How Renault Group Uses Celonis to Recover €15M in Procure-to-Pay

Renault Group is one of the world’s most storied automakers—125 years old, operating in 36 countries, with 98,000 employees and 2.265 million vehicles sold in 2024. As the company pushes toward next-generation mobility, operational efficiency at scale is no longer optional. Its Procure-to-Pay process, spanning thousands of global suppliers, had become a source of financial leakage and process friction that demanded a more intelligent approach.

The core challenge was a dual one. Renault’s P2P operations suffered from persistent issues—late supplier payments, overpayments, and duplicate invoices—that were difficult to trace to their root causes with existing tools. Julien Nauroy, IS/IT Domain Leader for Process Intelligence, needed to show fast, measurable ROI while simultaneously building a durable methodology for long-term process re-engineering. Those two goals often pull in opposite directions, and the team had to resolve the tension deliberately.

After evaluating the competitive landscape, Renault selected Celonis for its implementation speed, team productivity, platform scalability, and a marketplace of prebuilt apps that could deliver value immediately. The team used Celonis Marketplace apps to track credit memos and duplicate invoices, recovering cash from day one. In parallel, Celonis fed structured, quantitative data into a DMAIC framework, giving Nauroy’s team the “Measure” and “Analyze” phases with real precision. Renault also adopted the Celonis Machine Learning Workbench and Prediction Builder to forecast late payments, and deployed Process Copilot so business users could query live process data in plain language—no technical expertise required.

The results were immediate and compounding. Renault recovered its first €1 million within three months of going live. By the end of the first year, that figure reached €15 million. Nauroy credits a hybrid Center of Excellence—combining IT expertise, Lean/DMAIC methodology, and finance domain knowledge—with the speed of delivery. “Every euro of value that we make, we can track it down to the invoice or to the delivery note,” he explains. That level of traceability was decisive in securing continued investment.

Looking forward, Renault’s ambition is a C-suite control tower providing real-time visibility into all core operational processes across the enterprise. Nauroy is particularly focused on the convergence of Process Intelligence and AI: structured process data feeds better-quality inputs into predictive models, making the AI more accurate and the process improvements more durable. “Using object-centric process mining we can go from having the data as it is in the original system to a well-structured model that makes sense to the AI,” he says. The P2P success is a proof point, not a ceiling.