Global Manufacturers Turn to AI to Build More Resilient and Efficient Supply Chains

Global manufacturers are accelerating the use of artificial intelligence across supply chains as they seek to manage volatility, improve efficiency and reduce operational risk. A new Supply Chain Digital overview highlights how major companies are deploying AI, machine learning, digital twins, and connected data systems to make production, procurement and logistics more adaptive.

The trend reflects a broader shift in manufacturing. Supply chains have faced repeated pressure from geopolitical disruptions, rising demand for electric vehicles and clean energy technologies, energy price volatility, climate-related risks, and changing customer expectations. For companies with global supplier networks, the priority is no longer only cost reduction. It is also visibility, resilience, speed, and the ability to adjust production before disruption turns into financial loss.

AI Moves from Pilot Projects to Operational Infrastructure

Schneider Electric is one example. The company uses what Supply Chain Digital describes as a “self-healing” supply chain platform, combining adaptive machine learning, Internet of Things data, and real-time workflow optimization. The system can adjust parameters such as minimum order quantities, lead times, and safety stock levels, helping the company respond to delays, shutdowns, or supplier risks. According to the report, Schneider Electric has seen a 10% overall decrease in inventory, a six-day reduction in days-in-inventory, and more than €100 million in value generated.

For sustainability teams, inventory reduction is not only a financial metric. Better demand planning can reduce excess production, avoid unnecessary warehousing, and limit emergency freight, which is often more carbon-intensive. It can also help companies use materials more efficiently, especially in sectors exposed to constrained supplies of electrical equipment, semiconductors, or critical minerals.

Unilever is applying digital twin technology to create virtual versions of production lines. The company operates more than 280 factories and 200 warehouses, making even small efficiency gains significant at a global scale. Its digital twins use real-time operational data, including temperature and cycle times, to predict and optimise manufacturing performance. Supply Chain Digital reports that Unilever’s Valinhos facility in Brazil achieved energy cost savings of US$2.8 million and a 1% to 3% increase in productivity after implementing digital twin technology.

This is particularly relevant for companies working toward net-zero targets because factory optimization can support lower energy consumption without waiting for major capital upgrades. Digital twins can test changes before they are implemented physically, helping operators identify where energy, heat, water, or raw materials are being wasted.

Predictive Maintenance and Procurement Intelligence

Rolls-Royce is using AI and machine learning in its “signature analyser” approach, which detects anomalies in engines through vibration, thermal and acoustic patterns. The aim is to identify potential component failures before they happen, rather than reacting after faults appear. Predictive maintenance can reduce downtime, extend asset life, and lower the need for replacement parts, all of which can reduce material demand and lifecycle emissions when implemented effectively.

BMW Group is applying generative AI and multi-agent systems to purchasing. Its BMW Group AIconic Agent is designed to automate parts of global sourcing, scan historical information, generate more consistent request documents, assess bids, flag legal discrepancies, and identify supplier risks. With around 12,000 suppliers and an annual purchasing volume of about €90 billion, even incremental improvements in procurement decisions can have large implications for cost, compliance, and sustainability.

For manufacturers, procurement AI could become increasingly important as climate disclosure, supplier due diligence, and carbon accounting requirements expand. Companies need better data on where materials come from, how suppliers perform, and whether procurement choices align with emissions targets. AI can help process large volumes of supplier information, but only if the underlying data is reliable and governance rules are clear.

PepsiCo is also investing in AI-enabled digital twin technology through work with Siemens and NVIDIA. The company is using NVIDIA Omniverse libraries to simulate facility upgrades in the United States before scaling them globally. The approach allows PepsiCo to test facility layouts, validate changes, and optimize its physical footprint before committing capital to new builds or expansions.

Implications for Net-Zero and Sustainability

AI has the potential to support lower-emission supply chains in several practical ways. It can improve forecasting, reduce waste, optimize routes, identify energy inefficiencies, support predictive maintenance, and help companies compare sourcing scenarios. The World Economic Forum has argued that autonomous supply chains can contribute to sustainability goals by improving efficiency, reducing waste, and optimizing logistics.

However, AI is not automatically sustainable. The International Energy Agency has warned that AI development depends on affordable, reliable, and sustainable electricity, while training and deploying AI models takes place in power-hungry data centres. The IEA also notes that AI can transform the energy sector, but there is “no AI without energy,” making the source and efficiency of computing power a material issue for corporate climate strategies.

For manufacturers, this means AI deployment should be assessed on both sides of the ledger. A digital twin that cuts factory energy demand or avoids wasted production may deliver clear environmental benefits. A poorly governed AI system that increases computing demand without measurable operational gains may add cost and emissions with limited value.

Data quality is another constraint. Supply chain AI depends on accurate information from factories, suppliers, logistics providers, and enterprise systems. If emissions data, inventory records, or supplier risk data are incomplete, AI may scale poor decisions rather than improve them. This is especially important for companies reporting Scope 3 emissions, where supply chain data is often fragmented and difficult to verify.

The direction of travel is clear. AI is moving from an experimental technology into the operating systems of global manufacturing. The companies gaining the most value are not simply adding AI tools, but connecting them to measurable business outcomes: lower inventory, lower energy use, faster risk response, better procurement, and more efficient production.

For net-zero strategies, the opportunity is significant. AI can help manufacturers make supply chains more transparent, less wasteful, and more responsive to disruption. But it will need strong data governance, human oversight, and attention to its own energy footprint to become a credible tool for industrial decarbonisation.

Source: supplychaindigital.com