The Importance of Energy Monitoring in Manufacturing Production

Introduction: Why Energy Monitoring is Essential for the Manufacturing Industry

In modern manufacturing, energy plays an increasingly strategic role. Energy costs significantly impact company budgets – a study estimates that in 2022, energy represented about 8% of production costs in Italian industry, double compared to just a few years earlier (The impact of rising energy prices on production costs: comparing sectors between Italy, France, and Germany). Consequently, monitoring production consumption is no longer simply a good practice but a necessity to maintain competitiveness and sustainability. Every company aims for production optimization by reducing waste and inefficiencies: in this context, energy monitoring becomes essential to identify areas for improvement that would otherwise remain invisible.

Implementing a continuous energy monitoring system in production means gaining real-time visibility into how much energy is used by departments, lines, and individual machines. This allows understanding where the highest consumption is concentrated, which machines operate inefficiently, and where waste is generated. Without detailed measurement, many companies rely only on general meter data, losing detail: knowing instead how, where, and when energy is used enables informed decisions. In short, measure to improve: monitoring energy in production is the first step to optimizing it.

The benefits are concrete and tangible: reduced energy costs, leaner processes, lower emissions, and an overall increase in operational efficiency. Additionally, careful analysis of consumption often highlights direct correlations between energy and productivity, providing valuable insights on how to improve production line yields. In the following sections, we’ll explore how to best leverage this data, from the role of Artificial Intelligence in analysis, to identifying bottlenecks, to energy per piece as a new performance indicator and the possibility of upgrading older machines through these technologies.

The Role of Artificial Intelligence (AI) in Energy Consumption Analysis

Artificial Intelligence plays a key role in transforming raw energy consumption data into strategic insights for business. A manufacturing plant continuously generates information: IoT sensors can detect parameters in real-time such as motor power consumption, operating temperature, cycle times, etc. This enormous amount of data would be difficult to interpret if analyzed manually. This is where AI comes in: advanced machine learning algorithms can recognize patterns and anomalies in consumption that escape the human eye.

Thanks to AI, an energy monitoring system learns the typical behavior of each machine and can therefore automatically signal when something deviates from the ordinary. For example, if a machine starts consuming more than normal with the same output, the algorithm can highlight this deviation: it could be a symptom of a worn component or suboptimal adjustment. Similarly, AI can cross-reference energy data with other indicators (production rhythm, ambient temperatures, work shifts) and find non-obvious correlations.

Some examples of how AI applied to energy monitoring can help manufacturing companies:

• Anomaly detection: AI algorithms identify consumption spikes or sudden drops, signaling incipient failures or abnormal equipment use in real-time. For example, abnormal current consumption could indicate excessive friction in a motor or a lubrication problem.
• Predictive maintenance: By analyzing historical consumption, AI can predict when a machine needs maintenance. If a robot or press shows a slow increase in energy consumption per produced piece, it could mean that critical components are degrading. Intervening before the breakdown means avoiding unexpected and costly machine downtime.
• Load and production optimization: AI systems can suggest how to redistribute workloads among multiple machines to level energy consumption, avoiding peaks during peak hours (when energy costs more) and taking advantage of lower-rate time bands. They can also simulate scenarios (“what-if”) to understand the energy impact of a production change, such as introducing a new shift or a new line.
• Data-driven strategic decisions: By aggregating data, AI provides management with understandable reports and KPIs. Less energy-efficient lines can be identified and targeted investments planned. For example, if a certain machine disproportionately impacts consumption relative to the amount of product it produces, the company can evaluate whether it’s worth replacing it with a more modern one or upgrading it.

In summary, AI transforms energy monitoring from simple data collection to an expert system supporting decisions. It’s not just about seeing what’s happening, but understanding why and receiving indications on how to improve. This takes monitoring to a higher level: from a technical operation to a strategic lever for competitiveness.

Energy Monitoring and Production Optimization

A well-implemented energy monitoring system not only reduces bills but also becomes a tool for optimizing production itself. Each machine has its own “energy profile” that reflects how it operates. By analyzing these profiles, we can understand much about the actual functioning of the plants.

For example, consider a machine tool or press: it will consume a certain amount of energy to start up, then have stable consumption during operation, and finally minimal consumption in standby. If we compare the energy consumed with the production obtained, we get an indicator called energy per piece. This parameter tells us how much energy was needed to produce a single unit of product. Reducing energy per piece means making production more efficient (less costs and less waste for each manufactured unit).

How can this data be used? Imagine two production lines making the same item: if Line A consumes 10 kWh to produce a batch of 100 pieces and Line B consumes 15 kWh for the same quantity, it’s evident that Line A is more energy-efficient. We could then study why Line B consumes more: perhaps older machines, more downtime, or less optimized setups. Monitoring specific consumption by machine and product allows identifying these differences and acting accordingly. In some cases, simple interventions such as better machine parameter adjustment, replacing worn tools, or changing the production sequence can significantly lower consumption without impacting productivity.

Another crucial aspect is the relationship between energy consumption and OEE (Overall Equipment Effectiveness), the index that measures the total effectiveness of a plant considering availability, performance, and quality. By integrating energy monitoring data with OEE, the company obtains a 360° view of efficiency. For example, a drop in OEE due to machine micro-stops could also manifest as energy waste: machines that remain on but idle continue to consume electricity unnecessarily. Conversely, an improvement in OEE through reducing stops and increasing effective speed will be reflected in more productive energy consumption (more pieces per kWh spent). In practice, energy monitoring and OEE go hand in hand: one provides information on how the machine is used (consumption), the other on how well it is used (productive effectiveness). Combined, they help identify hidden inefficiencies and production optimization opportunities that would otherwise remain unknown.

The results of an integrated approach are not long in coming. Projects implemented with an Industry 4.0 perspective have demonstrated significant improvements: for example, the implementation of IoT sensors and AI analysis led in one case to reducing energy consumption by 25% along with a 45% decrease in machine downtime and 30% in production times (TechMakers - Production Optimization with IoT and AI). This means that acting on consumption not only saves energy but also makes production leaner, with machines active only when needed and more fluid processes.

Reducing Bottlenecks Through Energy Data

Beyond direct savings, energy monitoring offers another crucial benefit: helping to identify and reduce bottlenecks in the production process. A bottleneck is that phase or machine that limits the capacity of the entire line, slowing down overall production. Identifying it is not always immediate, especially in complex plants with many variables in play. However, energy data provides valuable clues to map the actual production trend.

How do these clues work? Imagine a line composed of multiple machines in sequence (for example, cutting, processing, assembly, finishing). If machine X is the bottleneck, it will tend to always work at maximum capacity, while upstream machines will accumulate materials waiting and downstream ones will sometimes remain idle for lack of pieces to process. From an energy perspective, we’ll see that X consumes energy constantly and at high levels, while subsequent machines show more intermittent consumption (turning on and off, or prolonged standby phases). This pattern suggests that X is slowing down the entire flow. Conversely, if an upstream machine is too slow, we might notice very low consumption (often stopped) in that phase and irregular consumption further downstream (because the subsequent machines have irregular waits for input material).

Through the monitoring dashboard, it’s possible to visualize these trends in real-time. Some systems allow overlaying consumption graphs of multiple machines: if you observe that when Machine A is on, Machine B is off, and vice versa, it could mean that B is forced to wait for pieces processed by A (a signal of a bottleneck in A). By intervening on that bottleneck – for example by enhancing Machine A, adding a second machine in parallel, or optimizing the upstream process – the line is rebalanced, making all stations work more uniformly. This eliminates downtime and increases productivity without necessarily adding new resources, simply better utilizing existing ones.

Furthermore, continuous monitoring can detect more subtle process inefficiencies. For example, unsynchronized work cycles between different departments can create accumulations of work-in-progress that mean not only occupied space and immobilized capital but also energy waste (machines heating material that then sits idle, lights and systems on in departments waiting for input, etc.). By analyzing total and segmented energy consumption by area, the company might discover, for example, that every day at 3:00 PM there’s a consumption drop in one department and a peak in another: why? Perhaps the upstream department finishes processing a batch and the next one doesn’t start immediately, leaving machines unnecessarily on elsewhere. By identifying these misalignments, production schedules or internal logistics can be modified to eliminate such hidden bottlenecks.

In conclusion, energy data acts as virtual sensors of the production process health. Reading between the lines of a consumption graph can reveal organizational or technical problems that, once solved, unlock additional production capacity and also improve indicators such as OEE (since downtime decreases and effective machine utilization increases). Once again, this demonstrates that energy is not an isolated factor but intimately connected to operational efficiency.

Practical Case: A Press with Anomalous Pauses Reveals a Layout Problem

To concretely understand how energy monitoring can help improve production, let’s consider a real case that occurred in a manufacturing company. The company had installed an intelligent monitoring system on several machines, including a large press used in metal component stamping. Data analysis revealed unexpected behaviors: the press showed anomalous pauses during the day, periods when energy consumption dropped dramatically indicating that the machine remained idle longer than expected. These interruptions were not in the production plans and initially had no immediate technical explanation (the press was new and working well from a mechanical standpoint).

Thanks to real-time monitoring, the production manager noticed that these pauses occurred mainly during certain time bands and lasted a few minutes each, enough to impact both productivity and energy per piece (each stop then required a restart of the press, with additional consumption). Initially, a problem with machine setup or raw material supply was hypothesized. Deepening the analysis, it was decided to correlate energy data with logistics data, discovering the real reason: the plant layout was hindering material handling with forklifts. In practice, the press went into waiting because the material to be processed didn’t arrive on time: forklifts had to follow a tortuous path in the building and often had to wait for other vehicles to clear narrow passages.

This discovery was illuminating. A problem that seemed technical (the press stopping) was actually organizational/logistic. Without energy monitoring data, the cause of the bottleneck would have taken much longer to identify, perhaps with long observations in the department or blaming false problems. Instead, the consumption graphs immediately showed when and for how long the press remained inactive, directing the search for the cause to factors external to the machine.

Once the problem was identified, the company was able to intervene quickly: it redesigned the layout of the warehouse and press feed lines, creating wider corridors and more direct routes for forklifts. After this modification, the anomalous pauses practically disappeared. The press now works continuously as expected, with more constant energy consumption and greater output. This has brought immediate benefits: increased daily productivity, reduced material transit times, and less stress for both logistics operators and machines (which no longer undergo frequent stops and restarts).

The press case demonstrates how energy monitoring in production can reveal hidden inefficiencies that don’t necessarily concern the machine itself, but the ecosystem in which it operates. It’s a concrete example of a data-driven approach: data guides problem identification and corrective decisions, with tangible results in terms of production optimization and savings.

Upgrading Existing Machinery Through Energy Monitoring

Many manufacturing companies operate with dated machinery which, while robust and still productive, are not natively equipped with digital monitoring systems. The good news is that it’s not necessary to purchase only new machines to benefit from consumption control: there is the possibility of upgrading older machines by integrating them into an advanced energy monitoring system. In other words, even older generation plants can become more “intelligent” thanks to additional sensors and devices.

How does the upgrade work in practice? IoT sensors can be installed (for example, non-invasive current transducers, power sensors, thermocouples, etc.) on electrical lines and critical parts of old machines. These sensors collect information on instantaneous consumption, vibrations, temperature, and other operating parameters, sending them to a central monitoring platform. In this way, the old machinery is connected to the company’s information system like any modern machine. The collected data allows evaluating its performance and efficiency in real-time, highlighting optimization opportunities.

Thanks to these insights, the company can improve and optimize older machines in various ways:

• Targeted adjustments and maintenance: if monitoring shows that an old machine consumes too much in certain phases, parameters can be adjusted or specific maintenance performed (for example, replacing worn components, lubricating mechanisms, better calibrating heating systems). A gradual efficiency decline is immediately noticed and can be addressed before it becomes a serious problem.
• Adoption of energy-saving components: the data might indicate that an electric motor from a 1990s machine draws much more current than a modern equivalent. In this case, installing a high-efficiency motor or an inverter (frequency variator) to modulate speed can be evaluated, reducing consumption. The upgrade investment is guided by numbers: if the consumption per piece of that machine is excessive, the retrofit intervention will have a measurable economic return.
• Reprogramming work cycles: some dated machines don’t optimize consumption because they follow fixed cycles despite variable loads. With monitoring, one notices if the machine remains unnecessarily on idle or waiting between cycles. In many cases, it’s possible to modify the operating logic (for example, introducing automatic standby modes or timed shutdowns) to eliminate waste without affecting productivity.
• Integration into factory systems: once collected, data from older machines can be sent to the central system (MES, ERP, or cloud platforms) to be analyzed together with others. This means that even old resources become part of the company’s digital ecosystem. OEE can thus be calculated including all machinery, old and new, and truly understand how they impact overall results. If an old but critical machine causes too many stops or high consumption, it will clearly emerge from comparative reports, supporting the decision for a possible replacement or further upgrade.

In essence, energy monitoring acts as a bridge between legacy technologies and Industry 4.0. It allows valuing existing assets, often the result of significant investments in past years, bringing them to a higher efficiency standard. A mixed plant of new and old machines, all monitored and optimized, can achieve remarkable performance without the financial burden of completely replacing the machine park. This gradual approach to innovation is highly appreciated in terms of economic sustainability: improvements are made where needed, guided by data, and measurable results are obtained both in terms of consumption reduction and productivity increase.

Conclusions

In conclusion, energy monitoring in manufacturing has proven to be a fundamental enabling factor for competitiveness and efficiency. Through intelligent data collection and analysis, companies can optimize production, reduce operating costs, and improve OEE, all while increasing the sustainability of operations. As we’ve seen, the advantages go well beyond savings on bills: it’s about making processes more lean and responsive, eliminating bottlenecks, valuing even older generation machinery, and making decisions based on concrete facts rather than assumptions.

For companies wanting to keep pace with the times, investing in energy monitoring systems and data analysis (perhaps leveraging IoT and AI) is an increasingly necessary step. The positive results obtained in various case studies confirm that these technologies can lead to significant improvements in short timeframes, often with notable returns on investment.

Next Steps

Is your company already leveraging energy monitoring? Do you want to discover how to apply it concretely and what benefits you could obtain? To delve deeper into the topic and see a real example of real-time monitoring and optimization, we invite you to visit the web page: TechMakers - Production Optimization with IoT and AI where you’ll find a detailed case study. Don’t miss the opportunity to see how energy consumption analysis can transform production and contribute to your company’s success.