i-Dave: Data-Knowledge Integration to improve the reliability of LCA projets in the enterprise of the future

Despite the growing interest in life cycle assessment (LCA) and product carbon footprint (PCF) methods and tools, their application faces three challenges:

• data collection
• the selection of relevant cost centers
• the robustness of reference databases.

These challenges are very difficult because experts mastering the industrial process do not necessarily have the LCA/PCF culture. In addition, validating and aggregating all factors is a very complex activity because the data is collected from very heterogeneous sources, in varied business contexts and life phases. Current collection methods remain mostly manual, based on questionnaires, and dedicated tools are often disconnected from the company's overall digital chain. These issues are more critical in the case of long-life systems.

Once the studies are completed, another challenge concerns the exploitation of the results for the prediction of carbon trajectories. 

The concept

These challenges are expected to be addressed through three complementary solutions:
 

• A central knowledge base defined as an ontology of the LCA/ECP domain and coupled with an inference engine that implements business rules to meet specific support requirements, such as the selection of scope and environmental cost centres, etc. This also involves supporting process traceability and the characterisation of systems in terms of LCA/ECP factors,
• Intelligent connectors to ensure the interoperability of LCA/ECP tools with the various modules of the company’s digital chain for the extraction of data from heterogeneous sources. The Product Lifecycle Management (PLM) approach will be used to ensure the cross-functional integration of all types of information systems and databases relevant to LCA/ECP studies.
• A tool for managing low-carbon strategies within the company, coupled with innovative dashboards. In particular, this tool contains algorithms for predicting future trajectories based on historical data, and classification algorithms to assist in selecting the best operational alternatives that generate the lowest carbon emissions. This module is based on a behavioural model containing useful performance indicators and cause-and-effect relationships between process parameters and environmental impacts.

Method

Scientific challenges

Understanding the most significant factors in terms of carbon impacts and the cause-and-effect relationships between organisational processes and environmental indicators. This presents a genuine scientific challenge due to the scope of the life cycle and the diversity of practices and specific cases within the field.

Data modelling amounts to building a unifying framework for a multitude of heterogeneous and fragmented databases spread across multiple sources. This leads to the problem of managing large volumes and a wide variety of data, much of which is difficult to access at its original sources.

Integrating LCA/ECP solutions into the company’s digital workflow introduces additional complexity due to the diversity of data formats and standards, which are not fully accounted for in current standards.

Human-machine interaction, both methodologically and in terms of information, is a key factor in the success or failure of LCA/ECP studies. This applies both upstream, when collecting data from experts, and downstream, when presenting results in a format suited to users’ expectations and profiles

Organisation into work packages

• WP2 Definition of LCA/ECP requirements for long-life systems and clarification of the project scope.
• WP3 Development of a knowledge repository to support LCA/ECP decision-making
• WP4 Design of a smart connector for LCA-PLM interoperability and the extraction of useful data from multiple sources
• WP5 Development of the AI module for predicting and steering low-carbon trajectories
• WP6 Software integration of the smart LCA-PLM framework
• WP7 Application in industrial cases
• WP8 Exploitation and dissemination of project results

Consortium

Scientific Coordinator

Emmanuel Rozière - Centrale Nantes / GeM

Partners

Centrale Nantes / LS2N

Trains engineering and master’s students to be versatile and equipped with managerial skills, enabling them to integrate technical subjects within a sustainability framework. Centrale Nantes is involved in the project through the CPS3 team at the LS2N laboratory. The team focuses on methods and tools for modelling, analysing, designing and managing complex socio-technical systems. The integration of data and knowledge for decision support, performance improvement and the design of business information systems are at the heart of its research.

Centrale Nantes
LS2N

UTC / Roberval Laboratory

Est un l'établissement d'enseignement supérieur et de recherche fondateur d'un modèle expérimental alliant université de recherche et école d’ingénieur généraliste. L’UTC va intervenir dans le projet par le biais de l’équipe SIPP (Systèmes Intégrés Produit-Processus) du laboratoire Roberval. L’équipe développe des modèles et outils pour la conception intégrée et collaborative des systèmes ainsi que l’amélioration de la continuité de la chaîne numérique sur l’ensemble du cycle de vie du produit.

UTC
Laboratoire Roberval

GreenWay Systems

Is a consultancy firm and publisher of innovative software solutions to support the joint development of carbon footprint assessments, LCA, and life-cycle cost analyses. Its founder, Dr Khalil Khalifa, was involved in the development of the first ISO 14040 standard and has taught since 1996 in numerous higher education and industrial training programmes for major corporations.

Greenway Systems

TOOVALU

Is the developer of the B-Corp software for climate and CSR management. This software also enables the creation of Bilans Carbone® or equivalent assessments, the definition of sustainable pathways, and the simulation and management of a low-carbon action plan. TOOVALU’s staff are thus specialised in supporting the deployment and monitoring of CSR and low-carbon strategies.

Toovalu

CETIM

Technology transfer centre and interface between the research and industrial sectors. With over 7,000 members. CETIM is involved in the project through its industrial sustainability performance division.

Cetim

Expected Scientific Advancement

A response to the above challenges is expected through three complementary solutions:
 

• A knowledge base defined in the form of an ontology of the LCA/PCF domain and coupled with an inference engine, implementing business rules to meet specific assistance needs such as the choice of scope and environmental cost centers, etc. It also involves supporting process traceability and the characterization of systems in terms of LCA/PCF factors.
• Intelligent connectors to ensure the interoperability of LCA/PCF tools with the different modules of the company's digital chain for extracting data from heterogeneous sources. The Product Lifecycle Management (PLM) approach will be used to ensure the cross-functional integration of all types of information systems and databases useful for LCA/PCF studies.
• A tool for managing low-carbon strategies in companies coupled with innovative dashboards. This tool contains in particular algorithms for predicting future trajectories based on history, and classification algorithms to help choose the best operational alternatives generating the minimum carbon emission impact. This module is based on a behavioral model containing useful performance indicators and cause-effect relationships between process parameters and environmental impacts.