Agile, flexible, fully utilized: these are the three qualities expected of the 2030 factory, according to an Accenture survey of over 500 plant directors. At Tech For Industry Show 2026, Paris Expo Porte de Versailles, a panel moderated by [Accenture — speaker to be confirmed] brought together three major manufacturers — Safran, Forvia and Renault Group — to compare their strategies toward the autonomous factory: physical AI, humanoid robots, industrial metaverse, smart automation and workforce transformation.

The Accenture survey highlights three requirements. First, agility: industrializing products much faster, echoing the shrinking development cycles driven by Chinese competition. Second, flexibility: reconfiguring almost dynamically in the face of demand hypersegmentation, supply disruptions and "permacrisis". Third, maximum asset utilization: a saturated asset able to produce different product types directly impacts ROCE — and helps keep industrial assets on French soil.

Three levers make this factory possible: hyperautomation — with the rise of physical AI and humanoids; orchestration, meaning process autonomy (detecting a problem, proposing a solution, implementing it with little human interaction — which is not mere automation); and talent transformation, since fully "lights-out" factories will remain the exception, reserved for high-volume standardized products.

Safran's context sets the scale of the challenge: 11 independent companies, over 220 factories in 70 countries, more than 200 programs, products manufactured for up to 50 years, and routings requiring up to 75 operations — half of them special processes, each representing around €2.5 million of investment.

Over the past 5 years, Safran has invested heavily in digital continuity — PLM, next-generation ERP, MES everywhere — to automatically guarantee traceability and conformity, aerospace's two imperatives. Another structural effort: bringing the supply chain under control (5,000 suppliers, over 900 production lines including 540 assembly lines) through control towers and early signal detection.

The benchmark verdict is clear: digitalization alone yields only 5–10% productivity. To target 30–40% gains across its 70 French factories, Safran is deploying smart automation: input/output cells (IOCI) for loading and unloading, AGVs for material flow — in discontinuous flow, parts wait 95% of the time — and a "third hand" for operators: computer vision, voice-activated work instructions, connected tools.

Between connected tools, computer vision, MES and part geolocation, an operator can end up facing five screens: cognitive load risks undoing the gains made on physical load. Safran therefore targets a single screen — one digital environment for its 60,000 frontliners — and now considers UX/UI a fundamental industrial discipline.

At Safran, POCs are now "strictly forbidden". Scaling relies on group standards (two MES, two PLM, framework contracts, architecture files, controlled cybersecurity) and production archetypes: final assembly lines (FAL), one-hour takt time lines, very high-speed fully automated lines, and algorithm-optimized job shops.

A tier-one automotive supplier, Forvia manages up to 35 technology categories on its shop floors — a major standardization challenge.

Forvia's 2030 factory vision rests first on lean: an excellence system for operations, lean industrial design to reach "just need" CAPEX, and lean program delivery — "the best way to lose money in automotive is to miss a program launch".

On these foundations, four building blocks: digital control of operations (an MES deployed across 200 plants in 5 years, now being modernized to the cloud so plants can build their own solutions); AI and GenAI as managerial and expertise support agents; digital twin and PLM for engineering-to-operations continuity; and AI for process control, prioritizing automated quality inspection through vision and sound.

These tools enable new operating models: centralized scheduling (all "planning" roles regrouped regionally with a transverse view of the value chain) and centralized expertise — top experts train the AIs that will handle 80% of common problems, intervening only on critical cases.

Renault started its industrial data journey in 2016 and "cracked it" in 2019. The result: over 20,000 connected assets across 22 plants, with a standard data model regardless of equipment — the precondition for scaling.

Every project in Renault's digital transformation delivers ROI in under one year, with double-digit gains on quality and energy consumption. The philosophy: don't scrap France's industrial heritage — bring legacy (brownfield) plants to top performance levels by combining lean and digital.

What separates a classic digital twin from the industrial metaverse? The real-time feedback loop: detecting a parameter drift and acting directly on the chain — supply, quality, maintenance. Day to day, it changes management: at the morning QRQC, complete incident data is on the table 15 minutes after the event. And the cockpit analogy applies: no frozen dashboards inside applications, but exposed data that each manager assembles into their own steering tools.

Three acceleration tracks: AI orchestrators already deployed on maintenance and energy supervision; applying VLA (Vision Language Action) models to the existing fleet of over 11,000 robots, making them able to perceive their environment; and deploying 300–350 humanoid robots from 2027 — not for dexterity demos, but for simple, strenuous, robust handling tasks. A complementary target: moving from 300 to 1,000 computer vision checks per plant. All underpinned by a transverse resilience program covering infrastructure and processes.

Three points of convergence emerged. On profiles: fewer data scientists "waiting for algorithms", more data engineers to structure data (UNS — Unified Namespace standards) and hardware/vision specialists — "hardware is hard", while algorithms become a commodity. On roles: the master scheduler evolves from "generating a schedule" to "steering an AI that steers the tool". On change management: suggesting a defect via algorithm to a level-3 aerospace inspector — who has been told for 50 years that planes fly thanks to him — is a cultural transformation, and the plant manager must be its first sponsor.

Three accelerators reached consensus: embedding digital inside the lean system rather than running it as a parallel track; offering platforms where 80% of the journey is pre-built, with plants completing the last mile themselves (citizen development, GenAI); and banning POCs in favor of standards and archetypes. GenAI is already a game changer: Renault reports 30–50% development time savings — some projects estimated at 6–8 months were completed in half a day.

What is the difference between automation and autonomy? Automation executes a programmed task. Autonomy is a system's ability to detect a problem, propose a solution and implement it with little or no human interaction — the core of the 2030 autonomous factory.

Are humanoid robots already an industrial reality? Yes: Renault Group plans to deploy 300–350 humanoid robots from 2027 in its plants, on simple, strenuous, robust handling tasks.

What ROI can be expected from factory digital transformation? Renault reports sub-one-year ROI on all its projects; Safran estimates digitalization alone yields 5–10% productivity, targeting 30–40% by adding smart automation.

Will the 2030 factory be a factory without humans? No, according to all three manufacturers: "lights-out" factories will remain limited to high-volume standardized production. The closer to the customer, the more central the digitally augmented human remains.

This panel was recorded at Tech For Industry Show 2026, the B2B trade show for industrial transformation (Smart Manufacturing, AI, Supply Chain) at Paris Expo Porte de Versailles. Watch the full discussion above, and join us on May 19–20, 2027, Hall 7, for the next edition.