FEATURE Supply chains
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
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.
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 businesses 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
planning. Currently, just 5% of organisations globally can proactively predict and mitigate disruption before it impacts their business. What’s more, 75% 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. 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Planning logic by suggesting what can be built with available inventory, rather than just what should be built based on outdated assumptions.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 alternative products that manufacturers can make based on the resources available to ensure the production programme is not disrupted.
IFS
www.ifs.com
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