Understanding brownfield mobility automation failures

Author:
Katie Windley
Marketing Manager

Four constraints decide whether industrial mobility automation works inside an existing plant: integration with the control systems already running, the physical layout of the building, standardization across sites, and the economics of repeating a design. Each one is an engineering problem, and each compounds the others. Deloitte’s 2025 Smart Manufacturing and Operations Survey of 600 executives found that 92% of manufacturers expect smart manufacturing to be their main competitiveness driver over the next three years, while only 45% work to a defined architecture standard. That gap explains most stalled deployments. For life sciences manufacturers, where every change inside a qualified area carries a revalidation cost, the gap is wider still.

What makes a brownfield plant harder to automate than a new build?

A greenfield line is designed once, around its automation. A brownfield line carries 20 or 30 years of accumulated decisions: control platforms from three vendors, safety zones drawn for a product that has since been discontinued, floor tolerances that suited pallet jacks, and validated processes that cannot move without a documentation trail. Capgemini’s analysis of intelligent factories identifies brownfield constraints and fragmented systems among the common reasons manufacturers accumulate pilots without ever reaching a scaled smart factory. Technology is rarely the limiting factor. The building it lands in usually is.

Key takeaways

Why does integration break industrial mobility automation first?

Mobile robots are useful in proportion to the systems that tell them what to do. A fleet manager needs production context from Manufacturing Execution systems, machine state from the PLC and SCADA layers, and inventory positions from the warehouse system. When each of those handoffs is custom-built per cell, every expansion becomes a fresh integration project rather than a repeat of a known one. Deloitte reports that 46% of manufacturers run Industrial IoT at the facility or network level, and that between 69% and 72% face moderate to significant difficulty hiring skilled IT and Operational Technology staff. Custom integration therefore consumes the scarcest resource on the project.

What happens when Operational Technology and IT stay separate?

Data that stays trapped in the OT layer cannot feed Predictive Maintenance models, Machine Vision quality checks, or the Industrial AI scheduling that justified the fleet in the first place. The robots move material on schedule. The plant still cannot explain why throughput moved with them, which makes the second business case harder to win than the first.

How does existing plant layout cap custom automation scaling?

Physical constraints decide more brownfield outcomes than software does. Aisle widths sized for counterbalance trucks, floor flatness outside mobile robot tolerance, doors and elevators that add minutes to every cycle, wireless dead zones in metal-heavy bays, and charging infrastructure competing with production power all set a ceiling on what a fleet can achieve. Digital Twins and traffic simulation let engineering teams test congestion and throughput against the real building before capital is committed. The International Federation of Robotics recorded 542,000 industrial robot installations in 2024 and a global operating stock of 4.66 million units, so proven design patterns exist. Fitting them to one specific floor is the engineering work.

Why does missing standardization stall industrial automation deployment?

Scale economics depend on repetition. A fleet designed around one site’s Manufacturing Execution configuration, one vendor’s traffic manager, and one team’s safety interpretation cannot be copied to the next plant without redesign, so the second deployment costs close to what the first did. Deloitte found that 54% of manufacturers have adopted a unified data model and 48% have a training and adoption standard, which leaves roughly half the industry rebuilding the same interface every time. ABI Research forecasts mobile robot shipments rising from 547,000 units in 2023 to 2.79 million by 2030, growth that rewards manufacturers holding a reusable architecture and penalizes those starting over at each site.

What does industrial automation scalability require?

Treat the first deployment as a template. Define the interface between fleet software and the PLC, MES, and SCADA layers once, then publish it as a site standard the next plant inherits. Survey the building before selecting a platform and validate the traffic model in simulation. Agree on measurable outcomes, throughput per hour, uptime, and manual touches removed, before the first robot arrives. McKinsey’s work with the World Economic Forum found at least 70% of manufacturers languishing in pilot purgatory, with under 30% rolling solutions out company-wide. Plants that escape it design for the second and third deployment while building the first.

Explore the possibilities

Wondering why your mobility automation pilot has not scaled past its first cell? Book a discovery call to learn how Eclipse Automation supports brownfield integration, layout planning, and repeatable industrial automation deployment across existing plants.