predicting Demand, Supply Chain, Automotiveاطلاعات بیشتر در مورد COI
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