News

Manufacturers have spent years treating automation as a straightforward answer to labor shortages, rising costs and growing production demands. Today, however, the conversation is becoming more nuanced. The question is no longer simply how much of a factory can be automated, but whether each automation investment solves a meaningful operational problem.

That distinction is becoming increasingly important as technologies such as robots, autonomous mobile robots (AMRs), automated guided vehicles (AGVs), connected controls and intelligent production systems become easier to deploy.

According to Deloitte's 2025 Smart Manufacturing Survey, 46% of manufacturing executives identified process automation as one of their top two investment priorities for the following two years, while 37% placed physical automation in their top two. The figures underline the continued appetite for automation, but they also highlight a challenge facing manufacturers: investment alone does not guarantee better performance.

A highly automated facility can still experience material shortages, production bottlenecks, equipment downtime, inefficient workflows and underutilized assets. In some cases, adding another automated machine can simply shift an existing constraint to another part of the production process.

For manufacturers evaluating new automation technology, the starting point should therefore be the operational problem rather than the equipment itself.

A production manager may identify repetitive material handling as an obvious candidate for automation. An AMR or AGV could reduce manual transportation and improve delivery consistency. Yet the business case becomes less convincing if production stations are frequently waiting for upstream processes, if materials are not prepared when vehicles arrive, or if charging requirements reduce fleet availability.

The same principle applies to robotic systems, automated storage, machine tending and other forms of factory automation. Technology should be introduced because it improves a defined business outcome, not simply because the technology is available.

That outcome should be measurable. Depending on the application, manufacturers may target shorter cycle times, fewer safety risks, higher equipment availability, reduced manual handling, lower operating costs or greater production capacity.

Without a clearly defined performance target, it becomes difficult to determine whether an automation project has delivered a genuine return on investment.

The wider production environment also needs to be considered. Automation rarely operates as an isolated technology layer. Robots depend on power, communications, controls, software and physical infrastructure. Mobile equipment requires charging strategies and traffic management. Connected machines depend on dependable industrial networks. Higher machine speeds can place additional demands on motors, drives, power delivery systems and mechanical components.

This makes infrastructure an increasingly important part of automation planning.

For example, a factory introducing a fleet of mobile robots may initially calculate fleet size according to production volume and travel distance. That calculation can change significantly once charging availability, battery capacity, traffic congestion and idle periods are included.

A vehicle that spends substantial time traveling to a charging station or waiting for access may contribute less productive capacity than expected. Purchasing additional robots might appear to be the simplest solution, but redesigning charging around scheduled production pauses or natural dwell periods could produce a better economic result.

Similar issues can emerge with industrial communication. Advanced control systems cannot deliver their expected benefits if data transmission is unreliable or if different automation platforms cannot exchange information consistently.

As factories become more connected, industrial Ethernet, control networks, sensors, PLCs, distributed I/O and supervisory systems increasingly form part of the same operational ecosystem. A weakness in one layer can reduce the effectiveness of an otherwise sophisticated automation investment.

Power infrastructure deserves similar attention. Increasing production speeds or adding additional automated equipment can change electrical loading and duty cycles. Existing power-delivery equipment may have been designed for an earlier operating profile, creating additional maintenance requirements or reliability concerns as the facility evolves.

For this reason, automation projects are increasingly being evaluated as complete systems rather than individual machines.

Manufacturers are mapping the movement of people, materials, equipment, energy and production data across the facility before deciding where automation should be introduced. This approach can reveal that the most valuable investment is not another robot or automated vehicle, but an upgrade to the infrastructure connecting existing equipment.

It can also expose process problems that automation alone cannot solve.

A poorly organized material flow, for example, may create unnecessary movement throughout a plant. Automating that movement without redesigning the underlying process can make an inefficient workflow faster without making it fundamentally better.

The distinction matters because automation can amplify both efficient and inefficient processes.

The same consideration applies after an automated system has been commissioned. A successful installation should not be judged solely by whether the equipment remains operational. Manufacturers need to determine whether the system is producing the business results originally expected.

Key indicators can include production throughput, equipment availability, downtime, maintenance workload, labor utilization, asset utilization and the amount of time employees spend on repetitive or low-value activities.

These measurements can reveal whether an automation project is still delivering value months or years after installation.

Operational conditions rarely remain unchanged. Production volumes fluctuate, product configurations evolve, facility layouts are modified and new equipment is introduced. A process that represented a major bottleneck several years ago may no longer be the primary constraint.

At the same time, automation itself introduces new requirements. Maintenance teams may need additional software expertise. Operators may require training for robotic or autonomous systems. Control platforms may require updates, while aging components can create unexpected lifecycle and spare-parts challenges.

This is why flexibility is becoming a more important criterion in industrial automation investment.

Systems that can be expanded, reconfigured or integrated with different equipment can give manufacturers greater freedom as production requirements change. Modular control architectures, scalable communication networks and adaptable automation platforms can reduce the need to replace entire systems when individual production requirements evolve.

The objective is not to predict every future requirement. Instead, manufacturers can design automation systems with enough flexibility to accommodate reasonable changes in production volume, product mix and facility configuration.

This approach also changes how companies should think about return on investment.

The value of automation may extend beyond direct labor reduction. Improved workplace safety, more predictable production, reduced unplanned downtime, better asset utilization and increased production flexibility can all contribute to the overall business case.

In some applications, the strongest justification for automation may be the ability to maintain production despite labor constraints. In others, the primary benefit may be consistency or the ability to operate a process that is difficult or unsafe to perform manually.

Consequently, there is no universal level of automation that represents a well-run factory.

A highly automated plant is not necessarily more efficient than a moderately automated one. The appropriate level depends on production volumes, process variability, product complexity, labor availability, safety requirements, maintenance capabilities and the economics of the operation.

Processes that require frequent human judgment or handle highly variable products may remain better suited to people. Other processes involving repetitive movement, hazardous environments or predictable high-volume production may offer a strong case for automation.

The distinction is particularly relevant as manufacturers explore newer technologies involving artificial intelligence, machine vision, autonomous robotics and advanced analytics. These technologies can create significant opportunities, but their value still depends on the operational environment in which they are deployed.

More intelligence does not automatically produce better manufacturing performance.

The next stage of industrial automation is therefore likely to be defined less by the number of machines installed and more by how effectively those machines work together.

Manufacturers that take a system-level view can identify constraints before investing in equipment, understand the infrastructure required to support new technology and establish performance indicators that remain relevant after commissioning.

Ultimately, the strongest automation strategies are deliberate rather than technology-driven. They begin with a production problem, evaluate the entire workflow, consider the supporting infrastructure and measure results over time.

Automation remains a powerful tool for improving manufacturing performance. But the competitive advantage does not come from automating every task that can technically be automated. It comes from knowing where automation can create durable value and where a simpler process, better infrastructure or improved use of existing assets may deliver a stronger result.

For manufacturers entering the next phase of smart manufacturing, that distinction could become just as important as the technology itself.

Written by: Daniel Mercer

Daniel Mercer is an industrial automation writer and technology analyst with more than 12 years of experience covering PLC systems, robotics, motion control, industrial networking and smart manufacturing. His work focuses on the practical impact of automation technologies on production reliability, maintenance and industrial operations.