AI in supply chain management: How enterprises are rethinking warehousing, forecasting and delivery

A product can be manufactured on time, stocked in a warehouse and still reach the customer late. The problem may lie in a demand forecast that missed a local surge, inventory sitting in the wrong location or a delivery route disrupted hours earlier. Across supply chains, small failures in coordination can become expensive by the time they reach the customer.

Artificial intelligence is giving businesses new ways to identify these problems earlier and respond before they affect operations. Its growing role in supply chain management is changing how companies forecast demand, allocate inventory, organise warehouses, and plan deliveries.

The decisions behind faster fulfilment

Quick-commerce companies have made inventory placement and order processing central to their business models. Their systems must anticipate what customers are likely to order in a particular neighbourhood and ensure those products are available nearby.


Zepto, for example, uses machine-learning models for demand forecasting and inventory management, alongside warehouse automation and route optimisation. Its technology helps determine where stock should be placed, how orders should move through fulfilment centres and how delivery partners should be assigned. These capabilities connect decisions that would otherwise be handled by separate teams or systems.

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Established businesses face a different set of constraints. Their supply chains may span factories, suppliers, distributors, warehouses and retail networks, often supported by systems built at different times. AI-driven forecasting can help planners anticipate changes in demand, while inventory analytics can identify where stock is accumulating or where shortages are likely. The commercial benefit depends on whether these insights lead to better purchasing, production and distribution decisions.

From warehouse efficiency to network-wide visibility

Warehousing is becoming an important testing ground for this approach. AI can help determine where frequently ordered products should be stored, improve picking routes, and flag disruptions as they emerge. Automated storage systems, warehouse management software and predictive analytics can work together to increase throughput without requiring businesses to expand physical capacity at the same rate.

The same logic applies beyond the warehouse. PepsiCo India has been expanding digital capabilities across agricultural procurement, manufacturing, warehousing, and transport planning. Its initiatives include digital shop-floor systems, warehouse management technology and AI-enabled transport control towers. The breadth of these applications illustrates how supply chain intelligence can extend from sourcing raw materials to moving finished goods.

The harder task is connecting these systems. A more accurate forecast has limited value if procurement cannot respond to it. Better warehouse productivity may fail to improve delivery performance if transport planning remains disconnected from order volumes. Supply chain gains increasingly depend on how well information moves between functions.

Can traditional supply chains learn from quick commerce?

Speed is one lesson. Quick-commerce operators have had to build processes around short fulfillment windows and constantly changing local demand. Established enterprises can apply similar principles to selected workflows, even when their operating models and delivery commitments differ.

However, faster decisions require reliable data, integration with existing systems and clear accountability when automated recommendations are wrong. AI implementation also carries costs, from infrastructure to staff training. Businesses need to establish where these investments will produce measurable improvements in service levels, inventory efficiency or operating costs.

These questions will be explored at the AI Business Transformation Masterclass & Awards 2026, presented by The Economic Times in association with IIM Bangalore. The supply chain track will examine AI deployment across procurement, warehousing and last-mile logistics, including what established enterprises can learn from newer, faster-moving businesses.

The masterclass will focus on the operational decisions involved in taking AI beyond isolated applications. The day will also feature the ET Most Innovative AI Product Awards 2026, recognising AI innovations delivering business value.

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