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• W3201482372 abstract "Supply chain literature reveals that study of resilient supply chains and bullwhip effect (BE) have been receiving special attention during pandemic for supply chains with seasonal as well as nonseasonal demand components. The BE phenomenon has been detected in various industries and sectors, and causes multiple inefficiencies such as higher costs of producing more than needed, wastage and transportation costs. As a result, BE forecast is of great importance for academics and supply chain managers. Despite the multitude of studies that have emerged addressing this issue, the impact of the quality of dynamic forecasts on the BE has received limited coverage in the literature. Optimal dynamic forecasts of the demand could allow managers to mitigate the upstream amplification of orders (and thus the BE), as well as reduce unnecessary inventory costs. Order quantity and BE in a supply chain depend on the forecast of the future demand. Usually minimum mean square error (MMSE) forecasts of the future demand are obtained by fitting an appropriate seasonal auto-regressive moving average (ARMA) time series model. However, a major drawback of the MMSE forecasting method is that it does not provide the associated risk forecasts. In this paper, a simple yet effective machine learning demand forecasting approach without fitting any time series model is presented.Specifically, a novel data driven machine learning algorithm that bypasses traditional forecasting steps and allows forecast weights to be optimized by minimizing the one-step ahead forecast error sum of squares (FESS) is proposed. A novel stability metric of a supply chain is proposed as the risk adjusted forecast of the future demand. It is shown that the risk adjusted forecasts can be used to check whether a given supply chain is resilient. In order to be more resilient and competitive in the current market, business leaders around the world agree that it is necessary to modernize and make major changes to their supply chain strategies. Demand risk forecasts obtained by the proposed machine learning approach allow supply chain managers to enhance the forecasting power of the order quantity and construct more resilient supply chains. The performance of proposed approach is evaluated through numerical experiments using simulated data and weekly demand data of two products. The results show that the performance of the proposed forecasts and risk adjusted forecasts of the future demand are better than the commonly used MMSE forecasts of the future demand." @default.
• W3201482372 created "2021-09-27" @default.
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• W3201482372 date "2021-07-01" @default.
• W3201482372 modified "2023-09-29" @default.
• W3201482372 title "A Novel Dynamic Demand Forecasting Model for Resilient Supply Chains using Machine Learning" @default.
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• W3201482372 doi "https://doi.org/10.1109/compsac51774.2021.00040" @default.
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