The manufacturing industry is facing a profound transformation: production processes must be further digitalized, automated, and made more sustainable. Modern Manufacturing Execution Systems (MES) play a crucial role in this by increasing transparency, optimizing processes, and enabling companies to make data-driven smart decisions.
With over 100 successfully implemented MES projects in various industries worldwide, we closely follow these trends and observe that they are highly relevant to all our customers.
It is evident that a combination of proven Siemens Manufacturing Software solutions with our in-house developed products enables manufacturing companies to comprehensively digitalize their processes, gradually automate them, and continuously optimize them to increase the transparency and quality of production processes.
For 2025, four central trends in MES development in the field of Smart Manufacturing are emerging: Artificial Intelligence (AI), digital twins, IIoT integration with edge computing, and sustainability. These trends address the increasing market requirements to make production processes more efficient, use resources more effectively, and secure long-term competitiveness as a producer.
Artificial Intelligence (AI): From Automation to Intelligent Assistance Systems
Initial practical examples show that Artificial Intelligence can elevate the potential of MES systems to a new level. While many production processes today are already partially or advanced automated, depending on the industry, AI additionally supports more intelligent decision-making and real-time reactions, for example, in scaling quality control or through more precise detection and interpretation of product deviations. This is not about fully automated manufacturing, but about advanced assistance systems that optimize processes and proactively prevent inefficiencies.
Benefits of AI-supported MES solutions:
- Optimized production planning: Real-time data analysis enables dynamic adjustment of production plans.
- Predictive maintenance: AI detects potential problems early and reduces unplanned downtime.
- Advanced analytics: Machine learning models improve data-based decision-making processes.
- Efficiency increase: Inefficiencies are detected in real-time, contributing to improved Overall Equipment Effectiveness (OEE).
Digital Twins: Simulation and Optimization with Real-time Data
The use of Digital Twins or their functional representation in an MES is gaining further importance. These virtual representations of physical systems or processes form the basis for simulations and analyses to improve production processes. However, the effectiveness of a digital twin as an integral part of Smart Manufacturing strongly depends on the data quality and IT infrastructure of a company.
Application areas:
- Process simulation: Production changes can be tested virtually before being implemented in the real environment.
- Optimized resource utilization: Simulation-supported optimization can achieve material and energy savings.
- Maintenance strategies: Simulations help optimize maintenance strategies and avoid unplanned downtimes.
Technological synergies: The combination of IIoT, sensors, and AI makes digital twins particularly powerful. They enable more precise decisions and contribute to sustainability by optimizing resource consumption.
IIoT Integration with Edge Computing: Higher-value Data from Shopfloor to Topfloor
By utilizing IIoT and edge applications, modern MES solutions receive valuable additional data for effective manufacturing control and Smart Manufacturing. This is particularly effective in conjunction with a powerful integration platform that enables machine connection while supporting the implementation of various automation scenarios. Corresponding solutions such as the Process Automation Controller (PAC) have been addressing these Industry 4.0 concepts for many years:
Example functions of the PAC integration platform in edge integration:
- Industrial Edge application: PAC runs directly on industrial edge devices and enables flexible data processing on the shop floor.
- Diverse connection options: As an integration platform, PAC uses a wide range of interfaces between equipment and MOM levels to seamlessly network machines and IT systems.
- Data contextualization: PAC not only processes raw data such as temperature or pressure but also links these with production data, thus supporting the transformation from Big Data to Smart Data.
- Scalable automation: PAC supports various automation levels – from simple data collection to basic automation to full machine integration with validations and feedback loops to the MES.
Technological synergies: IIoT, especially in conjunction with a powerful integration platform, helps bridge the gap between machine level and MES by providing standardized and semantically enriched data. This allows efficient integration of heterogeneous machine landscapes, regardless of the age or communication protocol of the systems.
Sustainability through MES: Efficient and Resource-conserving Production
Sustainability as a goal of modern manufacturing has gained enormous importance in recent years. MES solutions make an important contribution to reducing energy and material consumption. They are part of a more comprehensive sustainability approach that often also includes ERP and ESG systems.
Functions of sustainable MES solutions:
- Energy monitoring: MES-supported analysis tools identify optimization potentials in energy consumption.
- CO₂ tracking: MES can provide relevant data used for external environmental management systems.
- Scrap reduction: Optimized workflows minimize material losses and improve resource utilization.
These four trends make MES not only the backbone of modern manufacturing but also a driver for innovation and sustainability. But how can these trends and solution approaches be effectively integrated into a digitalization strategy? A good starting point is a Smart Manufacturing Audit.