Rising pressures on cost, carbon and continuity are forcing manufacturers to rethink supply strategies. Maggie Slowik and Andrew Burton show how AI is powering the shift
Recent geopolitical tensions, such as the Ukraine-Russia conflict and the US trade tariffs, have exposed the weakness of relying on single-source supply chains. Manufacturing decisions are no longer made based on sourcing the cheapest product, but sourcing what is available. In response, there’s been a strong push for manufacturers to shift from ‘just-in-time’ to ‘just-in-case’ models and to diversify their supplier networks. Now, sustainability regulations mean manufacturers are having ownership over their carbon footprint and taking the necessary steps to decrease their output.
Localisation has been redefined and become the order of the day. In fact, 68 percent of top management executives see supply chain localisation as a top priority on this year’s agenda. But localisation is not a quick fix. It comes with its challenges—outdated logistics networks causing delays and increases in distribution costs along with pivoting to locally-sourced raw materials, made worse by gaps in connectivity and advanced technology that further hinder visibility, tracking, and coordination across new supply chains.
Industrial AI is emerging as a key tool to help manufacturers overcome these hurdles and take the localisation leap. The payoff is certainly worth it. Even a small investment in localisation can yield significant supply chain performance improvements. A recent study found that 82 percent of companies were able to improve resiliency with localisation, and a further 77 percent benefited from cost reductions thanks to lower transportation expenses, reduced tariffs from international trade, and mitigating currency fluctuation risks. So, it is well worth overcoming the challenges!
1) Product design – start by making components easy to source
One of the most overlooked causes of supply chain vulnerability is product design. Highly customised 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 standardised 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 down by more than 12 percent compared to 2019. Standardisation 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 learnt from this experience?
Manufacturers that design with flexibility in mind and pivot to standardised, 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) Remanufacture to close the loop and turn waste into value
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 centres also bring production physically closer to the consumer, which creates regional loops that are more sustainable and responsive.
Research estimates that the European remanufacturing industry is on track to triple its market value by 2030, reaching a staggering €90bn, as manufacturers compete to keep costs low. But this barely scratches the surface of how remanufacturing can benefit manufacturing companies.
When manufacturers add Industrial AI into the mix, the potential to streamline remanufacturing processes becomes tenfold. 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 percent and save 3-5 percent in freight costs by reducing the cost of expedited shipping.
3) Ethics and emissions are the new bottom line
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 a fifth of Britons (21 percent) are willing to spend more on sustainable products, driven by their commitment to environmental health.
Supply chain localisation offers a way to reduce transportation emissions and allows for better oversight of supplier practices, including energy use and labour 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 business achieve accurate carbon insights at scale and embed sustainability into day-to-day operations.
4) Let AI connect the dots with real-time scenario planning
The final piece of the puzzle is scenario planning. Currently, just five percent of organisations globally can proactively predict and mitigate disruption before it impacts their business. What’s more, 75 percent of global manufacturers are still utilising static systems and siloed organisations 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.
A shift from reactive to proactive decision-making
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 programme is not disrupted.
Industrial AI holds all the aces in localising the supply chain
The traditional cost-optimise-supply chain model is no longer fit for purpose. Manufacturers must now operate in a world where disruption is the “new” norm, and where sustainability, ethics, and responsiveness are just as important. Manufacturers that take the time to rethink how products are designed, embrace circular models, act on emissions data, and leverage Industrial AI for planning, will be able to turn supply chain localisation into a new value-add.
This isn’t just about surviving the next shock, it’s about thriving in a new supply chain era where Industrial AI holds all the cards, and resilience and responsibility go hand-in-hand.
Maggie Slowik and Andrew Burton are Global Industry Directors for Manufacturing at IFS
