Your plant’s first step toward energy optimization: data visibility

Energy expenses make up a significant portion of a typical manufacturing company’s operating costs. But what’s even more concerning is that these costs continue to increase as production demands become more complex and equipment ages and becomes less efficient.

In the United States alone, energy consumption increased by 6% between 2018 and 2022, according to the 2025 Manufacturing Energy Consumption Survey. This report also reveals that the industrial sector consumes 32% of all U.S. energy, with the majority of that going toward manufacturing.

No plant wants to devote more money than necessary toward utilities, especially when there are so many other investment opportunities that offer a better return: research and development, hiring and training, automation and technology, etc.

On top of wanting to tame energy costs, plants also face increasing pressure from consumers, stakeholders and regulatory agencies to prioritize sustainability and energy efficiency.

Understanding energy consumption is also a critical component of establishing competitive pricing. Knowing the energy input for each product or batch you produce helps paint a clearer picture of true production costs so you can set prices that accurately reflect expenses.

Given these facts, it’s no surprise that energy optimization is a top priority for manufacturers. Along this journey toward operational excellence, however, make sure you don’t put the investment before the analysis. In order to effectively optimize energy use, you have to determine how much energy your plant consumes—and where that energy is going.

Where most plants miss the mark when trying to understand energy consumption

Visibility is the first step toward energy optimization. You must have access to granular data about things like machine-level performance and energy-usage patterns. But how can you gain those insights?

Looking at your plant’s utility bill doesn’t provide a complete picture of consumption. It simply represents usage across your entire facility. It can’t tell you:

• Where, when or how peaks occur
• Where inefficiencies or opportunities to reduce energy loss exist
• Where energy is being used or what it’s being used for
• How consumption is connected to operations (production schedules, downtime, maintenance, etc.)
• Which machines or processes consume the most energy

Some plants take analysis a step further by looking at data about energy usage, but teams often review historical consumption information: data about events that happened weeks or months ago, when it’s too late to act. In addition, this data is often siloed from production and maintenance data. Without bringing these data points together, it’s challenging to understand how production schedules or equipment performance impact energy use.

Even if you can identify possible patterns or trends by comparing historical energy-usage data to production and maintenance data, it’s outdated information that likely doesn’t reflect current operating conditions. Making decisions based on this data could lead to inaccurate assumptions.

Only by analyzing near-real-time data can you proactively:

• Respond to rapid changes in usage caused by shifts in production schedules, equipment performance, etc.
• Detect and respond to sudden spikes when they happen
• Identify and resolve equipment failures
• Optimize operational inefficiencies, like a drying process that’s taking too long
• Measure the impact of changes to assess whether strategies are working

Finally, some plant leaders assume that focusing on managing peak energy demand will effectively lead to energy optimization. But overlooking off-peak inefficiencies leaves significant savings on the table. For example, you won’t be able to identify:

• Equipment that runs during idle periods
• Processes running unnecessarily during downtime
• Systems operating at full capacity during low-production times

None of these strategies offer the level of near-real-time visibility you need to reduce energy consumption across machines and processes.

How near-real-time data turns energy awareness into operational success

Without analyzing near-real-time energy consumption and comparing it to operational data and machine-level performance metrics, you won’t know which pieces of equipment are operating efficiently or which processes are wasting energy. So how can you know where to start making improvements to boost energy optimization?

No matter which sensors, platforms or software you choose to deploy, Belden’s complete connection solutions help you seamlessly connect assets and systems plant-wide to track and manage energy-consumption data effectively. This can include data in critical areas like power stations and production lines, as well as from specific energy-intensive processes and systems, such as air compressors, motors, mixers, cone grinders or tumbling wheels.

Once everything is connected, we help you tap into detailed data on energy consumption and power usage across different parts of your facility. This gives you the visibility you need to understand how energy is being consumed at each location, within each area, within each process or even for each piece of equipment so you can make informed decisions about upgrades and optimizations.

By connecting these energy data points, we’ll help you identify inefficiencies so you know exactly where and how to optimize consumption. From there, you can develop a comprehensive optimization plan that will reduce costs, minimize waste and improve operational efficiency. Based on what you uncover, your strategies might include upgrading to more efficient motors, optimizing production processes or addressing inefficiencies revealed through the data. With this data in hand, you can be confident that you’re making impactful decisions to improve energy optimization.

Learn how our Customer Innovation Centers can help.

Related links:

• To optimize energy use, your plant needs the right data—here’s why
• Achieving Shop Floor Connectivity and Visibility Through Digitization
• Digital Transformation in Manufacturing: 4 Obstacles to Overcome