Which method BEST determines optimal reorder points for seasonal fashion inventory?

Statistical forecasting combined with safety stock calculation is the BEST method for determining optimal reorder points for seasonal fashion inventory. This approach acknowledges the unique challenges of seasonal demand patterns and the risk of stockouts. Statistical forecasting involves using historical sales data, trend analysis, and seasonality indices to predict future demand for specific items. Techniques like time series analysis (e.g., ARIMA models) can be used to identify patterns in past sales and project those patterns into the future. However, forecasts are never perfect, especially in the fashion industry where trends can change rapidly. Therefore, it is crucial to incorporate safety stock, which is extra inventory held to buffer against unexpected demand fluctuations or supply chain disruptions. The safety stock level is calculated based on factors like the forecast error, lead time variability, and desired service level (the probability of meeting demand during the lead time). The reorder point is then calculated as the sum of the average demand during the lead time plus the safety stock. For example, if a retailer forecasts that it will sell an average of 50 units of a particular dress per week, and the lead time for replenishment is 2 weeks, the average demand during the lead time is 100 units. If the retailer also calculates that it needs a safety stock of 30 units to achieve a desired service level, the reorder point would be 130 units. This ensures that the retailer has enough inventory on hand to meet demand while waiting for replenishment. While simpler methods like fixed-order quantity systems can be used, they do not adequately account for the seasonality and variability inherent in fashion demand.