Infographics

Intelligent Replenishment for Optimal Inventory

Crafting an Intelligent Replenishment Strategy for Optimal Inventory

As many as 70-90% of stockouts are caused by poor shelf replenishment practices that in turn lead to lost sales, erosion of customer loyalty, and directly impact retail profitability.

Crafting an intelligent replenishment strategy involves cross-functional integration, data-driven insights for smart ordering, automation-driven forecasting, and improved supply chain visibility.

Data-Driven Decision Making

40% of retailers cancel at least one in ten orders due to inaccurate inventory data.

Integrating data across all functions to draw actionable insights for inventory planning ensures accurate and responsive inventory management aligned with real-time customer demand.

Automated Forecasting

AI-powered forecasting can reduce supply chain errors by 30 to 50%, shrinking lost sales by 65% and warehousing costs by 10 to 40%.

Automating demand forecasting streamlines supplier communication and collaboration reduces lead times, and improves fulfillment.

Multi-Variate Demand Planning

Almost 60% of retailers have less than 80% inventory accuracy and face data challenges.

AI-powered demand planning considers external as well as internal sales influencers to generate highly accurate forecasts for product location-level precision.

In-Store Promotions Dynamics

Cannibalization wipes out as much as 17% of the extra sales volume generated by promotions.

ML-driven replenishment balances demand at the SKU-level to ensure minimal competition and keeps the profits intact across all categories.

Supply-Side Disruptions

45% of businesses have limited supply chain visibility, and only 15% have visibility beyond Tier 1 suppliers.

ML-driven self-learning models adapt to supply chain disruptions and generate optimal replenishment plans by modeling variations in lead times, fill rates, safety stock, and pending orders.