The goal of R-Learning is to transform theoretical quality standards into actionable, daily behaviors on the production line.

R is not just a programming language; it is an environment designed specifically for data analysis. While general-purpose languages treat data manipulation as an add-on feature, R puts data at the center of its universe.

As of mid-2026, Renault is advancing its quality initiatives through advanced technologies. The "askrnlt" platform represents the new era of customer experience and agentic AI, reflecting a commitment to innovation in quality control and customer interaction.

First, let’s decode the term. In the context of industrial manufacturing and quality management—specifically within the Groupe Renault (now part of the Renault-Nissan-Mitsubishi Alliance)— stands for Rigor, Repeatability, and Results-driven Learning.

It is a continuous improvement framework that blends Lean Management principles with hands-on, experiential training. Unlike traditional classroom learning, R Learning is embedded on the factory floor, in the design studio, and within the supply chain. It is a cyclical process of:

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This training is not only for internal Renault staff but also for partners. It is specifically designed for:

For suppliers, engineers, and project managers, mastering this framework through specialized training—often referred to in professional development contexts as learning Renault's extra quality standards—is crucial for success. This article explores the core components of Renault's quality approach and how learning these processes drives innovation and performance. 1. Understanding Renault RGPQP (Quality Project Management)

The spirit of the Extra is best captured by its community. On review sites like carsurvey.org, owners praise it as a "tough little workhorse" that is "very straightforward to work on". Its design prioritized function over form:

(often referred to as Qualité Extra Renault ) is a tiered standard applied to parts, processes, and supplier deliveries. It includes: