IBM's Pushpinder Singh: How AI Will Transform SCM

Supply chain performance has emerged as a huge challenge for businesses around the  world, with increased pressure to transform traditional logistics operations has never been greater. 

What was once considered a back-office function now sits at the heart of strategic business decisions, driving everything from customer satisfaction to competitive advantage.

The path forward is clear: organisations must evolve from reactive, data-driven operations to proactive, action-driven supply chains powered by AI. 

As forecast accuracy tops CEO priorities for the next three years, according to IBM Institute for Business Value research, supply chain leaders are turning to advanced technologies like AI, machine learning and IoT to revolutionise how they predict, plan and respond to market demands.

Pushpinder Singh, Partner Global Practice Leader for Driving Supply Chain Transformation & Strategic Offerings at IBM, discusses how cutting-edge technologies are reshaping supply chain forecasting, the critical strategies needed to build resilience in an increasingly volatile market and the practical steps leaders can take today to future-proof their operations. 

From agentic AI that can autonomously adjust inventory in real-time to simulation platforms that help companies prepare for disruptions before they occur, Pushpinder outlines the transformation that's already underway–and how organisations can position themselves to thrive in this new landscape.

How are advanced technologies like AI, machine learning and IoT transforming supply chain forecasting and demand planning and what impact do you expect these to have on accuracy and operational efficiency in the next few years?

Forecast accuracy is a key priority for organisations right now — and it’s not just supply chain leaders saying that. Our recent IBM Institute for Business Value study found that improving forecast accuracy is the top priority for CEOs over the next three years. That really underscores how critical this has become at the strategic level.

AI has become a critical enabler in this space, shifting us away from traditional, static forecasting models toward a much more dynamic and responsive approach.

AI’s strength lies in its ability to process and analyse vast amounts of data, faster and more accurately than any human could. 

Whether it's sales data, social media trends, economic indicators or even weather patterns, AI can bring all of this data together to help provide a more forward-looking, accurate picture of demand. That allows supply chain leaders to better anticipate needs, avoid overproduction and minimise stockouts.

The impact on accuracy and operational efficiency over the next few years will be significant, especially as adoption of agentic AI increases.

While traditional AI has transformed how we analyse, agentic AI will transform how we execute. For supply chain forecasting and demand planning, this means AI agents will be able to not only generate insights, but act on them autonomously — adjusting orders, reallocating inventory and even reconfiguring production schedules in real time, all with minimal human intervention.

What strategies do you recommend for organisations looking to enhance both agility and resilience in their supply chains, especially in response to increasing market volatility and global disruptions?

Today’s business realities demand a new approach—what worked yesterday might not work tomorrow. To stay ahead, supply chain leaders need to move beyond traditional methods and AI is a big part of that. 

Even with disruption as the new normal, it’s possible for supply chain leaders to stay one step ahead.

One particularly powerful strategy is leveraging AI to simulate disruption scenarios. This allows organisations to play out different “what-if” scenarios, analyse their impact and decide what is the best course of action — all before the disruption even occurs. 

We’re seeing this in practice with a global healthcare company we’re partnering with.

We’re developing an AI-powered platform that identifies risks and generates insights, enabling them to make faster, data-driven decisions to mitigate disruptions and build a supply chain that can absorb shocks and adapt quickly. 

How can companies better integrate cross-functional collaboration and real-time data sharing into their supply chain planning processes to improve responsiveness and decision-making?

Supply chain used to be considered a back-office function, but today it’s front and centre. More and more business leaders are recognising supply chains as a critical driver of business performance. In fact, supply chain performance now ranks as the number one challenge for CEOs, according to IBM research. 

As a result, cross-functional collaboration is more important than ever. CSCOs today must partner closely with IT, finance, marketing, sustainability and other departments to ensure decisions are aligned with the company’s broader strategic goals. 

AI can facilitate this collaboration by pulling data from across the supply chain to create a single source of truth —breaking down silos and ensuring everyone is working with the most accurate and up-to-date information.

This real-time data sharing allows teams to collaborate more effectively and respond quickly to market changes or disruptions.

Given the growing importance of cost-to-serve and granular supply chain analytics, how should businesses approach optimising their inventory management to balance service levels with cost efficiency?

Balancing inventory can be a fine line. On one hand, if you don’t have enough inventory available at the right time and place, you risk disappointing customers. On the other hand, holding too much inventory can be costly. 

Supply chain leaders need to know when to order, how much to order and where to store stock. AI can help with this in a few ways: 

• Real-time visibility: When paired with IoT, AI can provide a real-time view into inventory locations and conditions throughout the supply chain. IoT networks enable inventory items to be equipped with sensors and connected to the internet, allowing them to collect and share data. AI can then make sense of this data, looking for important patterns or problems, such as low stock levels or delayed shipments. 
• Warehouse operations: AI can help streamline warehouse operations, including optimising layout design and fulfillment processes. By analysing factors like product size, demand trends and turnover rates, it can recommend efficient configurations to help reduce lead times, cut costs and improve overall customer satisfaction.
• Demand forecasting and projection: As mentioned, AI has already proven to be a powerful tool for demand forecasting and agentic capabilities are only making it stronger. Unlike traditional models that rely on historical data, agentic AI can analyse customer behaviour and demand patterns in real time, leading to more precise inventory forecasting.

What steps can supply chain leaders take today to future-proof their operations and ensure they remain competitive as industry transformation and digitalisation accelerate?

The future of supply chains will be marked by a shift from data-driven to action-driven operations, thanks to Agentic AI. By 2026, 76% of surveyed CSCOs say their overall process efficiency will be improved by AI agents. 

This shift isn’t just about efficiency, it’s also introducing a new operation that combines autonomy, intelligence and speed to fundamentally change the way supply chains operate. But before this technology can fully come to bear across supply chain operations, there are critical considerations that must be in place. 

For example, leaders must ensure they have the right data — not just high quality, but also accessible and properly governed. Just as important is upskilling supply chain teams to work effectively with this technology. Teams need to understand how AI works, what it can and can't do and how to interpret its recommendations critically to ensure outputs are aligned with business goals and values.

The supply chain leaders who take action now — putting the right structures in place and embracing this shift with intention — will be the ones best positioned to navigate complexity, drive innovation and build long-term resilience.