FEATURE Industrial AI
STAY AHEAD OF DISRUPTION
Maggie Slowik, Global Industry Director for Manufacturing at IFS, explains how to keep your supply chain intelligence ‘always-on’ in the disruptive world we face today
F
or years, supply chain disruption has been treated as an occasional shock but now chokepoints on land, sea, and in the air are becoming the new norm. New Fed data shows global supply chain pressure has nearly tripled within one month, hitting its highest index point in four years. With the Strait of Hormuz blockade the latest example of how disruptions in energy, transport, and agricultural inputs can ripple across interconnected markets, it’s no surprise that supply chain pressures are on the rise. Manufacturers already have the data to help them take proactive measures to mitigate the impact on their operations. But to be able to use it they must transition away from their usual reactive, case-by-case decision-making, and move to a continuous intelligence approach with assistance from strong data governance and advanced technology. Geopolitical instability, material shortages, and climate-related events are no longer isolated events, they are a regular occurrence that disrupts operations globally. A gauge of UK manufacturer cost pressures by S&P Global’s UK Manufacturing Purchasing Managers’ Index shows a significant increase in April (53.7 up from 51.0 in March). What’s more, delivery delays are now the most widespread since mid- 2022 due to the standoff in the Strait of Hormuz, which is choking off 20% of the world’s supplies of oil and gas among other critical goods, such as fertiliser for agricultural use and other petrochemical derivatives that are used for vital medicines.
For manufacturers, the question is no longer if and when disruption will occur, but whether they are equipped to respond in real-time.
18 September 2026 | Automation
But what is holding organisations back from making the shift to continuous intelligence? Organisations are not short of data. In fact,
they are overwhelmed by it. A staggering 74% of manufacturing and engineering firms still rely on old systems for day-to-day work, with information being stored across various ERP systems, transport platforms, supplier portals, spreadsheets, and unstructured formats such as emails and PDFs. What manufacturers face today is not an access issue but an orchestration problem. Siloed data is often inconsistent and difficult to act on, which leaves organisations unable to build a coherent, real-time picture of their supply chain. This traditional model is too slow for today’s supply chain environment. By the time mitigation decisions are made, often with external consultant support, conditions have already changed! This episodic approach leaves companies structurally vulnerable to disruptive supply changes. When manufacturers spend around 10% of
revenue on transportation, even a 10% increase in control can make a big difference to the bottom line.
So how can manufacturers change their supply chain operations?
Turn early signals into early action You can’t predict the disruptions – but with AI you can turn early signals into early action. The aim today is not to predict every disruption but for manufacturers to understand their available options at any given moment and act decisively when conditions change – this is where AI becomes a competitive advantage. AI has the ability to extract and structure
data, making it more coherent and useable, even when it has been created or managed in siloed ways. Yet manufacturers can go one step further by using AI-enabled supply chain modelling and simulation tools, which can use data, even where gaps remain, to build and test scenarios across the supply chain. This allows manufacturers to see which parts of the supply chain are more or less resilient, and how different scenarios are likely to play out. Manufacturers don’t need to outsource this supply chain intelligence either. Rather than relying on third-party or consultant- led, periodic analysis, manufacturers can use AI-enabled supply chain intelligence tools internally on a regular basis to explore scenarios, test assumptions, and better respond to change.
For example, manufacturers can embed AI-driven transport planning within their ERP operational systems in order to move away from manual, spreadsheet-based decision- making and towards intelligent optimisation across trade lanes. Combined with zero-touch automation, this can minimise booking errors, lower operational costs, and provide real-time shipment visibility. The integration of freight audit capabilities can also validate every invoice at the line-item level and highlight billing discrepancies and manage dispute workflows.
But technology’s role in enabling the shift to continuous intelligence doesn’t stop here. There’s some really cutting-edge advanced technologies supporting and enhancing the shift to ‘always-on’ supply chain intelligence. Digital twin technology such as the Siemens Digital Twin Composer, allows organisations
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