For most manufacturers, energy has long been treated as a necessary cost of doing business — something to manage, but not something that fundamentally shapes how operations are designed. Production targets, throughput, and labor efficiency have historically driven decision-making. Energy, by comparison, has remained in the background, showing up mainly as a monthly line item.
That dynamic is starting to change.
Rising energy costs, growing pressure to improve sustainability, and increasing operational complexity are forcing manufacturers to take a closer look at how energy is used across their operations. At the same time, advances in automation, data collection, and artificial intelligence are making it possible to rethink the relationship between energy consumption and production output.
What’s emerging is a shift away from the idea that energy efficiency and productivity are at odds. Instead, more manufacturers are beginning to understand that the two can — and increasingly must — work together.
This shift is rooted in a more fundamental change in mindset, according to Josh Hoeing, manufacturing director at Bastian Automation. “We’re seeing a real shift in how manufacturers think about energy,” he says. “It’s no longer a fixed overhead; it’s a variable that should scale with output and reflect operating discipline.”
The idea that energy should scale with output marks a turning point. It reframes energy from something passive into something that can be actively managed, optimized, and aligned with production goals.
“With energy costs rising and margins tightening, efficiency has become a strategic priority, not a side project,” Hoeing adds. “If we don’t understand how energy consumption changes with production, we risk operating without the insight needed to improve efficiency and performance.”
Rethinking the Relationship Between Energy and Output
Even with that shift underway, energy is not always the first priority when manufacturers evaluate new investments. Most organizations still anchor their decisions in more traditional metrics, notes Josh Diaz, territory account manager at KUKA Robotics.
“Customers are still looking for labor payback, a quality payback, a throughput payback,” he says. “Energy is part of the equation, but it’s not always front of mind.”
Not to say that energy efficiency is being ignored. It’s often considered secondary — something that supports the business case rather than defines it.
But this also doesn’t mean that energy savings and productivity are working against each other. “I don’t see them as at odds,” Diaz says, pointing, as an example, to robots that are producing just as much while consuming less energy. “Our highest running product line is consuming 60% less energy than the model from about a decade ago.”
That perspective is important because it reflects a growing recognition across the industry: Improving efficiency does not have to come at the expense of performance. In many cases, the opposite is true.
The reason is straightforward. Inefficiency in manufacturing rarely shows up as a single issue. It tends to manifest as a collection of small, compounding problems — idle equipment, poor sequencing, inconsistent processes, unnecessary movement — that impact both energy use and productivity at the same time.
Addressing those inefficiencies through automation doesn’t just reduce energy consumption. It often makes operations run better.
Where Energy Gets Lost in the System
To understand how automation can help, it’s important to look at where energy is actually being used — and wasted — inside a typical manufacturing environment.
High-demand processes like machining, welding, and finishing are obvious energy consumers, but that’s especially true when equipment is running inefficiently. “The largest opportunities usually aren’t in the machines themselves, but in how they’re scheduled, sequenced, and loaded,” Hoeing says. “Processes with frequent start-stop cycles, long warmups, or poor coordination between operations tend to waste the most energy.”
These patterns are rarely intentional. They’re usually the result of trying to avoid downtime or maintain flexibility. But over time, they create a disconnect between energy consumption and actual production output.
“The biggest opportunities show up where energy is being consumed without creating value,” Hoeing says. “Intelligent automation can significantly reduce idle run time by aligning equipment operation with actual demand instead of running continuously ‘just in case.’ There’s also significant upside in automating material flow and sequencing, which smooths production, reduces stop‑and‑start behavior, and lowers peak energy loads.”
That insight shifts the focus away from individual machines and toward the broader system. Energy inefficiency is often less about how equipment performs and more about how it’s scheduled, coordinated, and utilized.
Automation as a Way to Tighten the System
This is where automation plays a critical role — not simply by replacing manual labor, but by bringing structure and consistency to how operations run.
“Well-designed automation reduces idle time, rework, and variability,” Hoeing explains, “so equipment is only consuming energy when it’s producing output.”
Automation makes it possible to control when equipment runs, how processes are sequenced, and how material flows through the system. Instead of relying on operators to manage these variables manually, manufacturers can build them into the operation itself.
One area where this becomes especially visible is in process applications. Diaz points to one customer’s cleaning operation that transitioned from a broad, inefficient approach to a more targeted one using robotics.
While the previous system used a large device with several nozzles to spray parts indiscriminately, the manufacturer now uses two robots that are fed a model of the part being cleaned.
“Now I’m spraying just enough water to clean the surfaces and the contours that I want,” Diaz says, noting the significant water savings. In fact, the savings in water costs helped to justify taking out all the old systems and replacing them with robots.
The same principle applies across a range of applications, from painting and welding to adhesive dispensing. “All of these process applications with a robot, if I’m doing it the way that I should be, should be using way less material than if it was a manual operation,” Diaz says.
The Often-Overlooked Role of Material Handling
While production processes tend to receive the most attention, material handling systems are often among the largest continuous energy consumers in a facility.
Conveyors, sortation systems, and vehicle-based transport frequently operate at a steady pace regardless of actual demand. When flow is inconsistent, these systems can spend long periods consuming energy without moving product.
That makes material handling a prime candidate for improvement.
“Smarter controls, better zoning, and demand‑based operation can significantly cut that waste,” Hoeing notes. “When material flow is smooth and intentional, energy use drops naturally while throughput improves.”
Even supporting systems can have a meaningful impact. Diaz highlights the energy demands associated with traditional forklift charging setups, where large banks of chargers draw power simultaneously.
“Those are massive power consumers,” he says.
Rethinking how and when equipment is charged, or how energy is distributed across the system, can create opportunities to reduce both consumption and peak demand.
Lion Power is doing exactly that. They’ve come up with a robotic battery swapping system for electric forklifts that not only has energy savings in mind but productivity as well. It replaces typical lead acid batteries with lithium batteries. A forklift pulls into a bay, a robotic arm pulls the depleted battery out and replaces it with a charged battery, and then the forklift goes back to work.
The fully autonomous system takes less than 2 minutes to swap the batteries, compared with about 12 minutes for a typical lead acid system, notes John Ward, director of engineering and strategic partnerships at Lion Power. At the same time, the system reduces energy by 30% over lead acid systems.
Why Data Changes the Conversation
One of the biggest challenges manufacturers face in addressing energy use is a lack of visibility.
“Real-time data from automated systems give operators and leaders visibility into when and where energy is being wasted, making it something that can be managed instead of guessed at,” Hoeing explains. But many plants don’t have good data, so they can’t see when energy is being consumed, he adds.
Without that visibility, energy remains abstract — something measured in aggregate rather than understood in context.
To make energy actionable, it needs to be tied directly to production. That means looking beyond utility bills and focusing on how energy is used at the level of individual lines, processes, or pieces of equipment.
Hoeing emphasizes the importance of connecting energy to output. “Manufacturers need to track energy in a way that’s tied directly to production, rather than relying solely on utility bills,” he says. “That means looking at energy consumption by line, process, or equipment and normalizing it against units produced, run time, or throughput.”
This kind of data makes it possible to identify patterns that would otherwise go unnoticed. It reveals when energy use is rising without a corresponding increase in output, or when certain conditions — like changeovers or downtime — are driving inefficiencies.
Once energy is viewed alongside production performance, it becomes something that can be managed, not just monitored.
The Expanding Role of AI
As data becomes more accessible, artificial intelligence is beginning to play a larger role in helping manufacturers interpret and act on it.
Hoeing sees AI as a way to make complex tradeoffs more visible and manageable. “AI can predict when and where energy will spike and recommend better ways to run the operation,” he says.
That capability is especially valuable in environments where conditions are constantly changing. Demand fluctuates, equipment performance varies, and production schedules shift. AI can analyze these variables in real time and identify opportunities to optimize both energy use and output.
Predictive maintenance is one of the most immediate applications. Diaz notes that as equipment wears, it often requires more energy to perform the same task.
“If a bearing or motor is wearing out,” he explains, “now I’m needing to draw more power to get through the same process.” By identifying those issues early, manufacturers can prevent both energy inefficiencies and unplanned downtime.
Looking ahead, the role of AI is likely to expand beyond maintenance into broader system optimization. Diaz points to the potential for smarter control strategies that adapt to operating conditions.
That kind of dynamic adjustment represents a shift from static operations to adaptive ones — systems that continuously refine how they run based on real-world data.
The Reality of Competing Priorities
Despite the clear opportunities, progress is not always straightforward.
Manufacturers operate in environments where production demands are constant and often increasing. In those conditions, efficiency improvements can take a back seat to throughput.
Diaz puts it plainly: “If a customer could double their equipment, consume twice the energy, and increase throughput, they would do that every day of the week.”
That mindset reflects the realities of the business. Meeting demand, maintaining delivery schedules, and staying competitive all depend on output.
But it also highlights why energy efficiency cannot be framed as a tradeoff. It needs to be integrated into the same solutions that drive productivity.
In some industries, energy is already a limiting factor. Diaz describes situations where large industrial facilities must coordinate directly with utility providers to manage consumption.
“They may be told when to shut down equipment based on grid demand,” he says.
In those cases, energy is not just a cost — it’s a constraint that directly affects operations. As energy pressures increase more broadly, similar considerations may become more common across other sectors.
Building a More Intentional Operation
The manufacturers that are making the most progress in this area are not treating energy efficiency as a separate initiative. Instead, they are building it into the way their operations are designed and managed.
That approach starts with a simple idea: Equipment should only run when it is creating value.
Achieving that requires a combination of automation, data, and operational discipline. It means aligning processes more closely, reducing unnecessary variation, and using technology to enforce consistency.
Hoeing emphasizes that the benefits come from how systems are run, not just what equipment is used. “I’ve seen operations move from continuously running conveyor and equipment to demand-based control, where systems only run when product is moving,” he says, pointing to measurable results in energy use. “At the same time, throughput improved because the operation was more stable and less start-and-stop driven.”
A Shift That’s Still Unfolding
The relationship between energy and manufacturing performance is still evolving. In many facilities, energy remains a secondary consideration. In others, it is becoming a central part of how operations are managed.
What’s clear is that the tools available to manufacturers are changing. Automation is becoming more capable. Data is becoming more accessible. AI is beginning to turn that data into actionable insight.
At the same time, expectations are rising. Customers, regulators, and internal stakeholders are all placing greater emphasis on efficiency and sustainability.
The convergence of these factors is reshaping how manufacturers think about energy — not as a constraint, but as something that can be controlled and optimized.
“I don’t think it is a zero-sum game,” Diaz says. “We’ll continue to be able to do more while the energy consumption goes down.”
Manufacturers that succeed in balancing energy consumption with production output gain more than just lower energy costs. They build operations that are more stable, more predictable, and more adaptable.
They also position themselves to respond to future challenges, whether those come in the form of rising energy prices, regulatory requirements, or changing market demands.
