AI in Supply Chain Management: Building resilient, predictive and efficient operations

From reacting to anticipating disruption

A shipment delayed at one port can affect production schedules, inventory levels and customer commitments thousands of kilometres away. By the time the delay appears in a conventional report, the options available to a supply chain team may already be limited. The advantage increasingly lies in identifying the risk early enough to change course.

For businesses managing complex supplier networks, AI offers a way to bring together signals that are often scattered across procurement systems, logistics partners, warehouse records and demand forecasts. Analysing these inputs can help teams spot unusual patterns, anticipate shortages and assess how a disruption in one part of the network could affect the rest.


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The need for stronger resilience remains clear. McKinsey’s 2024 Global Supply Chain Leader Survey, which included 88 senior supply chain leaders, found that 9 in 10 respondents had encountered supply chain challenges during the year. Although companies had made progress in improving visibility into their direct suppliers, gaps remained deeper in their supply networks. A disruption originating further upstream can be difficult to manage when businesses have limited information about the suppliers on which their own vendors depend.

Better forecasts, faster decisions

Demand forecasting is one area where AI can help change operational planning. Conventional forecasting methods can struggle when purchasing patterns shift quickly or when several external factors influence demand at once. AI models can analyze a broader range of historical and current information, helping planners identify emerging trends and revise forecasts as conditions change.

The implications extend to inventory management. Better demand estimates can help companies decide where to position stock, when to replenish it and which products require additional buffers. These decisions involve trade-offs: excess inventory ties up working capital, while insufficient stock can interrupt production or leave customer orders unfulfilled.

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AI can also support scenario planning. Teams can compare possible responses to a supplier delay, a sudden increase in demand or a change in transport availability before committing to a course of action. Human judgement remains essential, particularly when the available data are incomplete or the cost of a wrong decision is high.

Yet deployment remains a challenge. McKinsey’s 2025 supply chain risk survey found that three-quarters of respondents were planning, designing or piloting AI use cases, while only 19% said they were deploying AI tools at scale. The gap suggests that interest in the technology has moved ahead of implementation across much of the sector.

Building intelligence into the operating model

Predictive systems are only as useful as the information feeding them. Inconsistent product records, fragmented systems and limited visibility into supplier operations can undermine forecasts and risk assessments. Companies also need clear processes for deciding who responds when an AI system flags a potential disruption.

These questions will feature in “The Next-Generation Supply Chain: Using AI to Build Resilient, Predictive and Efficient Operations,” a panel at The Economic Times’ Future of Knowledge Work Summit 2026 in Mumbai. The discussion will examine how AI can support supply chain resilience and operational efficiency as businesses contend with increasingly complex networks.

For supply chain leaders, the objective is to make decisions earlier, with a clearer view of the consequences. AI can help surface risks and evaluate possible responses, but resilience ultimately depends on how effectively organisations connect those insights to procurement, planning and execution. The next generation of supply chains will be defined by their ability to anticipate change and respond before disruption reaches the customer.

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