SUPPLY CHAIN


How decision intelligence transforms supply chain risk response


By Shakeel Ahmed, procurement excellence leader, Belden W


hen the UK government ordered semiconductor manufacturer Nexperia to divest its Newport wafer fab in 2022, procurement teams across the electronics industry confronted the same three questions: where did this risk originate, which parts are affected, and which suppliers are connected. For many organisations, assembling an honest answer took days. The dependency at issue often came from several tiers down the supply chain. The episode illustrates a structural problem. Most disruptions originate below Tier 1, yet most visibility stops there.


From reactive scramble to informed response


The core challenge is fragmentation. Risk, compliance and procurement data typically live in disconnected siloes, and supplier knowledge is rarely exchanged beyond direct relationships. When an unexpected or unprecedented event takes place, teams pull information together manually across systems, functions and time zones. This means that speed and practical realities of communication become the constraint. Reacting to the event is rarely the hard part. Understanding exposure fast enough and making the correct decisions are.


Building the data foundation Effective risk intelligence starts with three connected data layers. A material breakdown decomposes fi nished products into modules, components, subcomponents and raw materials. Multi-tier mapping identifi es


Figure 1: Material breakdown: inside an industrial network switch


the suppliers behind each of those levels. A material-fl ow view links materials to manufacturing locations and the movement between them.


Sub-tier relationships cannot all be declared by suppliers, so modern approaches infer them from shipment and trade data, supplier portals, industry datasets and open sources. Each inferred relationship carries a confi dence score, from plausible to highly likely, strengthened as independent sources corroborate one another. The result is a living map that changes as the supply base changes.


From data to decisions


A map alone changes just very little. The step that matters most is connecting three inputs, spend, revenue exposure and supplier risk, into a composite score for every supplier.


That score determines how deeply the organisation engages.


Established frameworks are not discarded. Tools such as the Kraljic matrix and supplier pyramids remain, populated with live data rather than annual estimates.


Real-world impact


Coming from one of our practical case studies, impact analysis that previously took 40 hours now takes around 15 minutes, and the manual effort involved has fallen by 75-80 per cent: work that once occupied four to fi ve full-time roles is now handled by one. Adoption across global teams took approximately six months.


The operational value has also been


refl ected in in specifi c decisions. Early signals of DRAM shortages, mapped against affected products and revenue, prompted safety stock builds, exposed single-source dependencies and triggered dual-sourcing strategies well before allocation pressures peaked. A useful way to frame the benefi t is, of course, time: time to awareness, time to action, time to recover and time to survive.


Setting realistic expectations No multi-tier map is ever fi nished or fully verifi ed. Confi dence scores exist because much of the sub-tier picture has to be inferred and validating every relationship costs more than it returns.


The path forward


Figure 2: Mapping of partners and suppliers across various tiers to increase visibility as inputs come in real time


38 JULY/AUGUST 2026 | ELECTRONICS FOR ENGINEERS


There is no shortage of talk about visibility, resilience and AI in supply chains. What matters is how those ideas hold up in practice.


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