Are Cognitive Supply Chains the Future?

Cognitive supply chains are recalibrating how global logistics operate and how decisions are made. 

Deloitte, in its Redefining Supply Chain Agility Through Cognitive Automation report, explains the transition is from “people doing the work supported by machines” to “machines doing the work guided by people.” It’s a change that puts intelligent systems at the heart of operations, with human input steering them rather than powering them.

At the centre is what Deloitte calls a Cognitive Command Centre. This acts as a control tower for the supply chain, pulling in data from multiple sources, analysing root causes of disruption, suggesting solutions and, when ready, executing those decisions automatically. 

Deloitte also explores how consumer, market and stakeholder pressure shape this type of supply chain investment. The report found that 34% of respondents say better customer service and engagement is the number one outcome they expect as a result of digital transformation, which makes even more sense when paid with the fact 47% expect increased investment in consumer experience.

Businesses across all industries are now shifting their approach to match this demand; nearly 90% of surveyed organisations say they plan to invest in a more agile and resilient supply chain in the next two years. The vast majority (81%) list data sharing with partners as a key focus area, while almost 30% are building digital business models aimed at revenue growth.

How it all works in practice is a little more complex. 

Why grocery is in a league of its own

As Vice President of Product for Supply Chain at Ocado Group, Toni Radzihovska knows what it means to deliver precision at scale. 

Ocado has been building its reputation for 25 years, starting as one of the UK’s earliest online supermarkets. Today, it offers global retailers access to its own automation and fulfillment technology through the Ocado Smart Platform (OSP).

“When I tell people I build the software that helps manage global supply chains, I can often see their eyes rolling,” she says. “But the supply chain is so important. When it works well, it’s invisible. When it doesn’t, even the smallest snag can have a wide-reaching impact.”

Grocery, she explains, is a completely different game. 

“In many verticals, supply chains are relatively linear and demand is fairly predictable. But in grocery, retailers are required to deal with large baskets, multiple temperature regimes, perishable stock and constantly evolving customer preferences.”

Online grocery shopping adds a new level of complexity, as the typical customer might purchase about 50 products from a selection that can include up to 50,000 options, often adjusting their choices right up until checkout.

“This forces us to work harder on our end,” Toni continues, “replanning and recalculating to ensure we have the right products in stock.”

The solution lies in an interconnected, smart supply chain: “AI is embedded throughout our grocery operation: e-commerce, fulfillment, supply chain and last mile. This allows for operations to be seamlessly optimised.”

When a shopper edits their basket, the system knows exactly what’s in stock, when new stock is due and which supplier orders can still be changed. 

“We only show customers what we know we can fulfil, so they won't have to deal with disappointing substitutions on arrival.”

An assistant or co-pilot?

For Stéphane Piat, Senior Vice President of Strategy & Performance at Schneider Electric, the definition of cognitive supply chain also starts with intelligence – but it doesn’t end there.

“A cognitive supply chain goes beyond simply using AI for automation or prediction,” says Stéphane. “It’s about creating a self-learning, adaptive system that continuously improves by interpreting data in context, understanding cause and effect and making recommendations, or even decisions, in real time.”

According to Stéphane, traditional AI still leans heavily on static models or isolated applications. A cognitive system, on the other hand, is much more ‘alive’; always learning and always adapting.

“It’s like moving from a smart assistant to a strategic co-pilot,” he adds

Cognitive automation therefore not only responds more quickly, but helps plan more effectively in the first place. 

Predictive intelligence for resilience

Ocado’s automation and AI-backed forecasting give demand planners the tools to manage stock with precision and speed. 

Instead of waiting for problems to appear, they now receive real-time alerts that highlight potential operational issues before they disrupt KPIs. These alerts help teams identify what could have the biggest impact on cost or service, allowing them to act early rather than react after a stockout.

One of the platform’s key tools, Product Insights, helps diagnose the reasons behind unusual spikes or dips in supply and demand. These insights let planners make informed decisions when demand suddenly shifts, whether that’s because of seasonality or a social media trend sending a product viral. 

“Our automation,” Toni says, “allows us to scale up or down to respond quickly to spikes in demand. The goal is to shift from reactivity to proactivity by identifying and preventing upcoming stockouts.” 

Ocado’s forecasting system uses deep learning models, designed to mimic human information processing. It trains on billions of data points from the Ocado Smart Platform across multiple retailers, ensuring accurate demand forecasting for new items without long sales histories. The system continuously learns from customer behaviour and updates recommendations with fresh data.

Toni explains: “Our demand forecasting system helps retailers make smarter decisions on waste reduction and product availability. It runs on advanced models trained on billions of sales points across regions.”

This enables retailers to anticipate demand without relying on extensive sales histories, even for new products, while continuously adapting to shifting shopping behaviours and drawing on collective industry insights. During peak periods such as Christmas, customers frequently reserve delivery slots weeks in advance but continue modifying their baskets until the last moment. The forecasting system recalculates these updates in real time, allowing retailers to adjust purchase orders, meet customer commitments and minimise waste.

Other organisations use AI similarly for complex logistics. Deloitte’s Command Centre supports high-value supply chains like cell and gene therapies, costing US$500,000 to US$1m per batch and requiring storage as low as –150°C. The platform offers full traceability, predictive tools and temperature alerts to preserve quality.

For vaccine distribution, the Command Centre provides oversight and alerting during ultra-cold storage and transport. During COVID-19 vaccine rollout it detected sub-spec shipments in transit, reducing delivery risks.

Deloitte also shares how AI supports smarter delivery commitments. Instead of fixed rules, it uses dynamic lead times, supplier limits and transport availability to give accurate delivery promises and automatically respond to exceptions.

Meanwhile, Schneider Electric’s Zeigo Hub focuses on supply chain decarbonisation. It pairs guided supplier onboarding with tailored sustainability plans powered by AI. 

Stephane explains: “Resilience means anticipating, absorbing and adapting to shocks – geopolitical, environmental or market-driven. AI and analytics provide early warnings, scenario modelling and prescriptive insights.”

He points to an example from the Red Sea crisis: “Shipping routes were severely impacted. Our cognitive platform flagged the risk early through external data and lead-time deviations. It recommended alternative sourcing and logistics options, which our teams validated and executed within hours – not days. That agility helped avoid delays for critical deliveries and maintain continuity in key markets.”

Empower tomorrow’s supply chains

At Schneider Electric, AI plays a critical role in factory and logistics operations, helping the company respond to demand in real time while reducing energy use and cost.

Stephane explains: “Energy optimisation is where we see the most immediate and measurable impact. AI helps reduce energy consumption in our factories and logistics by adjusting operations based on demand, weather and energy pricing. 

“We’re also using AI to model the carbon footprint of our supply chain and make smarter trade-offs in sourcing and transportation. Over time, supplier engagement will become a major lever as we scale capabilities across our ecosystem.”

These capabilities go beyond reducing emissions. AI models help supply chain teams make faster decisions by predicting demand changes or flagging risks earlier. Cognitive supply chains can learn, predict and suggest actions.

Yet, introducing cognitive technology alone is not enough. 

Stephane says building these supply chains requires new ways of thinking and working: “We need people comfortable with data, questioning assumptions, trusting machine insights. That means upskilling in data literacy, digital twin modelling and scenario planning.”

This shift echoes Deloitte’s findings on cognitive supply chains. Its report describes a “self-driving” supply chain where up to “80% of decisions” are automated, leaving complex exceptions to humans. The Cognitive Command Centre enables better visibility, flexibility, collaboration and real-time control. These improvements allow organisations to “track and monitor events… take proactive actions” and “quickly adapt to disruptions” without large cost increases.

To move from strategy to action, the report recommends a phased approach: “Think big, start small, scale fast.” 

That means identifying where AI delivers value now, picking the right technology and evolving the business model around it.

Toni offers practical advice on starting the journey: “My key advice is simple: begin with the problem. For online grocery, AI was required to master three problems: offering enough choice to satisfy customers; ensuring this choice doesn't drive food waste; and guaranteeing choices can be fulfilled through delivery. We don’t throw sophisticated AI at every problem. Instead, we assess which problems deliver greatest value and choose the best tool.

“The real value comes from deeply understanding customer pain points and improving their experience.”

Stephane echoes this: “Start with a clear business problem, not technology. It’s easy to get caught in AI hype, but real value comes from solving pain points: improving inventory, reducing emissions, managing risk. Build from there, scale what works and keep the human in the loop.”

In other words, a cognitive supply chain can only thrive not by replacing human input, but supporting it.