Rockwell Automation Showcases Autonomous Operations at PACK EXPO 2026
Time:2026-10-13 Browse: 1
Rockwell Automation Highlights a Practical Path Toward Autonomous Manufacturing
Manufacturers are increasingly looking for ways to improve productivity, respond to changing demand, and manage production complexity without sacrificing operational reliability. On October 5, 2026, Rockwell Automation announced plans to demonstrate technologies supporting autonomous operations at PACK EXPO International, scheduled for October 18–21 in Chicago.
The company's planned showcase brings together advanced control systems, industrial software, artificial intelligence, digital twins, robotics, and autonomous production logistics. The central message is that manufacturers can move toward more autonomous operations by building on their existing automation infrastructure rather than replacing every system at once.
This approach addresses a challenge facing many industrial facilities. Manufacturing plants frequently operate a combination of programmable logic controllers (PLCs), human-machine interfaces (HMIs), industrial networks, motion controllers, robotic equipment, supervisory control systems, and production management software. These systems may perform their individual functions effectively while still leaving opportunities to improve coordination, visibility, and responsiveness across the production process.
Rockwell Automation's announcement reflects a broader industry direction in which automation is evolving from isolated machine control toward more connected and coordinated production environments.
For manufacturers in food and beverage, consumer packaged goods, packaging, logistics, and other industrial sectors, the important question is how these technologies can be introduced in a practical, measurable, and operationally responsible way.
What Autonomous Operations Mean for Manufacturing
Autonomous operations refer to industrial environments that can perform an increasing range of monitoring, decision-making, adjustment, and coordination tasks with reduced dependence on continuous manual intervention.
The term does not necessarily mean a factory operates without people. In many cases, the objective is to automate repetitive activities, identify production problems earlier, coordinate equipment more effectively, and provide operators with better information for decisions that still require human judgment.
For example, a production line may use PLCs to execute machine sequences, monitor sensors, and control motors. An HMI provides operators with access to machine status, alarms, and operating parameters. A supervisory system can collect production information across multiple machines, while manufacturing software supports scheduling, traceability, and performance analysis.
Additional digital capabilities can help connect these layers. Analytics may identify recurring downtime patterns, condition-monitoring tools may detect changes in equipment behavior, and simulation models may help engineers evaluate production changes before implementing them.
When these functions are properly integrated, a manufacturer can gain a more complete view of the production environment.
However, autonomy is not a single product or software feature. It is a system-level capability that depends on reliable data, well-engineered control logic, defined operating limits, cybersecurity, and clear responsibility for decisions.
PLCs Remain the Foundation of Reliable Machine Control
Although industrial AI and digital technologies receive significant attention, PLCs continue to provide the deterministic control functions required by many manufacturing applications.
A PLC processes input signals, executes programmed logic, and updates outputs according to its configuration and operating cycle. Depending on the system, it may control conveyor speeds, packaging sequences, filling operations, material handling, machine interlocks, and other production functions.
These functions require predictable behavior. A machine must respond appropriately to sensor inputs, equipment states, timing requirements, and fault conditions.
Industrial AI can complement this control architecture by analyzing historical data, identifying patterns, or recommending adjustments. It should not automatically be assumed that an AI application can replace a PLC's real-time control responsibilities.
A practical autonomous manufacturing architecture can instead separate responsibilities. The PLC executes validated control logic within defined operating limits, while higher-level applications support analysis, optimization, and coordination.
Where automated recommendations are permitted to influence machine operation, the integration must define which parameters can be changed, what limits apply, and how the system responds if data becomes unavailable or an application produces an unexpected result.
This separation helps manufacturers introduce new capabilities without compromising established control requirements.
Digital Twins Support Better Engineering Decisions
Digital twins and simulation tools are increasingly used to improve the design and operation of industrial systems.
A digital twin may represent a machine, production line, process, or broader manufacturing environment. Depending on its design, it can combine engineering models, operational data, equipment states, and simulation capabilities.
During system design, simulation can help engineers evaluate production sequences, material flow, equipment utilization, and potential bottlenecks before committing to a physical installation.
For existing facilities, suitable digital models can support troubleshooting, production planning, and the evaluation of proposed changes. Engineers may be able to investigate how different operating parameters affect throughput or how a new machine configuration could influence downstream processes.
In packaging and consumer goods manufacturing, where production requirements may change frequently, these capabilities can be particularly valuable. A production line may need to accommodate different product formats, package sizes, recipes, or operating speeds.
A digital model can help engineers assess the potential effects of these changes before they are implemented.
Nevertheless, simulation results depend on model quality and the assumptions used. A model that does not accurately represent equipment limitations, product behavior, control timing, or material flow may produce misleading results.
Digital twins should therefore be validated against actual equipment and process data. They are tools for improving engineering decisions, not guarantees that a proposed change will perform exactly as predicted.
Industrial AI and Production Data
Artificial intelligence can help manufacturers extract additional value from the data generated by automation systems.
Industrial facilities collect information from PLCs, sensors, drives, machine vision systems, quality inspection equipment, energy meters, and production software. When this information is organized and analyzed appropriately, it can support applications such as predictive maintenance, quality monitoring, production optimization, and operational decision support.
For example, condition-monitoring software may analyze vibration, temperature, current, or other measurements to identify changes that could indicate developing equipment problems.
Production analytics may compare output, cycle time, downtime, and quality results across shifts or product types. These comparisons can help engineering teams identify recurring problems and prioritize improvement projects.
AI-assisted systems may also help operators interpret large volumes of information, identify unusual trends, or investigate the possible causes of production interruptions.
However, collecting data does not automatically produce useful intelligence. Measurements must be sufficiently accurate, timestamps must be consistent, and equipment tags must have clear meanings. Missing data, sensor faults, changing operating conditions, and inconsistent production records can affect analytical results.
Manufacturers should begin with a defined operational objective rather than adopting AI simply because the technology is available.
A focused application, such as identifying repeated stoppages on a specific packaging machine, may provide a more measurable starting point than a broad project attempting to optimize an entire factory immediately.
Robotics and Autonomous Production Logistics
Robotics can extend automation beyond traditional machine control into material handling, packaging, inspection, and internal logistics.
Industrial robots are widely used for repetitive and precision-oriented tasks. Collaborative robotic applications may support selected activities where the system has been specifically designed and validated for the intended working environment.
Autonomous mobile robots can also support the movement of materials between designated locations, depending on the facility layout, navigation technology, traffic rules, and safety requirements.
When robotics and production logistics are coordinated with PLCs and manufacturing software, the resulting system can improve the flow of materials and reduce certain forms of manual handling.
For example, production equipment may signal that a material transfer is required, while a fleet-management system coordinates an appropriate mobile robot. The receiving machine and transport system must exchange the necessary status information to ensure that the transfer occurs under the correct conditions.
These applications require careful integration. Robot availability, battery condition, network connectivity, traffic congestion, equipment faults, and changing production priorities can all influence performance.
Manufacturers must also evaluate functional safety, emergency stopping, pedestrian interaction, and the consequences of communication failures.
The goal is not simply to add more robots. It is to create a coordinated production environment in which machines, people, and material-handling systems can operate safely and efficiently.
Why a Step-by-Step Automation Strategy Matters
Rockwell Automation's announcement emphasizes that manufacturers can progress toward more autonomous operations by building on existing investments. This is especially relevant for plants that cannot justify a complete automation replacement.
A phased strategy allows organizations to identify specific problems, establish measurable objectives, and introduce new capabilities according to operational priorities.
The first stage is usually an assessment of the existing environment. Engineers should document PLCs, control networks, machine interfaces, data sources, software dependencies, and production constraints.
The next stage is to identify an improvement opportunity. This might involve reducing unplanned downtime, improving changeover efficiency, increasing visibility into production performance, or coordinating material delivery more effectively.
The organization can then determine whether the solution requires additional sensors, revised PLC logic, a new HMI, better industrial networking, a data collection platform, or an advanced software application.
A pilot project should be evaluated using operational measurements such as downtime, cycle time, throughput, quality, maintenance effort, and energy consumption where relevant.
If the results are satisfactory, the approach can be extended to other equipment or production areas.
This process reduces the risk of deploying technology without a clear business purpose. It also provides opportunities to improve data quality, operator training, maintenance procedures, and cybersecurity before the system becomes more complex.
Cybersecurity and Workforce Readiness
More connected manufacturing systems introduce additional requirements for cybersecurity and workforce development.
As PLCs, robots, production software, and analytics platforms exchange more information, manufacturers must control who can access the systems and which communication paths are permitted.
Network segmentation, access control, secure remote maintenance, configuration management, and reliable backups remain important components of a modern industrial security strategy.
Any application that can influence production parameters should have clearly defined permissions and operating limits. Changes to control logic and machine configurations should follow approved engineering procedures.
Workforce readiness is equally important. Operators need to understand how to interpret new dashboards, recognize abnormal system behavior, and respond when automated recommendations conflict with established operating conditions.
Maintenance personnel may require additional training in industrial networking, data collection, software diagnostics, and cybersecurity.
Autonomous operations should therefore be introduced as an engineering and organizational program, not merely as a software installation.
Implications for Automation Buyers and Equipment Suppliers
The movement toward more connected production systems has implications for manufacturers purchasing automation components.
PLCs, communication modules, remote I/O, HMIs, industrial switches, power supplies, sensors, and motion-control equipment must work together within a defined architecture.
When upgrading existing machinery, buyers should verify exact part numbers, hardware revisions, firmware requirements, communication protocols, and engineering software compatibility. A component that appears physically similar to an installed device may still be unsuitable for the application.
Lifecycle support is another important consideration. Some facilities depend on established automation platforms that remain operational long after a product family has been superseded. In these situations, replacement components and accurate technical identification can help maintenance teams manage equipment availability.
Industrial equipment suppliers can support modernization by providing reliable product information and helping customers identify compatible replacement hardware. However, final compatibility should always be confirmed against the manufacturer's documentation and the actual installed configuration.
For system integrators, the expanding role of software and data creates opportunities to combine conventional control engineering with networking, simulation, analytics, and production management expertise.
What Manufacturers Should Watch After PACK EXPO 2026
Rockwell Automation's October announcement establishes its intention to demonstrate how advanced control, industrial software, AI, digital twins, robotics, and autonomous production logistics can work together.
The planned demonstration is not, by itself, proof that every technology will deliver a particular productivity improvement in every factory. Actual results will depend on application design, integration quality, production conditions, workforce readiness, and the reliability of the underlying data.
Manufacturers evaluating these technologies should look for concrete implementation details, compatibility requirements, demonstrated use cases, measurable results, and clear explanations of how the solutions interact with existing PLC and control infrastructure.
They should also distinguish between technologies already deployed in production environments and capabilities that are still being demonstrated or developed.
Conclusion
Rockwell Automation's planned PACK EXPO International 2026 showcase reflects a broader transformation in manufacturing: the gradual movement from isolated automation equipment toward increasingly connected and coordinated operations.
PLCs and other industrial controllers will remain essential for reliable machine control, while industrial AI, digital twins, robotics, and production analytics can provide additional capabilities when properly integrated.
For manufacturers, the most practical path toward autonomous operations is likely to involve incremental modernization, measurable improvement objectives, and careful attention to cybersecurity, safety, and system compatibility.
Rather than replacing existing automation infrastructure all at once, industrial organizations can evaluate how new technologies complement their current systems and introduce them where they provide demonstrable operational value.
