Optimal ordering period prediction method for seasonal and periodic goods

Abstract

The invention discloses an optimal ordering period prediction method for seasonal and periodic goods, and the method is suitable for the goods which is periodic and is sensitive to seasons, wherein the sales volume of the goods increases at first and then decreases. The method comprises the steps: predicting the sales demands of the goods in the current period based on the history sales data of the goods; considering the ordering cost of the goods, the stock cost and the interest cost of a bank loan; and calculating the optimal ordering period of the goods through a quantitative method. Output results enable the decision facilitating the ordering period of the current goods of an enterprise to be made in advance, thereby providing accurate and scientific judgment for the decisions of the requirements for stock capacity, the stock cost control and the amount of bank load of the enterprise.

Description

technical field [0001] The invention relates to a method for predicting the optimal order cycle of seasonal and periodic commodities, which is suitable for commodities with periodic phenomena and sensitive to seasons, whose sales volume increases first and then decreases in the cycle, through optimal ordering of the commodities The forecast of the period is beneficial to the enterprise to make an advance decision on the order period of the commodity in the current period, and belongs to the field of information forecast technology. Background technique [0002] The development of market economy has intensified the competition among enterprises, and the competition among enterprises has changed from price competition to cost competition. As an important part of it, inventory cost plays an increasingly important role in enterprise competition. The current fixed order batch model method has too many constraints, less consideration of the actual situation, and it is difficult to...

AI Technical Summary

Problems solved by technology

The current fixed order batch model method has too many constraints, less consideration of the actual situation, and it is difficult to well reflect the dynamic, complex and changeable actual market demand, resulting in frequent occurrence of slow-moving and out-of-stock phenomena, making the turnover rate of materials low, and there is no Consider available capital

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