Ecosystem for Collaborative Manufacturing Processes _ Intra- and Interfactory Integration and Automation


Data and services have become the key factor in optimising manufacturing processes. The need to react to dynamically changing market demands is dramatically rising. One of the most imperative problems so far is to connect supply chain data and services between enterprises and to connect value chain data within a factory so that it can meaningfully support decision-making.

COMPOSITION will create a digital automation framework, the COMPOSITION Integrated Information Management System (IIMS) that enhances the manufacturing processes by exploiting existing data, knowledge and tools to increase productivity and dynamically adapt to changing market requirements. This technology acts as the technical operating system for business connections between factories and their suppliers. Furthermore, it opens a new space for third party entities to actively interact along the supply chain, e.g., by providing services to optimise cycle time, cost, flexibility or resource usage. In addition to the supply chain improvements, the processes inside the company can be addressed and optimised.

Data across the (multi-sided) company internal value chain is integrated by the IIMS with optimisation and modelling tools for resource management including innovative, multi-level, real-time cross-domain analytics and a Decision Support System.

The technology will extend existing FI-WARE, FITMAN and LinkSmart catalogues, in addition to a Building Management System for environmental data gathering. Inter-operability, ease of retrofit and scalability are critical parameters that are taken carefully into account in devising such IIMS. 


Made In Europe - Draft R&I Objectives - (10)


This is a set of Specific Objectives and Research & Innovation Objectives that is subject to a consultation in preparation of the Made In Europe Partnership.  For more guidance about the consultation, please see

Significant innovations and lessons learned - (3)


    Comment: COMPOSITION defines innovations at factory performance level, but also at business model level. Collaboration among multi-disciplinary companies will be possible in the ecosystem.

    See also D2.6 Lessons Learned and updated requirements report II

    1. The COMPOSITION Marketplace Management System shall enable stakeholder to gain access to the COMPOSITION open marketplace.

    2. COMPOSITION Marketplace(s) should have possibility of restricted access. 

    3. The line visualization shall compare the actual processed units to the target ones. 

    4. Alarms/Notifications are forwarded to subscribers depending on their impact level. 

    5. It must be possible to reset an alert when the necessary measures have been taken. 

    6. Ecosystem components should be deployed as Docker images. 

    7. Agents shall be writable in any programming language. 

    8. The Decision Support System shall import data coming from the simulation and prediction engines. 

    9. Supplying companies register their products/services in specific topic(s) within the ecosystem. 

    10. The needs and requirements of companies shall be registered/published within the ecosystem. 


Project type - instrument - (1)


Manufacturing performance characteristics - (7)


      Comment: Real-time co-simulation methods will enable key stakeholders to simulate and forecast complex activities. Simulation will target increasing quality and flexibility as well as more efficient management of logistics by also decreasing service time and costs.
      Comment: Using the COMPOSITION platform, companies will be able to enter a supply chain and broaden their clientele, thus improving their economic performance. The COMPOSITION Marketplace will facilitate matching of demand and supply across the supply chain. Buyers and sellers will have close to perfect knowledge of offers, with searchable, low latency access to information. Agent technology and automated matching of requests offers lowers transaction costs. By providing a common interface for entering the marketplace, the platform also lowers the barriers of entry. In economics terms, these are all prerequisites for competitive market (from which everyone benefits).
      Comment: Improvements of the manufacturing process by using Process Modelling and Monitoring Framework combined with Integrated Digital Factories Models will lead to better resource management and will reduce the use of resources. Optimising the process involving factories with recycling companies will generate economic savings. Monitoring the health of machinery and using predictive maintenance and reducing energy consumption will reduce not only manufacturing efficiency (less downtime & material waste) but also the environmental impact of manufacturing.

        The COMPOSITION Integrated Information Management System will be a digital automation framework that optimises the manufacturing processes by exploiting existing data, knowledge and tools, integrating them with newly installed cyber physical systems (CPS) and automation software, in order to increase productivity and to allow for dynamic adaptation to changing market requirements. CPS using IoT devices such as wireless sensors nodes (WSN) can be easily retrofitted to existing equipment and infrastructure to gather sensory data and detect anomalies as well as opportunities to improve productivity and cvcle time.

        Comment: The reduction of scraps will be addressed in the sense of attempting to reduce unused material, rather than reducing the number of defective workpieces or rework operations. Furthermore, the project will reduce the amount of scraps by optimising the processes in a way that less material has to be scrapped because certain maximal idle times are not reached.
      Comment: The project will focus on the reduction of energy consumption via the detection of excessive consumption and anomalous equipment behavior. The health of equipment will be improved, using machine learning/deep learning algorithms for predicting failures & arranging preventative maintenance. It will be possible to monetise the results into suggestions for actions at specific processes. The possibility of letting the Marketplace matchmaker take externalities into account when rating offers and partners by providing it with information on e.g. environmental performance of marketplace actors, is being examined.

Manufacturing future products - (1)


Technologies and enablers - (14)


      Comment: Part of the scrap generated by the manufacturing industry is recyclable. The recycling companies need to stay connected with the industries and to plan for the further reselling and/or disposal of the material. COMPOSITION will connect the dots in the inter-factory pilot.
      Comment: Big data analytics in cloud environments to support manufacturing scenarios.
        Comment: Data acquisition tools to instantiate virtual manufacturing processes.
        Comment: Use of data analytics to provide automated knowledge extraction from big data generated in manufacturing in order to reduce factory and field failures and detect and remedy anomalies earlier.

        The COMPOSITION project contains a Digital Factory Model Component which offers representation of factory processes and resources in a common format based on well-known standards.

        Comment: The platform will apply smart data processing methods for investigating the dataset in order to find logical links. It will also investigate novel complex event processing approaches.
      Comment: The COMPOSITION Integrated Information Management System will be a collaborative ecosystem where all members of a manufacturing supply chain can connect in a secure environment. The Security Framework will implement the security core mechanisms aiming to ensure the security, confidentiality, integrity and availability of the managed information for all authorised COMPOSITION stakeholders.

      Requirements of modern production processes stress the need for greater agility and flexibility, for faster production cycles, increased productivity, less waste and more sustainable production. Human-machine interaction is put in the center, supporting the decision making process.

      Investigation of advanced HMIs for direct interaction with real-world objects. Consideration of mobile user interfaces that allow accessing crucial immediate information everywhere in the factory. Consideration will also be given to data gathering from ultra low power IoT devices such as wireless sensors where data can then be aggregated and visualised at an appropriate HMI interface. Where possible provision for self-powering these IoT devices using energy harvesting will be taken into account in order to avoid battery replacement. Flexible and dynamic data-driven solutions that can be adapted to different environments and needs.

        Comment: Decision makers are not at their desk the whole day. In order to reduce the probability that they miss important information, the project investigates user interface solutions for the shop floor and everywhere else in the factory, in order to provide data monitoring, control and notification systems, that help humans in interaction with machines and with the decision making process.
      Comment: The Digital Factory Model (DFM) will provide an integrated representation of the intra-factory domain at machine-level, end-user-level, and process-level. The DFM model will be used to exploit data coming from machinery, sensors and production lines and will offer interoperability in communication by providing all these heterogeneous data in a common format. The DFM is re-used in the Decision Support System, in Simulation and in the Deep Learning Toolkit. The integration of information will enable better use of the data available in the physical layer of the factories to be made.
      Comment: Design a technical operating system for the support connected and interoperable factories with their stakeholders and for the optimisation of manufacturing and logistics processes.

      A Simulation and Forecasting Toolkit analyses the production processes and required resources in an integrated way and extracts forecasts for possible failures. Also forecasting is provided in supply chain and logistics, especially in fill level monitoring of bins and boosts the waste management and recycling processes. Sustainable manufacturing will be assisted by a Decision Support System. The Marketplace will enable dynamic integration with actors in the supply chain.

      Comment: Continuous monitoring of the condition and performance of specific critical parts (equipment, infrastructure and environment) of the manufacturing system will provide feedback to the real-time brokering and message translations services allowing seamless integration of heterogeneous manufacturing components.

Manufacturing system levels - (5)


Digitalisation pathways - (21)


ICT performance characteristics - (12)


    Comment: Interoperability between installed equipment and infrastructure (machines, environment, people) is critical to capture and process data for the COMPOSITION IIMS components.
      Comment: The COMPOSITION project will address the issue of extended enterprises allowing the interaction and collaboration of all stakeholders in the COMPOSITION Marketplace as every business entity will be represented by agents at the ecosystem. Some of information from companies’ IIMS will be reflected at the ecosystem in a Collaborative Manufacturing Services Ontology which will be used as a central knowledge base. The Matchmaker will infer knew knowledge from the knowledge base by applying both syntactic and semantic matching in terms of manufacturing capabilities, in order to find the best possible supplier to fulfil a request. Different criteria for selection according to several qualitative and quantitative factors will be considered.
    Comment: The modular nature of the COMPOSITION system will allow easy and fast scalability for use in both large and small manufacturing sites with high or low complexity. The systems should also be modular such that multiple smaller systems & sub-systems can easily be integrated into larger systems

Standards, standardisation, certification and regulation - (2)


Business model aspects - (2)


    Comment: 1) Traffic Monitoring in IoT-enabled shopfloors, 2) Intra-factory Optimization & Decision support (i.e. forecasting services, trend analysis, predictive maintenance), 3, 4, 5) Robust module communication & message routing for Intra & Inter-scenarios