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Implementation of LARG (Lean, Agile, Resilient, Green) practices in the supply chain management is a complex task mainly because ecological, economical and operational goals are usually in conflict. To implement these LARG practices successfully, companies’ need relevant decision making tools allowing processes performance control and improvement strategies visibility. To contribute to this issue, this work tries to answer the following research question: How to master performance and anticipate problems in supply chain LARG practices implementation? To answer this question, a risk management approach (RMA) is adopted. Indeed, the proposed RMA aims basically to assess the ability of a supply chain, guided by “Lean, Green and Achievement” performance goals, to face “agility and resilience risk” factors. To proof its relevance, a logistics academic case study based on simulation is used to illustrate all its stages. It shows particularly how to build the “LARG risk map” which is the main output of this approach., {"references":["D. Ravet, \"Lean production and agile organization: the link between supply chain and sustainable development,\"Dorich House Group « Developing Sustainability, 2011 Area: Material Manufacturing and Design 2011.","Caroline THIERRY, Identifier les besoins en Supply Chain Risk Management Une méthodologie « orientée points de vue » fondée sur un système de représentation hiérarchique, RAPPORT INTERNE IRIT/RR--2008-7—FR, MARS 2008","Dumitriu, c., 2002-2007. « La gestion intégrée des risques: méthodes et outils, manuel de cours ». UQAM.","Dumitriu, c., 2005. « La gestion des risques et des crises: concepts, théories, outils et études de cas ». UQAM.","B. Verrier and B. Rose and E. Caillaud, Hakim Remita, \"Combining organizational performance with sustainable development issues: The Lean and Green project benchmarking repository,\" Journal of Cleaner Production, vol 1, 2013, article in presse.","AFNOR, ISO 9001, Systèmes de management de la qualité : Exigences, AFNOR, 2015.","Eppen, G., D., 2000. « Garder le cap à travers les pièges de la production ». Dans L'art de la gestion des risques, Les Echos, e-Learning HEC. . Consulted the 01.02.2007.","Simons, R., 1999. « How risky is your company? ». Harvard Business Review. vol. 77, no. 1, p. 85-94.","Adrian J. Slywotzky et John Drzik (2005), « Strategic Risk Management (Gestion des Risques Stratégiques) ». Consulted the 14.04.2008. http://www.12manage.com/methods_slywotzky_strategicJisk_management_f r.html.et \n[10]\tDuc Pham-Hi, 2000, PHAM HI, D., 2000. « Traitement quantitatif des risques ». Dans L'art de la gestion des risques, Les Echos, e-Learning HEC. . Consulted the 15.03.2008.\n[11]\tMarch J., Shapira Z. (1987), \"Managerial perspectives on risk and risk taking\", Management Science, Vol. 33, N° 11, pp. 1404-1418.\n[12]\tZsidisin G.A., Panelli A., Upton R. (1999), \"Purchasing organization involvement in risk assessments, contingency plans and risk management: an exploratory study\", Supply Chain Management; An International Journal, Vol. 5, N° 4, pp. 187-197.\n[13]\tZsidisin, G., A., 2003. « A grounded definition of supply risk ». Journal of Purchasing and Supply Management. Vol 9, no 5, p. 217-224\n[14]\tIntroduction aux plans d'expériences par la méthode Taguchi, Maurice Pillet, les éditions d'organisation, 1997.\n[15]\tLean logistics, v1 1er Novembre 2008, Christian HOHMANN. Consulted the 01.08.2016. http://chohmann.free.fr/SCM/lean%20logistique_fr.pdf.\n[16]\tJohnson, M. E. (2001). Learning from toys: Lessons in managing supply chain risk from the toy industry. California Management Review, 43(3), 106-124.\n[17]\tJuttner U. (2005), \"Supply Chain Risk Management\", International Journal of Logistics Management, Vol. 16, Is. 1, pp. 120-141.\n[18]\tMason-Jones R., Towill D.R. (1998), \"Shrinking the Supply Chain Uncertainty Cycle\", Control, pp. 17-22.\n[19]\tKraljic P. (1983), \"Purchasing must become supply management\", Harvard Business Review, Vol. 61, N° 5, pp. 109-117.\n[20]\tHauser L.M. (2003), \"Risk-adjusted Supply Chain Management\", Supply Chain Management Review, Vol. 7, Is. 6, pp. 64-71.\n[21]\tHarland C., Brenchley R., Walker H. (2003), \"Risk in supply networks\", Journal of Purchasing and Supply Management, Vol. 9, N° 1, pp. 51-62.\n[22]\tKleindorfer P., Saad G. (2005), \"Managing disruption risks in supply chains\", Production and Operations Management, Vol. 14, N° 1, été, pp. 53-68.\n[23]\tHallikas J., Karvonen I., Pulkkinen U., Virolainen V.-M., Tuominen M. (2004), \"Risk Management Processes in supplier networks\", International Journal of Production Economics, Vol. 90, Is. 1, pp. 47-58.\n[24]\tSrivastava, S.K., 2007. Green supply-chain management: a state-of-the-art literature review. International Journal of Management Reviews. 9(1), 53-80.\n[25]\tP. Hines, \"Lean & Green,\" Source the Home of Lean Thinking, 3rd edition, Summer 2009.\n[26]\tA. Charles, M. Lauras, L. N. van Wassenhove, \"Towards an agile supply chain: lessons learnt from humanitarians\" in Proceedings of the International Conference on Industrial Engineering and Systems management (IESM), Montreal, Canada, May 2009 Prix du meilleur article \"Systems Management\".\n[27]\tT.J Pettit, J. Fiksel and K.L. Croxton, \"Ensuring Supply Chain Resilience: Development of a Conceptual Framework,\" Journal of Business Logistics, Vol. 31, No. 1, 2010, pp. 1-22.\n[28]\tG. Bergmiller, P. R. McCright, \"Are Lean and Green Programs Synergistic?\" Proceedings of the 2009 Industrial Engineering Research Conference.\n[29]\tEvrard Samuel K., « Concevoir des supply chains résilientes : simple évolution du management des risques ou mutation stratégique majeure ? », Logistique & Management, Vol.21, n 2.\n[30]\tEvrard Samuel K., Ruel S.,«Contribution des écosystèmes informationnels à la résilience des chaînes logistiques globales», Systèmes d'Information et Management, vol.18, n 1, 2013, 57-85.\n[31]\tGuide AFNOR SD 21000 « Développement durable – Responsabilité sociétale des entreprises – (FD X30-021), mai 2003."]}

International audience; Green supply chain management has emerged as a green innovation that integrates environmental aspects in the supply chain. The objective of this paper is to answer the following question: "How TRIZ (Theory of Inventive Problem Solving) can contribute to solve the problems of green logistics?". To do this, a literature review was constructed in order to understand and categorize problems of green logistics, and qualify them in terms of their resolution. This in order to identify the contributions types made by methods of TRIZ, and to solve the green logistics problems.

The difference between inventive problems and optimization ones is defined in this article. There exist among the engineering practices different kind of tools and methods aiming at designing, but which are not specified for the same nature of problem. It is thus relevant to be able to recognize the two kinds of problems: optimization ones, for which a solution can be found by adjustment of the value of problem parameters; and inventive problems, for which no solution is known. If no solution is known, either a solution exists and has to be found, it means that it has not been formulated the right way; either no solution exists and it is required to use a method to invent a solution. For these two cases, the matter is the problem, as it is modeled has to be reformulated, the model has to be changed, in order to build a representation enabling the resolution of the problem. The article will be focused on the question of problem model change and will compare the mechanisms to change this model for inventive problems from two problem solving theories: dialectical methods and models, on the one hand; and constraint satisfaction problem (CSP), on the other hand.

Different kinds of problem solving methods exist for different kinds of problems. One can recognize two kinds of problems: optimization ones, for which a solution can be found by adjustment of the value of problem parameters; and inventive problems, for which no solution is known. This problem insolvability can be due to the lack of a "good" solving algorithm or to a non adequate problem representation. If so, the problem, as it is modeled has to be reformulated, the model has to be changed, in order to build a representation enabling the resolution of the problem. The article will be focused on the question of problem model change and will compare the mechanisms to change this model for inventive problems from two problem solving theories: dialectical methods and models, on the one hand; and constraint satisfaction problem (CSP), on the other hand.

Problems in design of technical systems can be solved by optimization or inventive solving principles. Two representation models are studied: Generalized System of Contradictions (GSC) as inventive principle and Design of Experience (DoE) as optimisation principle. Our purpose is to improve the capacity of design problems resolution by using the both solving principles articulated to one representation model. We will show how it is possible to shift from DoE representation model to GSC representation model by using different methods. On the one side this transition can be done by the identification of Generalized System of Contradictions out of Design of Experiments based on a set of equations to resolve. On the other side methods of data analysis can be used to visualise and reorganise the DoE matrix in the form of "contradiction blocks " reflecting the set of equations. This reorganisation of representation model will be illustrated on a simple technical system.

Dans la résolution des problèmes de conception, l'idée de proposer un modèle unifié de représentation des problèmes pour être utilisé à la fois par les approches d'optimisation et d'invention se révèle très prometteuse. Les deux approches sont en effet complémentaires et l'évolution des systèmes techniques passe par des phases successives d'optimisation et d'invention. Dans un article précédent, les parallèles entre les modèles de représentation des problèmes ont été établis. L'objectif de cet article est de présenter les différences et les complémentarités entre les modes de résolution de problèmes issus de l'optimisation et de l'invention. Cette analyse se fait à travers l'étude des mécanismes de changement de modèle de la résolution des problèmes par contraintes (CSP) et de la théorie de résolution des problèmes d'invention (TRIZ). Afin d'être en mesure de comparer les mécanismes de changement de modèle, les espaces de connaissances parcourus par ces changements de modèle seront décrits et comparés.

L'objectif de ce papier est de lister et caractériser différents systèmes informatiques existants dans le domaine de la maintenance industrielle afin de proposer une classification des différentes architectures de ces systèmes. Deux critères de cette classification s'imposent : l'évolution de l'information utilisée et la relation entre les systèmes intégrés dans les architectures. Quatre architectures génériques sont identifiées, à savoir maintenance, télémaintenance, e-maintenance et s-maintenance. Le type d'architecture de maintenance sémantique : la s-maintenance est proposée prenant appui sur des ontologies communes aux différents systèmes et sur la technologie émergente du Web sémantique. Ce nouveau concept représente une architecture adaptée aux besoins d'intégrer les différents systèmes d'aide aux opérateurs et aux experts de maintenance et ouvre également la possibilité d'utiliser les techniques de gestion des connaissances dans ces systèmes.

International audience; Although the elaboration of the case representation is the key problem of the case-based reasoning system conception there is no proved methodology targeted to this task for now. This paper deals with this lack in the maintenance domain precisely in the equipments diagnostic and repair help. A methodology of the case representation elaboration is proposed based on knowledge management techniques and existing engineering analytical tools used in the industry. Different ontological models are proposed to take into account similarity and adaptability aspects of the case representation and to optimize the case base size.

Faced with the technological developments, the increasing complexity of the industrial plants and the processes dynamics as well as with organisational changes and staff mobility, maintenance managers want to formalise and capitalize the know and know-how of maintenance operators and experts. To deal with these factors, our objective is to provide a service of maintenance assistance that uses and capitalizes knowledge. Our work has been part of European project Proteus which goal was to develop a generic distributed platform of e-maintenance to integrate and to provide a set of different maintenance systems and applications. We specified four levels of maintenance applications associated each one with a set of decision help systems: equipment analysis, diagnosis and expertise, resource management and maintenance strategy management. These tools require an expertise which we propose to capitalize and preserve in a corporate enterprise memory. In order to create this memory and to develop our diagnostic and repair help system, we introduced a methodology based on the association of knowledge capitalization and knowledge intensive case based reasoning. The development of our system is based on knowledge modeling that consists of a representation model (domain ontology) and a problem solving model (case based reasoning). The suggested models use emerging technologies from the semantic Web which make possible the evolution of e-maintenance concept in a new concept of s-maintenance (semantic maintenance)., Face aux évolutions technologiques, à la complexité croissante des équipements industriels et à la dynamique des processus ainsi qu'aux changements organisationnels et à la mobilité du personnel, les responsables de maintenance cherchent à formaliser et pérenniser le savoir et le savoir-faire des employés. Pour répondre à cette problématique, notre objectif est de fournir un service d'aide à la maintenance qui utilise et capitalise les connaissances. Nos travaux se situent dans le cadre du projet Européen Proteus qui a permis de développer une plateforme distribuée d'e-maintenance intégrant les différents systèmes et applications existants en maintenance. Nous avons déterminé quatre niveaux d'applications associés chacun à un ensemble d'outils d'aide : le niveau d'analyse d'équipement, celui de diagnostic et d'expertise, celui de gestion des ressources et celui de gestion des stratégies de maintenance. Ces outils d'aide nécessitent une expertise capitalisée que nous proposons de préserver dans une mémoire d'entreprise. Afin d'élaborer la mémoire ainsi que notre outil d'aide au diagnostic et à la réparation, nous avons introduit une démarche de capitalisation des connaissances articulée autour d'une méthodologie de raisonnement à partir de cas (RàPC) guidé par les connaissances. La conception de l'outil passe par la modélisation des connaissances qui se décline en un modèle de représentation (une ontologie du domaine) et en un modèle de résolution de problèmes (RàPC). Les modèles proposés utilisent des technologies émergeantes du Web sémantique permettant de faire évoluer le concept d'emaintenance vers un nouveau concept de s-maintenance (maintenance sémantique).