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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; 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.

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; This paper considers the configuration of physical systems in a business to business environment (machine tool, aerospace equipment, cranes …). In this kind of business, knowledge-based configuration software are frequently used when dealing with "infinitely routine design" situations where the entire customer's requirements can be fulfilled with standard systems. However, in "less routine design" situations where non-standard systems must be designed in order to fulfill the entire customers' requirements, existing knowledge-based configuration software cannot be used. In fact, the configuration hypothesis state that all configured systems are assembled from standard subsystems and components. The aim of this paper is therefore to investigate how the existing products/systems configuration hypothesis, problems' definitions, and models can be modified or adapted in order to allow the use of configuration software in "less routine design" situations. In this purpose, first, the main differences between standard and non-standards systems are analyzed. Then, six cases of systems configuration that differentiate "less routine design" from "infinitely routine design" are identified and discussed. Finally, some Constraint Satisfaction Problems (CSP) based modeling extensions are proposed to allow the use of configuration software in these situations.

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.

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.

International audience; End-of-life management of complex systems is increasingly important for industry because of growing environmental concerns and associated regulations. In many areas, lack of hindsight and significant statistical information restricts the efficiency of end-of-life management processes and additional expert knowledge is required. In this context and to promote the reuse of secondhand components, a methodology supported by risk assessment tools is proposed. The proposal consists of an approach to combine expert and statistical knowledge to improve risk assessment. The theory of belief functions provides a common framework to facilitate fusion of multisource knowledge, and a directed evidential network is used to compute a measure of the risk level. An additional indicator is proposed to determine the result quality. Finally, the approach is applied to a scenario in aircraft deconstruction. In order to support the scientific contribution , a software prototype has been developed and used to illustrate the processing of directed evidential networks. Index Terms-Belief functions, decision-support system, directed evidential networks, end-of-life management, risk assessment.

International audience; This article proposes a model and rules dealing with the management of the interaction between system design processes and project planning ones. An industrial benchmark analysis has reinforced our belief that the interaction between the two processes has to be supported by models, processes and relevant tools. Firstly, after presenting the results of the analysis, the different entities are defined and the one-to-one relationship or bijection between the structure of the system and the structure of the project is made. Then, a model, taking into account design activities and planning activities as well as management of interactions, is proposed in compliance with existing project and design standards. A process of interaction is presented to carry out design and project management. Two interaction modes have been proposed. On the one hand, the structural interaction establishes links between entities of the two domains. On the other hand, the behavioral interaction (subject of this paper) is based on the definition of states for each entity following feasibility and verification criteria, and can thus manage the changes between states. Some rules are defined (precedence and synchronous rules) to forbid certain changes when they are inconsistent and to synchronize them.

Part 1: Knowledge Discovery and Sharing; International audience; This article presents the first results of a study which deals with the detection and the correction of abnormal values in data series intended to forecast demand. This work fits in the broader context of performance management for proximity retailers. Indeed, when this kind of point of sales (POS) is studied, sales volumes are often too small to be effectively exploited by statistical processing methods. It is therefore useful to consolidate the information with expertise and additional knowledge resulting from similar POS. It is also relevant to take into account the inherent uncertainty of such information. The proposal of this paper is a methodological contribution which uses consolidated knowledge to detect and correct abnormal values and to improve the quality of data used to implement forecast methods.

This paper addresses the fulfillment of requirements related to case-based reasoning (CBR) processes for system design. Considering that CBR processes are well suited for problem solving, the proposed method concerns the definition of an integrated CBR process in line with system engineering principles. After the definition of the requirements that the approach has to fulfill, an ontology is defined to capitalize knowledge about the design within concepts. Based on the ontology, models are provided for requirements and solutions representation. Next, a recursive CBR process, suitable for system design, is provided. Uncertainty and designer preferences as well as ontological guidelines are considered during the requirements definition, the compatible cases retrieval, and the solution definition steps. This approach is designed to give flexibility within the CBR process as well as to provide guidelines to the designer. Such questions as the following are conjointly treated: how to guide the designer to be sure that the requirements are correctly defined and suitable for the retrieval step, how to retrieve cases when there are no available similarity measures, and how to enlarge the research scope during the retrieval step to obtain a sufficient panel of solutions. Finally, an example of system engineering in the aeronautic domain illustrates the proposed method. A testbed has been developed and carried out to evaluate the performance of the retrieval algorithm and a software prototype has been developed in order to test the approach. The outcome of this work is a recursive CBR process suitable to engineering design and compatible with standards. Requirements are modeled by means of flexible constraints, where the designer preferences are used to express the flexibility. Similar solutions can be retrieved even if similarity measures between features are not available. Simultaneously, ontological guidelines are used to guide the process and to aid the designer to express her/his preferences.

To take into account the experience feedback on solving complex problems in business is deemed as a way to improve the quality of products and processes. Only a few academic works, however, are concerned with the representation and the instrumentation of experience feedback systems. We propose, in this paper, a model of experiences and mechanisms to use these experiences. More specifically, we wish to encourage the reuse of already performed expert analysis to propose a priori analysis in the solving of a new problem. The proposal is based on a representation in the context of the experience of using a conceptual marker and an explicit representation of the analysis incorporating expert opinions and the fusion of these opinions. The experience feedback models and inference mechanisms are integrated in a commercial support tool for problem solving methodologies. The results obtained to this point have already led to the definition of the role of ‘‘Rex Manager’’ with principles of sustainable management for continuous improvement of industrial processes in companies.

International audience; The goal of this paper is to show how it is possible to support design decisions with two different tools relying on two kinds of knowledge: case-based reasoning operating with contextual knowledge embodied in past cases and constraint filtering that operates with general knowledge formalized using constraints. Our goals are, firstly to make an overview of existing works that analyses the various ways to associate these two kinds of aiding tools essentially in a sequential way. Secondly, we propose an approach that allows us to use them simultaneously in order to assist design decisions with these two kinds of knowledge. The paper is organized as follows. In the first section, we define the goal of the paper and recall the background of case-based reasoning and constraint filtering. In the second section, the industrial problem which led us to consider these two kinds of knowledge is presented. In the third section, an overview of the various possibilities of using these two aiding decision tools in a sequential way is drawn up. In the fourth section, we propose an approach that allows us to use both aiding decision tools in a simultaneous and iterative way according to the availability of knowledge. An example dealing with helicopter maintenance illustrates our proposals.

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector.

9th International Conference on Evolution Artificial, Strasbourg, FRANCE, OCT 26-28, 2009; International audience; Several recent works have examined the effectiveness of using knowledge models to guide search algorithms in high dimensional spaces. It seems that it may be a promising way to tackle some difficult problem. The aim of such methods is to reach good solutions using simultaneously evolutionary search and knowledge guidance. The idea proposed in this paper is to use a bayesian network in order to store and apply the knowledge model and, as a consequence, to accelerate the search process. A traditional evolutionary algorithm is modified in order to allow the reuse of the capitalized knowledge. The approach has been applied to a problem of selection of project scenarios in a multi-objective context. A preliminary version of this method was presented at EA' 07 conference [1]. An experimentation platform has been developed to validate the approach and to study different modes of knowledge injection. The obtained experimental results are presented.

International audience; A better integration of preliminary product design and project management processes at early steps of system design is nowadays a key industrial issue. Therefore, the aim is to make firms evolve from classical sequential approach (first product design the project design and management) to new integrated approaches. In this paper, a model for integrated product/project optimization is first proposed which allows taking into account simultaneously decisions coming from the product and project managers. However, the resulting model has an important underlying complexity, and a multi-objective optimization technique is required to provide managers with appropriate scenarios in a reasonable amount of time. The proposed approach is based on an original evolutionary algorithm called evolutionary algorithm oriented by knowledge (EAOK). This algorithm is based on the interaction between an adapted evolutionary algorithm and a model of knowledge (MoK) used for giving relevant orientations during the search process. The evolutionary operators of the EA are modified in order to take into account these orientations. The MoK is based on the Bayesian Network formalism and is built both from expert knowledge and from individuals generated by the EA. A learning process permits to update probabilities of the BN from a set of selected individuals. At each cycle of the EA, probabilities contained into the MoK are used to give some bias to the new evolutionary operators. This method ensures both a faster and effective optimization, but it also provides the decision maker with a graphic and interactive model of knowledge linked to the studied project. An experimental platform has been developed to experiment the algorithm and a large campaign of tests permits to compare different strategies as well as the benefits of this novel approach in comparison with a classical EA.

1st International Conference on Complex System Design and Management (csdm 2010), Cite Int Univ Paris, Paris, FRANCE, OCT 27-29, 2010; International audience; This paper proposes first integrated models dealing with the management of the coupling between system design environment and project planning one. A benchmark done with fifteen companies belonging to the world competitiveness cluster Aerospace Valley has highlighted a lack of models, processes and tools for aiding the interactions between the two environments. An integrated model taking into account design and planning requirements as well as management of coupling is proposed in compliance with existing project and design standards. A process of coupling, carrying out design and project management in case of innovative design is presented. It is based on the generic formalization of the interactions and the propagation of decisions taken within an environment to another one.

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving the missions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable.

International audience

This paper studies the co-design paradigm which refers to the concurrent design of products, their associated services, internal processes and networks of partners. The co-design is a powerful strategic positioning technique that will give opportunities to firms to reach and maintain a profitable position on the market. This means that future co-working needs should be taken into account from the beginning of the product life cycle. In this paper, we define some of the basic co-design concepts and also provide a set of indicators that allow the qualification of the collaboration of partners. These indicators are illustrated on a car assembly example.

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In an other context, many studies are carried out aiming at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We suggest in this paper to study the natural convergence between these two approaches and we propose an integration architecture dealing with machine tool and machining control that enables the exploitation of distributed smart sensors and actuators in the decisional system.