The 2025 Guide to Smart Factory MES: Comparing Composable and Traditional MES Vendors

Manufacturing leaders across industries are rethinking how they run their operations, leading many to re-consider their options when it comes to manufacturing execution systems.

With so many different solutions on the market, you'll likely find yourself asking: which MES vendors are the top choice for manufacturing?

In this guide, we'll look at some of the biggest brands in the MES space, and compare their solutions to the modern, composable approach manufacturers are taking to digitize production processes today.

The history of MES: Built for a different era

Finding the best MES vendor for your operations depends on your specific industry and needs. As these systems have evolved over time, many legacy vendors may no longer be the right fit for today's complex production environments.

For decades, this solution category was defined by companies like GE Digital, Rockwell, Siemens, Honeywell, and Dassault. While their solutions addressed real challenges when they were initially developed, they were inherently designed for a world that looked very different from the one we work in today.

Manufacturing execution systems emerged in the early 1990s, when globalization and lean manufacturing were reshaping production. Factories needed a bridge between ERP planning and the shop floor. Regulations were tightening, and companies needed better traceability and consistency. MES filled that gap by standardizing production, capturing data, and enforcing compliance without disrupting enterprise systems.

At the time, that structure made sense. Plants were centralized. Product lines changed slowly. IT dictated technology, and operators followed defined processes. MES was designed for that environment—rigid, hierarchical, and predictable. At this time, accuracy and control mattered more than flexibility.

Throughout the 90's and into the early 2000's, early MES solutions became a key driver of digitization within factories as manufacturers slowly transitioned from the Industry 3.0 to the Industry 4.0 era. These systems gave manufacturers:

• Standardization that reduced variability

• Compliance with the documentation regulators demanded

• Data integrity through centralized collection

• Traceability across complex workflows
  

Additionally, MES brought digital structure to environments still rooted in paper and manual processes.

Instead of eliminating those practices, MES solutions created a hybrid way of working—one part digital, and one part traditional. They helped standardize operations, made data more visible, and introduced the first real step toward digital transformation in manufacturing.

A generation later, the conditions that shaped those systems haven’t disappeared. They’ve evolved, and they continue shifting more quickly than rigid systems can adjust.

Today's manufacturers face a completely different set of pressures. Supply chains stretch across continents. Product variants have multiplied. Markets are shifting faster than most systems can respond. A delay halfway around the world can stop production at home.

The workforce has changed, too. As Deloitte notes, “As more baby boomers and Generation X workers move closer to or into retirement, the workforce may be made up more of millennials and Generation Z workers, who can have a different set of expectations when it comes to work culture and the working environment itself.”

Operators today expect intuitive, responsive tools similar to the ones they've become familiar with in their personal lives. They learn fast and move between roles. Static terminals and menu-driven systems common among legacy solutions don’t match that reality.

Smart factory initiatives are widespread, yet we consistently see them stall when companies look to upgrade their tech stack without fundamentally rethinking their approach.

The very systems that once helped manufacturers modernize are now holding them back.

Traditional MES was built for stability. It performed well in predictable environments where change was rare. But even the most traditional operations now face volatility in supply, demand, and regulation. New materials, new customer requirements, and faster product cycles have become the norm.

The result is tension between the need for agility and the weight of legacy infrastructure. Updating a workflow can take months. Global rollouts can take years. Every small change requires consultants. That pace might have worked in 1995, but it doesn’t in 2025.

These challenges are often exacerbated by company culture. While legacy MES assumes control from the top, a true smart factory thrives on collaboration and iteration. As traditional systems try to modernize their interfaces and add APIs, the underlying structure remains slow and difficult to change.

Smart factories aren’t defined by the product they produce. They’re defined by how they operate. True smart factories prioritize:

Connected systems: Machines, sensors, and people share data in real-time.

Decisions at the edge: Operators act on live data, not historical reports.

Human + machine collaboration: Workflows guide actions and capture insights.

Fast iteration: New processes roll out in days, not quarters.

Continuous improvement: Teams use data to solve problems daily.

AI for insight: Data is used to predict and prevent issues, not just display them.

Notice these are operational behaviors, not software features. And they depend on technologies that evolve with change, not resist it.

Factories deal with shifting inputs, machine behavior that changes by the hour, and people making quick calls to keep production moving. Any system that supports this environment has to keep pace. A composable MES approach matches that reality by letting teams shape and adjust digital workflows inside the operation without waiting on long development cycles.

The architecture relies on modular pieces that you can reassemble as processes evolve. The model rests on a few practical ideas:

Modular components: Apps pull from a shared library that expands as teams build more capability into the system.

Low-code development: Engineers, supervisors, and operators can create and refine tools directly instead of routing everything through central IT.

Open integrations: Standard APIs tie machines, sensors, and business systems together without piling on custom middleware.

Human-centered interfaces: Workstations present clear context so people can respond quickly to conditions on the line.

Native AI tools: AI speeds up development work, surfaces patterns in production data, and supports operators during tasks.

This structure fits how OT environments actually run. Production relies on real-time data flow, two-way communication with equipment, and applications that respond immediately when conditions shift. A composable MES gives teams a digital layer that supports continuous improvement and local problem-solving, even in high-variability processes.

Legacy MES vs. Composable MES

Adopting a composable approach to MES creates an operational backbone that connects people, information, and equipment in a way that can be reshaped as the factory changes. This offers the flexibility and responsiveness that manufacturers expect while keeping control close to the teams doing the work.

AI is quickly becoming a defining capability for modern smart factories, but its value depends entirely on where and how it’s embedded.

We typically find that manufacturers don’t necessarily struggle with data availability. They struggle with fragmentation, slow insight cycles, and systems that can’t adapt quickly enough to operational complexity. AI only delivers transformative impact when it’s part of the core execution layer, not an accessory bolted onto legacy infrastructure.

For operations leaders evaluating MES strategies, the question isn’t whether AI is present. The real question is whether the system provides the conditions for AI to work: unified operational context, flexibility to iterate, and governance that matches the stakes of production. In environments driven by change, variation, and frontline decision-making, AI must be able to interpret real-time signals, support human judgment, and close loops between insight and action.

Embedded AI enables this shift. It accelerates how teams create digital workflows, enriches how they understand performance, and supports operators with timely guidance during production.

Instead of isolated models producing static recommendations, AI becomes part of daily execution, reducing manual effort, elevating expertise, and helping teams improve faster.

Here's how we're seeing customers incorporate AI into their operations:

AI for Faster Development
Building digital tools used to involve long handoffs between teams. With Tulip, AI removes that friction. Engineers can upload existing SOPs, PDFs, or even short videos. AI then helps translate that content into functional apps—mapping logic, creating workflows, and suggesting visualizations that turn ideas into usable tools in minutes.

AI for Operational Insight
Once data is connected, AI helps reveal what’s behind the numbers. Tulip’s AI features enable process engineers to probe their production, quality, and machine data to uncover context that would take hours to piece together manually. Users can ask direct questions and get clear, contextual answers—identifying bottlenecks, tracing quality issues, or explaining downtime trends without leaving the platform.

AI Agents on the Shop Floor
Tulip also supports AI agents that assist operators in real time. These agents suggest next steps, detect anomalies, and even adjust workflows as conditions change. As Mike Rousch, Director of Manufacturing at TICO put it, “We could use AI to research data and build tables, but we couldn’t act on it until the agents came out. Seeing what they could do—actually manipulating data and doing something useful—that changes everything.”

Manufacturers are pushing for quicker changeovers, tighter coordination across plants, and sharper visibility into what’s happening on the floor. The pressure on systems grows as data volumes rise and product mixes shift. In that environment, a fixed, monolithic MES starts to slow teams down.

The Deloitte 2025 survey highlights where companies are putting their money: analytics, cloud infrastructure, AI, and connected equipment. Those investments point to a clear direction. Operations groups want systems that can absorb new data sources, support iterative process improvements, and adjust when supply chains or production priorities move.

A composable MES gives them that footing. Teams can build what they need, plug it into existing workflows, and adjust without waiting for long release cycles. You keep control over quality and compliance while opening room for changes driven by the people running the lines.

A modern factory benefits from software that evolves at the same pace as its processes. When engineers and supervisors can shape their own tools, daily improvements become part of how the plant operates.

If you're interested in seeing how Tulip's MES can help improve the way you're running your operations, reach out to a member of our team today!