Optimizing energy consumption in manufacturing with Power BI dashboards
Energy accounts for a major portion of manufacturing businesses’ overhead costs, with direct impacts on their bottom lines, and leads to an environmental footprint. Today, where manufacturing is a competitive arena, being able to optimize the use of energy is no longer an environmental perspective but a critical business perspective for being profitable and competitive. Power BI dashboards provide manufacturing companies with the right mechanism to reflect on, analyze, and optimize their use of energy so that they can view raw data in a meaningful way to generate insights for increasing efficiencies and reducing costs.
The Silent Profit Killer: Energy Waste in Manufacturing
Most enterprises in manufacturing consume between 15,000 and 25,000 kWh of energy per annum, but this will vary significantly with the diameter of a business, nature of the industry, and patterns of operations. The smallest manufacturing plants would usually consume around 10,000 kWh of gas and about 15,000 kWh of electricity annually, while the opposite scenario may generally be expected for larger establishments of about 65,000 kWh and 50,000 kWh of gas and electricity, respectively.
The volatility of energy consumption prices over recent years kept rising, stabilizing towards 2024 but still remaining to be much higher than they were before the pandemic. Announcements were that prices would steadily increase in 2025, thus highlighting energy waste as a key matter to address for a manufacturing concern’s profitability.
Efficiently wasting energy gets overlooked in many manufacturing operations and stands there silently reducing the already thinning profit margins. But in 2022, the industrial sector accounted for 25.1% of final energy consumption in the European Union, with nearly two-thirds of that energy being electricity and natural gas.
Identifying the Problem: Where is Energy Being Wasted?
An attempt at optimization requires the determination of specific sources of energy waste. Typical sources of inefficiency are:
1. Equipment Maintenance Issues
Poorly maintained equipment often uses excessive energy due to dirty lubricating fluids, misalignments, or worn parts. Being able to monitor energy consumption might even forewarn when the machinery fails and is overly consuming energy, thereby providing means for maintenance intervention. In the case of air compressors, for example, the use of some specialized monitoring equipment can easily detect leaks that are forcing the system into consumption of energy at a rate higher than is justifiable to maintain the required pressure.
2. Inefficient Production Processes
Energy inefficiency arises during downtimes, startups, and shutdowns in production. Equipment may very well be left on during periods when it is not being used to carry out work because there is no monitoring system enforced to prevent this. Inefficient processes and operation of equipment can result in losses of up to 30% of the energy they consume in the manufacturing plants, energy management specialists attest.
3. Heating, Cooling, and Lighting Systems
Energy waste is recorded mostly through inefficient HVAC and lighting systems in manufacturing facilities. Timed machinery and lighting should be set to curtail such waste during times when the plant is not operational. They might also be missing an opportunity for heat recovery from waste-gases and hot waste solids-energy which could be recycled within the plant or exported to the electrical grid.
4. Water Usage
One more often-overlooked energy cost in manufacturing is water consumption. Indeed, heating water or even passing it through small chillers so that it may be cooled or simply pumped from one point to another requires a large amount of energy, making the concept of water conservation essential in any energy optimization project.
The Role of Data Analytics in Energy Optimization
Data analytics change raw Japanese energy-consumption data into finer action strategies for optimization. The emerging technologies such as Internet of Things (IoT) devices, big data analytics, and artificial intelligence (AI) enable manufacturers to measure, analyze, and optimize their energy usage with utmost perfection.
Real-time Energy Monitoring
Smart meters and sensors connected to and using IoT will document energy consumption in real time through various manufacturing systems and processes. Plant managers are instantly faced with the ability to make adjustments once inefficiencies occur, e.g., if machines are running idle and sucking power.
Historical Data Analysis
The analysis of energy consumption with the consideration of past time patterns is very important in understanding seasonal variation and inefficiencies occurring at specific intervals. This opens up room for long-term strategic planning for energy optimization. Months of production data may reveal that certain production lines consume more energy per unit produced compared to others and thus offer avenues for improvement
Read More :- https://megamindstechnologies.com/blog/optimizing-energy-consumption-in-manufacturing-with-power-bi-dashboards/
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