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      2. APPROACH

        We predicted store replenishment quantities for each item sold across the retailer expanse using a combination of internal shipping data, point of sales, inventory and invoice inputs at the retailer level. We did this by:

        • First, determining product level seasonality and underlying daily sales variations
        • This information was used to forecast expected future sales
        • Multiple forecasting techniques using time series trends, in combination with store and product related factors were considered
        • Relevant constraints were applied to forecast quantities such as the minimum order quantity required for a replenishment trip to a store, the total capacity of a store, and the threshold below which an inventory replenishment will be required

        KEY BENEFITS

        • Our solution provided an intuitive interface for over 500 users, with reports on sales performance and sell-in recommendations
        • Provided a single, comprehensive veiw of total business performance by Integrating information across retailers

        RESULTS

        The intuitive and easy-to-use interface provided critical metrics, which helped the client reduce onground execution of tasks such as:

        • Adherence to route plan
        • Root cause for missed replenishments
        • “Loss tree” that captures value lost at various stages of execution owing to non-adherence, Standard Operating Procedure

        The client was able to generate precise ROI by capturing trends with respect to lost opportunity and stock outs, in near real-time.

        亚洲 欧洲 日韩 综合在线