Abstract
This paper proposes a stock dynamic sizing optimization under the Logistic 4.0 environment. The safety stock is conceived to fill up the demand variability, providing continuous stock availability. Logistic 4.0 and the smart factory topics are considered. It focuses on vertical integration to implement flexible and reconfigurable smart production systems using the information system integration in order to optimize material flow in a 4.0 full-service approach. The proposed methodology aims to reduce the occurring stock-out events through a link among the wear-out items rate and the downstream logistic demand. The failure rate items trend is obtained through life-cycle state detection by a curve fitting technique. Therefore, the optimal safety stock size is calculated and then validated by an auto-tuning iterative modified algorithm. In this study, the reorder time has been optimized. The case study refers to the material management of a very high-speed train.
Highlights
This study proposes a new inventory management method that can ensure companies the right service level, despite the possible complex operating conditions
The Economic Order Quantity (EOQ) model of inventory management is used to mark the optimum size of delivery and to choose the cheapest deliverer [3]
The used approach is parametric and it deals with general modeling whose input is given by supply chain characteristic parameters together with wear-out rate detections
Summary
This study proposes a new inventory management method that can ensure companies the right service level, despite the possible complex operating conditions. Many inventory management models, such as economic order quantity or probabilistic models, are present. Demand uncertainty is the risk factor that is supposed to have the most significant impact on supply chain performance [2]. This consideration is one of the major problems of companies; they are not able to figure out historical data because they are affected by extreme variability. It develops a model to evaluate the stock demand analytically. The Economic Order Quantity (EOQ) model of inventory management is used to mark the optimum size of delivery and to choose the cheapest deliverer [3]. Traditional data warehouse systems have static structures of their schemas and
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