The digitisation of manufacturing processes can offer energy saving solutions. For instance, the optimisation or replacement of specific technologies and the application of new software tools may result in significant reductions in energy consumption. The EU-funded E2COMATION project will address the optimisation of energy usage at various stages of the manufacturing process as well as considering the whole life-cycle perspective across the value chain. To monitor, predict, evaluate and optimise the energy and sustainability impact of the behaviour of a factory, E2COMATION will develop a cross-sectoral methodological framework and a modular technological platform. For production performance forecasting, it will enable a holistic analysis of energy-related data streams, leveraging also on a life cycle conceptual paradigm applied to digital twinning of factory assets.

Future Factories Running Sustainably on Less Energy
Project Runtime11/2020 – 10/2024
Funding BodyEuropean Union
Involved Partners19 Partners from EU
1 Partner from Turkey
My RoleProject Leader
My Project ParticipationSince 07/2022


Improving industrial energy efficiency at European Manufacturing level requires the integration of energy data with advanced optimization techniques to guide a company decision making.
E2COMATION intends to address the optimization of energy usage, at multiple hierarchical layers of a manufacturing process as well as considering the whole life-cycle perspective across the value chain. To this purpose, it aims at providing a cross-sectorial methodological framework and a modular technological platform to monitor, predict, evaluate impact of the behavior of a factory across energy and the life-cycle assessment dimensions, in order to adapt and optimize dynamically not only its real-time behavior over different time-scales, but also its strategic and sustainable positioning with respect to the complex supply and value chain it belongs to.
Its major objectives are:

  • Holistic analysis of energy-related data streams for production performance forecasting;
  • Life-cycle conceptual paradigm applied to digital twinning of factory assets;
  • Factory-level integrated multi-objective optimization architecture;
  • Modular and scalable automation platform for distributed monitoring and supervision;
  • Comprehensive simulation environment enhanced with energy and environmental performance;
  • Energy Aware Planning and Scheduling tool (EAP&S);
  • Life Cycle Assessment and Costing tool (LCAC) integrated in a company Decision Support System;
  • Sustainable Computer Aided Process Planning (s-CAPP);
  • LCA-driven supply chain management (SCM) and business ecosystem.

For E2COMATION to be successful, it is fundamental that the effectiveness of its methodological approach and technological framework is proved in complex industrial scenarios, involving several factories of different sectors. This will be achieved by implementing the project platform in 2 completely different value chains, the food and drink one and the woodworking one, with 5 concurrent industrial use-cases.


One goal of E2COMATION is proposing and validating a generic and adoptable IT infrastructure to address and optimize the resource and energy efficiency in industrial production. The outcome is a layered architecture, shown below, where the individual layers can be freely distributed either on premises or in the cloud.

Data is flowing from bottom to top, firstly collected at the shop floor, and then aggregated in the CPS-izers Layer shown in light green. To achieve the decoupling of the upper two layers we use industry standard MQTT brokers for a flexible publish/subscribe mechanism. This allows each component to decide for itself what data they need for their task.

The central component in the DSC Layer, the layer that uses the aggregated data from the shop floor and connects it with other relevant production parameters, e.g. from MES or SCADA systems, is the real-time processing engine Apama. It is a commercial product from Software AG and was first used in fraud detection scenarios in the banking and telecommunication sectors. However, real-time detection of anomalies becomes more and more important for industrial production processes as well to save time and money. For our use cases the Community Edition, see https://apamacommunity.com, is sufficient and freely usable.

The general usage patterns of Apama are given in the figure below which are:

  • Analyze your business data in real time
  • Enrich your analytics with historical data
  • Visualize your real-time and historical data in business dashboards
  • Trigger actions through detection of pre-defined conditions

An important role for Apama in the E2COMATION platform is the triggering part which is used to inform the Digitals Twin models or the Plant Simulation models that the shop floor values have so significantly changed that an update of the models is needed. The triggering condition can be written in many different ways, e.g. in Apama’s own Event Processing Language (EPL) or in other supported languages like Java, C++ or Python. In addition, support is given via an additional component for industry-standard Machine Learning (ML) models in PMML or ONNX format.

Since the triggering conditions can be adopted to any use case the proposed architecture including Apama is suitable to address all of E2COMATION use cases.

Project website: https://e2comation.eu.