Industrial energy management is no longer just about checking the electricity bill and reviewing a chart from the main meter. Today, it is also worth knowing how much energy a process, production line, work order, or batch consumes and how that result relates to what actually happened on the shop floor. This data helps manufacturers control energy intensity and costs while organizing information that may later be needed for digital product passports.

This brings Industry 4.0, industrial sustainability, industrial energy management, and the digital product passport in manufacturing into the same conversation. What connects them is data about processes, materials, machines, and products, as well as the ability to link that information reliably across different stages of production.

Some of this information is generated directly on the shop floor, while other data comes from ERP systems, quality systems, technical documentation, or suppliers. As reporting becomes more detailed, the ability to connect these sources without manually reconstructing a product’s history becomes increasingly important.

A production line consumed 12 MWh. That is a specific number, but it is still difficult to evaluate the result based on that figure alone because, during the same period, the plant may have produced a large batch with high equipment utilization or manufactured significantly fewer units with frequent startups, changeovers, and downtime.

That is why industrial energy management becomes truly useful only when energy measurements can be compared with the actual course of production. Batch size, product type, cycle time, machine status, and reject count all matter, and instead of monitoring only a line’s total consumption, manufacturers can calculate energy use per unit, ton, batch, or hour of actual operating time.

This approach is supported by ISO 50001:2018, which defines a framework for energy management systems and improving an organization’s energy performance. ISO reviewed and confirmed this version again in 2024, so it remains the current reference standard for organizations working to manage energy performance systematically.

In day-to-day operations, there is a significant difference between measurement and information that is actually useful for production. A meter shows consumption, while connecting that reading with a process and work order makes it possible to understand why the result changed and whether it genuinely requires action.

The information manufacturers need usually already exists, but it is often distributed across different systems. Power analyzers provide energy data, PLCs and SCADA describe equipment status, MES tracks the progress of production orders, and ERP stores information about products, materials, and planning.

When these sources operate separately, calculating the result for a specific batch requires matching production times with energy readings and checking exactly what happened on the line. Industry 4.0 makes it possible to connect these layers and assign energy consumption to specific production events instead of treating energy data as a separate stream of information.

Consider a simple hypothetical example in which a process furnace consumes 420 kWh while producing a batch of 700 units. The result is 0.6 kWh per unit, while for the next comparable batch the result increases to 0.75 kWh per unit.

That difference alone does not prove that the equipment is operating incorrectly because the load may have changed, the process may have taken longer, the number of startups may have increased, or process parameters may have been different. Only by comparing these pieces of information can the cause be determined with confidence.

This type of industrial energy management separates increases in total consumption from deterioration in energy intensity. A facility may use more energy because it produced more while still requiring fewer kWh for each unit manufactured, which is a very different operational conclusion than simply observing a higher utility bill.

According to Statistics Poland, between 2014 and 2024, energy efficiency in Poland improved by an average of 1.2% per year, while the average improvement in industry was 2.8% per year. During the same period, final energy consumption in the Polish economy increased from 60.2 to 69.2 Mtoe, although these figures apply to the national economy and therefore cannot be directly translated into the performance of an individual plant.

They do, however, illustrate something useful for manufacturing: higher overall energy consumption does not necessarily mean lower efficiency. A company can increase its production scale while simultaneously using less energy per ton, batch, or unit of product, which is why total consumption should always be interpreted in context.

Industrial sustainability increasingly requires environmental data to be combined with operational information. Monthly energy consumption is a useful control point, but evaluating a process requires a result that is related to what was actually produced and under what conditions production took place.

This information is also valuable outside production itself because finance teams can allocate energy costs to products more accurately, sustainability teams can use the same underlying values, and production teams can compare the energy intensity of similar batches without creating separate reports. Using the same data foundation also reduces the risk that different departments calculate similar indicators in different ways.

The Digital Product Passport, or DPP, is one of the mechanisms introduced under the Ecodesign for Sustainable Products Regulation, or ESPR 2024/1781. Its purpose is linked to making selected product information available in a structured digital form throughout relevant stages of the product lifecycle.

Detailed requirements depend on the product category and the applicable legislation. Among other things, the ESPR provides for defining the scope of information, the data carrier, and the level at which the passport is created, for example for a model, batch, or individual item.

From a manufacturer’s perspective, the digital product passport in manufacturing is therefore primarily a data organization challenge. A QR code or NFC tag may provide access to the passport, but before that can happen, the required information must be assigned to the correct product and maintained in the required structure.

Not all of this data originates on the shop floor because process information may come from MES and automation systems, material information from ERP, and some component-origin data from the supply chain. Poland’s Ministry of Development and Technology indicates that the scope of the DPP may include information such as the origin of raw materials, manufacturing processes, the supply chain, repair, recycling, and end-of-life product management, while the exact dataset will depend on the regulations applicable to the relevant product group.

Manufacturing nevertheless remains one of the key data sources because it creates a large part of the operational history associated with the product. If you later need to reconstruct the history of a batch or the conditions under which it was manufactured, it is much easier to ensure proper data identification when the information is created than to assemble the entire history several months later.

As of September 2026, the DPP has entered the technical implementation stage, and the European Commission provides an official registry and a test environment. Organizations can register and work with the system’s functionality as the technical infrastructure develops alongside sector-specific requirements.

It is important to distinguish the registry from the passport itself because the EU DPP Registry acts as a common index that stores information including unique identifiers, registration data, and metadata. It is not a centralized database containing the complete, detailed documentation for every product covered by future DPP requirements.

The full dataset remains decentralized and is maintained by the relevant economic operator or DPP service provider. The registry enables identification and access in accordance with the applicable regulations, and the European Commission has also provided for its use by customs and market surveillance authorities.

This distinction matters for anyone planning a company’s data architecture because preparing for the digital product passport in manufacturing does not simply mean uploading all product documentation to a single European Commission database. Manufacturers still need their own data sources, ownership rules, integration mechanisms, and a reliable way to maintain information over time.

Poland’s Ministry of Development and Technology states that implementation of the regulations and technical DPP infrastructure is already underway. Adoption of a delegated act establishing sector-specific requirements for iron and steel is planned for Q4 2026, while further work is expected to cover areas including textiles, tires, aluminum, furniture, mattresses, and ICT products.

This is a schedule for developing requirements, not a single compliance date for the entire industrial sector. Companies should therefore monitor the regulations applicable to their own product categories rather than base their preparations on a general assumption that DPP requirements will simply begin across the board in 2027.

The first clearly defined passport obligation applies to batteries, and starting February 18, 2027, every LMT battery, every industrial battery with a capacity greater than 2 kWh, and every electric vehicle battery placed on the market or put into service will be required to have a battery passport. This requirement follows from Article 77 of Regulation (EU) 2023/1542.

Manufacturers cannot currently assume that every company will be required to publish the exact amount of energy consumed to manufacture an individual product. There is no single, universal set of DPP fields for all products, because the required information depends on the product category and the applicable sector-specific requirements.

This distinction matters because it is easy to connect the two topics too closely. Industrial energy management can provide data needed for environmental calculations or process descriptions, but an individual kWh reading does not automatically become a mandatory field in every digital product passport.

A plant that only knows the monthly energy consumption of the entire facility has limited options when it needs to determine the impact of a specific process. Measurements linked to an operation, its execution time, and a work order are far more useful because such data can later support cost analysis, process comparisons, and preparation of specified product information.

The best way to assess the state of your data is to start with one product or one product family instead of trying to cover the entire portfolio from the beginning. This makes it easier to see where data already connects automatically and where gaps still require manual work.

A useful progression can be divided into five levels:

A plant can have highly accurate meters and still remain at the first or second level if the readings cannot be connected to a work order. In that situation, the usefulness of energy data at product level remains limited even though the measurement infrastructure itself may be advanced.

The opposite situation is also possible because an MES may precisely represent the course of production while energy data is only available for the entire facility. In that case, the product history is known, but the energy measurement lacks the level of granularity needed to connect consumption reliably with a specific process or product.

The scope of measurement should depend on how the data will be used rather than on how many points can technically be metered. With a stable production-line configuration, a shared meter may be sufficient for comparing the energy intensity of the overall process and identifying meaningful changes over time.

Separate measurement is more justified for equipment that represents a significant share of total consumption, operates independently, or has clearly different energy states. In those cases, the reading can support both product-level allocation and analysis of the equipment’s own behavior under different operating conditions.

Metering everything simply because it is technically possible quickly increases infrastructure costs and the volume of data that must be maintained. A better approach is to start with measurement points that matter for specific production decisions and expand the system where additional detail can create measurable value.

For industrial energy management, more data is not automatically better data because usefulness depends on context, quality, and the ability to connect measurements with production events. The goal should be to collect information at a level of detail that supports a meaningful operational, financial, or sustainability decision.

An Energy Management System, or EMS, is responsible for the energy data layer, but it does not create the entire digital product passport because the required information may also come from MES, ERP, quality systems, technical documentation, and suppliers. Its role is therefore one part of a broader manufacturing data architecture.

Once integrated with production systems, an EMS can connect energy consumption with a production line, operating time, work order, or batch. MES provides the operational context, ERP contains information about the product and materials, and additional systems supplement the remaining data, while our EMS is designed to collect and analyze energy data from equipment, production lines, and facilities.

In this architecture, industrial energy management supports a broader data environment instead of functioning as an isolated monitoring application. It creates a connection between energy consumption and what is actually happening in manufacturing, making energy information more useful for both operational analysis and product-level reporting.

Product information is created across different parts of the organization, which means the DPP cannot realistically be treated as the responsibility of one isolated team. Production understands the course of operations, maintenance and energy teams are responsible for some measurements, IT/OT manages integration, quality teams own process-related information, and purchasing departments and suppliers provide some of the data about materials and components.

This does not mean that one person needs to be responsible for everything. What matters much more is assigning responsibility for specific data so that each piece of information has a defined source, owner, and update method.

The same discipline also supports industrial energy management because if production, finance, and sustainability teams use the same KPI, it should be based on the same calculation method. Otherwise, two departments may report values that have both been calculated correctly but are still different because they were built on separate assumptions.

A consistent data model also makes industrial sustainability easier to manage because operational, environmental, and financial decisions can rely on the same source information. This reduces manual reconciliation and makes future reporting requirements easier to address.

You do not need a company-wide DPP platform or energy meters on every machine to get started. Select one product or product family and trace its path through production, including operation times, equipment used, and energy consumption, while checking which information is captured automatically and which still has to be reconstructed manually.

If the information connects automatically, the company already has a solid foundation for further development. If gathering it requires several exports, manual timestamp matching, and combining data in Excel, it makes more sense to improve integration first and only then evaluate the need for additional metering, EMS expansion, MES integration, or a dedicated solution for the digital product passport in manufacturing.

Industrial energy management delivers the most value when consumption can be assigned to a specific process and product. Today, this helps manufacturers control energy intensity and costs, while future DPP obligations may make the same quality of data useful for reducing the amount of manual work required to prepare product information.

The digital product passport therefore starts much earlier than the QR code because, for a manufacturer, it begins at the point where information about what a product was made from, where it was made, when it was made, and how it was produced can still be reliably connected. This is also where Industry 4.0, industrial sustainability, and the digital product passport in manufacturing meet: in the ability to collect, connect, and maintain reliable production data across the product lifecycle.

The obligation depends on the product group, and DPP infrastructure is already being implemented while the European Commission’s registry is available to organizations. Detailed obligations are being introduced in stages, and for specified batteries a passport will be required starting February 18, 2027.

The requirement will cover every LMT battery, every industrial battery with a capacity greater than 2 kWh, and every electric vehicle battery placed on the market or put into service. These categories are covered by the battery passport requirements established under Regulation (EU) 2023/1542.

There is no single dataset that applies to every product, because the required scope depends on the requirements for the relevant product category. Energy data may be used for calculations or process descriptions, but manufacturers should not assume that exact energy consumption will be a mandatory field in every passport.

No, because the registry operates as a common EU index and stores information including identifiers, registration data, and metadata. The DPP itself uses a decentralized model, with the detailed data remaining under the responsibility of the relevant economic operators or service providers.

Yes, because integration makes it possible to combine energy consumption with information about work orders, batches, operating time, and process execution. This allows industrial energy management to move much closer to the level of an individual product and makes the data more useful for operational, cost, and sustainability analysis.