How industrial AI is powering localized and resilient supply chains

Single-source supply chains are no longer fit for purpose as manufacturers navigate the ripple effects of the U.S. trade tariffs and recent geopolitical conflicts. As manufacturing organizations adapt, there’s been a noticeable shift towards localization, with more than 50% of senior executives considering supply chain localization a key strategic goal for their organization this year. 

Even a modest commitment to localizing supply chains can lead to significant improvements in performance. A recent PwC- MIT study found that 82% of companies were able to improve resiliency with localization, and a further 77% benefited from cost savings due to reduced transportation costs and a decrease in risks associated with currency fluctuations. 

However, localization is not without its obstacles. Manufacturers must now navigate the complexities of sourcing local materials as well as working with outdated logistic systems that can cause delays and increase distribution costs. But technology has a solution! Industrial AI is becoming a vital asset for manufacturers looking to localize their operations in four key areas: product standardization, remanufacturing, ESG reporting, and scenario planning.

1. Standardize product design to expand supplier options

One of the most overlooked causes of supply chain vulnerability is product design. Highly customized components, for instance, can limit a manufacturer’s flexibility by tying them to single-source suppliers or long-lead-time-parts that are difficult to replace during supply chain disruptions. This is why manufacturers that simplify product designs by shifting from bespoke to standardized components can open themselves up to a wider pool of suppliers, including those closer to home.

Agile automotive manufacturers led by example during the semi-conductor shortage by making decisions to replace custom chips with more commonly used, multipurpose ones that are found in consumer electronics. In doing so, they were able to offset the initial dip in revenue, which saw global car sales in 2021 decline by more than 12% compared to 2019. Standardization helped the industry become less dependent on certain critical resources and allowed companies to build more resilient and shorter supply chains. So, what key lessons can be learned from this experience?

Manufacturers that design with flexibility in mind and pivot to standardized, modular designs can support faster procurement, reduce lead times, and make it easier to manage inventory, all while enabling quicker responses to shifts in customer demand and raw material availability.

2. Leverage industrial AI for ESG compliance and low-emission local supply chains

Sustainability practices are no longer just good for the planet, they’ve become essential for long-term business success. Regulators, investors, and consumers now expect greater transparency from companies, especially around Scope 3 emissions. Witness the fact that 80% of American consumers would be willing to pay more for sustainable products, driven by their commitment to environmental health.

Supply chain localization offers a way to reduce transportation emissions and allows for better oversight of supplier practices, including energy use and labor conditions, which can help ensure manufacturers meet regulatory targets. But how can manufacturers clearly display that their companies are meeting these?

Sustainability at the back end needs to be visible, transparent and auditable, which is where AI-driven data collection and analysis is key in producing these records. Manufacturers can use Industrial AI to automate emissions calculations and embed sustainability into daily operations. This can help businesses achieve accurate carbon insights at scale and embed sustainability into day-to-day operations.

3. Use remanufacturing to reduce waste and strengthen resource resilience

As remanufacturing reduces the need for raw material extraction and long-distance transport, it can be a crucial strategy for manufacturers to reduce carbon footprints and supply risk. In fact, the Environmental Protection Agency (EPA) calls out remanufacturing as one of the most effective ways to lower environmental impact while conserving resources. Local dismantling and repair centers also bring production physically closer to the consumer, which creates regional loops that are more sustainable and responsive.

Research estimates that the automotive remanufacturing market in the U.S. is projected to grow significantly, reaching an estimated value of $24.30 billion US by 2030, as manufacturers compete to keep costs low. But this barely scratches the surface of how remanufacturing can benefit these companies.

When manufacturers add Industrial AI into the mix, the potential to streamline remanufacturing processes scales quickly. Industrial AI can assess which components are reusable, match recovered parts to new production needs, predict failures to improve recovery planning, identify the shortest supply chain, and even flag companies that can use one company’s waste as their raw material. When it comes to core forecasting, Industrial AI tools can even help remanufacturers reduce core safety stock by 2-4% and save 3-5% in freight costs by reducing the cost of expedited shipping.

4. Use AI-driven scenario planning to predict disruptions and improve supply chain agility

The final piece of the puzzle is scenario planning. Currently, just 5% of organizations globally can proactively predict and mitigate disruption before it impacts their business. What’s more, 75% of global manufacturers are still utilizing static systems and siloed organizations with minimal collaboration between engineering and supply chain teams. This is where real-time intelligence and always-on insights can enable a more proactive approach to supply chain risks—and Industrial AI holds the key.

Manufacturers can use agentic AI systems embedded into their enterprise systems to say goodbye to what-ifs and instead simulate disruptions and re-plan in minutes. Where previously scenario planning would have taken a week for a human-led team to test a few key factors, AI agents can ingest massive datasets—be that supplier performance, geopolitical risk, weather—and suggest real-time actions based on learned patterns.

AI also enables upside-down Material Requirements Planning logic by suggesting what can be built with available inventory, rather than just what should be built based on outdated assumptions. For instance, if a supplier experiences delays during a specific holiday season, AI can flag the risks and suggest alternative products that manufacturers can make based on the resources available to ensure the production program is not disrupted.

Say goodbye to what-ifs and build intelligent supply chains closer to home
Today’s manufacturers must now adapt to supply chains that are constantly disrupted and where sustainability, ethics, and agility considerations are equally weighted. Future-ready organizations will consist of those using Industrial AI to localize supply chains by reimaging product designs, cutting down on their emissions, and remanufacturing to keep the product lifecycle going.