Abstract:

Traditional retail has experience of supply, which seriously affects the development of the retail industry. According to different application scenarios and needs, different replenishment strategies are proposed. In the intelligent vending machine use scenario, an optimized replenishment strategy of a dynamic matrix model is proposed, which realizes intelligent replenishment on demand. This strategy calculates the replenishment value at different times of the day based on the relationship between sales statistics, predicted sales value, zero inventory, the number of merchandise lanes of the vending machine, and time series. The table dynamically adjusts the replenishment cycle. According to the output results, compared with other replenishment methods such as supply based on sales experience in the past, this replenishment strategy can improve the turnover rate of goods, increase the sales of goods, and also save the replenishment operating costs.

Key words: replenishment strategy, time series, vending machine, matrix model, replenishment cycle

摘要：

传统零售存在经验供货，严重影响了零售业的发展，针对不同的应用场景和需求需要提出不同的补货策略，在智能自动售货机使用场景下提出动态矩阵模型的可优化的补货策略，实现按需智能补货。该策略根据销量统计、预测的销量值、零点库存、售货机的商品货道数以及时间序列等之间的关系计算每天不同时间段的补货值，实现补货人员可根据输出的补货矩阵表动态调整补货周期。根据输出结果表明，对比以往根据销量经验供货等其他补货方法，此补货策略可以提高商品周转率，提升商品的销量，同时也能够节约补货运营成本。

关键词: 补货策略, 时间序列, 售货机, 矩阵模型, 补货周期