A prerequisite for optimising energy efficiency is monitoring the energy consumption of individual assets. Are you monitoring your energy consumption via your primary energy meter(s)? Although it will give you a general overview of your plant’s energy consumption, it will not tell you where you can find opportunities for energy savings.
Machine-level data analysis helps you identify energy waste and act upon anomalies and consumption spikes when necessary. Energy management systems that give insights on a machine-level and in real-time, generally provide industrial plants with these benefits:
1. Granular insights
2. Reduced operational risk
3. Reduced operating costs
4. Efficient reporting
For organisations that wish to optimise their energy consumption, it is important to understand their energy consumption on a machine-level. Key here is that this puts you and your team in the position where you can check and evaluate how well every machine performs on energy-efficiency. Only then, you are able to make valid investment decisions based on actual data.
For example, if you find that one of your machines consumes its energy inefficiently, you can decide if it’s relevant for you to invest in a new similar machine, or in a hybrid version.
Another benefit that comes with granular insights, is that you are able to combine energy and production data. This methodology (benchmarking) is often used to calculate the production costs per produced kg of goods.
Reduced operational risk
The high volatility in demand, need to protect brand equity and the complex operating environment make it challenging for operators to maintain and optimize business processes. Failing to act anomalies will impact an organisation’s business goals on the longer term. Among others, one of the top risks that impact operations in the manufacturing industry are failure of critical assets, environmental impact (spills, leaks, etc.) and non-compliance to regulations.
By monitoring the energy consumption of individual machines, you enable insights that help you efficiently identify the potential risks and make data-driven decisions. Through smart energy management, these insights can be leveraged to reduce the risk of asset failure, energy waste and to comply with regulatory standards.
Reduced operating costs
The insights provided by machine-level analysis help you make data-driven decisions. By pinpointing where the inefficiencies are situated, it becomes easy to take action and decrease the costs and planning associated with machinery maintenance, replacement and changes in employee behaviour. Having such detailed insights is an important indicator for how well you are able to identify and efficiently invest in energy saving opportunities. A few examples of reductions in operating costs are:
- Reducing standby power
- Reducing protocol deviation
- Identifying & upgrading highest energy consumers
To comply with audits and regional regulations, it is highly recommended and sometimes even a prerequisite to analyse energy consumption on a machine-level. To be efficient in compliance and reporting, industrial plants can have an EMS set up that supports machine-level energy analysis.
The accurate energy data acquired from individual machines can be leveraged with a smart energy management system to automate reporting, calculations and task creation.
If you are interested to learn more about managing the energy of your plant in an efficient way, download our whitepaper on Smart Energy Management or contact one of our industry specialists.