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Space Restrictions
Convenience retailers operate 2000 sq ft stores with <2 days of stock.
The non-existent backroom storage, coupled with limited shelf space per SKIJ, poses a replenishment puzzle beyond the computational abilities of traditional planning tools.
AI-led demand forecasting and auto-replenishment seamlessly integrate hyperlocal forecasting with replenishment planning to enable proactive auto-replenishment, ensuring just-in-time orders and deliveries.
Planogram Adherence
Planogram adherence is critical, so replenishment is always 'adjusted'.
Retailers are constantly adjusting replenishment plans to accommodate planogram constraints, causing stock imbalances and bloating inventory costs.
AI-powered replenishment algorithms factor in all planogram constraints like minimum display quantities, maximum on-shelf quantities, promotional displays, and more, and automatically generate optimal order plans.
Space Restrictions
Demand for Ready-to-Eat items varies significantly by location, day, weather, special events, etc.
Traditional demand modeling and order planning tools don't offer holistic inventory planning for all stock locations, leading to stock imbalances and higher costs thereafter.
AI-driven retail-native C-store inventory optimization solutions can optimize all kinds of orders - direct to store, WH orders, Distribution Centre orders, and dark store orders. Thus, inventory stays optimal at all locations.
Heavy Reliance on Warehouse Inventory
Spoilage in RTE CategorySpoilage in RTE CategorySpoilage in RTE Category
The non-existent backroom storage, coupled with limited shelf space per SKIJ, poses a replenishment puzzle beyond the computational abilities of traditional planning tools.
AI-led demand forecasting and auto-replenishment seamlessly integrate hyperlocal forecasting with replenishment planning to enable proactive auto-replenishment, ensuring just-in-time orders and deliveries.
Need for Optimal Stock Allocation
Key value items require optimal stock allocation across all store locations as per demand.
Retailers forecast demand in a one-size-fits-all manner. So, revenue from key value items stays low due to inefficient stock allocation.
AI-led demand forecasting and auto-replenishment solutions offer configurations to allocate inventory based on store ranks, sales performance, equitable allocation, etc. Hence, the key value items are allocated in the most optimal manner boosting sales and profits.
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