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Presented by Celonis<\/i><\/p>\n
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When tariff rates change overnight, companies have 48 hours to model alternatives and act before competitors secure the best options. At Celosphere 2025 in Munich, enterprises demonstrated how they\u2019re turning that chaos into competitive advantage \u2014 with quantifiable results that separate winners from losers.<\/p>\n
Vinmar International<\/b>: Theglobal plastics and chemicals distributor created a real-time digital twin of its $3B supply chain, cutting default expedites by more than 20% and improving delivery agility across global operations.<\/p>\n
Florida Crystals:<\/b> One of America's largest cane sugar producers, the company unlocked millions in working capital and strengthened supply chain resilience by eliminating manual rework across Finance, Procurement, and Inbound Supply. AI pilots now extend gains into invoice processing, predictive maintenance, and order management.<\/p>\n
ASOS<\/b>: The ecommerce fashion giant connected its end-to-end supply chain for full transparency, reducing process variation, accelerating speed-to-market, and improving customer experience at scale. <\/p>\n
The common thread here: process intelligence that bridges the gap traditional ERP systems can\u2019t close \u2014 connecting operational dots across ERP, finance, and logistics systems when seconds matter. <\/p>\n
\u201cThe question isn\u2019t whether disruptions will hit,\u201d says Peter Budweiser, General Manager of Supply Chain at Celonis. \u201cIt\u2019s whether your systems can show you what\u2019s breaking fast enough to fix it.\u201d<\/p>\n
That visibility gap costs the average company double-digit millions in working capital and competitive positioning. As 54% of supply chain leaders face disruptions daily, the pressure is shifting to AI agents that execute real actions: triggering purchase orders, rerouting shipments, adjusting inventory. But an autonomous agent acting on stale or siloed data can make million-dollar mistakes when tariff structures shift overnight. <\/p>\n
Tariffs, as old as trade itself, have become the ultimate stress test for enterprise AI \u2014 revealing whether companies truly understand their supply chains and whether their AI can be trusted to act.<\/p>\n
Supply chain leaders face a paradox: drowning in data while starving for insight. Traditional enterprise systems \u2014 SAP, Oracle, PeopleSoft \u2014 capture every transaction meticulously. <\/p>\n
SAP logs the purchase order. Oracle tracks the shipment. The warehouse system records inventory movement. Each performs its function, but when tariffs change and companies need to model alternative sourcing scenarios across all three simultaneously, the data sits in silos.<\/p>\n
\u201cWhat\u2019s changed is the speed at which disruptions cascade,\u201d says Manik Sharma, Head of Supply Chain GTM AI at Celonis. \u201cTraditional ERP systems weren\u2019t built for today\u2019s volatility.\u201d<\/p>\n
Companies generate thousands of reports showing what happened last quarter. They struggle to answer what happens if tariffs increase 25% tomorrow and need to switch suppliers within days.<\/p>\n
Global trade volatility has transformed tariffs from predictable costs into strategic weapons. When new rates drop with unprecedented frequency, input costs spike across suppliers, finance teams scramble to calculate margin impact, and procurement races to identify alternatives buried in disconnected systems where no one knows if switching suppliers delays shipments or violates contracts.<\/p>\n
By hour 48, competitors who already modeled scenarios execute supplier switches while late movers face capacity constraints and premium pricing. <\/p>\n
Process intelligence changes that dynamic by allowing businesses to continuously model \u201cwhat-if\u201d scenarios, showing leaders how tariff changes cascade through suppliers, contracts, production lines, warehouses, and customers. When rates hit, companies can move within hours instead of days.<\/p>\n
AI and supply chains are mutually dependent: AI needs operational context, and supply chains need AI to keep pace with volatility. But here's the truth \u2014 there is no AI without PI. Without process intelligence, AI agents operate blindly.<\/p>\n
The ongoing SAP migration wave illustrates why. An estimated 85\u201390% of SAP customers are still moving from ECC to S\/4HANA. Moving to newer databases doesn\u2019t solve supply chain visibility \u2014 it provides faster access to the same fragmented data.<\/p>\n
Kerry Brown, a transformation evangelist at Celonis, sees this across industries. <\/p>\n
\u201cOrganizations are shifting from PeopleSoft to Oracle, or EBS to Fusion. The bulk is in SAP,\u201d she explains. \u201cBut what they really need isn\u2019t a new ERP. They need to understand how work actually flows across systems they already have.\u201d<\/p>\n
That requires end-to-end operational context. Process intelligence provides this by enabling companies to extract and connect event data across systems, showing how processes execute in real time.<\/p>\n
This distinction becomes critical when deploying autonomous agents. When visibility is fragmented, autonomous agents can easily make decisions that appear rational locally but create downstream disruption. With real-time context, AI can operate with clarity and precision, and supply chains can stay ahead of tariff-driven disruption.<\/p>\n
The companies highlighted at Celosphere all applied the same principle: understand how processes run across systems in real time. Celonis PI creates a digital twin above existing systems, using its Process Intelligence Graph to link orders, shipments, invoices, and payments end-to-end. Dependencies that traditional integrations miss become visible. A delay in SAP instantly reveals its impact across Oracle, warehouse scheduling, and customer delivery commitments.<\/p>\n
\u201cThe platform brings together process data spanning systems and departments, enriched with business context that powers AI agents to transform operations effectively,\u201d says Daniel Brown, Chief Product Officer at Celonis. <\/p>\n
With this cross-system awareness, Celonis coordinates actions across complex workflows involving AI agents, humans, and automations \u2014 especially critical when tariffs force rapid decisions about suppliers, shipments, and customers.<\/p>\n
A key advancement unveiled at Celosphere \u2014 zero-copy integration with Databricks \u2014 removes another barrier. Traditionally, analyzing supply chain data meant copying from source systems into central warehouses, creating data latency.<\/p>\n
Celonis Data Core now integrates directly with platforms like Databricks and Microsoft Fabric, querying billions of records in near real time without duplication. When trade policy shifts, companies model alternatives instantly, not after overnight data refresh cycles.<\/p>\n
Enhanced Task Mining extends this by connecting desktop activity \u2014 keystrokes, mouse clicks, screen scrolls \u2014 to business processes. This exposes manual work invisible to system logs: spreadsheet gymnastics, email negotiations, phone calls that keep supply chains moving during urgent changes.<\/p>\n
Most companies can\u2019t rip out and replace systems running critical operations \u2014 nor should they. Process intelligence offers a different path: compose workflows from existing systems, deploy AI where it creates value, and adapt continuously as conditions change. This \u201cFree the Process\u201d movement liberates companies from rigid architectures without forcing wholesale replacement.<\/p>\n
As global trade volatility intensifies, the companies that model will move faster, make smarter decisions, and turn tariff chaos into competitive advantage \u2014 all while existing ERPs keep running.<\/p>\n
When the next wave of tariffs hits \u2014 and it will \u2014 companies won\u2019t have days to respond. They\u2019ll have hours. The question isn\u2019t whether your ERP captures the data. It\u2019s whether your systems connect the dots fast enough to matter.<\/p>\n
Missed Celosphere 2025? <\/i><\/b>Catch up with all the highlights here<\/i><\/b>. <\/i><\/b><\/p>\n
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