Periodic Reporting for period 1 - STRATEGUS (STRATEgic GUide to Smart manufacturing)
Berichtszeitraum: 2023-10-01 bis 2025-09-30
Manufacturing is undergoing a major digital transformation. Over the last decade, advances in low-cost sensors, high-performance computing, and artificial intelligence have made it possible for factories to become smart, adapting to changing demand, integrating new processes, and cooperating with human operators. Initiatives such as "Industry 4.0" in Germany and Italy's "Piano Nazionale Industria 4.0" have encouraged this transition. The next step, "Industry 5.0", focuses on human-machine collaboration and worker well-being.
Many European small and medium-sized enterprises (SMEs) still face difficulties in using the large amounts of data now collected on their shop floors. Integrating advanced scheduling, automation, and control technologies into existing production lines can be complex and carries the risk of reduced productivity, lower quality, or safety issues. At the same time, market trends are pushing a shift from make-to-stock to make-to-order production, which increases the need for agile and reliable manufacturing systems.
The STRATEGUS project aims to address these challenges by developing intelligent scheduling and control methods that coordinate machines and human workers in flexible manufacturing systems. The core concept is a digital twin, a detailed virtual replica of a production line that is built from mathematical models and real-time sensor data. The digital twin is used to predict performance, optimise operations, and respond to unexpected events.
The project will design and integrate three main components into an open-source software platform:
- Digital twin: models the physical and operational behaviour of manufacturing equipment and workers, using both physics-based and data-driven approaches.
- Scheduler (strategist): uses the digital twin to create optimal schedules, balancing cost, energy use, deadlines, safety, and worker capabilities.
- Analysis tools: monitor performance, detect bottlenecks or failures early, and adjust plans automatically to minimise disruption.
The work will be carried out by the University of North Carolina (USA) and the University of Verona (Italy). It will be tested on advanced industrial demonstrators and through partnerships with both multinational companies and SMEs.
STRATEGUS will produce an open-source framework for digital-twin-based production simulation, scheduling, and analysis. The framework will help manufacturing companies to:
- Reduce production time, energy consumption, and operational costs
- Increase flexibility in fulfilling customised orders
- Improve worker safety and well-being by taking human factors into account
- Reduce downtime by detecting and mitigating problems earlier
In the longer term, the methods can be extended to predictive maintenance and other optimisation tasks. STRATEGUS will enhance the competitiveness of European manufacturing, particularly for SMEs, and contribute to achieving EU goals in digitalisation, sustainability, and a human-centred industry. It will also train researchers in combining advanced modelling, optimisation, and industrial collaboration, ensuring long-term benefits.
Many European small and medium-sized enterprises (SMEs) still face difficulties in using the large amounts of data now collected on their shop floors. Integrating advanced scheduling, automation, and control technologies into existing production lines can be complex and carries the risk of reduced productivity, lower quality, or safety issues. At the same time, market trends are pushing a shift from make-to-stock to make-to-order production, which increases the need for agile and reliable manufacturing systems.
The STRATEGUS project aims to address these challenges by developing intelligent scheduling and control methods that coordinate machines and human workers in flexible manufacturing systems. The core concept is a digital twin, a detailed virtual replica of a production line that is built from mathematical models and real-time sensor data. The digital twin is used to predict performance, optimise operations, and respond to unexpected events.
The project will design and integrate three main components into an open-source software platform:
- Digital twin: models the physical and operational behaviour of manufacturing equipment and workers, using both physics-based and data-driven approaches.
- Scheduler (strategist): uses the digital twin to create optimal schedules, balancing cost, energy use, deadlines, safety, and worker capabilities.
- Analysis tools: monitor performance, detect bottlenecks or failures early, and adjust plans automatically to minimise disruption.
The work will be carried out by the University of North Carolina (USA) and the University of Verona (Italy). It will be tested on advanced industrial demonstrators and through partnerships with both multinational companies and SMEs.
STRATEGUS will produce an open-source framework for digital-twin-based production simulation, scheduling, and analysis. The framework will help manufacturing companies to:
- Reduce production time, energy consumption, and operational costs
- Increase flexibility in fulfilling customised orders
- Improve worker safety and well-being by taking human factors into account
- Reduce downtime by detecting and mitigating problems earlier
In the longer term, the methods can be extended to predictive maintenance and other optimisation tasks. STRATEGUS will enhance the competitiveness of European manufacturing, particularly for SMEs, and contribute to achieving EU goals in digitalisation, sustainability, and a human-centred industry. It will also train researchers in combining advanced modelling, optimisation, and industrial collaboration, ensuring long-term benefits.
During the reporting period, the project focused on two main technical streams.
First, a novel "dynamics-aware" scheduling methodology was developed. This approach departs from traditional methods by modeling manufacturing operations as continuous physical processes described by mathematical equations, rather than as simple blocks of time. This allows for a more accurate representation of equipment behavior, including factors like acceleration and thermal effects. The main achievement of this work is the FlexMan library, an open-source tool that implements these advanced scheduling algorithms.
Second, a high-fidelity digital twin simulation platform, named GLACIER/Frost, was designed and built. This platform allows for the creation of detailed, continuous-time virtual models of factory equipment. A key scientific achievement is its deterministic execution engine, which guarantees that simulations produce bit-exact, reproducible results across different computers and operating systems—a critical requirement for industrial validation.
These two platforms were integrated and validated using an industrial demonstrator. The results confirmed the effectiveness of the approach, demonstrating that the schedules produced by FlexMan could achieve up to a 75% reduction in energy consumption and a 57% reduction in processing time for certain manufacturing tasks when compared to traditional methods.
First, a novel "dynamics-aware" scheduling methodology was developed. This approach departs from traditional methods by modeling manufacturing operations as continuous physical processes described by mathematical equations, rather than as simple blocks of time. This allows for a more accurate representation of equipment behavior, including factors like acceleration and thermal effects. The main achievement of this work is the FlexMan library, an open-source tool that implements these advanced scheduling algorithms.
Second, a high-fidelity digital twin simulation platform, named GLACIER/Frost, was designed and built. This platform allows for the creation of detailed, continuous-time virtual models of factory equipment. A key scientific achievement is its deterministic execution engine, which guarantees that simulations produce bit-exact, reproducible results across different computers and operating systems—a critical requirement for industrial validation.
These two platforms were integrated and validated using an industrial demonstrator. The results confirmed the effectiveness of the approach, demonstrating that the schedules produced by FlexMan could achieve up to a 75% reduction in energy consumption and a 57% reduction in processing time for certain manufacturing tasks when compared to traditional methods.
The main result of the STRATEGUS project that goes beyond the state of the art is the shift from discrete, event-based scheduling to a dynamics-aware, continuous-time optimization approach. By integrating mathematical models of physical processes (such as motion and heat) directly into the scheduling logic, the project's software can co-optimise for multiple, often competing, objectives like production speed and energy efficiency. This provides a much finer level of control and a more accurate prediction of factory behaviour than what is possible with current state-of-the-art tools.
The potential impact of this result is a new paradigm for factory management where operational efficiency and sustainability are no longer treated as separate goals.
To ensure further uptake and success, several key needs have been identified:
- Further Research & Demonstration: The core methodology needs to be tested on a wider range of industrial case studies to demonstrate its scalability and applicability to different manufacturing sectors. Future research could also focus on integrating machine learning to automatically learn and refine the physical models from sensor data.
- Access to Markets & Commercialisation: The project has adopted a permissive open-source licensing model (MIT/BSD) as its primary strategy for uptake. This removes barriers to adoption for SMEs and creates a platform for a community of researchers and industrial users to build upon.
- Standardisation: The project's data models are inspired by industry standards like OPC UA. Further work could contribute to formalizing a standard for representing continuous physical dynamics within digital twin frameworks, ensuring interoperability.
The potential impact of this result is a new paradigm for factory management where operational efficiency and sustainability are no longer treated as separate goals.
To ensure further uptake and success, several key needs have been identified:
- Further Research & Demonstration: The core methodology needs to be tested on a wider range of industrial case studies to demonstrate its scalability and applicability to different manufacturing sectors. Future research could also focus on integrating machine learning to automatically learn and refine the physical models from sensor data.
- Access to Markets & Commercialisation: The project has adopted a permissive open-source licensing model (MIT/BSD) as its primary strategy for uptake. This removes barriers to adoption for SMEs and creates a platform for a community of researchers and industrial users to build upon.
- Standardisation: The project's data models are inspired by industry standards like OPC UA. Further work could contribute to formalizing a standard for representing continuous physical dynamics within digital twin frameworks, ensuring interoperability.