Global value chains are facing unprecedented challenges, with a series of disruptions exposing vulnerabilities and costing billions of dollars. The solution to these upheavals could lie in an advanced analytics system that tirelessly collects and processes information from a plethora of sources to minimise or neutralise the impact of such disruptions.

Generative artificial intelligence (GenAI) is fast emerging as a potential silver bullet, with companies investing in GenAI across functions. A McKinsey report projected GenAI could add $4.4 trillion to the global economy annually. That’s greater than India’s GDP in FY2023. And this is just an early estimate (the study only looked at 63 business use cases for GenAI).

From helping a leading automobile manufacturer to optimise production schedules, to enabling a global logistics company to optimise warehouse layouts and picking/packing processes, GenAI has already showcased its potential to revolutionise supply chain management (SCM). A recent McKinsey SCM capability assessment saw GenAI-powered SCM beat over 90 per cent of traditional SCM practitioners.

GenAI can use existing datasets to generate new data, designs, or solutions for more flexible supply chains. One food and beverage multinational employed GenAI to create eco-friendly supply-chain scenarios that helped identify opportunities to reduce waste, minimise carbon footprint and optimise resource usage.

Accurate forecasting

By integrating diverse data sources — economic indicators, social media trends and consumer behaviour patterns — GenAI enhances demand-sensing and accurate forecasting. A global consumer goods company used GenAI for more precise demand forecasting, a valuable capability in today’s volatile markets.

Beyond forecasting, GenAI can simulate market scenarios — such as power shortages and competitor actions — and their impact on demand, allowing businesses to prepare for different outcomes and meet consumer needs despite market fluctuations.

Besides improving operational efficiency, GenAI can ensure compliance with growing regulatory demands for sustainable, ethical business practices through its ability to trace material origins. This enhances transparency and builds consumer trust. GenAI can also assist design teams to innovate on product designs.

The implementation of GenAI in SCM is not without challenges, however. Much of the training data for GenAI models is sourced from web-crawl only post 2008, raising questions about data validity and applicability. GenAI models could also improve accuracy ( 60-70 per cent currently) with more specific training.

As the variability in prompts increases, issues around consistency become more pronounced. The computational intensity required to build and deploy GenAI models poses another challenge: being both expensive and resource-intensive, it could raise carbon emissions and complicate ESG compliance.

These concerns should not deter companies. They are rather a reminder to adopt a measured approach. GenAI isn’t going away. Its transformative potential will only grow as the technology matures. Embracing it could help companies to surge ahead in an increasingly competitive market.

Ganesh is a partner in McKinsey and Company’s Chennai office, and Varma is a knowledge expert in the Gurugram office