Minimize warehousing, logistics, and buyers' labor costs with automated inventory management and replenishment.
The solution uses a proprietary time series forecasting library based on machine learning, which can dynamically adapt to your data and pick the best model.
The solution calculates the economically optimal stock buffers to achieve a given service level or, vice versa, selects the optimal service level to balance the risk of deficit and the warehousing storage costs due to the stock buffer.
The solution calculates the optimal number of units to buy for a specific SKU-store pair in the assortment matrix taking into account the supply shoulder, delivery schedules, current stock, orders in transit, storage and transportation costs. It can be configured to generate plans periodically on schedule, based on the SKU-level stock triggers, or in a hybrid manner.
The solution calculates the optimal number of units to buy for a specific SKU-store pair in the assortment matrix taking into account the supply shoulder, delivery schedules, current stock, orders in transit, storage and transportation costs. For DCs, it can operate in two modes: on the basis of the demand forecasts (top-down) and on the basis of retail stores or regional warehouse's needs (bottom-up).
Optimal distribution of goods from the DCs in the situation when the total demand exceeds the available stock in all warehouses (for example, in the case of a serious supply shortage). The goods are distributed to maximize revenue based on the store performance while minimizing the overall risk of deficit and logistics costs.
Determines the best order structure for a specific supplier taking into account all business agreements and constraints, such as trade volume, core SKU groups, and etc. The solution will recommend expanding the order to increase the truck utilization or asking for a discount in the case of large order. In addition, it can simulate and automatically evaluate the business proposals coming from the suppliers, e.g. "a discount for volume", monitor their soundness over time, and notify the company buyers when it makes sense to accept the corresponding offers.
A flexible tool for measuring and analyzing the effect of the past promotional marketing campaigns and planning the future campaigns. It takes into account the historical SKU and store performance data, finds similar campaigns to ameliorate the data sparsity problem, and conducts automated affinity analysis to make predictions.
Helps choose the optimal delivery frequency for each SKU in the assortment matrix given the minimum stock policy and volume-weight characteristics.
Accurate data-driven replenishment and demand forecasting can significantly reduce stock buffers while maintaining or even improving the service levels. This applies to all elements of the supply chain including stores, distribution centers, and suppliers.
By maintaining optimal inventory levels across the entire supply chain, one can significantly reduce the turnover and, hence, scale up the business.
By maintaining an optimum level of stock buffers, the number of deficit events is significantly reduced resulting in a greater level of customer satisfaction. The SKUs are more likely to be on the shelves and guaranteed to be in the store warehouses.
The solution automatically generates replenishment plans making the complex process more transparent.
Automation and standardization decrease the hiring requirements and allow onboarding new employees faster by simply teaching them how to use the tool.