Nicolas Perry

Samar Ammar-Khodja

Kbe Application

A knowledge engineering method for new product development

Keywords: Knowledge-based Engineering, Knowledge Processing, Capitalization, MOKA 1 Knowledge Base Information Corpus Data Model Class Diagrams

Engineering activities involve large groups of people from different domains and disciplines. They often generate important information flows that are difficult to manage. To face these difficulties, a knowledge engineering process is necessary to structure the information and its use. This paper presents a deployment of a knowledge capitalization process based on the enrichment of MOKA methodology to support the integration of Process Planning knowledge in a CAD System. Our goal is to help different actors to work collaboratively by proposing one referential view of the domain, the context and the objectives assuming that it will help them in better decision-making.

Introduction

In recent years, engineering systems have moved from being information-intensive towards knowledgeintensive systems [START_REF] Sainter | Proceedings of DETC'00 ASME 2000 Design Engineering Technical Conference And Computers and Information in Engineering Conference[END_REF] [START_REF] Ishikawa | Activity model for product development/production, CE: The Vision for the Future Generation in Research Applications[END_REF]. The information is thus constantly refined by clarifications, discussions and evaluations, until an optimised or compromised solution is agreed. In the framework of Product design and manufacturing, Weber [START_REF] Weber | A different view on PDM and its Future Potentials[END_REF] argue that today's PDM and PLM systems provide infrastructures to store and move data, but not retain knowledge about the content and the interrelationships of the data they handle. But, decisions are based upon the designers' intellectual assets and vary from one expert to another.

It becomes crucial to develop a method for take the benefit of this intellectual capital. The Knowledge-Based Systems (KBS) are one solution. Their development relies on the transformation of human informal knowledge into formal knowledge with some support from knowledge engineering techniques [START_REF] Studer | Knowledge Engineering: Principals and methods[END_REF].

The purpose of this paper is to introduce an engineering process to structure the transfer of expertise from experts' minds to an automated system in the manufacturing domain. Assuming that capitalizing knowledge consists of working on the content and form of the knowledge, the proposed process is structured in two major phases: the capture phase and the formalisation phase. Focusing on the first phase, the main objective of this work is to define a capitalization process to support knowledge capture and Product Development in a collaborative environment, Hermes Ed°, ISSN 1246-0125. (sous presse) representation for the specification of a knowledge-based engineering (KBE) system, which is a specific type of knowledge-based systems.

This study is based on the USIQUICK project which. First, we will start by describing its global context and objectives. Then, the problematic will be presented before developing the capitalization process proposed.

Knowledge-based methods and tools

A knowledge-based system can be defined as a computerised system that uses knowledge about some domain in order to deliver a solution concerning a problem [START_REF] Fasth | Knowledge-based Engineering for SMEs[END_REF]. The first generation of knowledge-based systems was expert systems using a set of facts and rules [START_REF] Ulengin | Cognitive map-KPDSS integration in transportation planning[END_REF]. This kind of systems is composed of essentially two components: a knowledge base (KB) and an inference engine. It applies specific domain or domain-specific knowledge to problem-specific data to generate problem-specific conclusions [START_REF] Ritchie | Expert systems in transportation engineering[END_REF]. The next KBS generation was the case-based systems. These systems use previous solutions to problems as a guide to solving new problems. Knowledge-based systems are widely acknowledged to be the key for enhancing productivity in industry, but the major bottleneck of their construction is knowledge acquisition, i.e. the process of capturing expertise before implementation in a system [START_REF] Christine | Knowledge modelling techniques for construction of knowledge and databases in industrial applications[END_REF]. Some methodologies assist the developers in defining and modelling the problem in question, such as Structured Analysis and Generation of Expert Systems (STAGES) and Knowledge Acquisition Documentation System (KADS) (an acronym that has been redefined many times, e.g. Knowledge Acquisition Documentation System and Knowledge-based system Analysis and Design Support). Moreover, these approaches get enriched in order to take into account the project management, organisational analysis, knowledge acquisition, conceptual modelling, user interaction, system integration and design [14] [15].

Consequently, knowledge modelling in engineering must be based on a rich and structured representation of this knowledge, and an adequate way of user interaction for modelling and using this knowledge [START_REF] Klein | Knowledge Modelling in Design -the MOKA framework[END_REF].

Due to the complexity of engineering knowledge, knowledge modelling in engineering is a complex task.

KBE has been defined as being an engineering methodology in which knowledge about the product, e.g.

the techniques used to design, analyse, and manufacture a product, is stored in a special product model.

The product model represents the engineering intent behind the geometric design. The KBE product model can also use information outside its product model environment such as databases and external company programs. KBE has been defined as "a computer system that stores and processes knowledge related to and based upon a constructed and computerised product model" [START_REF] Fasth | Knowledge-based Engineering for SMEs[END_REF]. The encoding of design knowledge from domain experts into computer codes that can generate complex geometric data, has demonstrated significant savings in manpower and time resources for routine design problems [START_REF] Bermell-García | A KBE System for the design of wind tunnel models using reusable knowledge components[END_REF], and has also provided a high degree of design integration and automation in well-defined and complex design Product Development in a collaborative environment, Hermes Ed°, ISSN 1246-0125. (sous presse)

tasks. The MOKA methodology has been proposed to address methodological issues during KBE systems development for our case study.

The modelling approach in KBE has to structure the engineering knowledge. In terms of developing KBE applications, this structuring process involves the configuration of the objects that model the engineering design environment and the rules that control the behaviour of the objects [START_REF] Sainter | Proceedings of DETC'00 ASME 2000 Design Engineering Technical Conference And Computers and Information in Engineering Conference[END_REF]. Current KBE systems are based upon a combination of the production rules and the object-oriented knowledge representation. Both elements together offer an automated way to introduce design requirements, model design constraints and provide a product description.

Knowledge base project: USIQUICK

Engineering knowledge tends to be very complex, diverse, and interrelated in many ways. Consequently, knowledge modelling in engineering must be based on a rich and structured representation of this knowledge, and an adequate way of user interaction for modelling and using this knowledge [START_REF] Klein | Knowledge Modelling in Design -the MOKA framework[END_REF]. Still, due to the complexity of engineering knowledge, knowledge modelling in engineering is a complex task.

Many relations and interdependencies have to be taken into account in order to come up with a model that is as precise, generic, consistent and concise as possible [START_REF] Sainter | Proceedings of DETC'00 ASME 2000 Design Engineering Technical Conference And Computers and Information in Engineering Conference[END_REF]. So, each new piece of knowledge, which should be inserted into an existing knowledge model, has to be related in many ways to the already contained knowledge. Thus, during modelling, a maximum of information about the already existing model has to be available and easily accessible by the knowledge engineer.

The other main knowledge-related issue in engineering is the application of knowledge-based technologies, i.e. the automatic computer-based processing of knowledge in KBE systems.

The following two sections define the concept of KBE, the most well-known methodologies and mostwidely-used modelling techniques to support such technology.

a. Context

The works presented are part of the output from an industrial project (USIQUICK [25]). The project aimed at developing a knowledge-based engineering system to help experts during the process planning for mechanical parts. The project involves eight partners. An aircraft manufacturer is the final user and the initial expert. He specified the expected results and its manufacturing expertise on complex part design and on process planning. A CAD/CAM developer supported the industrialization in its software solution.

Five laboratories ensured the scientific coherence, enriched and solved the strategic keystones of the project. A French-government institute helped to switch these project results in other area of mechanical manufacturing. The partners started working together in a same setting domain with different cultures, contexts, goals and backgrounds. These differences led to different viewpoints, assumptions and needs. In order to optimise the information flow from design to production, a three-step method is proposed [START_REF] Candlot | Deployment of an Innovative Resource Choice Method for Process Planning[END_REF]:

-Transformation phase: an analysis of the part to compute a maximum of information registered at an appropriate level of feature. In this phase computer assesses the machinability of faces.

-Preparation phase: the synthesis templates of the previous phase are presented to the user. Then with appropriate tools, the process plan skeleton can be built and constrained.

-Automation phase: the unconstrained choices are automatically optimized and a complete documentation is proposed by the system.

These phases would become the three major modules of the engineering tool based on the formalisation and the integration of expert knowledge.

We play a role in the project in order to propose solutions to allow to effectively cooperate on the same objective despite the mentioned differences, and to reduce the communication gap between the domain expert and the developer. Contextualised and structured information was shared, in the form of knowledge, to help all the actors to have a same understanding of the domain, the context and the goals.

However, to develop a KBE system, we need first to acquire, represent, reason and then communicate the intent of the design process. The problem is first understood at a conceptual level, and then decomposed into understandable working objects, developed further through an iterative process until a satisfactory outcome is reached. Then, product and process development are defined as a logical sequence of stages or activities, which may be documented, disseminated and understood by all the actors [START_REF] Al-Khudair | A system to support configuration version management in a distributed concurrent engineering design environment, CE: The Vision for the Future Generation in Research Applications[END_REF].

One of the project's challenges is to translate knowledge that has been expressed in the form of legacy specifications for the development of the system into a computerised form so that the computer can use it.

The difficulty is thus to select the right methods and tools for supporting and structuring such a transfer.

One solution could be to structure the knowledge within a knowledge base (KB) (figure 1-a). The building of this KB implies the deployment of a capitalization process to help and guide the knowledge treatments.

Capitalizing knowledge consists in processing and treating knowledge to prepare it for management activities. This capitalization will enable knowledge to be shared through a specific form making it understandable by each actor of the project.

The next section details the definition of such a process and highlights its major steps. This definition represents an introduction to the process we are proposing (following section). An overview describing MOKA methodology principals and ontology will be presented.

b. Knowledge capitalization

Knowledge capitalization is the process of capturing and formalising expertise before its implementation in a system. This process can be refined into four major stages:

-Knowledge elicitation, also known as acquisition, the process of obtaining knowledge from an expert; -Knowledge analysis, the process of making sense of the information collected in the first step;

-Knowledge structuring, the process of expressing the analysed knowledge in an understandable and usable form, for enhancing communication between the expert and the knowledge engineer, and for validation purposes;

-Knowledge representation, the process of rearranging and expressing knowledge in a format that facilitates its encoding and thus its handling by a computer.

The aim of knowledge capitalization is to develop methods and tools that make the task of capturing and validating experts' knowledge as efficiently and effectively as possible. Experts tend to be important and busy people; hence it is vital that the methods used minimise the time each expert spends off the job taking part in knowledge acquisition sessions [START_REF] Fasth | Knowledge-based Engineering for SMEs[END_REF].

To reach the multi-experts collaboration, the knowledge sharing and reuse within the USIQUICK context, we propose to capitalize the knowledge in two major phases: a capture phase and a formalisation phase.

The capture phase gathers the elicitation, the analysis and the structuring stages, while the formalisation phase is the representation stage. In the following sections only the capture phase will be detailed.

Capitalization process proposal

According to the KBE systems development principle, knowledge must be identified, acquired, analysed, structured and formalised in such a way that it could be accessible and reusable by each one. However, this principle does not allow any distinction between the activities handling the knowledge content (this means knowledge itself) and those handling its form.

What we are proposing in this paper is not completely different from or contradictory to the KBE development principle. Our aim is to structure all these activities according to the knowledge aspect addressed at each stage of the capitalization process. This structuring consists in separating the activities that handle the knowledge itself from those handling its form. This distinction tends to help knowledge engineers during capitalization activities deployment.

This structuring can also be considered as working on the state of the knowledge. Working on the knowledge content consists in transforming its state from a raw state (independently of being explicit or tacit) to a structured one. Working on the form, deals with the representation of the knowledge in order to go from a structured state to a formalised state, and onwards toward an automated one.

The transition between the two phases is based on the design of a knowledge base. This base constitutes a knowledge repository that can be accessible and which will be the knowledge reference for all the partners involved (figure 1-b).

Knowledge Capture Phase

Knowledge capture is the process that tries to transform the human experts' knowledge into a formulated knowledge that can be used directly by an expert system or by a computer system.

As defined in the previous section, this process can be broken down into three major steps: the elicitation step, the analysis step and the structuring step.

a. Elicitation step

The terms "knowledge elicitation" mean "how to obtain (or collect or acquire) knowledge from an expert". Diaper [START_REF] Diaper | Knowledge elicitation: principles, techniques and applications[END_REF] has extended this definition to include elicitation from other sources, such as documents, existing computer systems and the physical or the social environment.

Many elicitation techniques exist depending on the type of the knowledge source. The most common way to elicit knowledge from an expert is interviews. These interviews can be structured or unstructured depending on their context and on the knowledge engineer's strategy. On the other hand, eliciting knowledge from documents can be done by data mining techniques resulting from artificial intelligence.

Within the USIQUCK project, the elicitation had to be done from documents that represent legacy specifications for the development of the final system.

Among the existing methodologies for KBS and KBE development, the only one that can meet our needs is MOKA. This is because it offers the possibilities of eliciting knowledge from documents within engineering domains through its ontology. Ontology is a set of different interrelated concepts that describe a given domain [START_REF] Gruber | A translation approach to portable ontologies[END_REF]. However, this does not mean that MOKA does not allow the eliciting of knowledge from experts by using the proposed ontology.

To do so, we chose to deploy the proposed ontology within MOKA in order to identify the concepts that should be acquired from the specifications we obtained. However, before explaining this deployment, we will present MOKA.

MOKA methodology

MOKA, for Methodology and software tools Oriented to Knowledge Engineering Applications, describes in terms of rules, processes, modelling techniques and definitions, the necessary stages for the specification of KBE systems. MOKA provides a framework both for capturing and for representing knowledge. This framework works at two levels: informal level and formal level. The first one is relatively simple and oriented to represent and formalize knowledge in language that can be understood by experts without being a specialist in formalization languages. The advantage of this level is that it makes the validation of the acquired knowledge possible. This level also facilitates the communication between the expert, the knowledge engineer and the software developer.

The second level is more formal and aims to represent and store knowledge in an encoding form in order to plug it into computers.

The MOKA spirit is not different from the approaches proposed within the other knowledge management methodologies, the difference lies in the deployment strategy.

The other point that differentiates it from the other methods is the concepts it proposes to analyse the application domain. MOKA proposes five generic knowledge object types and relations among them to describe the domain. These objects as well as their use constraints are also defined. These object types are:

• Illustrations representing comments, past experiences, specific cases and complex explanations;

• Constraints describing the product's or its component's limitations;

• Activities to describe problems resolution stages;

• Rules to describe knowledge that directs the choices in the activities;

• Entities to represent knowledge elements that describe the product, its components, its assemblies, parts and features. An entity can be structural or functional.

Starting from this ontology, our first step was the identification of the knowledge objects. The identification step is a preliminary domain investigation and analysis that aims to recognise the knowledge elements or objects that must be acquired. The specifications we obtained consisted of texts, tables, and images in MS Word format. The domain library, which approximates domain ontology, consists of technical sentences condensed from legacy specifications.

The use of the MOKA ontology enabled us to identify a great number of knowledge objects. However, there is some knowledge related to, for example, resources and functions that have been missed.

The insufficiency of the ontology in this case study is due to the fact that in our context the final product is a process planning which is a process. The object's types do not become reusable as proposed. For example, if we consider the structural entity, it describes a physical component of the product but within our context the product is not a set of physical components but a set of activities that consist in geometry recognition, manufacturing mode identification, manufacturing operations definition and organization, etc.

They represent domain activities. This implies that we have two types of activities, those related to the domain and those related to the reasoning that allows definition of the process planning. The reasoning activities represent the design process and each one covers one or several domain activities. This insufficiency led to a need for ontology enrichment.

MOKA ontology enrichment

Facing this insufficiency, we propose to define the concept of resource to encapsulate the knowledge of the different tools and machines used by manufacturing processes (or operations) to realize geometries.

Hence, this object should be considered at the same level as the entity and the activity. It should also be related to both of them.

We also propose to define a concept of function to identify what is the objective of the reasoning activities. During the design of the system, some reasoning activities that have to be encoded aim to list results or to check if some parameters values are correct or not. This kind of activity should be attached to the concept of function to allow the differentiation of the activities related to a problem solving from those related to the presentation of the solution. It will be linked to the activity. The concept of entity in our context will represent the manufacturing features to be realized. We also distinguished the representation constraints from the product constraints and also the expert rules from the domain rules.

The representation constraints describe the constraints related to the presentation of the knowledge to the end user, and the product constraints enable the definition of all the constraints related to the product and its design.

The domain rules cover generic rules defined in the domain and the expert rules describe rules, applied by a specific expert that can vary from one expert to another.

According to these new object types, we propose ICARREF ontology to cover the manufacturing domain, in this case study, and for capturing knowledge about a product that is a process considering that these object types are generic. Figure 2 illustrates all object types and their interrelations. This figure also shows the ICARREF forms to fill in, and the ways to navigate within the knowledge base.

ICARREF Form

Knowledge Base reading interface Once the knowledge is extracted it must be analysed. This analysis has two objectives: its structuring and its evaluation.

b. Knowledge analysis

The analysis step is the most difficult step in the Knowledge-capture process because the belief is that a "magical one-to-one correspondence" between the expert's verbal comment and the real items of knowledge is misleading [START_REF] Buchanan | Constructing an expert system[END_REF]. Data and information obtained from manuals, textbooks, experts, and even users need to be converted into knowledge before they can be used.

The intermediate step of knowledge analysis is important, because its result will enable the building of a first knowledge model of the domain and the reasoning. It consists, first, of identifying the interrelated knowledge components, and after, on defining the right relation for each linked components.

Different relation types can be defined, for example: has constraint to link entities to constraints, has function between functions and activities, etc.

Once the interrelations and the relations have been defined, the knowledge should be structured.

c. Knowledge structuring

The structuring step will be achieved using trees and diagrams according to the MOKA approach.

Knowledge objects having the same type are linked using trees with "Is a" and/or "Is composed of" relation types. Knowledge objects having different types are linked using diagrams (figure 3-b). For diagram building, the relations are defined according to the objects they link. It can be "Has a rule", "Has a constraint", "Has a function", etc.

At this stage, the three steps of the capture phase have been done and a first representation of the knowledge is built. This representation will enable the evaluation of the knowledge.

The evaluation consists in analyzing the knowledge according to two criteria: completeness and feasibility.

The completeness indicates if, as transmitted by the expert in the specifications, this knowledge is enough to define the process planning for specified geometries. It also allows identification if, for a specified utilisation of the application, the context for each knowledge object is well described. This criterion highlights the additional knowledge to capture or to explain further if this has already been done.

Each one knows that there is a gap between the "real world" and the "computer world". The analysis of the feasibility to point out the knowledge that cannot be coded as specified by the expert and that requires the development of additional algorithms to make its automation possible. This need of comparison introduces the need of knowledge traceability. This means that the capitalization process has to take into account the organizational aspect of the project in addition to the product and the process aspects.

Knowledge completeness

To consider this new aspect an analysis of the developed algorithms has been done. The objective was to establish the correspondence between the algorithms and the design process activities in order to determine which activities have been effectively developed and, for each activity, the percentage of domain activities that has been automated (figure 4-c).

For this analysis the attribute "State" has been attached to each knowledge object to identify its state at a given time. The state can have one of the four following values: in progress, implemented, dismissed (ruled out: the implementation of the object is not envisaged), not treated.

Conclusion

Most KBE applications have been developed for solving large design problems in the aerospace and automotive industries where the main concern is the functionality to automate a complex design problem, rather than the reusability of engineering knowledge by the human expert.

However, to get such a result, disparate know-how and heterogeneous viewpoints have to be managed, integrated and stored in different forms that should be easily accessible, usable and maintainable.

Ontology approaches can propose solutions that could help integrate knowledge in KBE environments.

The USIQUICK experience has shown that considering the two knowledge aspects separately, the content and the form, helps to decrease the complexity of knowledge-based engineering system development. The capitalization process we propose aims to structure knowledge engineering activities deployment.

It also aims to help the knowledge engineer capture all the knowledge he has in order to capitalize and to facilitate the communication between the different experts (or actors) and to have indicators regarding the project's lifecycle.

Furthermore, they used

  different jargons and terminologies sometimes diverging or overlapping, generating and becoming unclear. Product Development in a collaborative environment, Hermes Ed°, ISSN 1246-0125. (sous presse)

Figure 1 :

 1 Figure 1 : knowledge transfer possibility (a) and the knowledge capitalisation phases (b)

Figure 2 :

 2 Figure 2: ICARREF conceptual model and working interface At this stage the knowledge to be kept has been identified and the elicitation can be done completely by an extraction strategy. The extraction consists of recognizing a subset of knowledge objects and their relationships, and then associating them with applicable fragments of the specifications (figure 3-a). The eventual output of extraction can be in plain text, in XML, or in Excel form, depending on the application of the supported software. In this example the output is in plain text.

Figure 3 :

 3 Figure 3: Knowledge acquisition (a) and structuring (b) step

Figure 4 Figure 4 :

 44 Figure4illustrates the automatic semantic enrichment of surfaces that will be machined. The type of these surfaces (colour identification in 4.a) depends on the rules and constraints linked to the tools access, machining strategy, settings, etc. The automatic proposal and selection of tools and machining parameters will be generated in accordance with the process of the expert's or experts' decision coded in the knowledge-based system. The user can access the contextual information and the selected rules and


Magali Bosch-Mauchand
email: magali.bosch@utc.fr

Ali Siadat

Nicolas Perry

Alain Bernard

A Siadat Lcfc

VCS: Value Chains Simulator, a Tool for Value Analysis of Manufacturing Enterprise Processes (A Value-Based Decision Support Tool)

Keywords: Value Chain, value, AHP, performance indicator, discrete event simulation

Manufacturing enterprises are facing a competitive challenge. This paper proposes the use of a value chain based approach to support the modelling and simulation of manufacturing enterprise processes. The aim is to help experts to make relevant decisions on product design and/or product manufacturing process planning. This decision tool is based on the value chain modelling, by considering the product requirements. In order to evaluate several performance indicators, a simulation of various potential value chains adapted to market demand was conducted through a Value Chains Simulator (VCS). A discrete event simulator is used to perform the simulation of these scenarios and to evaluate the value as a global performance criterion (balancing cost, quality, delivery time, services, etc.). An Analytical Hierarchy Process (AHP) module supports the analysis process. The value chain model is based on activities and uses the concepts of resource consumption, while integrating the benefiting entities view point. A case study in the microelectronic field is carried out to corroborate the validity of the proposed VCS.

Introduction

Competition between firms in a same market and field is increasing all the time. Therefore, all efforts need to concentrate on how to obtain a competitive advantage. Great efforts must be done to improve enterprise organisation in order to ensure the durability and prosperity of the enterprise. In terms of competitive advantage, the value chain approach developed by Porter proposes a vision by activities [START_REF] Porter | Competitive Advantage -creating and sustaining superior performance[END_REF]. The adopted point of view concentrates on strategic decisions. Therefore, the weak point is that this position cannot be considered in the design phase. To be competitive, the industrial organisation needs to reconsider its own activity and to increase its activity result. Continuity and growing challenges are the aims of all industries. Different ways to reach this goal can be followed. Berrah et al. said that the concept of performance is aimed at durability but with different requirements [START_REF] Berrah | Global vision and performance indicators for an industrial improvement approach[END_REF]. On the one hand, to achieve this aim the enterprise needs to be considered as a whole. On the other hand, it is necessary to make permanent and continuous improvements of the enterprise with regard to its structure and its means, as well as its organisation, management, flows, etc. Focusing on the design of a decision support tool, our research work concentrates on performance analysis in an industrial context, based on value concepts and models [START_REF] Mauchand | Modélisation pour la simulation de chaînes de production de valeur en entreprise industrielle comme outil d'aide à la décision en phase de conception / industrialisation[END_REF][START_REF] Mauchand | Enterprise processes performance analysis based on value chain modelling and simulation[END_REF][START_REF] Mauchand | Manufacturing system performance analysis based on value chain modelling and simulation[END_REF]. The evaluation process is crucial when we want to carry out a performance analysis that is the source of decision processes based on relevant criteria. Cost is one of the major performance criteria taking into account by benefiting entities. Moreover, in the design phase, this criterion is a growing concern since cost reduction is crucial for enterprise durability. In order to use a global indicator, this cost point of view will not be the only criterion to be taken into account [START_REF] Olivares-Benitez | A metaheuristic approach for selecting a common platform for modular products based on product performance and manufacturing cost[END_REF]. Value becomes a common concept, integration more focuses than only cost. That's why the value indicator has to be considered: it aggregates cost, quality, time to delivery, and function satisfaction criteria. So, value is defined as a balanced sum of criteria. But depending to the point of view, the value evaluation of a product or a service can be highly different. It results a need of managing the multi view point results.

Problem statement

Why Value? And what is its definition?

Our purpose is to make relevant decisions on product design and manufacturing processes using a tool-based method of value estimation for each benefiting entity (id. es. stakeholders). In this context, a benefiting entity is defined as the entity or the human organisation that takes advantage of the value created by the enterprise and/or that can have an impact on this value. There is a need to react at the beginning of the product development process in order to make early relevant decisions.

In the design and manufacturing planning phases, we try to enrich the Value Engineering method using an integrated method and different tools to complete the value-based decision support. Financial data is no longer the discriminatory argument in decision and negotiation. For instance, product and service quality (regarding delivery time) ensure client loyalty, which is a strong point in a competitive context. It is crucial to understand that cost performance is not the only criterion to be taken into account in order to define the best technical product solution and the best associated-manufacturing process. Some factors such as quality and delay create complementary constraints on performances. Therefore, performance indicators (relative to multiple objectives) need to be measured. Moreover, the value criterion depends on the point of view of the benefiting entities. Each of them has its own judgment of the major criteria for value evaluation. The use of discrete event simulation contributes to performance evaluation by adding contextual performances. The need to know the results of an enterprise in terms of its activity performance and its technical and managerial processes performance is increasing due to competitive markets. Furthermore, the impacts of decisions must be forecast and evaluated. An analysis of the manufacturing performances must be done to underline the best decisions to make such as to create the best manufactured product value. But the performance cannot be evaluated easily. The potential performance of a manufacturing system depends on many criteria. A well-argued decision process is strategic when considering the definition of manufacturing process plans and technological development in the production systems of the company [3; 7]. It becomes crucial to have a global performance evaluation. The ISO 14000 constraints emphasize the necessity to proceed with this global evaluation when taking efficient decisions. Hence, the simulation process will help to compare different solutions. Depending on the point of view of the benefiting entity (client, manufacturer, supplier, designer, commercial engineer, etc.), the performance is evaluated in different ways and by different criteria. It is necessary to work on complementary aspects for instance cost / price / value [START_REF] Mévellec | Whole Life Cycle Cost: a new approach[END_REF] in order to compare all points of view. In this work, value and its different understandings are crucial in order to evaluate the enterprise activity. So, a hierarchy of the components of the value criterion is proposed for this evaluation analysis.

The main issue of our decision process based on value criterion is: how to support a value-based simulation approach in the product design phase?

In the following sections, the research results on Value Chain modelling, simulation and evaluation are depicted [START_REF] Mauchand | Modélisation pour la simulation de chaînes de production de valeur en entreprise industrielle comme outil d'aide à la décision en phase de conception / industrialisation[END_REF]. The tools and methods system, called Value Chains Simulator (VCS) is presented in two phases, and its use is described and explained by a case study. The first section of the paper focuses on related research on performance indicators, on value chains and value concepts, on simulations for organisation improvements (such as lean manufacturing) and on a Multi-Criteria Decision Making (MCDM) approach (the AHP process). This review will highlight the already existing concepts and methods used to perform value simulation and analysis. Then we shall point out the lacks of information or the misunderstanding that should be improved on in order to propose a global value model and a value chain simulator. The second section deals with the architecture of the proposed tool, the Value Chains Simulator (VCS) and the description of the method used to estimate values. The methodology for using VCS is depicted by, on the one hand, the formalism designed to facilitate users' communication and modelling phases, and on the other hand, the formalization of the structure and deployment of the tool. To carry out the evaluation, the AHP module configuration is introduced in details. To end the VCS description, the evaluation process and models are explained. The last section relates to the application and illustrates the results of the VCS approach in a case study in the microelectronics field.

Related Research Evaluation process: Performance indicators definition

The definition of the performance indicators of a system is contingent on this system. They are dependent on each other. A performance indicator system is contextual. The performance evaluation of manufacturing processes is generally performed by using models developed from a particular point of view (temporal, financial, etc.). Bennour and Crestani postulate that human competences are the source of value creation [START_REF] Bennour | Using competencies in performance estimation: From the activity to the process[END_REF]. Therefore, they link human capacities and process performances (in terms of quality, time or financial performances). For manufacturing enterprises, a classification of the most useful performance criteria has been established in order to determine what criteria must be evaluated to give a measure of the product value and also the value provided by the value chain.

The product value is seen as a global indicator that relates to functional, structural and behavioural aspects [START_REF] Bernard | LC universal model for the enterprise information system structure[END_REF] [START_REF] Labrousse | Proposition d'un modèle conceptuel unifié pour la gestion dynamique des connaissances d'entreprise[END_REF]. The value of an enterprise object is relative to the benefiting entities (for instance, the shareholders). The benefiting entities can benefit or act on this value. They can be external and/or internal actors of the enterprise. Their appreciation of the product and enterprise performance is modelled in order to give a value indication. The appraisal of value is the result of a combination of several performance indicators such as cost, quality and time [START_REF] Chen | Performance measurement for a manufacturing system based on quality, cost and time[END_REF]. These criteria could be mixed with more subjective indicators such as customer perception, associated services, and environmental impact. Performance and value indicators, presented in Figure 1, come from a reflection on the benefits of product manufacture for each benefiting entity [START_REF] Mauchand | Enterprise processes performance analysis based on value chain modelling and simulation[END_REF]. From the client point of view, technical performances are required and must be respected. The cost, quality and delivery time can evolve and so the client value (element "a" in Figure 1) of a product can increase or decrease. From the point of view of the supplier (element "b" in Figure 1), the process value benefits from his delivery time and product quality since it can create manufacturing delays or increase the scrap rate of products. A link and a parallel can be established between some kinds of performance such as cost and value. To explain this idea, Perrin describes a product by its two distinct views (left and centre blocks of Figure 2) [START_REF] Perrin | Analyse de la valeur et valeur économique des biens et services[END_REF].  The first view, functions/value, depicts the product appreciation in terms of utility. Product value characterizes the expected product performance relative to the required functions.  The second, artefact/cost, describes the physical object produced to respond to this utility [START_REF] Lonchampt | Assessment of Mechanical Products during the Conceptual Design Stage Using Value Criteria[END_REF]. The relation between the solutions and the activities that could be applied to realise them is not underlined. In order to evaluate the product, a set of activities is defined to characterize the realization of the technical solutions that compose a product. So our proposal is to add a third face Value Chain / Performance that gives the concepts to be able to link the product solution to its development process and to add some criteria to the second view [START_REF] Mauchand | Manufacturing system performance analysis based on value chain modelling and simulation[END_REF]. Consequently, we propose to add to the Perrin proposal a third view to perform a pertinent evaluation of product functions. This provides the elements needed to evaluate the product value of the manufacturing system performance. A set of activities is defined to characterize the realization of the technical solutions that make up a product.

Value Chain / Performance View

Evaluation process: Value chain and value notions integration

Value is a multi point of view and multi-criteria concept. Elhamdi underlines this aspect of multi points of view by introducing the notion of benefiting entities [START_REF] Elhamdi | Modélisation et simulation de chaînes de valeurs en entreprise -Une approche dynamique des systèmes et aide à la décision : SimulValor[END_REF]. In the context of Value Analysis, AFNOR X50-151 standard gives the definition of the term "value" as: "The value is the judgement related to the product on the basis of the user's expectations and motivations, expressed by a ratio which increases when, all other things being equal, the satisfaction of the user's need increases and/or the expenditure related to the product decreases"1 [START_REF] Lonchampt | Assessment of Mechanical Products during the Conceptual Design Stage Using Value Criteria[END_REF].

"In competitive terms, value is the amount buyers are willing to pay for what a firm provides. Value is measured by total revenue, a reflection of the price a firm's product orders and the units it can sell" [START_REF] Porter | Competitive Advantage -creating and sustaining superior performance[END_REF] p. 38. To define "value", we propose the following, based on the definition by Lonchampt [START_REF] Lonchampt | Co-évolution et processus de conception intégrée de produits : Modèle et support de l'activité de conception[END_REF]: Value reflects the judgement related to the product on the basis of the user's expectations and motivations, expressed by a ratio which increases when, all other things being equal, the satisfaction of the user's need increases and/or the consumption of resources required by all the life cycle phases decreases. The characterization of the processes that create value is strategic in order to estimate the impact of a given production on the triptych Value/Cost/Risk. An approach based on value chain modelling and simulation is used to support the manufacturing processes performance analysis. This notion of value is essential for analyzing and comparing the value chain and its components with the same criterion.

Value nets can be used to model the enterprise dynamic regarding material and information flow [START_REF] Michael | An Introduction to Simulation[END_REF]. Their purpose is to evaluate various alternatives driven by high-level decision criteria to assess the managerial decisions relative to these criteria. There is a need to structure the concepts in order to create a basic model for value chains that could support performance criteria evaluation and thus, value assessment. Since the modelling should be used in the design phase, the industrial system is modelled by a Process / Product / Resources model coupled with a Function / Behaviour / Structure approach [START_REF] Labrousse | Proposition d'un modèle conceptuel unifié pour la gestion dynamique des connaissances d'entreprise[END_REF]. This enables us to describe the product and a part of its relative life cycle phases. The proposed approach based on value chain modelling and simulation is used to support the manufacturing processes performance analysis. From our point of view, the value chain is a tool of analysis and evaluation that takes into account all the activities that create value. We model these chains by a breaking down of the industrial system into relevant activities regarding strategic, organisational and operational plans. The purpose is to understand the variation of performance with product characteristics and with possible manufacturing process choices (constrained by these characteristics). A simulation of the activities effects is carried out based on the modelling from the supplying of raw materials or parts to the delivery of the final products, as well as the manufacturing process plan. The decisions have an impact on the value chain. These decisions change the performance level of the enterprise. For the benefiting entities, specific evaluation criteria are defined. For instance, the information needed by the commercial engineer is relevant to global indicators such as manufacturing product cost and delivery time; the designer is much more interested in functions or cost of technical solutions; the manufacturing expert looks at manufacturing activity and process cost; the cost analyst must obtain analytical information. The proposal of a value chain model is given in order to create the support of the manufacturing performance modelling and evaluation of activities. The first choice of such a model is justified and explained with regards to the related research and to our industrial experience. The understanding of the value concept is a pre-requisite for using the value chain approach. As said previously, the appraisal of value is the result of a combination of several performance criteria: cost, quality, time and functional satisfaction.

Cost is a problematic value criterion. The relevance of its estimate is a critical point. For this reason, cost estimation is explained in a following section.

Model and simulation: Simulation tool for lean manufacturing

It is stated that the use of a discrete event simulation tool can permit to reduce costs and also to provide a training tool [START_REF] Lian | Analysing the effects of Lean manufacturing using a value stream mapping-based simulation generator[END_REF]. In this lean manufacturing context and in this intra-enterprise context, this tool can analyse the effects of the introduction of lean manufacturing improvements into a particular industrial context. The authors' proposal is based on a value chain mapping simulation generator. They analyse Value Stream Mapping constructs symbolised by standard icons used to describe only manufacturing processes. A new classification is highlighted, the Value Stream Mapping Paradigm (VSMP). Then, each icon (graphical representation) is linked to a specific module that models the function and behaviour of the atom (manufacturing process modelling entity). In an inter-enterprise context, Rabelo et al. propose a very original approach [START_REF] Rabelo | Value chain analysis using hybrid simulation and AHP[END_REF]. They combined discrete-event simulation with a dynamic system to model the service and manufacturing activities of the global supply chain of a multinational construction equipment corporation. Moreover, the AHP process is integrated to overcome the potential limitations inherent in simulation models and to take into account qualitative criteria in the decision making process of the managers. Discrete-event simulation is applied to model the manufacturing function and the operational level tasks.

Results analysis: AHP process

Few Multi-Criteria Decision Making (MCDM) approaches have been developed to deal with complex decision problems. The AHP [START_REF] Ayag | A hybrid approach to machine-tool selection through AHP and simulation[END_REF] [21], the Multiple Attribute Utility Theory (MAUT) [START_REF] Keeney | Decisions with Multiple Objectives. Preferences and Value Tradeoffs[END_REF] and Out-ranking Methods [START_REF] Roy | The European School of MCDA: Emergence, Basic Features and Current Works[END_REF] are wellknown and typical of multiple criteria decision making methods. The AHP method was developed by Saaty (1981) and it consists of a systematic approach based on breaking down the decision problem into a hierarchy of interrelated elements [START_REF] Ayag | A hybrid approach to machine-tool selection through AHP and simulation[END_REF]. This method enables us to take advantage of experts' experience and expertise. The formalization uses a tree of criteria that is established to compare several solutions. At each node of the tree, there is a decision matrix where the pair-wise comparison is entered. In our module, the results of the simulation provide the value of the selection attributes on which the comparison is based. These results are compared and their scores are evaluated in the scaling system. The expert needs to mark the priority system. In Table In the AHP process, the deficiency of a criterion can be compensated by the good performance of another criterion that characterizes an alternative. So the process gives a compromise between various alternatives among all their attributes. Then, this process is adapted to value evaluation. Specific performance indicators have been chosen to evaluate the value of each benefiting entity by using a modified AHP method. These values give the basis of the argument and the negotiation in order to make relevant decision on product functions.

The following section describes the method, the concepts and their structuring and the application tool which has been developed.

Tool Architecture

Method proposal and limitations

The proposed value evaluation integrates only measurable and observable data (i.e. tangible value, like production volume or scrap rate for quality evaluation) and not inaccurate and subjective aspects (such as perceptive or aesthetic values). Moreover, the scope of the proposed approach has been limited to the manufacturing phases of the product life-cycle. The first steps (pre-design, detailed design etc.) cannot be modelled and integrated since they cannot be evaluated by the expected value indicator measured in this work.

The proposed system is based on the evaluation of product modelling and of the performance criteria. All these processes are supported by an application tool (Figure 3). Product modelling combines the requirement of the functions and technical solutions specifications and the requirement of the value chain alternatives defined by activities and processes. The Value Engineering process provides the descriptions of the product and the performance evaluation process is based on these descriptions. This gives indicators for Value Engineering decisions process by using performance analysis and multi criteria analysis. In terms of the application tool (see Figure 4), the description of the product is registered in a database. The simulation module gives the results exploited by the analysis module (AHP analysis). This result supports the performance evaluation of each value chain. The AHP method was chosen for its previous applications for design functions and manufacturing processes selection and for its qualitative and quantitative attributes. Other MCDM methods can gives good results such as Choquet integrals.

Database

Concepts involved in the modelling

The goal is to evaluate the manufacturing system performance regarding the creation of value that defines the value chain. The concepts involved in this evaluation are as follows:

 Industrial system modelling: value chain, activities, resources, expert knowledge, industrial potential;  Product modelling: product, functions, components;  Performance criteria: value, cost, time, quality, reference (objectives), functional satisfaction;  Point of view (benefiting entities): shareholder, client, supplier, manufacturer, designer, etc. The product is manufactured using resources in a set of activities (the process). The process generates a sum of value (i.e. the value chain). Finally, this value chain makes the industrial potential operate in order to respond to the expected functionalities and the industrial potential limits the product. In fact, the production process that meets the product requirements varies depending on the industrial potential requested. The product must give solutions to the functions required by the functional specifications. These functions consume resources such as components (material resources) to realize the technical solutions (Figure 5). A Product/Process/Resource model is the base of modelling. The product is obtained by the use of a value chain that makes the industrial potential work in order to respond to the expected functionalities (Figure 6). Product The proposed solution that adopts value chain modelling enables the integration of all these concepts. The effectiveness of the industrial system gives indications of the resources needed and consumed in order to give performance results; the efficiency of the industrial system is measured by the ratio between the actual performance criterion and the estimated performance criterion. In this way, the activities (that consume resources) must be studied to evaluate the global resources consumption.

Value Chain

Structuring of the proposal

The proposal is based onthe adoption of a value chain simulation approach used to face a multi-criteria analysis for decision-making support in the design and manufacturing plan phase. The means aimed at helping the expert are divided into two groups:

-a method based on a value-chain approach that has four main steps: 1. formalisation of the value-added activities and value-chain solutions responding to required product function; 2. value chain modelling; 3. simulation that supports the evaluation of the various value chains and that gives the value of criteria; 4. analysis using the AHP process to draw a multi-criteria analysis including the judgment of the benefiting entities. -an integrated tool that enables: the modelling of the activities in a generic manner and there chaining to specify the product realization; the simulation process of each of these value chains in order to evaluate performance criteria. This tool also provides help in analysing these results with an application tool using procedural calculations based on the value of the decision criteria and taking into account the weight attached to these criteria by the benefiting entities. A database is required to manage and capitalise the enterprise information.

Formalism

The activity concept is the central concept that creates value by resource consumption. The organisation is broken down into various activities. Activity-Based Costing method is based on the principle of causality logic and is used to improve the analysis and evaluation of the activity cost. The activity is seen as the basic component of the value chain of the enterprise [START_REF] Porter | Competitive Advantage -creating and sustaining superior performance[END_REF]. Function/Behaviour/Structure model is used in the design phase for behavioural performance evaluation of product maintainability [START_REF] Mévellec | Whole Life Cycle Cost: a new approach[END_REF]. The contiguous concepts to the performance analysis are cost, quality, time and value. The concepts that support the modelling for the evaluation are: the benefiting entities, the enterprise object that are the resources, the activities, the functions, the technical solutions, the products, the value chains, the processes and the industrial potential.

Deployment

The deployment of such a tool is supported by a platform application based on a database, a discrete-event simulation tool and an exploitation tool.

User interface

The database user interface is shown in Figure 7.

Simulation module

In order to obtain an evaluation of the performance indicators, manufacturing and support activities chaining are defined according to specific decision criteria. A flow simulation tool is then used to estimate the performance criteria of the processes [START_REF] Miller | Introduction To Manufacturing Simulation[END_REF]. In this way, comparative studies can be conducted to evaluate distinct value chains that are capable of producing the same items or complex items (interface Figure 8). The data are extracted from a Microsoft Access© database in which all the activities of the enterprise are depicted by a certain number of attributes. The database also contains value chain definitions used for specific products (for instance, ballast for automotive lighting systems) and the amount of consumption of the activities cost drivers concerned by the value chain. An interface called Modelling Assistant has been developed to facilitate the modelling of the value chain in the software (Figure 8). In this way, comparative studies (scenarios) are carried out to evaluate distinct value chains that lead to the creation of a same product or system. A useful interface supplies the launching of a series of simulations on the same product with different models or contextual variables in order to generate an Excel file that structures the comparison of these scenarios. The simulation provides the performance results of the expected (ideal) process chain which are considered for the product manufacture. Each activity is modelled by the same model in the Arena 9.0© software (Figure 9). This simulation can be useful for several users. For example, the information needed by the marketing manager is relevant to global indicators such as manufacturing product cost and delay (delivery time); the designer is much more interested in functions or technical solution costs; the manufacturing expert looks at activity cost; the cost analyst must obtain analytical information. Hence, some of these points of view have been implemented to respond to specific user needs. In order to apply and validate the approach, a case study is performed in microelectronics to evaluate various possible manufacturing processes.

AHP module

Rabelo et al. couple the simulation system and the AHP approach to make relevant decisions on machine tool selection [START_REF] Rabelo | Value chain analysis using hybrid simulation and AHP[END_REF]. This demonstrates that a decision process can take advantage of simulation results. The steps of the AHP method developed are as follows (Figure 10):

Step 1: Structure the hierarchy (the tree) from the value components (cost, quality, time and function satisfaction) which reflect the objectives of the decision-maker viewpoint through the intermediate levels that characterize the attributes of the value component;

Step 2: Construct the matrix in which the results of each simulation are entered relatively to the criteria given in the lower level;

Step 3: Enter the results for each alternative;

Step 4: Construct the set of judgment matrices of each intermediate level; enter the judgments for one point of view;

Step 5: Construct the set of pair-wise comparison matrices for each of the lower levels (leaves of one branch);

Step 6: Analyse the AHP process result for each point of view.

An interface helps the user to score his preferences on performance criteria. The results are presented in a table on which the expert can base his argumentation.

Evaluation models

A final notation is defined to give an assessment of the product value relative to the performance criteria: cost, risk, product conformity, time, function satisfaction.

The evaluation function of performance indicator defines the relation between the objective (o) and the measure (m) [START_REF] Porter | Competitive Advantage -creating and sustaining superior performance[END_REF]. The authors address the declaration of the objective, its representation and its measure for performance analysis.

In our work, the comparison is relative. The objective is the value of the best criterion and then the evaluation function is based on the AHP scale.

For this analysis an activity-based approach is proposed. The goal is to report financial and non-financial information such as those put forward by Gunasekaran et al. [START_REF] Gunasekaran | Activity Based Costing In Small And Medium Enterprises[END_REF]. In their development, Activity-Based Costing provides the structuring support (the activity) to keep non-financial information such as defect rates (quality), throughput rates (effectiveness of the industrial process) and delivery time (delay/effectiveness).

1. Cost model One of the main elements of the modelling is the one that enables the application of the Activity-Based Costing method. This is based on the principle that activities consume resources that drive costs (Figure 11). Consequently, a value chain has to support the modelling of resources, activities, cost objects (product and process) and three kinds of drivers, and to model the relation between them. A cost driver is defined for each activity and thus the activity cost is expressed as the product of the consumption of this cost driver by the associated rate. The correlation method is used to determine the relevant cost driver of an activity [START_REF] Huber Swinarski | Les inducteurs de coût et l'approximation des coûts par produits: application dans le secteur hospitalier[END_REF]. Therefore, historical data must be exploited. The principles of resource consumption and cost drivers come from the ABC method and the Cost Entity approach. Product cost drivers are extracted and analyzed by screening the value drivers of each activity. In order to obtain performance indicators, manufacturing activities are defined by decision criteria. Mévellec advocates the use of non-conventional cost models for industrial contexts based on the activity principle ABC Activity-Based Costing [START_REF] Mévellec | Les systèmes de coûts -Objectifs, paramètres de conception et analyse comparée[END_REF].

i CI : cost of the product i i Mp : cost of the material and components incorporated in the product i i MOD : cost of the direct labour consumed by the product i j CI : cost of the driver of the analysis unit j (activity j) ij NbCI : consumption of the cost driver j by the product i Thus,

        n 1 j j ij i i i CI NbCI MOD Mp CI (1)
Prerequisites:

-Activity analysis with relative expenses for all the products manufactured during a fixed period -Cost drivers identification by activity -Establishment of the cost of the driver for a specific activity 2. Function satisfaction This criterion is relevant to the designer that establishes its assessment for each function and each solution. It reflects the satisfaction of the solution to respond to a specific functional requirement.

Delay model

The time evaluation is based on historical data since the calculation is time-rate based. For each activity, the process time to transform one entity that passes through the activity is estimated. Also, the time to process one entity is known immediately. The time cycle is calculated by the simulation tool. It gives an average time cycle for each product. For product value evaluation, the time criterion is defined by two measurements: cycle time per product and lead time.

Quality evaluation

Quality is defined by the level of the global defective rate that is represented by the number of defective products. Statistical defective rates are defined for quality controls. A quality criterion is thus estimated by the cost of defective products.

Value evaluation

In the design step and during Value Analysis, the expert works on product functions. These required functions are the mediator for the choice of technical solutions and their evaluation is needed to define the final product design. A raw interpretation of the norm of a product value definition is expressed as [START_REF] Yannou | Proposition de deux nouveaux outils d'aide à la décision en analyse de la valeur bases sur une définition opérationnelle de la valeur[END_REF]

with, I j : Importance of the function j for the product, S ji : Satisfaction of the solution j to respond to the function i , C j : Cost of the solution j .

In that case, solution costs are known; one of our goals is to estimate the solution cost to obtain more accurate evaluation results. The attributes of Value are Cost, Quality, Time and Function Satisfaction (see Figure 12). So Value is measured by a weighted method, the AHP process. Benefiting entities express their point of view of the importance of a criterion for the evaluation of the product value.

Case study

In this illustrative study, the industrial context is reduced to a manufacturing firm in microelectronics field. The set of activities (manufacturing, commercial, administrative, supplying) needs to be taken into account to simulate the global product value chain. These activities are not directly linked with the technical solutions of the product but it is necessary to achieve them in order to provide the customer with the product in best conditions. The microelectronic field studied for this value evaluation application is justified by the fact that many details and information on its manufacturing activities were available. The aim of this analysis is to check if our model would be able to support the analysis of decisions made during the design phase on manufacturing processes and to show their impacts on product value.

First step: management and capitalisation of the enterprise information in a specific database;

The analyzed product is a ballast used in automobile lighting system for power regulation in an electrical circuit. The major functions are linked with the electrical power, should be contained in the lighting system, in order to be connect to the lighting system and to be adaptable to the lighting system technology. These functions limit the choice of technical solutions, microelectronic components and printed board.

To build the cost model, the resource consumption process has been done to define the factors that drive activity cost: time, number of pins per component, number of components per system, batch size, etc.

Second step: modelling of the activities in a generic manner and there chaining to specify the product realization;

Globally the manufacturing process type is the assembly of microelectronic devices on a board where activities' chaining is linear except for the rework process.

Third step: simulation process of each of these value chains in order to evaluate performance criteria by a discrete event simulation system;

Case study includes a value analysis based on two action variables: the panel size and the scrap rate of in-circuit test activity with an aim to determine which arrangement of action variables give the best result for the enterprise activity. For the experiment, two factors are taken into account as decision criteria for the design and manufacturing planning steps: first the number of boards per panel (batch factor) and second the in-circuit test scrap rate. For each of them, two potential values are acceptable and finally, four alternatives are compared. In Table 2, results directly provided by the simulation module are tabulated under four scenarios. Notice that the delay time is evaluated relatively to the delivery time imposed by the client.

Fourth step: analysis using the AHP process to include the benefiting entities judgment.

A decision must be taken according to these types of results but decision is not obvious; a rough comparison of the results by performance criteria (cost, cycle time, quality indicator) gives a basic classification in Table 3.

Table 3 -Basic classification

Due to various positions of the scenarios according to the performance indicator chosen, the decision must be a compromise. This compromise can be finding by integrating the benefiting entities point of view through AHP. The AHP process treats the simulation results presents in These last results demonstrate that: 1) scenario 1 is the best regarding preferences of designer and manufacturer;

2) scenario 2 is the best regarding preferences of client and scenario 1 is at the second place; 3) from the shareholders point of view, scenario 4 is the best since cost is the primordial criterion.

Globally, scenario 1 is the one to be chosen in order to best satisfy all the benefiting entities. The results of scenario 1 can be then analysed to ensure that unit cost is higher but the client shall be served in time.

The purpose of this case analysis is to provide comparison indicators to take a decision on a dimensional parameter and on the choice of a manufacturing resource. This kind of approach can be applied to decision support on technical choices. From the same functional definition, depending to the enterprise technologies possibilities and limits, the optimal product-process solution can be evaluated, taking into account all the benefiting entities point of view. Technical solutions and choices can be then adjusted to strategically axes.

Conclusion

The improvement expected is to enhance the performance evaluation process of a product and its manufacturing process. A value-based approach is used to define manufacturing process chain modelling and is applied to support its performance evaluation. The evaluation methodology is assisted by our specific simulation tool proposal: VCS. This tool and methodology is based on a modelling of the manufacturing enterprise system through the concepts and their relations. This entity relation model structures the information system.

Value chain modelling gives an appropriate support to performance evaluation. This kind of modelling is understood by all the actors. The basic principle of activity links (or is linked to) all the modelling elements. Moreover, each participant, in the decision process, can express his/her point of view which is taken into account in the analysis process. The first experiment shows that the use of VCS provides consistent results.

Our final purpose would be to simulate value chains to generate alternative chains coupled with optimization methods to find the best solutions. We limit our work to measurable values and to manufacturing steps. Then other aspects could be investigated to consider less observable information in order to tackle the whole product life cycle and non-tangible value aspects. A new study covering logistic processes and/or support activities must be carried out to assess the relevance of our approach. Moreover, complementary research is being done to integrate risk factors in our model. As a perspective, integration of resource competencies can be done in order to measure the impact of resource choice in the value system [START_REF] Bennour | Using competencies in performance estimation: From the activity to the process[END_REF]. Also, others value metrics can be addressed [START_REF] Otto | A multi-criteria assessment tool for screening preliminary product platform concepts[END_REF].

Figure 1 .

 1 Figure 1. Performances that impact value and their interactions with benefiting entities.

Figure 2 .

 2 Figure 2. The three views of a product

Figure 3 .

 3 Figure 3. Value Chains Simulator Architecture

Figure 4 .

 4 Figure 4. Integration of the simulation and the AHP process in the decision process.

Figure 5 .

 5 Figure 5. Choice process of value chains alternatives.

Figure 6 .

 6 Figure 6. Structuring of the concepts for industrial system modelling.

Figure 7 .

 7 Figure 7. VCS database user interfaceExperts have to record all the information relating to the order, the definition of the product and the definition of the potential value chains. This database is the basis for the creation of the activity modules needed in the simulation tool. The described value chain structures the activity sequence. The simulation provides the performance results of the expected (ideal) process chain that are considered for the manufacture of a product. This simulation can be used by incorporating several points of view. Some of these views are implemented to respond to specific user needs relative to process choice: what activities, what sequence of activities, what resources, etc.

Figure 8 .

 8 Figure 8. Simulation user interface (developed in Arena 9.0).The application used to implement our modelling is a discrete-event flow simulation software, Arena 9.0©. The main notion is based on the entity concept. The entity is what follows the flow and what is transformed along this flow[START_REF] Miller | Introduction To Manufacturing Simulation[END_REF]. The modelling of the activity concept calls upon modelling blocks such as Submodel, Process, Decide, Hold, Assign, ReadWrite etc.

Figure 9 .

 9 Figure 9. Arena 9.0 modules to model the activity construct.

Figure 9

 9 Figure9is an illustration of the model implemented in Arena 9.0© to simulate an Activity. The model represents the basic element called "activity". Each activity is modelled by a similar sequence of blocks that models the resource consumption law and the performance estimate. The data are extracted from a Microsoft Access© database in which all the activities of the enterprise are depicted by a certain number of attributes. The database also contains value chain definitions used for specific products (for instance, ballast for automotive lighting systems) and the amount of consumption of the activities cost drivers concerned by the value chain. An interface called Modelling Assistant has been developed to facilitate the modelling of the value chain in the software (Figure8). In this way, comparative studies (scenarios) are carried out to evaluate distinct value chains that lead to the creation of a same product or system. A useful interface supplies the launching of a series of simulations on the same product

Figure 10 .

 10 Figure 10. AHP evaluation process.

Figure 11 .

 11 Figure 11. Process of resource consumption by cost object, according to ABC method from[START_REF] Huber Swinarski | Les inducteurs de coût et l'approximation des coûts par produits: application dans le secteur hospitalier[END_REF].

Figure 13 .

 13 Figure 13. The simulation modelling for the evaluation of the ballast is based on a sequence of activities.

Figure 13

 13 Figure 13 represents a part of the value chain modelling in Arena for the simulation of the ballast.

Concrete set of ACTIVITIES defining a VALUE CHAIN Characterised by manufacturing system PERFORMANCE.

	Abstract set of	Concrete set of
	FUNCTIONS	SOLUTIONS
	defined in term of	defined in dimension,
	PERFORMANCES	shapes, materials and
	to achieve	forming an
		ARTEFACT
	Characterised by a	Characterised by a
	purchased,	purchased, utilisation
	utilisation,	COST, QUALITY,
	estimation VALUE.	DELAY.
	Functions/Value	Artefact/Criteria View
	View

Table 1 .

 1 1, the nine-scale comparative points are explained. AHP scale.

	1	Equal importance for both elements compared
	3	Element A is moderately important compared with Element B
	5	Element A is strongly important compared with Element B
	7	Element A has demonstrated importance compared with Element B
	9	Element A is extremely important compared with Element B

  This definition does not explicitly give the measurement process. For that reason, Yannou breaks down the value as the sum of the value of each function required by a product. The logical equation used to evaluate a function value is:

	Function	Value		function the on towards the produce satisfacti o solution t user on to the contributi of Cost Function	product	(3)
	For a function, the formula proposed by Yannou to define the function value for a
	specific technical solution is:
	I j : Importance of the function j for the product,
	S ji : Satisfaction of the solution j to respond to the function i ,
	C j : Cost of the solution j .
	Function contribution to user satisfaction =	j I 	S	ji
	Value	of	a	i Function	for	a	Solution	j 	I	j	Cj S 	ji
													:
	Value	of	a	Product		User	cost on towards Product satisfacti	the	product	(2)

Table 2 -

 2 Results given by the simulation of the product value chains

Table 4 -

 4 Table 3 to establish the results of the Table 4. AHP analysis results for each benefiting entity.

	Scenario	Designer	Manufacturer	Client	Shareholders
	Scenario 1	0,3912	0,3912	0,3794	0,1941
	Scenario 2	0,3894	0,3719	0,3854	0,2405
	Scenario 3	0,1015	0,1070	0,0981	0,2106
	Scenario 4	0,1174	0,1298	0,1371	0,3549

Translation of « jugement porté sur le produit par l'utilisateur sur la base de ses attentes ou de ses motivations. Plus spécialement, grandeur qui croît lorsque la satisfaction de l'utilisateur augmente ou que la dépense afférente au produit diminue. »