| Summary: | 碩士 === 國立臺灣大學 === 工業工程學研究所 === 105 === The research aims to strengthen the recent inventory management model which focus on low-volume and high-mix production. The low-volume and high-mix production method usually couldn’t satisfy the demand on time due to the fluctuation of demand caused by market all the time. According to the high-mix product, it is hard to make material control decision clearly and quickly under abundant barriers.
Therefore, the research formulates four different inventory management model including statistic forecasting model and safety stock, and the cost is the point of view for each product to choose a better management model.
However, classification is a common method to manage the inventory in the practice of enterprise nowadays. It conducts different inventory management strategies by considering different categories of products. The categories are based on the product attributes and the operational efficiency. The research classifies rapidly learns the attributes of the training set and constructs the classification model by machine learning. The new model simplifies the complicated procedures of the low-volume and high-mix inventory management in practice.
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