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International audience; Smart manufacturing (SM) or Industry 4.0 is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution and roadmap paths for industries such as biochemistry or biology. In this paper, we analyzed evaluation in smart manufacturing systems based on modern industrial revolution. Since, Industry 4.0 holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Moreover, we review key technologies for information and communications technology (ICT) that are used to enable intelligent manufacturing. Also, the major key technologies related to smart Manufacturing were identified through the analysis of the policies and technology roadmaps of Germany, Chania and the U.S for smart manufacturing. In addition, we answered the three-key question in this paper. First, how small and medium enterprises (SMEs) are build and setting-up from conventional manufacturing practices to the smart manufacturing/Industry 4.0? Second, comparative analysis of the key architectures adopted for the Industry 4.0. Third, review, select and suggest the standards from the pool of standard bodies for Industry 4.0. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.

International audience

Nowadays all industrial sectors are increasingly faced with the explosion in the amount of data. Therefore, it raises the question of the efficient use of this large amount of data. In this research work, we are concerned with process and product traceability data. In some sectors (e.g. pharmaceutical and agro-food), the collection and storage of these data are required. Beyond this constraint (regulatory and / or contractual), we are interested in the use of these data for continuous improvements of industrial performances. Two research axes were identified: product recall and responsiveness towards production hazards. For the first axis, a procedure for product recall exploiting traceability data will be propose. The development of detection and prognosis functions combining process and product data is envisaged for the second axis.

International audience

International audience

International audience

International audience; This research proposes implications of application functions by using the chain traceability data acquired from the Smart Object attached with Extended Real-time Data (SO-ERD: e.g. smart container, smart pallet, etc.) to improve risk management at the level of the logistics chain. Recent applications using traceability data and major issues in traceability systems have been explored by an academic literature. Information is classified by the usage of current traceability data for supporting risk detection and decisions in operational, tactical, and strategical levels. It is found that real-time data has been a significant impact on the usage for the transportation activity in all decision levels such the function of food quality control and collaborative planning among partners. However, there are some uncertainties in the aggregation of event-based traceability data captured by various partners which are preventing the adoption of data usage for the chain. Under the environment of Industry 4.0 and the Internet of Things (IoT), the SO-ERD enables independent data tracing through the chain in real-time. Its data has potential to overcome current issues and improve the supply chain risk management. Therefore, Implications of risk management are proposed with the usage of SO-ERD data based on the literature review which reveals current concerns of decision functions in the supply chain. The implications can be an impact to the domain needs.

With the emergence of new requirements in manufacturing, mainly the product customization trend, implementing a solution for product quality control becomes an increasingly complicated task. In this modern industrial context, those solutions need to take into account the constant updates in the product design and combine its resources in the most effective manner in order to meet the new product specifications. By reason of the lack of available technical solutions, this task is usually carried out by human operators which is time consuming, tiring and not always a reliable solution. In the present work, a self-* automatic quality control platform based on cyber-physical systems and multi-agents paradigm is presented. The solution is validated within a real industrial usecase.

International audience

International audience

The problematic of this thesis concerns mainly the development of simulation and decision-making models and architectures for the collaborative enterprise (supply chain, clusters...). My researches started fifteen years ago by several works about the analysis and performance evaluation of production and manufacturing systems. After I have studied the different approaches and methods for the enterprise modeling and the manufacturing control, I proposed in my PhD a simulation model which integrates a Discrete-Event Simulator for the analysis of the physical processes, and a multi-agent system for the decisional and the organizational processes. The cognitive agents include some learning algorithms and case based mechanisms to simulate the decision makers reasoning processes. This model was dedicated to the intra-enterprise and allowed us to better understand the complexity of the production system and evaluate its performance after some organizational changes (Push/Pull system, Just in Time, Kanban...). After this first phase of work, I have enriched the proposed model to take into account the interactions between the enterprises within the supply chain. The main idea was to propose a distributed simulation model to evaluate the impact of the information sharing on the global performance of the whole supply chain. For that, we proposed an information system architecture connected to a simulation model to build a distributed Decision Support System (DSS). This architecture has been developed in two PhD thesis (co-supervised with Professor Abdelaziz Bouras) and validated in a research project (COPILOTES) and in different industrial applications. I also worked on another research problematic, which concerns the integration of information systems and knowledge management. In fact, the management of the collaborative processes in the supply chain supposes that there is an alignment of the information systems (ERP, PLM, MES...) to the business processes of the companies. Efficient information systems facilitate the data and knowledge sharing between the actors of the systems. The current information systems, as the PLM (Product Lifecycle System), are defined to collect, store, treat, and share data and information related to the business processes and the product for the whole lifecycle. But it is still difficult to make these systems interact and communicate easily. In this context, we worked on different research subjects related to this alignment and supported by industrial and research projects. Four PhD works are presented in this thesis, two of them are focusing on applications for Information Systems Integration, and the two others on knowledge management systems for supply chain and product lifecycle. The added value and the originality of these works are the approach that we used, combining computer sciences models, and architectures with management and decision making theories; mixing engineering and human sciences approaches and methodologies. The evolution of my research works could be presented and summarized in three main dimensions, corresponding to the enlargement of the problematic, from the simulation models for the decision making in the production system (intra-enterprise) to the modeling of the collaborative processes and the distributed decision making for the supply chain and the product lifecycle management (inter-enterprise): a. Development of simulation models and decision support systems to analyze the dynamic of industrial organizations and the evaluation of their performance. The main theories and approaches used are: mathematical models (operational research), multi-criteria analysis, decision theories, multi-agent systems, distributed simulation... b. Development of architectures and technical data referential for the information systems alignment and the data management: Model Driven Application (MDA), Interoperability, Services Oriented Applications (SOA)... c. Development of Knowledge Management Systems to support the collaborative processes in the virtual enterprise (supply chain, clusters...). The main methods and tools used are: hybrids methods for knowledge engineering, models for knowledge capitalization, agent models for the cooperation/negotiation for the collaborative processes, standards for long term knowledge retention (OAIS)... These three dimensions converge towards a larger paradigm, which concerns the decision support systems for the enterprise networking, the paradigm of Agile and Sustainable enterprises. The enterprise performance requires new reactive and collaborative organizations and a good synchronization of the information systems; and this to be able to connect themselves to international supply chain and to develop new product development projects, and enhance new efficient partnerships easily reconfigurable., Les travaux de recherche présentés dans mon mémoire d'HDR s'articulent autour de la problématique de la collaboration et de la prise de décision dans les entreprises en réseau, dans des contextes de chaînes logistiques et d'analyse du cycle de vie des produits. Ces travaux ont débuté sur la thématique des modèles informatiques pour la prise de décision dans les systèmes de production. Après avoir étudié les différentes méthodes de modélisation d'entreprise ainsi que les approches liées au pilotage dans les entreprises industrielles, nous avons proposé un modèle de simulation des processus industriels permettant de coupler un simulateur a événement discret, dédié à l'analyse des flux physiques, à un modèle de simulation décisionnel et organisationnel à base d'Agents cognitifs. Nous avons par la suite abordé la problématique de la prise de décision et montré que l'efficacité de la décision dépend de sa capacité à exploiter des mécanismes d'apprentissage comparables à ceux utilisés par l'homme ; pour cela nous avons proposé une évolution du modèle de simulation en intégrant dans les agents logiciels des mécanismes de raisonnement et d'apprentissage (raisonnement à base de cas). Après cette phase d'étude des organisations et des processus intra-entreprise, ces architectures ont ensuite été enrichies de manière à intégrer des problématiques liées à la dimension collaborative dans le cadre des entreprises en réseau et des chaînes logistiques. Pour cela, nous avons proposé des modèles de simulation multi-agents afin de modéliser et de simuler les comportements des acteurs décisionnels dans le cadre des processus collaboratifs, et en particulier, le lien entre collaboration et partage d'information et l'amélioration des performances locale (à chaque entreprise) et globale (à l'ensemble de la chaîne logistique). En parallèle des travaux menés sur la modélisation et la simulation des processus décisionnels dans les entreprises en réseau, il est vite apparu la nécessité d'approfondir les problématiques liées aux systèmes d'information et à la gestion des connaissances. En effet, les processus décisionnels dans des contextes collaboratifs nécessitent un alignement des systèmes d'information aux différents processus organisationnels. Les principaux systèmes d'information existants, par exemple les ERP, ou les systèmes PLM pour la gestion du cycle de vie des produits, ont pour objectif principal d'organiser, de stocker, et de restituer les informations liées aux processus et aux produits dans les différentes phases de leurs cycles de vie (de la conception au recyclage, en passant par les phases de fabrication, de stockage, de maintenance, de distribution,...). Nous avons donc traité de certaines problématiques posées par cet alignement, et en particulier l'intégration des systèmes d'information, l'interopérabilité des systèmes, et la gestion des connaissances. L'évolution de mes préoccupations peut ainsi s'analyser selon quatre phases successives, qui correspondent à un élargissement progressif des problématiques traitées : de la modélisation et la prise de décision dans les systèmes de production, vers la modélisation et la simulation des processus collaboratifs dans les chaînes logistiques et les entreprises en réseau, puis l'intégration par les systèmes d'informations des processus PLM et Supply Chain, et enfin le développement d'architectures et de solutions distribuées pour le partage et l'échange d'information et de connaissances tout au long du cycle de vie des produits. Ces travaux de recherches se sont développés selon trois principaux axes scientifiques : a. Conception de modèles de simulation et d'aide à la décision pour l'étude de la dynamique des systèmes complexes et l'évaluation des performances : modèles formels de décision multicritères, décision collective et distribuée,... b. Intégration des systèmes d'information et mise en place d'architectures et de référentiels techniques pour la gestion et le suivi des données : approches par les modèles, MDA, interopérabilité... c. Développement de systèmes de gestion des connaissances (méthodes hybrides d'ingénierie des connaissances, modèles pour la capitalisation et la réutilisation des connaissances, formalisme de modélisation de la coopération/négociation entre acteurs dans les systèmes distribués... Ces trois dimensions convergent en fait vers un paradigme plus large pour l'aide à la décision dans le cadre des entreprises en réseau : le paradigme de l'entreprise cognitive et collaborative. Ce paradigme place au cœur de la performance des entreprises la nécessité de mettre en œuvre des modes d'organisation réactifs et des modes de collaboration et de synchronisation des systèmes d'information leur permettant de s'intégrer à des chaînes logistiques en fonction des besoins, ou de participer à des projets de développement de produit, et de mettre en place des partenariats et des échanges de manière efficace et facilement reconfigurable. Ceci rejoint les préoccupations actuelles sur les principes d'agilité des entreprises, d'urbanisation des systèmes d'information, et de cloud computing par exemple. Dans ce contexte, et en synthèse de ce mémoire, nous proposerons des pistes de réflexion et des perspectives de recherche issues de ces axes scientifiques, dont les orientations principales sont : vers la définition de modèles cognitifs d'aide à la décision pour les entreprises en réseau, basés sur la simulation et l'optimisation, et intégrant des notions de durabilité des systèmes et des produits et de capitalisation des connaissances à long terme.

International audience; Today, the trend of doing business concerns not only the efficiency but also the sustainability of product and the health of environment. In order to reduce waste generation and improve product or product development process in term of time, cost and quality, organization focuses more and more on Product lifecycle sustainability. It helps reducing waste, conserving resources, using recycling materials, designing product for easy disassembly and avoiding using hazardous material. This paper proposes a knowledge management architecture, based on a multi-agent system, which focuses on the “sustainability” in order to manage knowledge in each stage of the product lifecycle, and particularly in the recovery process. The aim of this research work is to distribute the environmental knowledge about sustainability (e.g. environmental regulations, rules, norms...) by using multi agent system architecture. The proposed system will make the link between the agent's knowledge base and PLM (Product Lifecycle Management) systems. It will help the decision makers in each stage of the lifecycle and make them take into account the environmental impact of their decisions. The proposed architecture will be illustrated on an industrial case study.

International audience

Right PLM components selection and investments increase business advantages. This paper develops a PLM components monitoring framework to assess and guide PLM implementation in small and middle enterprises (SMEs). The framework builds upon PLM maturity models and decision-making methodology. PLM maturity model has the capability to analyze PLM functionalities and evaluate PLM components. A proposed PLM components maturity assessment (PCMA) model can obtain general maturity levels of PLM components based on key performance indicators. Investment decisions should be made from the relatively weaker PLM components based on the results of PCMA. One developed method of the fuzzy analytical hierarchy process (Fuzzy AHP) is applied to extract the premier improvement component needed. The results of a first empirical assessment in a swimming industry are presented, which could be used as benchmark data for the other Small and Medium sized Enterprises (SMEs) to develop their own PLM components monitoring framework to increase the success of their PLM implementation. Scopus

International audience; Throughout product lifecycle, the definition of product data is confronted to the diversity of the business activities and influenced by their specific information systems. This paper is related to the analysis of technical and functional product data exchange solutions throughout product lifecycle. In such context, the communication between partners is ensured by setting agreements, methodologies, and adopting standards. However, these communication issues have to deal with systems heterogeneity. We will mainly focus on two components of the lifecycle: Design and Supply chain. Design teams must collaborate effectively with their internal and external partners to develop competitive products. Product data must be reviewed according to the product specifications throughout its supply chain to ensure the quality of product data definition. In both cases, the interoperability between computer systems and applications presents a vital issue and an important challenge to address. Today, the OMG's model-driven architecture (MDA) makes available an open approach to write specifications and develop applications, separating the business and application functionalities from the platform technology. We will focus on the use of MDA technologies to improve the interoperability and illustrate its application through two case studies related to the Design and Supply Chains. The first case study shows how to enhance the interoperability between design experts exchanging heterogeneous CAD/CAM models and using the formulation of business knowledge throughout a Model-Driven Architecture approach. The second case study illustrates the contribution of a model-driven interoperability approach to ensure data quality in the logistic perimeter of the vaccine supply chain.

International audience; We present in this paper the benefits of a Model-Driven Architecture (MDA) to ensure the interconnection of different business contexts' specifications by providing a linked structure of models. This enables to generate bridges that connect implementations in different platforms. In this way, the systems interoperability is satisfied. In fact, the concepts of interoperability are useful to enable the interconnection between different enterprise services and divisions at four levels: business level, process level, service/application level and data level. When the enterprise modeling concepts structure some models to enable the connection between business and process levels, the software engineering concepts structure the models between applications and manipulated data. The MDA approach is widely considered as a methodology to software generation from models, with a focus on enterprise and business models. Deploying an MDA approach in vaccine industry allows us to deal with business product complexity and regulatory constraints. It helps their translation to some business rules and recommendations when we try to communicate data models from heterogeneous information systems. This will finally ensure product data quality in different information systems throughout the product lifecycle.

International audience; This paper applies the multi-agent systems paradigm to collaborative coordination and negotiation in a global distribution supply chain. Multi-agent computational environments are suitable for a broad class of coordination and negotiation issues involving multiple autonomous or semiautonomous problem solving contexts. An agent-based distributed architecture is proposed for better management of rush unexpected orders for which the quantity of product cannot be delivered partially or completely from the available inventory. This type of orders can be generated by unexpected swings in demand or unexpected exceptions (problem of production, problem of transportation, etc.). This paper proposes a first architecture and discusses an industrial case study.

21 pages; National audience; Dans un contexte de plus en plus concurrentiel et dans lequel la complexité des produits et des services associés est croissante, les entreprises ont pris conscience que leur niveau de performance dépend de leur capacité à se coordonner avec leurs fournisseurs et leurs clients. C'est pourquoi le concept de chaine logistique est un concept pertinent pour étudier et analyser les éléments de coordinations entre acteurs. Dans cet article, nous proposons de modéliser et de formaliser des processus d'échanges de connaissance et des modes de collaboration entre acteurs de la chaîne logistique. Nous allons nous intéresser plus particulièrement aux relations inter-entreprises et à la notion de projet afin de favoriser le partage d'information et de connaissances et d'améliorer ainsi la performance globale de la chaîne logistique. Nous finirons en présentant la mise en œuvre de notre démarche au sein de la chaîne logistique.

International audience

International audience; no abstract

National audience; The constraints on business process applications in terms of automation and reusability makes their development a challenging issue. To manage the variety of business process requirements, agile methods are required. Classically, business processes are implemented in a static way, what makes any evolution costly to realize. To cope with those needs, we propose the use of Web services to implement business processes. Web services and the designed business processes are stored in ontologies. An efficient multi-criteria service selection algorithm performs a semantic matching between the ontologies. It selects the most appropriate Web services among the existing ones for implementing a business process. This algorithm considers the business context, functional and QoS properties of the Web services. The user is asked for weights on QoS attributes. WordNet is integrated to solve the synonym problems. To be closer to reality and for more accurate results, our algorithm takes into account the changing values of QoS over time.

International audience; no abstract

International audience

International audience

International audience; Natural language is understandable by human and not machine. None technical persons can only use natural language to specify their business requirements. However, the current version of Business process management and notation (BPMN) tools do not allow business analysts to implement their business processes without having technical skills. BPMN tool is a tool that allows users to design and implement the business processes by connecting different business tasks and rules together. The tools do not provide automatic implementation of business tasks from users' specifications in natural language (NL). Therefore, this research aims to propose a framework to automatically implement the business processes that are expressed in NL requirements. Ontology is used as a mechanism to solve this problem by comparing between users' requirements and web services' descriptions. Web service is a software module that performs a specific task and ontology is a concept that defines the relationships between different terms.