4 real-life Business Process Automation examples for Supply Chain

4 real-life Business Process Automation examples for Supply Chain

Automation is different in supply chain because performance depends on a network of suppliers, inventory positions, transport capacity, service levels, and disruptions that propagate quickly across the system.

The highest-value use cases improve planning, exception management, supplier communication, and inventory visibility so teams can act faster on shortages, delays, and demand shifts without losing control of cost or resilience.

Industry

Sector

Flipkart's AI-Powered Supply Chain

Flipkart has optimized its complex supply chain by implementing an AI-based demand sensing and automated replenishment platform to manage the massive surges in consumer activity during peak events like "Big Billion Days." By forecasting demand at both the SKU and warehouse levels, the system ensures that high-demand products are pre-positioned in regional fulfillment centers, minimizing stockouts and drastically reducing last-mile delivery times.

Use Case
AI demand forecasting & inventory optimization
Tools
Internal Tools
Input
Historical sales data, festival/seasonal calendars, market trends, real-time inventory levels, and supplier lead times
Process
The AI forecasts demand spikes at the SKU and warehouse level; automated systems then trigger the restocking of fast-moving products and pre-position inventory at regional fulfillment centers to shorten delivery distances
Output
Reduced stockouts during peak sale events, lower warehousing costs due to decreased overstock, and faster delivery to customers
Outcome
Significant reduction in warehousing expenses, inventory obsolescence, and carrying costs; improved seller performance and customer satisfaction during major 2024 sale events
Pure Play Ecommerce
Supply Chain

Nestlé and Demand Forecasting

Nestlé S.A utilizes AI-driven analytics and machine learning to refine demand forecasting and inventory management, allowing for precise stock planning and real-time logistics tracking to minimize waste and ensure product availability.

Use Case
Improving Demand Forecasting Accuracy and Inventory Optimisation
Tools
Coupa
Input
Historical sales data, seasonal trends, consumer behaviour data, and container arrival predictions
Process
AI analyzes multi-variable inputs to generate statistical demand forecasts while predicting container arrival times at destination ports to mitigate external disruptions
Output
More accurate stock planning, fewer stockouts, and reduced overproduction
Outcome
Streamlined supply chain workflows and optimized inventory levels
Food and Beverage Manufacturing
Supply Chain

Walmart's Retail Shelf Recognition and Inventory Management

This computer vision automation enables major retailers like Walmart and Carrefour to maintain optimal inventory levels by utilising smart-shelf cameras and robotic image capture to monitor stock in real-time, ensuring shelves are correctly stocked and priced.

Use Case
Retail Shelf Recognition and Inventory Management
Tools
IoT Sensors & Internal Tools
Input
High-resolution annotated images, fine-grained product classifications, and data regarding shelf structures, pricing labels, and promotional materials.
Process
The system performs real-time detection and classification of products and pricing. It evaluates shelf layouts against expected configurations and identifies discrepancies between digital pricing systems and physical shelf displays. Continuous scanning allows the AI to flag missing products and track inventory levels autonomously.
Output
Automated analysis of shelving and pricing, compliance scores for store layouts, real-time alerts for out-of-stock items, and automated stock reorder requests.
Outcome
Detection of out-of-stock conditions with over 90% accuracy, leading to a dramatic reduction in lost sales. Brand managers, such as those at Nestle, further utilise these insights to adjust regional promotional budgets in real-time.
Grocery and FMCG Retail
Supply Chain

JD.com's Self-Operating Fulfilment Centres

JD.com has pioneered the transition to self-operating fulfillment centers by integrating ForwardX Robotics and AI-driven Warehouse Management Systems (WMS). By combining machine learning demand forecasting with autonomous case-picking robots, the company has transformed its logistics arm into a near-fully autonomous operation that optimises storage and picking speed with minimal human intervention.

Use Case
Warehouse Automation with AI
Tools
ForwardX Robotics
Input
Historical order data, real-time inventory, product dimensions and weights, warehouse layout data, and demand signals
Process
AI identifies optimal storage locations and predicts order volumes to pre-position stock, while autonomous robots execute the physical picking and movement of inventory orchestrated by the WMS
Output
Near-fully autonomous warehouse operations with accelerated order processing and improved space utilization
Outcome
136% increase in units processed per hour and a 300% boost in operational efficiency (expanding storage units from 10,000 to 35,000)
Pure Play Ecommerce
Supply Chain
See all 60 Business Process Automation examples