Flexxbotics

What Are Modern Factory Automation Autonomy Architectural Principles?

Separate Interoperability and Orchestration and Make Them Software-Defined

A modern factory automation architecture for autonomy requires two distinct but connected capabilities:

1) Interoperability & Line/Cell Coordination (Edge)

Purpose:

Enable communication for interoperability across machines, PLCs, tools, equipment, and automation

Characteristics:

> Supports all major industrial protocols
> Extends to legacy equipment
> Operates in real time at the edge

2) Orchestration for Autonomy (Control Plane)

Purpose:

Coordinate behavior across plant assets, machines, and systems separate from deterministic control

Characteristics:

> Normalizes and contextualizes data
> Applies process operating rules and drift adjustments
> Automates and governs actions and corrections

3) Key Design Insight

True coordination emerges when interoperability is combined with orchestration that doesn't effect deterministic control

> This is the missing capability in most factory automation architectures today

Does this make sense in your factory? Let us know your experiences (good, bad, or just observations) in Comments below

Find out more about these challenges and how to solve them at: https://flexxbotics.com/blog/interoperable-orchestration-in-factory-architecture/

What Do Existing Factory Automation Approaches Miss when attempting to enable Manufacturing Autonomy?

1. What Are the Implicit Assumptions in Scaling Automation for Greater Autonomy?

Most approaches assume:

> Standardizing on a single vendor stack will make everything work together
> A successful pilot architecture can be copied elsewhere
> Standardization means forcing sameness across plants, lines, and cells

These assumptions ignore a critical gap:

Scaling factory automation for autonomy requires an architectural model that is interoperable, governable, fault-tolerant, and adaptable as different plant’s production environments change over time

2. Why Is “More Standardization” Not Enough?

Historical thinking includes:

> Standardize on one PLC brand
> Standardize on a set of function blocks / AOIs
> Standardize on a single interfacing workflow

Standardization is beneficial although becomes problematic when operational realities are disregarded leading to:

x Unrealistic architectures that do not fit real plant requirements or variations
x Hidden customizations that cause divergence and complexity
x Suppressed optimization in each plant’s lines and cells causing inefficient workarounds

Standardization must take into account plant operational requirements that change over time to increase autonomy

3. Why Is Reuse More Important Than Uniformity?

Standardization of requirements for interoperability between heterogeneous equipment into the future is important to enable reuse and repeatable scaling as the factory changes

It should enable:

> Reusable interoperability interfaces
> Reusable orchestration and traceability patterns
> Reusable governance models
> Reusable recovery and deployment approaches

Without this:

x Every plant’s lines and cells become a separate automation project irrespective of the vendor’s standard hardware
x Engineering effort increases exponentially
x Scaling remains slow and expensive

Read about these issues and how to address them: https://flexxbotics.com/blog/scalable-factory-automation-architecture-for-greater-autonomy/

Why is extending process control beyond individual machines across entire automated production process so complex?

Most factories already implement some level of process control:

- PLC-based control loops
- Machine-level feedback systems
- Inspection, test, and quality checks often later in the process

Historical this mostly manual approach works:

> Operators and inspectors monitor outputs
> Adjustments are made manually
> Variability is manageable

As production automation density scales, new challenges emerge:

❌ Variability accumulates across machines and processes
❌ Process drift is detected too late
❌ Adjustments are inconsistent across cells, lines, and shifts

The question is no longer:

“How do I control this machine?”

It becomes:

“How do I continuously control production outcomes across a wide range of machines, automation systems, and processes in real-time?”

Is process control becoming increasingly difficult as more automation is introduced in your factories?

Read more about this issue and what can be done at https://flexxbotics.com/blog/autonomous-process-control-in-factory-automation-architecture/

Why Does Process Control Break Down at Scale?

1. Why Is Control Limited to the Machine Level?

Traditional factory architectures concentrate control within:

> PLCs
> Machine controllers
> Local feedback loops

This creates:
❌ Strong local deterministic control
❌ Complicated system-level coordination

Result:
- Machines operate correctly in isolation
- Production outcomes vary across cells, lines, and plants

2. Why Is There Not More Closed Loop Between Production Operations?
Most factories separate:

> Production systems (machines, equipment, automation)
> Inspection & test systems (automated testers, inspection, optical, CMMs, etc)
> Factory data analysis (manufacturing, quality, maintenance event data)

This leads to:

x Long feedback loops requiring human interpretation and intervention
x Inspection results analyzed after production occurs
x Adjustments applied manually or inconsistently after the fact

Result:

❌ Delayed corrections and prolonged downtime
❌ Increased defects, rework, and scrap
❌ Missed opportunities for proactive optimization

True Autonomous Process Control requires closing this loop, where feedback directly drives production adjustments in real-time

3. Why Does Process Variability Persist?

Production variability is introduced by:

- Material differences
- Environmental conditions
- Tool wear and degradation
- Machine drift

Without coordinated control:

x Variability compounds across operations
x Intervention is reactive, not proactive
x Adjustments do not occur continuously

Result:

❌ Throughput bottlenecks and reduced output
❌ Unplanned downtime
❌ Inconsistent quality and yields

Read more about these problems and what can be done at https://flexxbotics.com/blog/autonomous-process-control-in-factory-automation-architecture/