In the contemporary manufacturing landscape, the ability to perform sophisticated analytics at the module level has become essential for achieving true distributed intelligence and collective decision-making capabilities. The integration of local analytics enables individual production modules to process, analyze, and respond to data in real-time, creating a foundation for autonomous and adaptive manufacturing systems.
Within the MODAPTO project framework, Local Analytics, which is also used by optimization, not only by virtual commissioning, encompasses two fundamental technological pillars: the integration of sustainability analytics into Virtual Commissioning processes and the implementation of standardized co-simulation capabilities through the Functional Mock-up Interface (FMI) standard. Virtual commissioning of a production module generally combines three important pieces: a digital model (referred to as a digital twin), the controller code that governs the motion and response to sensor feedback, and simulation environment that allows the two to be run together. The digital model is a virtual representation of a corresponding physical entity.
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6.1.1 Introduction standard FMI
6.1.2 Integration of FMUs as simulation model in standardized digital twins based on AAS standard
This comprehensive approach enables manufacturers to not only optimize their production processes but also to embed sustainability considerations directly into their operational decision-making during the planning phase.
The transformation from traditional commissioning to virtual commissioning with integrated local analytics represents a paradigm shift in manufacturing system design. Virtual commissioning is the practice of using “virtual” simulation technology to “commission”—design, install or test— – control software with a virtual production module model before you connect it to the physical production system. The aim of using virtual commissioning is to allow for early validation of controlling code.
By incorporating sustainability analytics into this process, manufacturers can evaluate environmental impact, energy consumption, and resource utilization during the design phase, leading to more sustainable production systems from inception.
The implementation of local analytics through standardized interfaces, particularly the FMI standard, ensures interoperability between different simulation tools and manufacturing systems. The Functional Mock-up Interface is a free standard that defines a container and an interface to exchange dynamic simulation models using a combination of XML files, binaries and C code, distributed as a ZIP file.
This standardization is crucial for creating modular manufacturing systems where components from different vendors can seamlessly work together while maintaining their analytical capabilities.
The purpose of this learning module on Local Analytics is multifaceted and directly addresses the evolving needs of manufacturing professionals in the era of sustainable and intelligent production systems. This module has been specifically designed to equip learners with the knowledge and skills necessary to implement, operate, and optimize local analytics solutions within modular manufacturing environments.
This module provides essential understanding of how sustainability analytics can be integrated into their simulation workflows. Virtual commissioning, apart from optimization, is reshaping the manufacturing landscape by employing computer simulations for testing and optimizing production systems before they’re physically built. This approach not only simplifies the setup process and reduces expenses but also boosts efficiency, elevates worker proficiency, and enhances factory output.
Learners will gain the ability to extend their existing simulation capabilities to include environmental impact assessments and energy consumption analysis, enabling more informed design decisions.
Learners will also benefit from understanding how local analytics can enhance their process optimization efforts. The module addresses their need to evaluate not just operational efficiency but also sustainability metrics during process design and improvement initiatives. By simulating and validating automation equipment virtually, one can confirm that they will work as expected and significantly reduce system startup time. Manufacturers who have used virtual commissioning have reported significant reductions in engineering time.
This knowledge enables process engineers to balance productivity requirements with environmental responsibility.
Furthermore, the module provides crucial insights into how their programming decisions impact sustainability metrics. Using FMUs as black boxes for sustainability analytics simulation models allows programmers to optimize robot movements and sequences not just for speed and accuracy, but also for energy efficiency and reduced environmental impact. This holistic approach to robot programming is increasingly important as manufacturers face pressure to reduce their carbon footprint.
The target audience for this Local Analytics module encompasses a diverse range of manufacturing professionals who are involved in the design, implementation, and operation of modular manufacturing systems. Each audience segment brings unique perspectives and requirements that have been carefully considered in the module design.
Robot Simulation Users form a primary audience segment, typically comprising engineers and technicians who utilize simulation software to model and validate robotic systems before physical implementation. These professionals require practical knowledge of how to extend their existing simulation capabilities to include sustainability analytics. They need to understand how tools like RF::ViPer can be enhanced with FMUs to capture energy consumption patterns and environmental impact metrics during simulation runs. Their learning objectives include mastering the integration of sustainability FMUs into behavior simulation projects and interpreting the resulting analytical data to make informed design decisions.
Process Engineers represent another crucial audience segment, responsible for designing and optimizing manufacturing processes across entire production lines. These professionals need to understand how local analytics can provide insights into both operational efficiency and environmental sustainability. A model of the plant and control system enables more than virtual commissioning. Engineering teams can reuse models throughout the design, implementation, integration, and operation of the equipment. They can apply their models in the form of digital twins for tasks such as monitoring machinery or performing predictive maintenance.
Process engineers must learn to balance multiple objectives, including productivity, quality, cost, and now increasingly, sustainability metrics. The module provides them with tools and techniques to evaluate process alternatives based on comprehensive criteria that include carbon footprint and resource efficiency.
Robot Programmers constitute a technically focused audience segment that directly influences the operational behavior of robotic systems. These professionals traditionally focus on optimizing robot paths for speed, accuracy, and collision avoidance. However, with the integration of local analytics, they must now consider energy consumption and environmental impact in their programming decisions. The module teaches them how their programming choices affect sustainability metrics and provides techniques for optimizing robot movements to minimize energy consumption while maintaining operational requirements.
Virtual Commissioning Engineers represent the most specialized audience segment, with deep expertise in virtual validation of manufacturing systems. Virtual commissioning adds real value to savvy organizations. It uses simulation approaches to test production systems in the virtual world, before companies commission systems physically. They identify potential issues early in the commissioning process, eliminating expensive late-stage errors. This drives time and cost savings for organizations while allowing them to identify new operational opportunities and expedite innovation across their business.
These professionals are responsible for ensuring that production systems meet all performance criteria before physical implementation. The module extends their capabilities by teaching them how to incorporate sustainability analytics into their validation processes, enabling them to verify not just functional performance but also environmental compliance and efficiency.
Upon successful completion of this Local Analytics module, learners will have developed a comprehensive set of competencies that enable them to effectively implement and utilize local analytics in modular manufacturing environments. These learning outcomes have been carefully aligned with industry requirements and the specific needs of each target audience segment.
Proficiency in FMI Standard Implementation: Learners will gain practical skills in working with the FMI standard, including the ability to create, validate, and integrate FMUs for analytical purposes. They will understand the differences between Model Exchange, Co-Simulation, and Scheduled Execution interfaces, and be able to select the appropriate interface type for specific analytical requirements. This includes understanding version compatibility issues and best practices for ensuring interoperability between different FMI implementations.
Capability to Implement Sustainability Analytics in Production Modules: Learners will develop the ability to design and implement sustainability analytics solutions for specific production modules. This includes creating mathematical models for energy consumption and carbon emissions, implementing these models as FMUs, and integrating them into virtual commissioning environments. They will be able to analyze recorded data from virtual plants to identify optimization opportunities and validate sustainability improvements.
Skills in Co-Simulation and Analytical Workflows: Learners will master the techniques for implementing co-simulation scenarios that combine multiple FMUs and simulation tools. They will understand how to orchestrate complex analytical workflows that process data from multiple sources, including virtual plant simulations, external databases, and real-time sensor feeds. Co-simulation is a technique to combine multiple black-box simulation units to compute the combined models’ behavior as a discrete trace. The simulation units, often developed and exported independently from each other in different M&S tools, are coupled using an orchestration algorithm, often developed independently as well, that communicates with each simulation unit via its interface. This interface, an example of which is the FMI standard interface for co-simulation, comprises functions for setting/getting inputs/outputs and computing the associated model behavior over a given time interval.
Integration with Digital Twin Frameworks: Learners will understand how to integrate local analytics capabilities into standardized digital twin frameworks based on the Asset Administration Shell (AAS) standard. The Asset Administration Shell (AAS) is a digital representation of a physical asset. One AAS can hold of a number of Submodels, in which all the information and functionalities of a given asset — including its features, characteristics, properties, statuses, parameters, measurement data, and capabilities — can be described. The AAS provides interoperability and seamless communication between stakeholders, enabling the integration of heterogeneous systems.
They will be able to create AAS-compliant implementations that expose analytical capabilities through standardized interfaces, enabling seamless integration with broader digital manufacturing ecosystems.
Understanding of Virtual Commissioning with Sustainability Analytics: Learners will be able to explain the fundamental concepts of virtual commissioning and demonstrate how sustainability analytics can be integrated into the virtual commissioning process. They will understand the role of FMUs in capturing and analyzing environmental impact data during virtual plant simulation. The Functional Mock-up Interface (FMI) has gained widespread acceptance in industrial usage both at the level of users and simulation tool vendors as a mechanism for the interchange of models for integration into other environments. This document provides best practice recommendations to implementers of FMI, focusing on FMI 3.0, derived from the industrial experience of the Smart SE project members and the MAP FMI community in employing FMI. The overarching goal of the recommendations is to aid interoperability of FMI implementations and ensure good ease-of-use for the user in employing FMI.
To successfully engage with and benefit from this Local Analytics module, learners should possess certain prerequisite knowledge and skills. These requirements ensure that participants can fully comprehend the advanced concepts presented and effectively apply them in practical scenarios.
Familiarity with Production Processes: Learners must have a solid understanding of modern manufacturing processes, particularly those involving robotic systems and automated production lines. This includes knowledge of typical production workflows, understanding of process parameters, and awareness of key performance indicators used in manufacturing. Experience with at least one manufacturing domain (such as automotive, electronics, or general assembly) is highly beneficial. This foundational knowledge enables learners to contextualize the analytical techniques within real-world manufacturing scenarios.
Profound Knowledge in Robot Simulation: Given the module’s focus on virtual commissioning and simulation-based analytics, learners need substantial experience with robot simulation tools. This includes familiarity with 3D modeling concepts, understanding of kinematic and dynamic simulation principles, and experience with at least one major simulation platform. Simulation software, such as Simulink®, lets you simulate systems, design industrial control algorithms, verify and validate designs, and generate code for industrial controllers within a comprehensive engineering software environment.
Knowledge of simulation model creation, validation, and debugging is essential for understanding how analytical capabilities can be integrated into simulation workflows.
Awareness of Sustainability Concepts: As sustainability analytics forms a core component of this module, learners should have basic awareness of environmental sustainability concepts in manufacturing. This includes understanding of energy consumption metrics, carbon footprint calculations, and resource efficiency principles. While deep expertise in sustainability science is not required, learners should appreciate the importance of environmental considerations in modern manufacturing and be motivated to integrate sustainability metrics into their decision-making processes.
Technical Computing Skills: The implementation of local analytics requires certain technical computing competencies. Learners should be comfortable with basic programming concepts, data analysis techniques, and mathematical modeling. Experience with scripting languages (such as Python or MATLAB) is beneficial for understanding FMU implementation and data processing workflows. Additionally, familiarity with XML structures and file formats will aid in understanding FMI model descriptions and AAS implementations.
Understanding of Control Systems: Since local analytics often interfaces with control systems, learners should have fundamental knowledge of industrial control concepts. This includes understanding of PLC programming basics, familiarity with control logic structures, and awareness of real-time system constraints. Virtual commissioning combines three pieces: a digital twin, the controller code, and a development environment that allows the two to run together.
Awareness of usage of functional behavior models, e.g. PLCOpenXML/Codesys or IEC61131-3.
While expertise in control system programming is not mandatory, understanding how control systems interact with analytical components is crucial for successful implementation.
