Energy efficiency is no longer a discussion limited to reducing energy costs or meeting environmental targets. In industrial environments, energy consumption, capacity, productivity, equipment utilization, and production flow are directly connected. Evaluating these dimensions separately can lead to incomplete conclusions and, in some cases, decisions that improve one indicator while negatively affecting another.
The scale of this challenge is significant. A 2025 study published in the Journal of Manufacturing Systems indicates that industry accounts for approximately 38% of global energy consumption and 24% of CO₂ emissions. The authors also highlight that traditional production objectives, such as productivity, quality, and costs, need to be balanced with energy efficiency, increasing the complexity of industrial decision-making.
A review published in Energy Conversion and Management reinforces this perspective. The study identifies energy efficiency as one of the pillars of industrial decarbonization and highlights that improvements depend on the interactions among processes, production systems, and energy supply.
Consuming less energy does not necessarily mean producing more efficiently
One of the metrics used to assess industrial energy efficiency is Specific Energy Consumption (SEC), calculated as the ratio between total energy consumption and production output. This relationship makes it possible to evaluate the amount of energy required to produce a given quantity of products, providing a more representative view of efficiency than analyzing energy consumption alone.
This distinction is important because a reduction in total energy consumption does not necessarily indicate greater efficiency.
The energy consumption of an industrial system varies according to equipment behavior, production volume, waiting times, resource utilization, and production flow conditions. Therefore, analyzing only total consumption may not reveal where the main efficiency opportunities are.
A study published in the Journal of Manufacturing Systems demonstrates this relationship in an automated production line in the automotive industry. By simultaneously evaluating Specific Energy Consumption, throughput, an indicator that represents the volume processed by the system over a given period, and work-in-process inventory, researchers identified scenarios capable of reducing SEC by 23.9% while increasing throughput by 27.9%. The results also showed that these gains need to be evaluated considering their effects on work-in-process inventory levels.
Digital Twins expand the analysis of energy efficiency and demand
Understanding the energy performance of an industrial system requires more than analyzing how much energy has already been consumed. It is also necessary to evaluate how changes in capacity, equipment, processes, and resource utilization can simultaneously affect consumption, productivity, and energy demand.
In this context, Digital Twins make it possible to represent system behavior, evaluate different conditions, and anticipate impacts before implementation. A systematic review published in 2025 analyzed 50 studies on Digital Twins applied to energy efficiency across different sectors. Among the studies reviewed, energy savings of up to 30% were reported, with relevant applications in manufacturing, industrial processes, and smart buildings.
In a specific manufacturing application, researchers developed an Energy Digital Twin for a heating tunnel. The application reduced energy consumption by up to 40% while maintaining the maximum temperature range required by the process. The study demonstrates how a digital representation can support not only monitoring but also the adjustment of parameters related to energy consumption.
This approach extends the analysis beyond historical consumption to the evaluation of future scenarios. Capacity changes, new equipment, or process modifications can alter the demand profile and create new energy demand peaks, potentially requiring additional infrastructure.
As a result, total energy consumption and power demand can be evaluated as distinct dimensions. Two scenarios may present similar energy consumption over a given period but require different infrastructure capacities if one concentrates greater demand within specific time intervals.
Energy costs and sources also affect the outcome
The amount of energy consumed is not the only factor that determines its economic and environmental impact. The timing of consumption, energy costs, and the source of energy can also affect the outcome of each scenario.
A study published in 2025 in Production Engineering demonstrated this relationship through a Digital Twin applied to an industrial cleaning machine. The application used information related to energy demand and prices to adjust consumption, demonstrating the potential of demand response strategies to reduce energy costs and better align consumption with renewable energy availability.
This expands the analysis beyond the efficiency of individual equipment. Supply strategies, renewable energy availability, tariff variations, and periods of higher demand can also be considered when comparing alternatives.
Combining this information makes it possible to evaluate not only how much energy will be required, but also when that demand will occur, which sources can meet it, and the economic and environmental impacts associated with different strategies.
Energy efficiency is also a matter of planning
When consumption, demand, costs, emissions, and productivity are analyzed together, energy efficiency becomes more than an initiative to reduce waste. It becomes part of industrial planning.
Before increasing capacity, installing new equipment, or modifying processes, companies can evaluate questions such as: What will future energy demand look like? Will the available infrastructure be sufficient? Where will the highest demand peaks occur? Which energy sources can be considered? How will different alternatives affect costs and emissions?
This analysis also supports investment planning. Rather than sizing energy infrastructure based solely on current conditions, different growth scenarios can be evaluated to identify future requirements and compare alternatives.
The studies therefore show that energy efficiency should not be analyzed separately from productivity, capacity, and the characteristics of the production system. Evaluating these relationships in a digital environment expands the information available to define strategies for efficiency, waste reduction, and energy planning.
Incorporating energy intelligence into FlexSim models
Helios, an additional FlexSim module, incorporates energy intelligence into scenario analysis, enabling companies to evaluate the energy impacts of different strategies before implementation.
The solution enables the analysis of resource energy consumption across different states, demand behavior over time, costs, indirect carbon emissions, and different energy sources. This information can therefore be evaluated alongside the behavior of the system represented in the FlexSim model.
The analysis can support energy efficiency and waste reduction initiatives, as well as assessments related to demand peaks, infrastructure, energy capacity, supply alternatives, and future requirements associated with new investments.
Evaluate the energy impacts of your strategies before implementation. Discover Helios and talk to our specialists.
References
Schmitt, T., Olives Juan, S., Amouzgar, K., Hanson, L., & Urenda Moris, M. (2025). Optimizing energy efficiency and productivity in industrial manufacturing: A simulation-based optimization approach with knowledge discovery. Journal of Manufacturing Systems, 82, 748–765.
Naumann, M., et al. (2025). Energy efficiency improvement for decarbonization in manufacturing industry: A review. Energy Conversion and Management, 338, 119763.
Ba, L., Tangour, F., El Abbassi, I., & Absi, R. (2025). Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review. Sustainability, 17(8), 3560.
Billey, A., & Wuest, T. (2024). Energy digital twins in smart manufacturing systems: A case study. Robotics and Computer-Integrated Manufacturing, 88, 102729.
Fuhrländer-Völker, D., Lindner, M., von Elling, M., Frieß, T., Karnapp, S., & Weigold, M. (2025). Method for the development and application of digital twins in manufacturing. Production Engineering, 19, 959–973.