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Expertise is an activity carried out by experts that contributes to societal progress, as it helps to elucidate unknown situations. Forexample, accident expertise eases accident understanding, by describing how it happened, and by identifying its causes and conse-quences. As a result, the design of accident expertise in a convenient human-machine structure will enable the querying, reasoning,and reuse of accident knowledge in other tools, such as safety and decision-making systems. However, existing representations ofaccident knowledge, such as documents, relational databases, or accident ontologies, do not fulfill accident expertise expectations.Moreover, these representations are unlikely to provide the appropriate use of accident expertise knowledge. This study presents a base ontology for accident expertise knowledge representation designed with a model-driven methodologyand implemented with semantic web technologies. The study obtained satisfactory results from the evaluation and application ofextension and reuse of this ontology with aircraft accident expertise taken from the French bureau of Enquiries and Analysis (BEA)for civil aviation safety.

International audience; In Engineer-To-Order (ETO) industrial contexts, decision-making about which systems to offer for sales or which subsystems to integrate into the systems for sales is based on various performance indicators. However, the lack of relevant information to evaluate these indicators challenges the decision-making process. In this article, to face this issue, a CBR approach is proposed for the evaluation of the performances of ETO systems. The main contributions are: (i) an object-oriented case representation model which allows to store previous evaluated systems for an effective and time efficient similar systems retrieval, and (ii) a method that allows to compute similarity between two systems that can have different structures.

This work deals with the customer-supplier relationship and concerns offer definition in Engineer-To-Order situations (ETO), by adopting the supplier point of view. In such cases, when the offer definition relies simply on key design choices without a detailed design, there is a specific risk (ETO-specific risk) that customer expectations cannot be fulfilled. This kind of risk is in addition to the conventional risks (non ETO-specific risk) involved in any delivery process (machine break down, resource not available, scrapped part…). In order to minimize the supplier risk of not being able to complete the offer as accepted and contracted by the customer, a knowledge-based system can be used to assist risk engineering. Consequently, this article proposes two interrelated knowledge modeling contributions. Firstly, a risk knowledge model which, when implemented in a knowledge-based system that supports risk characterization and risk treatment by using knowledge re-use techniques, is proposed and discussed. Secondly, two knowledge typologies for risk characterizations and treatments (both for ETO and non-ETO situations) in order to support risk knowledge, identification and modeling are also proposed and discussed. These contributions are innovative and groundbreaking in terms of both academics and applications: they provide a formal model to structure risk knowledge and a first list of risks and treatments to be taken into account in ETO and non ETO situations. After an introduction that presents the research gap, our objectives and an analysis of related works, our two contributions are described in two sections with respect to ISO31000 recommendations. The first section covers risk identification and evaluation while the second deals with risk treatments.

International audience; To increase their business volume and remain competitive, systems contractors must propose competitive and feasible solutions to customers. However, Engineer‐To‐Order industrial situations become challenged by the lack of relevant information. This article, to help companies to overcome this problem, presents two confidence indicators, their evaluation methods, and a way to use them during a design process. These indicators allow evaluating a company's future ability to offer a solution during a bidding process.

International audience; In many design problems, the designer must select the design solution from among several alternative design solutions. In situations where relevant information to evaluate the design solutions are not available, the evaluation is imprecise and uncertain. Therefore, in order to make the right decision, it is necessary to take into account imprecision and uncertainty in the decision process. In this context, this article presents a multi-criteria decision support approach for the complete ranking of alternative design solutions. The proposed approach allows to take into account the imprecision and uncertainty related to the evaluation of the decision criteria. It is based on the possibility theory and PROMETHEE II ranking method. It is validated using an application dealing with the design of a technical bid solution (a couple of technical system and delivery process) during a bidding process.

International audience; Successful bidding involves defining relevant technical bid solutions that conform to the customers' requirements, then selecting the most interesting one for the commercial offer. However, in Engineer-To-Order (ETO) industrial contexts, this selection process is complicated by issues of imprecision, uncertainty and confidence regarding the values of the decision criteria. To address this complexity, a Multi-Criteria Decision Making (MCDM) support approach is proposed in this study. This approach is based on possibility theory and the Pareto-dominance principle. It involves three main stages. First, a method is proposed to automatically model the values of the decision criteria by possibility distributions. Second, four possibilistic mono-criterion dominance relations are developed to compare two solutions with respect to a single decision criterion. Finally, an interactive method is devised to determine the most interesting technical bid solutions with respect to all the decision criteria. The method is applied to the design of a technical bid solution of a crane. The results show that this approach enables bidders to select the most interesting solution during a bidding process, while taking into account imprecision, uncertainty and their own confidence regarding the values of the decision criteria.

Intending to lead organizations to continuous improvement, this chapter proposes a methodology that involves three axes: risk management, problem- solving, and feedback experience. This methodology allows organizations to characterize the experiences they have already confronted, as well as new experiences (which can be risks or problems) with the use of taxonomies established by the organization. It also enables them to capitalize and exploit their knowledge base. This work proposes a best-use approach of the past experiences that are similar to a current event and facilitate their treatment and provide solutions. The authors take the feedback as a point of articulation between the two methodologies because it is a mechanism that offers knowledge where it can be found that the organizations must avoid and take advantage of.

Le congrès a pour titre "Nouvelles avancées et défis pour des industries durables et avisées"; International audience; L'élaboration d'une offre, lors d’une réponse à appel d'offres, est un processus complexe, généralement caractérisé par des informations partielles et imprécises ainsi qu’un temps de réponse limité. Adoptant le point de vue des entreprises soumissionnaires, cet article se concentre sur l'ingénierie des risques portant sur les activités du processus de fabrication d’un produit, de sa conception à sa livraison. Plus précisément, cet article propose un système à base de connaissances pour aider à l'identification et l'analyse (probabilité / impact) des risques qui peuvent survenir au cours du processus de fabrication. Cette identification des risques a lieu durant la phase de réponse à appel d'offres, de sorte que les risques identifiés et leurs impacts peuvent être pris en compte dans l’offre faite au client. Grâce au système proposé, les connaissances en matière d'ingénierie des risques peuvent être capitalisées et réutilisées pour aider à l’élaboration de nouvelles offres. Les connaissances en matière d'ingénierie des risques sont structurées par un modèle unifié. Pour aider les décideurs dans leur processus d'ingénierie des risques, une méthode de réutilisation, inspirée du raisonnement par analogie, est proposée pour exploiter les connaissances pertinentes. Le système proposé permet d'obtenir des offres plus précises, moins risquées et dans lesquelles il y a une forte confiance à livrer ce qui a été présenté au client. Ce travail peut être étendu pour modéliser et choisir les meilleures stratégies de mitigation des risques.

International audience; Collaboration is a key factor that encourages an efficient running of industrial processes. The measurement of the collaboration performance is necessary to allow experience capitalization and reuse in order to support decision making about efficient collaborations in future processes. This article describes a proposition of collaboration and performance evaluation methodology in industrial processes for experience capitalization. For this purpose, a collaboration model is introduced in order to develop an evaluation methodology. Finally, a case study applied to the aeronautical domain is presented to illustrate the methodology and validate the proposals

International audience; Collaboration is a key factor for carrying out activities in industrial processes and an efficient collaboration is essential to accomplish an overall improvement of any process. In this article, we introduce a collaborative process-modeling framework, which allows evaluating collaboration throughout all the activities of an industrial process. The proposed framework uses experience management notions towards the creation of a repository of collaboration experiences. This experience base facilitates the reuse of past experiences to support decision making for the organization and execution of future collaborations. The article concludes by discussing the contributions and limitations of the proposed collaboration model.

International audience; The bidding process is one of the most important phases for system contractors. A successful bid implies defining and implementing attractive and realistic systems solutions that fulfil customer expectations. An additional challenge arises with the increase in systems diversity resulting from growing customization needs. As a result, for standard customizing offers, bidders find good quality support with configuration software for assemble/make-to-order situations. But when requirements exceed the standard offers, bidders need extended support to fulfil Engineering-to-Order requirements. In this context, this article shows how configuration knowledge models, which support configuration in assemble/make-to-order situations (AMTO), can be extended and used in engineer-to-order situations (ETO). Modeling is achieved assuming that the configuration problem is considered as a constraint satisfaction problem. Six key requirements that differentiate ETO from AMTO are identified and modeling extensions are proposed and discussed. An example illustrates all the contributions.

International audience; This paper deals with the first ideas relevant to a knowledge based support for risk engineering when answering tenders or direct customer demands. Indeed, when an offer is defined, it becomes more and more important to analyze the possibilities of: risks occurrence, their consequences and their potential avoidance. Most of the time if it is done, this analysis is conducted manually thanks to a risk expert. In this paper, we propose to assist the expert with a risk engineering aiding tool that relies on a knowledge base and which allows to define and evaluate: (i) the risk and its probability, (ii) the main risk impacts and (iii) the interests of various corrective and preventive actions (impact and probability reductions). We first detail the problem. Then we identify risk knowledge and risk processing. This allows us proposing a knowledge model relevant to the risk engineering entities and some knowledge retrieval queries to support risk engineering.

International audience; During a bidding process, bidders have to submit offers that will suit the best to the customers’ requirements. The OPERA project aims at developing method, model and tools to help bidders to develop more accurate offers. One of the major tasks during the bidding process is the elaboration of offers. In this paper, we present a first version of a constraint-based model for offers (bids) elaboration which gathers three types of data: (1) general data characterizing the potential customer and the overall contexts, (2) data defining the technical system or service and (3) data defining the delivery process relevant to the technical system. The system or service will be limited to a 3-level decomposition. The process is composed of activities characterized by a couple (resources, workload). Four end user companies are involved in the OPERA project: two in the industrial sector and the two others in the service one.

During a bidding process, bidders have to submit offers that will suit the best to the customers' requirements. The OPERA project aims at developing method, model and tools to help bidders to develop more accurate offers. One of the major tasks during the bidding process is the elaboration of offers. In this paper, we present a first version of a constraint-based model for offers (bids) elaboration which gathers three types of data: (1) general data characterizing the potential customer and the overall contexts, (2) data defining the technical system and (3) data defining the delivery process relevant to the technical system. The system will be limited to a 3-level decomposition. The process is composed of activities characterized by a couple (resources, workload). Four end user companies are involved in the OPERA project: two in the industrial sector and the two others in the service one.

International audience; In order to respond to a call for tenders, bidders must define and evaluate potential solutions, based upon the specifications of customer’s requirements and their capabilities (skills, existing solutions, resources … etc.). The definition and the evaluation of potential solutions are not trivial activities. The lack of relevant information makes the evaluation imprecise and uncertain. Therefore bidders choose the most suitable solution based upon the standard indicators (cost, performance and lead time) and their subjective feeling. Unfortunately, this may leads to the choice of unfeasible solution regarding customer’s expectations (cost, performance and delivery time). Therefore, the aim of this paper is twofold: (i) first, to clarify the notion of imprecision and uncertainty in the evaluation of potential solutions; and (ii) second, to propose two Confidence Indexes (CI) in order to take into account uncertainty in offer elaboration. The first one (CIS) characterizes the confidence in the technical system solution and the second one (CIP) the confidence in the implementation process of the technical system. The proposed CIS and CIP will enable bidders to choose the most relevant solution not only based upon the standard indicators but also considering the confidence in the potential solutions.

International audience; In a bidding process, the bidder must define and evaluate potential offers in order to propose the most suitable one to the potential customer. Proposing attractive but also realistic offers to various potential customers is a key factor for the bidder to stay competitive. In order to achieve this, the bidder needs to be very sure about the technical specifications and the constructability of the proposal. However, performing a detailed design is resource and time-consuming. This article proposes the foundation of a new framework which can help bidders to define the right offer: (i) in the context of a non-routine design process, while avoiding a detailed design and (ii) taking into account two new indicators that reflect the bidder’s confidence that they can meet the commitments once the offer is accepted. The first indicator (OCS) characterises the Overall Confidence in the technical System, while the second one (OCP) gives the Overall Confidence in the delivery Process. Both OCS and OCP are based firstly on two factual objective indicators, Technology Readiness Level (TRL) for OCS and Activity Feasibility Level (AFL) for OCP, and secondly on two human-based subjective indicators, Confidence In System (CIS) for the OCS and Confidence In Process for the OCP. An illustrative application shows how this framework can really help bidders define an offer, while avoiding detailed design and enable them to evaluate the confidence level in each potential offer.

International audience; Nowadays, in customer/supplier relationship, suppliers have to define and evaluate some offers based on customers' requirements and company's skills. This offer definition implies more and more some design activities for both technical solution and its delivery process. In the context of Engineering-To-Order, design and engineering activities are more important, the uncertainties on offer characteristics is rather high and therefore, suppliers bid on the calls for tender depending on their feelings. In order to provide suppliers with metrics that enable him/her to know about the confidence level of an offer, we propose a knowledge based model that includes four original metrics to characterize the confidence level of an offer. The offer overall confidence relies on four indicators: (i) two objectives ones based on Technology Readiness Level and Activity Risk Level, and (ii) two subjective ones based on the supplier's skills and risks aversion. The knowledge-based model for offer definition, offer assessment and offer confidences is based on a constraint satisfaction problem.

International audience; Companies need to become more agile to survive to the unstable and highly changing market-place. This can be achieved through the adaptation and control of their business processes. A process sufficiently structured but not over constrained by standards and based on experience feedback principles is necessary. This article describes a proposition of agile process driven by the reuse of experiences and knowledge. For this purpose, based on Case-Based Reasoning (CBR) principles, the complete lifecycle of an agile process is introduced, from requirements definition, retrieval, reuse, adaptation, and storage steps. Finally, an example applied to the domain of industrial problem solving is presented to illustrate the methodology.

Modern organizations generally define and use standardized models of their processes. They manage their activities using such standards. In these processes, the generalization and reuse of knowledge is facilitated by the standardization. But it is sometimes difficult to react to unexpected events due to over-constrained standards. Companies need to become agile to survive to continuous changes in their environments. There is a requirement of agility for the processes in order to ensure constant responsiveness and flexibility. This necessity of agility can be achieved through a knowledge-based system. Therefore, this article proposes a knowledge-based agile process model in which agility is driven by the reuse of knowledge and experiences. For this purpose, agility operators are defined as formalized pieces of knowledge. A model of an agile process in which these operators are used is presented. The basis of a methodology describing incremental versions of the model is also presented. This "versioning" allows to formalize experiences and to capitalize them for future reuse. Finally, an application of the method to the problem solving domain is presented. It is shown how the standard 9 Steps process becomes more agile by deploying the proposed methodology.