How does AI help manufacturers reduce energy consumption?

Energy as an Often Overlooked Efficiency Opportunity

Energy costs represent a significant and ongoing operating expense for most manufacturers, yet energy usage has historically been harder to monitor and optimize in detail compared to more visible factors like labor and material costs. Many facilities have relied on relatively coarse, facility-wide energy monitoring — a single utility bill covering an entire plant — which makes it difficult to pinpoint exactly where energy is being used efficiently versus where it’s being wasted. This lack of granular visibility has meant that meaningful energy efficiency opportunities have often gone unnoticed simply because nobody had the detailed data needed to find them.

Bringing Granular Visibility to Energy Use

AI-driven energy optimization starts by bringing much more granular visibility to energy consumption, typically through sensors and meters installed at the level of individual pieces of equipment, production lines, or process areas rather than relying on a single facility-wide total. This detailed data allows machine learning models to establish what efficient energy usage looks like for specific equipment under specific operating conditions, and then to flag deviations — a motor drawing more current than expected for its output, a compressor cycling inefficiently, or a heating process consuming more energy than comparable equipment doing similar work elsewhere in the facility.

This kind of granular analysis can surface inefficiencies that would be completely invisible when looking only at total facility energy consumption, since a specific piece of underperforming equipment might represent only a small fraction of a plant’s total energy use but still offer a meaningful, identifiable savings opportunity once flagged.

Optimizing When Energy-Intensive Work Happens

Beyond identifying equipment-level inefficiencies, AI can also help optimize the timing of energy-intensive operations. Many utility pricing structures charge different rates depending on the time of day or overall demand on the electricity grid, and for production processes that have some flexibility in scheduling, AI-driven optimization can recommend shifting certain energy-intensive operations to periods when energy costs are lower, without necessarily changing the total amount of production completed. This kind of scheduling optimization needs to be balanced against other production priorities, like meeting delivery deadlines, but for processes with genuine flexibility, it can meaningfully reduce energy costs without any change to underlying equipment or operations.

Waste Reduction Rather Than Output Reduction

An important distinction in this space is that AI-driven energy optimization generally aims to eliminate genuine waste — energy consumed inefficiently or unnecessarily — rather than reducing actual production output or line speed. Examples include reducing energy consumed by equipment left running during idle periods, correcting inefficient equipment settings that don’t affect output quality, and identifying maintenance needs, such as a component causing excess friction and energy draw, that would otherwise go unnoticed until they became more serious. This distinction matters because it means energy savings from AI optimization don’t necessarily come at the cost of reduced manufacturing capacity or output.

Bottom Line

AI helps manufacturers reduce energy consumption by analyzing granular, real-time energy usage data to identify inefficient equipment and processes, optimizing the timing of flexible energy-intensive operations around cost and demand patterns, and flagging waste that would otherwise go unnoticed in facility-wide energy monitoring. This approach generally targets eliminating genuine energy waste rather than reducing production output, though the achievable savings depend heavily on a facility’s existing equipment and processes.

Go deeper

• Can the Environmental Benefits of AI Outweigh Its Energy Costs?
• Is It Ethical to Build Energy-Intensive AI Systems During a Climate Crisis?
• How Does AI’s Energy Use Compare to Other Major Industries?