Focusing on sustainability during a global energy crisis is no easy task. Manufacturers must prioritise the efficiency of current operations to reduce their environmental impact and mitigate the effects of rising energy costs. Through a data-driven approach, this can be achieved to improve energy efficiency throughout manufacturing.
Challenging times
Sustainability is a high priority in the manufacturing industry. Achieving sustainability goals has become increasingly challenging in recent times. Raw material and component shortages in the supply chain coupled with increased costs for raw materials and components, combined with a global energy crisis where energy prices are higher than ever, have complicated the situation.
Energy conservation is a win-win
The development of new and renewable production technologies will be extremely important in helping to establish a more sustainable manufacturing industry in the future. However, these will take time to develop and implement.
It is therefore important to push ahead with the development of existing production capacity to make it more environmentally friendly and sustainable. With the current uncertainty of energy supply and rising costs, there is now also an economic incentive to invest in energy conservation and efficiency measures.
While this may seem like common sense, it is not the reality for much of the manufacturing industry. There are examples of industries that currently use 20-30% more energy than they need to achieve the same volume of output per unit of time.
By managing and reducing energy consumption, companies can have a significant positive impact on the environment, energy savings and their own bottom line.
Data-driven insights
But how do you start working on energy efficiency in complex industrial environments? As with all aspects of manufacturing improvement, you need to build an understanding of your current situation and track changes and results over time.
By generating relevant and timely information, you can create the basis for data-driven decision-making and continuous improvement programmes. Energy efficiency involves measuring energy consumption related to specific production processes and equipment and using these measurements over time to evaluate improvement efforts against the results.
This approach is well suited to most companies as they already use some form of system support to help them become more data-driven in their development efforts. However, in most cases there is still a huge potential to improve data-based decision support using modern industrial IT solutions.
Deeper insights lead to better results
Basic information is of course already available. Companies usually have a good understanding of their energy consumption and costs at company level. What many companies do not know is who the energy consumers are at a detailed level, how much energy is wasted and where greater energy efficiency can be achieved.
With a data-driven approach to energy consumption, you can gain a clearer understanding of your energy use with deeper insight. Each new level of insight adds more detailed knowledge, opening up more and greater opportunities for improvement.
LEVEL 1: The first level is to simply measure the energy consumption related to the whole production activity, i.e. the energy used to produce a certain amount of product (total production minus discard) over a certain time.
LEVEL 2: The next level is to be able to correlate energy consumption with primary production activities, such as production availability, plant utilisation, uptime, changeovers, proactive and reactive maintenance, etc. This can provide insights into your operational efficiency and its effect on energy consumption.
LEVEL 3: At the third level, energy use is correlated with how production is run. Here you can understand the impact of specific aspects related to production, from specific products, production lines and shifts, down to individual machines or even operators.
Identify opportunities for improvement
The ability to drill down from the plant level to individual assets and resources provides almost unlimited opportunities to gain new data-driven insights. For example, a comparison between different shifts, products or machines can reveal differences in energy efficiency. This provides an opportunity to understand why differences occur and thus standardize operations around best practices.
To take full advantage of any improvements, it is necessary to establish a process that works for your organisation. The key step is to measure energy consumption at a detailed level, i.e. equipment level, and then correlate it with relevant aspects of production. You also need to be able to visualise this data in a way that is easy to access and easy to review and go through.
Data is combined into views or reports to focus attention on specific aspects of production. In this way, new insights are gained and areas for improvement can be identified. This provides a basis for data-driven decision-making with clearly defined goals and measurable results.
In addition to providing a framework for effective improvement programmes, this approach can also be used to identify and pursue broader investment opportunities such as production development, process design or investment in new equipment.
The end result is a powerful data-driven solution that provides all the insights and support you need to run your business as energy efficiently as possible.
