Agentic AI is redefining the trajectory of Industry 4.0. Traditional systems have largely been reactive—analyzing data and supporting human decision-making. Today, a new paradigm is emerging: autonomous agents that independently make decisions, optimize processes, and adapt to changes in real time. In modern smart factories, these systems integrate with MES, IIoT, and ERP platforms to create self-optimizing environments. Machines no longer wait for instructions—they identify issues, anticipate disruptions, and act before production is affected.

The concept of Industry 4.0 is based on continuous monitoring of production processes and decision-making driven by real-time data. Without reliable information, it is impossible to implement predictive maintenance, energy optimization, automated planning, or meaningful OEE analysis. If data is delayed, incomplete, or inconsistent, digitalization becomes only superficial. IT systems may be in place, but they do not deliver real business value. That is why it is critical to build an architecture in which data is collected automatically, consistently, and centrally.

Implementing a Manufacturing Execution System (MES) rarely begins with a full-scale rollout across the entire production environment. In practice, the most effective approach is incremental—starting with a pilot implementation on a selected production line or area. This allows companies to validate the system in real operating conditions, collect data, and fine-tune the configuration before scaling across the entire machine park. More and more organizations choose a pilot not only for technical reasons but also for organizational ones. MES impacts the daily work of operators, production managers, maintenance teams, and planners. Therefore, implementation should be a controlled process rather than a one-time deployment.

In Industry 4.0 and 5.0 discussions, a common argument appears: AI models reach around 90% accuracy, and in industrial environments a 10% error can cost millions. The reasoning sounds compelling because it is framed numerically and linked to operational risk. However, this argument assumes something critical — the existence of a fully digitized factory. In reality, across many manufacturing plants in Poland and Europe — especially in the SME sector — that level of digital maturity simply does not exist.

Why Connect Older Machines to an MES System? Legacy machines are often a crucial part of the production fleet, yet their actual performance frequently remains unknown. Data is collected manually, reports are delayed or missing entirely, and operational decisions rely on experience rather than facts.

Digital twins are increasingly appearing in modern factory strategies. Process simulations, virtual production lines, scenario testing without the risk of stopping production – this sounds like the future of manufacturing. One of the most recognizable tools in this area is NVIDIA Omniverse. The problem begins when a digital twin is supposed to stop being a visualization and become a reflection of actual production. For this, data is needed. And this is where MES-class systems play a key role.

Nvidia and the data problem at a broader scale

For the last two decades, most digital innovation has focused on mass-market IT: e-commerce, social platforms, and office SaaS. But the largest untapped opportunities now lie in highly specialized, industrial domains — where processes are physical, regulated, and operationally critical. Manufacturing, energy, logistics, and infrastructure do not need more generic software. They need systems that understand how the real world of machines actually works. That is where the next decade of industrial innovation will be built.

System MES (Manufacturing Execution System) - If someone in 2025 still thinks of an MES system as "terminals at workstations and reports from the department," it's about as current as a fax machine in OT-IT integration. The Manufacturing Execution System has ceased being a shop floor application. MES has become the operational layer of truth between the world of automation (OT) and the business world (IT). MES systems in 2025 are operational platforms that determine whether a company thrives on availability, quality, lead time, and energy efficiency. The MES system has stopped being a cost—it has become a mechanism for steering competitiveness.

The role of a production manager is becoming more complex every year. Modern factories demand not only smooth operations and on-time execution, but also cost optimization, rapid reaction to deviations, and building a culture of continuous improvement. In such an environment, traditional Excel sheets, manual reports, or intuition-based decisions are no longer enough. This is where OmniMES proves its value — a system that gives the production manager tools to make informed, fast, and accurate decisions.

Every manufacturing company has activities that do not add value to the product yet consume time, resources, and employee energy. In the book Toyota Production System: Beyond Large-Scale Production (1978), Taiichi Ohno identified seven of the most common types of waste. In Lean Manufacturing philosophy, these activities are called Muda – meaning wastefulness or uselessness (Japanese: muda = useless, unnecessary). Japanese companies, led by Toyota, have been effectively eliminating Muda for decades, achieving high production efficiency and flexibility. Understanding the seven classic wastes of Muda helps identify where productivity may be leaking in your company — and how to fix it.

OmniMES is a modern Manufacturing Execution System (MES) designed on a microservice architecture. This model ensures scalability, flexibility, and independent development of individual modules — from machine communication to data analytics and production management.

In modern manufacturing plants, every second of machine operation matters. Even short, seemingly insignificant interruptions – so-called micro-downtimes – can generate substantial losses on a production line. Research (Aberdeen Research) shows that unplanned downtime can cost manufacturing companies from hundreds to even thousands of dollars per minute. This is one of the reasons why more and more enterprises implement MES (Manufacturing Execution Systems). They support maintenance, enable real-time machine performance analysis, and help minimize the negative impact of micro-downtimes.

Industry 5.0 is not just another stage of digital transformation – it's a fundamental shift in approach to manufacturing that places humans and sustainable development at the center of advanced technologies. In this new reality, analytical platforms like BigQuery AI become a key component of intelligent production systems, enabling the transformation of vast amounts of data into concrete business insights.

In the era of industrial digital transformation, the global human-machine interface (HMI) market reached USD 24.5 billion in 2024 and is projected to grow to USD 55.2 billion by 2033, with a compound annual growth rate (CAGR) of 9.7%. At the same time, the simulation software market expanded from USD 19.95 billion in 2024 to an expected USD 36.22 billion by 2030 at a CAGR of 10.4%. These dynamic trends reflect a fundamental shift in the approach to optimizing production resources.

The integration of the Sparkplug B protocol in MES systems enhances energy efficiency and reliability in production.

The digital transformation of manufacturing requires a strategic approach to managing production batches. The use of Manufacturing Execution Systems (MES) combined with Industry 5.0 technologies can reduce downtime by 50% and cut maintenance costs by 40%, according to the latest Deloitte research.

Modern industry is at the heart of digital transformation, where Manufacturing Execution Systems (MES) with real-time data analytics form a cornerstone of Industry 5.0. According to a 2023 McKinsey Global Institute report, companies that deploy advanced analytics in production achieve, on average, a 15–20% productivity increase and a 25% reduction in defects.

ROI: up to 15% efficiency gains and 10–20% better energy storage performance – this is the real power of AI in modern energy. In the era of Industry 5.0, where technology merges with a human-centric approach, the renewable energy sector is growing at an unprecedented pace – with 18.6 GW of additional solar capacity installed in the first nine months of 2024. Manufacturing Execution Systems (MES) enhanced with machine learning are becoming a cornerstone of this transformation, delivering both business and technical benefits.

In the age of Industry 5.0, where energy efficiency becomes a key differentiator, manufacturing enterprises face unprecedented challenges. According to the new EU Directive 2023/1791 on energy efficiency, member states must achieve an average annual energy savings rate of 1.49% between 2024–2030. This means that companies must radically rethink their approach to energy management. A crucial tool in this transformation is the optimization of the OEE (Overall Equipment Effectiveness) index through intelligent energy management in MES-class systems. In both Industry 4.0 and 5.0, OEE plays a key role in ROI planning, reliability, and production stability.

Integrate MES with IoT to enhance quality control and reduce production downtime.

Comparison of MQTT and Sparkplug B Communication Protocols in the Context of Industrial Internet of Things (IIoT). Learn about their advantages, differences, quality of service (QoS), and message retention. Discover which protocol better meets the requirements of your IoT project.