Chérif Aidara
email: c.aidara3345@zig.univ.sn

Moussa Bala

Serigne Biaye

Khalifa Diagne

Amadou Gaye
email: kgaye@univ-zig.sn

Coulibaly

Ahmed Tidiane Chérif

Aidara

Bala Moussa Biaye

Serigne Diagne

Khali- Fa Gaye

Amadou Coulibaly
email: amadou.coulibaly@insa-strasbourg.frsdiagne

Towards a Conceptual Design and Semantic Modeling Approach for Innovative Modular Products

Keywords: Inventive Design, Conceptual Design, Semantic Modeling, Modularity, Innovation

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

From the Russian acronym, TRIZ is a theoretical approach for inventive problem solving. Indeed, several works have been done in this direction, from the ontology proposal of the main notions of the concepts associated with the acquisition of knowledge [START_REF] Zanni-Merk | An ontological basis pour computer aided innovation[END_REF], to the formal definition of the contradiction and its potential manipulations in the inventive conception according to the basic principles of TRIZ [START_REF] Rousselot | Towards a formal definition of contradiction in inventive design[END_REF], the application of TRIZ to real industry problems [START_REF] Sheu | TRIZ-based trimming for process-machine improvements: Slit-Valve Innovative Redesign[END_REF] or the proposal for an integrated framework for systems to support individual creativity [START_REF] Wang | A literature review on individual creativity support systems[END_REF]. However, setting up a product requires consideration of several factors that will reflect the product. The complexity of the design lies in the fact that the products must be innovative and more efficient, that is to say more reliable, resistant, easy to assemble and disassemble, etc. It is in this sense that it is said in [START_REF] Menye | Validation of maintainability and design availability of a multi-component system[END_REF] that the designer must opt for product solutions that are simple to manufacture, ergonomic, very reliable, safe, easy to maintain and have an overall cost over the entire life cycle that is attractive to the consumer.

Being more and more complex, the products imply an interaction between several actors [START_REF] Diagne | Towards a conceptual design for mechatronic product's family development[END_REF], [START_REF] Casner | An approach to design and optimization of mechatronic systems based on multidisciplinary optimization and based on the feedback of experiences[END_REF]. Managing all the factors in the design process requires taking into account the environment and evaluating the performance of the future product or system [START_REF] Casner | Design of mechatronic systems by topological optimization[END_REF], [START_REF] Coulibaly | Maintainability Assessment at Early Design Internship Using Advanced CAD Systems[END_REF]. Thus, we intend to conduct a scientific and technical study to propose innovative products through a semantic modeling method and the identification of innovative modular solutions concepts by the resolution of technical contradictions. In this article, we propose to implement a semantic design approach for modularity and to identify innovation paths, in terms of both modularity and other product-related behaviors.

Start of the art

Following a literature review [START_REF] Motte | A Review of Fundamentals of Systematic Engineering Design Process Models[END_REF], [START_REF] Gausemeier | Nes guideline VDI 2206 -A flexible procedure method for the design of mechatronic systems[END_REF], [START_REF] Ziemniak | Mechatronics Engineering on the Example of an Innovative Production Vehicle[END_REF], it should be said that design methodologies have been continuously improved in recent years. Thus it has been proposed in [START_REF] Rahman | Systematic Mechatronic Design of a Piezo-Electric Brack[END_REF], the combination of the systemic model and the V-model. On the one hand, they focus on design and modeling, and on the other hand, they help to improve the evaluation of products in the early stages of their design [START_REF] Motte | A Review of Fundamentals of Systematic Engineering Design Process Models[END_REF]. Others have done a lot of research on design and methodologies [START_REF] Partto | Explaining Failures in Innovative Throught Processes in Engineering Design[END_REF], [START_REF] Chandrasegaran | The evolution chalanges and future knowledge representation in product design systems[END_REF]. While some have highlighted the steps of the conceptual design of mechatronic products [START_REF] Diagne | Towards a conceptual design for mechatronic products family development[END_REF]. In addition, there are several product design models among which, V design, spiral design, unified methods, agile methods, etc., [START_REF] Serigne | Conceptual semantic modeling for the behavioral performance engineering of complex products[END_REF]. Being the result of the decomposition of a product or system, the modularity makes it possible to solve several aspects of the product manufacture. Various works have been carried out in this sense [START_REF] Baldwin | Modularity in the Design of Complex Engineering Systems[END_REF], [START_REF]Contributions to the Design of Configurable Products in Advanced CAD Systems[END_REF] and approaches have been developed to make the systems modular and to simplify their links. Let us note generative grammars for generating and classifying the components or the use of the concept of "holons" to model the connections between structure and functions [START_REF]Contributions to the Design of Configurable Products in Advanced CAD Systems[END_REF]. Boothroyd et. al [START_REF] Boothroyd | Product Design for Manufacture and Assembly[END_REF] propose a systematic methodology to assess the influence of Geometry, Material, Tolerance effects on Assembly. In this methodology, the emphasis was to relate Product design, Assembly operations, and Assembly Method to the single decision factor: the cost. Hitachi Assembly Evaluation Method is another approach based on very similar principals. The basic idea is the reduction of cost of a product through simplification of its design by :

-Reducing of number of components -Ensuring that parts are easy to assemble -Increasing the use of standardized parts across entire product range -Designing with widest possible tolerances -Material selection must consider manufacturing also, not just function. Boothroyd-Dewhurst method includes:

-Preliminary (rough) design -Selection of Assembly Method (manual, robotic, or high-speed automated) -Design/Redesign of product for selected method -The selection of assembly method must be done at an early stage in the product design process. In most of these previous works, the authors highlight a structural and functional modularity of products or systems in order to obtain at a final product with a more flexible configuration. These different approaches were not intended to offer innovative and modular products in terms of new technological concepts. In this article we propose an approach of designing innovative and modular products based on the TRIZ method. Thus our design method allows to identify the problems, the solutions as well as the technical contradictions that result. Our goal is to propose an innovative product design approach, modular, easily repairable and adaptable to different user profiles, we will in this article work on a semantic modeling method, in order to obtain modular solutions concepts innovative.

An approach for modularity Assessment

Starting from the conceptual design, the work in [START_REF] Diagne | Towards a conceptual design for mechatronic product's family development[END_REF] defined a field of eligible solutions (instances, SS (Space of Solution), classes of solutions) respecting certain requirements and constraints as illustrated in the Fig. 1. We will consider a solution instance in the area of eligible solutions to highlight the modular aspect. Moreover, to find the connected components, we will first build the adjacency matrix denoted M of the graph G and the matrix of the transitive closure of the same graph noted M *.

X1 X2 X3 X4 X1 0 1 1 0 X2 1 0 0 1 M = X3 1 0 0 0 X4 0 1 0 0
The adjacency matrix is obtained by noting:  1 if there is a relationship between two components  0 otherwise Thus, we will be able to determine the matrix of the transitive closure denoted M *.

M * = I + M 1 + M 2 + M 3 (1) I: matrix unit (put on the main diagonal of the 1 and the rest we put 0) M n-1 : adjacency matrix (n-1) we stop at 3, since n = 4 for this graph So :

X1 X2 X3 X4 X1 1 1 1 0 X2 1 1 0 1 M* = X3 1 0 1 0 X4 0 1 0 1
From the matrix of the transitive closure, we deduce the following connected components: {X1}, {X2}, {X3} and {X4} because the lines are not alike, which is why each line is considered as a component related in itself. To calculate the modularity of an instance, we will use analogy based on the cyclomatic number to determine the complexity of a program (computer) by counting the number of paths. The cyclomatic number is defined by the following equation:

M = E -N + 2P (2)
with M: cyclomatic complexity E: the number of edges of the graph N: the number of nodes of the graph P: the number of connected nodes of the graph

In software engineering, a simple code with a low cyclomatic number is theoretically considered as easier to read, test and maintain. It is in this same logic that fits our approach to define a product modularity with the same equation:

M = E -N + 2P (2)
where M: Modularity of the product E: the number of links between components of the product N: the number of components of the product P: the number of connected components of the product For that we will take into account two graphs representing two instances of products, then will determine the most modular between these 2 products. If we consider a first graph G (C, L) in Fig. 3, the connected components of the graph G are: {X1}, {X2}, {X3}.

Then, consider a second graph G' = (C', L') whose connected components: {X1, X2, X3}.  For the graph G we will have:

• M = 3 -3 + 2*3 M = 6  For the graph G' we will have:

• M' = 3 -3 + 2*1 M' = 2
M' < M so the solution M' is theoretically more modular

In the next section, we discuss about the influence of the different parameters involved in the modularity expression.

Discussion and contradictions in design for modularity

To improve the modularity it is necessary that the value of M is the smallest possible. When is the best modularity for a product reached? If we take the example above, it should be said that M' of the graph G' is more modular since it is less than M of the graph G. However if we come to find that the value of M is equal to 1 (M = 1), but we will have a monobloc product. On the other hand, if we increase the value of M by acting on its parameters (e.g.: M = N), we will arrive at a product that is too fragmented. In addition to what should we tend to have an optimum value of M in terms of modularity and it is in this sense that we will talk about the notion of contradiction in the inventive design. The optimal value of M would be a solution that would optimize the modularity. So would be a way to look for a solution of a product not too compact or too exploded. Thus we can act on the variables of the equation (M = E -N + 2P) to make a product more modular. We have three variables in our equation. Indeed, let's try to enlarge or minimize the modularity according to these parameters.  For P: the number of related components o P is minimized if P = 1 o P is maximized if P = N therefore P: the number of connected components is the largest possible, o We will have:

M = E -N + 2N so M = E + N  For E: the number of links:
o E is minimized if E = N-1 (allowing to traverse all the components of the product) o E is maximized if E >= N (E greater than or equal to N making it possible to cycle or even multi-instance links).  For P = N and E = N o Then M = 2N (aberration)  For N: the number of components:

o N must be different from zero (0), N ≠ 0 o If N = 1 then M = 0 -1 + 2 (1) => M = 1 5

Innovative design for modularity

Modularity remains one of the best ways to facilitate the maintainability of products once designed. The evaluation of the modularity in the above shows us that for n product we will be able to choose the most modular and therefore the most easily maintainable. However, the modularity must respect a certain threshold in order to reach a compromise with other behavior of the product. Because a product can be modular and less reliable or less ergonomic. It is in the sense that the resolution of technical contradictions takes into account all the functional characteristics and the behavioral performances. In our case, it would be important to act on the modularity by playing on the parameters of the equation of modularity. Therefore, we will propose a case of illustration allowing implementing our solution of research of optimum modularity. For this, we will take the example of an electric wheelchair that allows people with reduced mobility to move.

Illustration example

As an example, we will consider two wheelchairs (Instance I1 and Instance I2). Indeed these are manufactured with a mechatronic device to roll without help. The first chair Fig. 4. (Instance I1) comprises eight (8) components as illustrated in Fig. 5. By applying the procedure proposed above to the instance I1 of Fig. 4, we will evaluate its modular. Thus for the chair we will take into account the components shown in Fig. 5.

Fig. 5. Wheelchair components

If we consider the first instance I1 in Fig. 4. the matrix of the following transitive closure is obtained: M2 < M1 so the solution I2 is more modular than I1 Thus, we clearly see that Instance (I2, M2) is more modular than Instance (I1, M1), with fewer components because some are grouped into modules (3. Arm + support) and facilitate assembly and disassembly, hence the interest of maintainability.

Conclusions and perspectives

The manufacture of innovative and modular products is a. good way to facilitate its maintainability, including the replacement of a defective part. Indeed, evaluating their behavior in the early stages of their design could help to improve the implementation of innovative products. Thus, these products will meet several requirements of the user before manufacture. This work will facilitate the use, maneuverability and especially the repair of future products. In our future work, we plan to carry out an ergonomic study of the products in order to alleviate the difficulty of use related to certain tools of work but also to solve the technical contradictions to get innovating products with respect to different behaviors.

Fig. 1 .

 1 Fig. 1. SS and DES

Fig. 2 .

 2 Fig. 2. Representation of a Product Instance by a Graph G (C, L)

Fig. 3 .

 3 Fig. 3. Graphs (G (C, L) -G' (C', L'))

Fig. 4 .

 4 Fig. 4. Wheelchair (Instance I1)

  of I1 are: {1}, {2}, {3.a}, {3.b}, {4}, {5}, {6}, {7}, {8}. Let's not forget that for instance I1 component 3 (3. Arm + support) is multiplied by two(3.a, 3.b). However, we will consider a second instance I2 having almost the same characteristics as the first but with a detail, the grouping of the component 3 of Fig.5. (3. Arm + support) in one module. In this sense, we will act on the parameters of equation M to arrive at a more modular product.

Fig. 6 . 1  9 M1 = 35 

 61935 Fig. 6. Wheelchair (Instance I2)


Jean Renaud
email: jean.renaud@insa-strasbourg.fr

Rémy Houssin

Mickaël Gardoni

Mhamed Nour

Multi-users of a Product: Emergence of Contradictions

Keywords: Usage, User experience, Multi-user, Contradiction, Design, Functional analysis

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

When a customer wishes to acquire a commercial product, tool or technology system, they choose it according to its ultimate intrinsic use, in a well-defined situation at a specific time. The customer does not care if the product may be perceived or used by other users at other times for different purposes or objectives. These products, which have several users, will be called 'multi-user products'. The product is designed and manufactured generally for the main function, a purpose of the product for a need of the user. The designer designs a product through experience, out of habit, assuming how the product can be used or handled. The version or model of the product is generally related to the designer's personality, character, choice criteria and design service. -

In general, the designer does not specify how the user wishes to use the product [START_REF] Sun | Integration of user behaviour and product behaviour during the de sign phase: Software for behavioural design approach[END_REF], [START_REF] Sun | Integrating user information into design process to solve contradictions in product usage[END_REF], [START_REF] Sun | Towards a use information integration framework in the early product design phase function-task-behaviour[END_REF] or how the user should use the product due to the critical lack of tools and methods of design help available for designers. The designer works by habit, persuaded that the product he designs will be suitable for the customer's use, that the product will seduce him in priority over its use.

Generally, the designer puts himself in the user's place, he considers that his design criteria are the best, without worrying about the real needs to integrate the conditions of use of the product [START_REF] Fadier | L'intégration des facteurs humains à la conception, Phoebus, la revue de la sûreté[END_REF], [START_REF] Darses | How do designers represent to themselves the users' needs ?[END_REF]. It is not uncommon to find that the product is designed to be used manipulated, or stowed by a single user considered to be the primary user without worrying about potential users at different times in the product life cycle. The designer proposes, generally a user's manual of the product [START_REF] Renaud | Aide à la rédaction d'une notice d'utilisation à partir de l'analyse fonctionnelle comportementale prenant en compte l'usage[END_REF]. In this manual, only one user is mentioned. Many authors have focused on the anthropo-centered approach. This approach concerns the improvement of the design from an ergonomic point of view [START_REF] Das | Industrial workstation design: A systematic ergonomics approach[END_REF]. It is used for custom design because of its high cost and because it is only feasible for large projects and luxury goods [START_REF] Carballeda | La contribution des ergonomes à l'analyse et à la transformation de l'organisation du travail : l'exemple d'une intervention dans une industrie de process continu[END_REF][9][10] [11][12][13] [START_REF] Obradovich | Users as designers: how people cope with poor HCI design in computer-based medical devices[END_REF]. The techno-centric approach, the technical system is at the center of the design problem. Designers have only the product standards at their disposal. These standards have the obligation of results without specifying how the designer could obtain these results.

Some authors have been interested in the needs of the client. Anthony W. Ulwick [15][16] proposes the method of Outcome Driven Innovation (ODI). It focuses on the customer's "primary" need rather than the solution they use. The method focuses on the "what" and not on the "how". Other methods, such as the QFD [START_REF] Chang | Quality function deployment (QFD) processes in an integrated quality information system[END_REF] or the "Voice of the customer" are commonly used to identify the needs of the client.

It is known that the product can have multiple users. Users have different needs that can lead to contradictions of solutions to be solved early in the design phase. In the next section, we present the notion of the user experience. In section 3, an application on a baby seat, a product that requires several users, is presented to illustrate our point.

User experience

Analyzing a need means translating the product into "Customer specifications" or "User specifications". The "client user" reasons in solutions rather than needs [START_REF] Marchat | Gestion de projets par étapes, analyse des besoins[END_REF]. When there are multiple users, we talk about multi-users. The functional analysis approach is a response to the search of the needs of the users.

In recent years, some authors have focused on the user experience, called "user experience (UX)". This refers to the experience of a person using a particular product, system or service. It's about making a product, a system easy to use, understandable (immediately, ergonomic, logical ...) by integrating the user experience into product design based on ergonomics and human sciences, the goal of the user experience is to increase satisfaction with the use of functions by continuously improving the form, content and accessibility of the product [19][20][21] [START_REF] Maguire | Socio-technical systems and interaction design-21st century relevance[END_REF].

UX stands out as a major marketing asset and becomes at the heart of business strategies. The user comes back to the center of all marketing concerns. The UX therefore contributes to increase the act of purchase. So it is proven that user satisfaction is as much related to the product / service marketing ability as to the perception of the brand by the user, the term refers to essential notions of communication, design and marketing. It is no longer based solely on ergonomic criteria.

In the continuity of the user's explanation, design studies must be able to identify or register different users according to the life cycle of the product. It is not uncommon to find that the product in its operation phase has several users. Designers must transform customer requirements into product performance. It should be noted that the requirements of the customer are not the same as those of the user (there is the first level of contradiction). Subsequently, the designer will have to prioritize these users, to better understand the functionality of the product according to the type of user. The functional analysis makes it possible to answer it. -Usually, the customer is the person who buys the product and uses it. It is never specified all of all users and how they are used. The customer takes note of the overall product function, cost, efficiency ..., while the end user wants to pay more attention to product reliability, security, usability and operability. This means that designers must transform not only the customer's requirements, but also the user's requirements in product performance.

Case study: baby car seat

In our study, we chose to study the baby car seat. This product can potentially be unsafe. It presents several users at different level of use, not always identified. The baby seat must fulfill two important conditions, be well installed on the car seat and be adapted to the weight and size of the child in case of an accident. Hence, a deadline for using the seat and the safety standards to be respected. Depending on the size of the child or baby, the seat is either facing backwards or forwards. It is mandatory up to 10 years in Canada for example. There are five groups of seats depending on the size and weight of the seat. There are "adaptable" seats depending on the size of the child and his weight.

The design of the seat and its use introduce many criteria or parameters very varied and very complementary. The client (parents) is not the privileged user, it is the baby but who cannot express himself. The parents are also users because they are the ones who install and fix the seat in the car. Hence the interest of taking into account the different types of users. In the following paragraphs, we detail the different phases of the product life cycle, in use.

---

Product-study-background

The usage context must be explained by the use that will guide the product design result. The context must be exhaustive (ease of assembly, use, maintenance, respect of security ...).

Identification of the study phases of the product cycle

A product does not only have one main function to be used. It has several, depending on where we are in the phase of the product life cycle. Thus, it is proposed to detail the different stages of the product life cycle, such as:

-Study and design of the baby seat (seat, headrest, harness, the shape of the secure shell, mandatory standards...), The seat can be in one piece or detachable -Unpacking the product and reading the instructions for assembly and use of the product, -Installing the baby seat and checking it, -Installing the baby seat on the car seat and securing the seat, -Safe installation of the baby in the seat and its safety, -Unlock the baby's seat belt.

-Remove the baby safely from the seat, -Removing the baby seat from the car seat, -Storing the baby seat in a storage place, -Drafting and validation of the instructions for use. Among all these steps, it is necessary to make apparent the various functions of the users which can be different.

We use functional analysis to better understand the product's functionalities according to the type of user and the phase of the product's lifecycle. Before identifying the product's functionalities according to the type of user, it is necessary to position the different users of the product's parts of use. Figure 2 describes the product and the different users.

In this example, we have identified four types of child seat uses for children less than 2 years of age and their function in using this seat (Table 1). The person storing the child seat in the storage area

In the case of Figure 2, the aim is to identify the different links between the different types of users and the different main parts of the product. This link graph makes it possible to visualize the functional roles of the users. These functions can also be specified as: safety, fixing or tying, storage, confidence building... The links can also be contact, removable, manual...

Fig. 2. Diagram of links between product parts and users

From figure 2, we report on table 2, the types of users, the types of malfunction, the types of operation or tasks to be performed by the user and the type of phase of the product lifecycle. Each user thinks differently according to their missions or objectives in relation to the product. We propose for each phase of the life cycle of the product, to carry out the corresponding functional analyzes. Each functional analysis corresponds to a different user. These functional analyzes are a first reflection. These four functional analyzes focused on the missions of the different users according to the phases of the product life cycle.

Discussion

In our example, we can note the presence of four types of users who have four different profiles, four different objectives or missions.

The UX1 user has the concern to make solid the fixing of the child seat. That the fastening system (straps) of the car is efficient, practical and secure. The shape of the seat must marry at best the seat of the car. The shape of the seat for the baby is not the priority for the user UX1. The knowledge of the user UX1 is rather in the technicality and the practical sense. It usually refers to the manufacturer's instructions. Security is at stake in this phase.-

The UX2 user gives priority to the child, his well-being and his safety on the seat, but also the cleanliness of the seat and the ease of its maintenance. The technical knowledge of the UX2 is mainly practical sense, not requiring technical knowledge. Generally the UX2 user looks little or not the product designer's manual. The user UX2 combines the safety and comfort of the child in its implementation on the child seat.

The UX3 user, the baby, must be able to move without the risk of being detached or touching safety components of the car. The child's tether straps must resist the child's mobility force in the seat.

The UX4 user must be able to detach the child seat quickly and efficiently. It does not refer to the manual. His only concern is to untie it, carry it and put it away. The ergonomic shape of the seat must allow the UX4 user to carry it effectively. Reading the missions of the four users, we can observe that their level of technical knowledge, practical, safety, well-being... is totally different from one user to another and taking their needs into account could cause contradictions:

UX 4 needs the seat to be as light as possible, but for UX2 it must be as strong as possible and for UX2 the seat must be as soft as possible.

UX1 is interested in baby seat attachment systems in the car, UX2 is interested in baby seat attachment systems. Then there are two TRIZ point of view fastening systems.

There should only be one system.

The UX1 needs the seat to have the simplest form, on the other hand, the UX3 needs the seat to take at most the shape of car seat on course (more complex).

UX2 needs the seat surface to be made of easy to clean material (plastic for example), whereas UX3 needs the seat surface to be made of cotton, for example, because it is more pleasant.

In Annex 1, we summarize all functions, criteria, levels and contradictions by different phases

Conclusion

In this research work, we wanted to show that products designed today no longer have a single user, but several who may have contradictory needs. Hence the importance of identifying the different users and their missions or functions, in order to better design the product under the angle of the "experienced user".

In our case study, we have broken down the life cycle of product use in stages. Then, we used the functional analysis to list all the features by user type and step. Each function is characterized according to different levels of importance and flexibility.

Then we identify the criteria that conflict or complement each other. Unlike other studies where we seek to better define the functionality of the product according to a single client, here we seek to better define the functionality of each user according to the study of use of the product. All for the purpose of better designing the products.-This work of identification of contradiction between the criteria or users makes it possible to propose recommendations for the designer of the product in order to eliminate or minimize the consequences of these contradictions on all the uses of the products.

Annex 1

Fct.

FP4

The straps on the bench must adapt in an efficient and solid way to the child seat

FP14

The child seat must be able to be stored easily in its place of storage by the user UX4 C25 C26

Storage volume Accessibility

Same as the seat Handy

Fig. 1 .

 1 Fig.1. Car seat model for baby or child < 12 years old

3 FP9FC10

 3 The user, UX3, child can move freely in the child seat fixed on the bench of the car The child is held in the seat safely, attached and free of movement

Table 1 .

 1 The different users of the child seat

	N°	Type of use	UX j	Description or function of the user
	1	Parent (1)	UX 1	The person who puts the child seat on the seat.
	2	Parent (2)	UX 2	The person who gets the child to cooperate
	3	Child < 2 years	UX 3	Securing the child in his seat
	4	Parent (3)*	UX 4

Table 2 .

 2 Comparison of potential malfunctions between the three users

	Type	User	Types of operations	Type of	Phase of the
	UX i		or tasks	malfunctions	product cycle
	UX 1	Father	Attaching the car seat	Strap passage, locking	Functional
		or	to the seat of the car	system, seat position,	Analysis 1
		Mother		secure fastening,	Phase 1 (fixing
					the seat on the
					bench)
	UX 2	Father	Position of the child	Baby attachment	Functional
		or	in the child seat and	system, strength of	Analysis 2
		Mother	attachment of the seat	the attachment,	Phase 2 (Child
			belt	optimal position	positioning and
					fixation)
	UX 3	Child <	Be properly installed	Freedom of	Functional
		2 years	and able to move	movement in	Analysis 3
		old	freely	complete safety	Phase 3 (Child in
					safe position)
	UX 4	Father	Easy to handle and	Weight too heavy,	Functional
		or	store	fastening systems too	Analysis 4 Phase
		Mother		loose, volume	4 (Storage)


Hicham Chibane

Sébastien Dubois

Roland De Guio

Automatic Extraction and Ranking of Systems of Contradictions Out of a Design of Experiments

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.


Sebastien Dubois
email: sebastien.dubois@insa-strasbourg.fr

Roland De Guio

Aurélien Brouillon

Laetitia Angelo

A feedback on an industrial application of the FORMAT methodology

Keywords: Analysis of Initial Situation, FORMAT method, long-term prospective

One of the main issues of industrial product evolution planning is the current state of the art, related to the product itself, its market competitors, and also the available resources that can become parts of the future product. Moreover, to plan evolution, it is required to well understand how the performance of the product will be evaluated, on a future market, and surely it won't be the same performance criteria as today habits. This issue has been tackled and defined as Analysis of Initial Situation. A combination of TRIZ based approaches and Design of Experiments has been defined to clarify the problem to be solved. But all these approaches are dedicated to analyze today product and to choose the prior problem to be considered, but these methods have not been defined to analyze long-term evolution planning of products. For this long-term planning, a method, FORMAT, has been developed and proposed. The purpose of this article is to describe the application of this methodology on an industrial case, to plan the evolution of kitchen hoods. The article will state the different methods to perform the Analysis of Initial Situation but also the benefits and the difficulties of FORMAT application.

Introduction

Solving problems has always been one of the main concerns for industrial engineers. And lot of methods to help them in this task have been developed, with different aims and ends [START_REF] Carmichael | Problem Solving for Engineers[END_REF][START_REF] Horvath | Modeling and Problem Solving Techniques for Engineers[END_REF]. One can recognize various methods to:

 help in describing the design process at an operational level [START_REF] Pahl | Engineering design: a systematic approach[END_REF] or at a cognitive level [START_REF] Masson | Design Theory: Methods and Organization for Innovation[END_REF],  help in defining the problems to be tackled during design process in regard of the generated value [START_REF] Afd | Analyse de la Valeur[END_REF],  help in generating design concepts either algorithmically [START_REF] Haupt | Practical Genetic Algorithms with CD-ROM[END_REF], either with humans [START_REF] Altshuller | Creativity as an Exact Science[END_REF],  help in evaluating design concepts [START_REF] Suh | Axiomatic Design: Advances and Applications[END_REF].

Among these different proposals, TRIZ has been widely recognized as increasing the inventive level of proposed concepts and as enabling engineers to propose new solutions' concepts. From the end of 50's till the end of 80's, TRIZ has been developed to help engineers to be more creative. This development has lead to the proposal of a general approach based on 5 steps:

1. The analysis of initial situation, which objective is to elicit the specific conditions under which problem appears, the inherent contradictions and also to choose the root cause of problem, the prior contradiction to be solved.The definition of the problem. The objective of this step is to clarify the limits of the problem, i.e. the borders in which the solution can be found and thus to specify if the problem is a maxi or a mini problem. 2. The definition of the ideal solution aims to separate lower-level solution from higher-level ones by intensifying the constraints for solution concept synthesis. 3. The definition of the physical solution enables to select relevant information for concept solution synthesis, i.e. to identify inventive mechanisms enabling to satisfy the conditions of the ideal solution. 4. The definition of the technical solution reinforces the concept of the physical solution by defining which resources can be used and how these resources are used to fit the physical solution.

In border of this 4-steps general approach, several versions of ARIZ [START_REF] Altshuller | The Innovation Algorithm: TRIZ, systematic innovation and technical creativity[END_REF] have been defined, but the last one defined by Altshuller [START_REF] Altshuller | Algorithm of Inventive Problem Solving (ARIZ-85C)[END_REF] does not fit with the first step. The analysis of initial situation has not been developed anymore and is still one key stake in problem solving process. The matter was the recognition that the proposed method for this step was not as developed as the methods inherent to the other steps [START_REF] Zlotin | [END_REF].

In [START_REF] Burgard | Sequential experimentation to perform the Analysis of Initial Situation[END_REF], the authors proposed to perform the Analysis of Initial Situation with sequential experimentation. This enabled to create a continuum between methods dedicated for optimization and the ones developed for invention. This continuum is still under development today [START_REF] Lin | Optimization Methods for Inventive Design[END_REF][START_REF] Dubois | From simulation to contradictions, different ways to formulate innovation directions[END_REF].

In parallel the consideration of Technical Systems' Laws of Evolution has also been considered as a way to state the level of maturity of a considered problem and thus enable the elicitation of the key problems to solve [START_REF] Kucharavy | Long-Run Forecasting of Emerging Technologies with Logistic Models and Growth of Knowledge[END_REF]. An European Project has been developed in this direction and lead to the proposal of the FORMAT method [START_REF] Consortium | FORMAT -the handbook[END_REF]. Thus an "innovative forecasting methodology" has been proposed "to support strategic decisions in industrial R&D activities, by managing the multi-disciplinary complexity of current systems and by anticipating the future characteristics of products and processes" 1 .

In this paper, the authors will first introduce three known ways to perform the Analysis of Initial Situation: ARIZ-85A, the use of Design of Experiments, and the FORMAT method, then the general required steps will be elicited. The application of the FORMAT method will then be illustrated. And at last some discussion and conclusion will be proposed.

Analysis of Initial Situation

In this part, three ways to perform the Analysis of Initial Situation will be presented: the one developed by G. Altshuller, ARIZ-85A, the proposal of the authors about the way to build a continuum between optimization methods and inventive ones, and, finally, the FORMAT project.

ARIZ-85A

Throughout the development of a method to tackle inventive problems, several versions of ARIZ (the Algorithm for Inventive Problem Solving) have been proposed as the results of seminars in which several problems have been tackled by groups of people, to identify the strong and weak points of the proposed method. By the end of the 50's the patterns of technological evolution have been included in the method.

During the 60's the versions of ARIZ pointed out the importance of the problem statement, as ARIZ-64 introduced the section "Clarifying and verifying the problem statement", which has been decomposed in two different parts in 1968. In the 80's, starting with ARIZ-82, the method has been targeted specifically to solve difficult non-typical problems. And in 1985, "the former first chapter is no longer part of the algorithm as it is not rigorous enough compared to the other chapters" [START_REF] Kucharavy | Theory and practice of ARIZ, in Materials for Master of Innovative Design[END_REF]. Thus the last version of this first chapter, that can be used to perform the Analysis of Initial Situation is ARIZ-85A [START_REF] Altshuller | Analysis of the Initial Situation[END_REF]. ARIZ-85A is a set of 9 steps:

1. Definition of the final goal, which aims at defining the global objective of the problem, from technical and economical point of view 2. Investigate the bypass approach, to state how the problem occurs at different levels of the system 3. Choice of the considered problem, at which level it will be tackled 4. Determination of the required characteristics, to state the list of Evaluation Parameters 5. Intensification of the required characteristics, the intensification being one the TRIZ mean to propose more robust concept solution. 6. Definition of the constraints 7. Application of the inventive standards, to state if the problem is inventive or not 8. Analysis of the patents, also a mean to clarify if an existing solution, even coming from another domain can be applied. But this step raises the question of the non application of the laws of technical systems evolution. 9. Application of the STC operator, another mean to apply intensification.

In these 9 steps, one can formulate 4 main questions to be tackled: What is the scope of the problem? What is the system to consider? What is the strategic context? What is the problem? These 4 main questions, which will be used to represent the complementary aspects of the different methods, and the related questions of ARIZ-85A are represented on figure 1.

Design of experiments based methods

In [START_REF] Burgard | Sequential experimentation to perform the Analysis of Initial Situation[END_REF] the authors proposed a 6 steps approach to merge optimization and invention methods. The use of sequential experimentation, a well-known sequence of different mathematical tools, aimed at fulfilling Analysis of Initial Situation and to identify the contradiction to consider for problem resolution. The 6 steps are:

1. Principal component and factor analysis to identify how many action parameters (AP) influence the evaluation parameters (EP) 2. Fractional design of experiments to identify which AP influence each EP 3. Factorial design of experiments with center-points to identify the direction which will allow to move towards an optimum 4. Response surface method to locate the optimum solution 5. Generalized System of Contradictions if the optimum does not satisfy the objective 6. OTSM-TRIZ techniques to solve the contradiction These steps mainly participate to the questions of the system to be considered and to the analysis of the problem, as illustrated on figure 2.

Fig. 2. Design of Experiments based method contributions on the 4 questions

It is thus already obvious that the methods based on optimization approaches could bring benefits to the Analysis of Initial Situation, and that they way the Design of Experiments should be defined would also benefits from TRIZ-based methods. But one aspect that is still missing with these two methods is the consideration of the maturity level of the considered system and more generally the evolution of this system.

Format

FORMAT method focuses on making long-term prospective, and thus is mainly built on the crossed use of logistic and substitution curves [START_REF] Kucharavy | Long-Run Forecasting of Emerging Technologies with Logistic Models and Growth of Knowledge[END_REF] and qualitative elements to interpret them. These project FORMAT aimed at answering "the need to introduce structured methods and tools capable to support strategic decisions in industrial R&D activities, by managing the multi-disciplinary complexity of current systems and by anticipating the future characteristics of products and processes."2

Transfer results of study to decision makers

Transfer the forecasting results to beneficiaries / decision makers All these steps also aim at identifying the general trends of evolution of technical systems, and thus, at formulating which problems are to be considered for resolution. Then, the method also enables to perform an Analysis of Initial Situation, and the benefits of each stage in regard of the 4 generic questions is presented in figure 3.

Comparison of the presented methods

The three presented methods, ARIZ-85A, Design of Experiments based method to hierarchize contradictions, and FORMAT, could be recognized at three ways to per-form the Analysis of Initial Situation. Each being more complete, or at least different, in the way to answer to each of the 4 mains questions. It seems thus quite obvious that complementary aspects could be built based on these differences. In this article, the authors will present their application of the FORMAT method, on a case study, and will show the benefits they recognize in this application, but also, the difficulty they met.

3

Case study

Presentation of the case

The presented article is the application of the FORMAT method on an industrial case to investigate the long-term evolution of the kitchen hoods. The objective was to make a state-of-art of the current technologies, their advantages, their limitations, and to understand which of these technologies will be the leading technology in the future.

To tackle this question, a group composed of 3 experts from enterprise and 2 TRIZ experts from laboratory was formed. The company aimed at creating differentiation on the market by proposing high quality products. The initial purpose was to apply FORMAT method as is, but, quickly, it has been quite obvious that complementary tools were necessary to answer the questions, due to lack of formalization in the FORMAT method, and also because we could not obtain required data to perform relevant analysis of logistic curves. The available information the group had at the beginning of the project was:

 Historical data of the enterprise products from 1966 to 2015  Functional analysis of kitchen hood  Safety and operating standards  Partial studies of filtration technologies and of the elements to be filtered Thus, there were some missing information that have been capitalized throughout the project:

 The clarification of the enterprise strategy  The market tendencies and the customers' needs  The differentiation criterions for the customer  The alternative technologies, not reduced to those available on the market  Current and objective performances  Technical constraints  Pareto front and trade-off of current existing systems

Based on this missing information and trying to fulfill the first steps of FORMAT method, some ascertainments were made and some limitations of the direct applicability of the method were pointed out.

Ascertainments about FORMAT method

Performing the FOR step of the method tackled some strategic questions, which enabled to clarify the previously cited missing information and to well clarify what the project aimed at. It also clarified the necessity to better investigate current systems, the market and technological competitors and a clear formulation of what performance means for the system. In fact, some elements of the M step were required to well answer FOR step.

The starting point of the method of the M step is to define the Main Useful Function of the observed system, thus the limits of this system and also its operating principle. One first conclusion about the analysis of the system was that the experts of the company were mainly oriented by the Evaluation Parameters defined in border of the standards. And one of the conclusion of the group was that the standards are proposed to evaluate existing systems, in regard of safety purposes, but not appropriate to evaluate future, not yet existing systems, in regard of performances.

In the method it is proposed to complete a multi-screen analysis of the system and to use the Element-Name-Value (ENV) model to formulate the criteria to measure the performances and expenses [START_REF] Chandia | A Method for Forecasting Design Requirements Based on Experts' Knowledge and Logistic Growth Model[END_REF]. Only analyzing system through the TRIZ based models gives a partial understanding of this system, mainly functional. It was thus decided to complete this TRIZ system description with an analysis of the system also based on activities, using an IDEF0 3 and SADT 4 analysis, as proposed in the method.

It was also decided to have a global understanding of who is the customer, and this customer has been analyzed throughout a systemic perspective, then 4 kinds of potential future users were interviewed to point out their main concerns:  The demanding customers for their high demand on functionalities  The private users, as they will be the end-users of the product  The kitchen furniture dealers to understand the constraints linked with the assembly constraints, transportability, and so on of the future system  The professionals, renowned chefs for their high understanding of the current systems' limits and main functional advantages of future systems Moreover, to better state the current limits of known working principles, the analysis of the market has been completed by kitchen hoods modeling and simulation. At last, the use of logistic curves in order to plan substitution of technologies has led the group to the difficulty in having robust long-term data of past systems enabling to fit with accuracy the curves. It has then been difficult to have reliable conclusions on the analysis of logistic curves.

Realized process and main results

The figure 4 details the process as it has finally been conducted, which starts at the top left of the figure. After a first realization of the stage "FOR" we could have access to a further marketing study and were able to question the participants of the whole value chain of the kitchen hoods. The first pass provided a first family of assessment criteria that were adjusted in the second study. Let us notice that it is not a linear process; lot of backtrackings were necessary to validate answers to initial questions, and the benefits of adding model and simulation to poor descriptions provided by the state of the art have been well appreciated to demonstrate the potential and limits of current working principles.

The available data about the sales on the market enabled to propose logistic curves, but not on technological information, only on the kind of settlement of the system. The analysis was performed with data from the world sale volume from 2004 to 2014. The collected data were then transposed, with dedicated tools (Logistic Substitution Model II 5 and Loglet Lab 6 ), into logistic curves, which are a graphical representation of the innovations' diffusion in the technologies' life cycle [START_REF] Kucharavy | Logistic Substitution Model and Technological Forecasting[END_REF]. However, this analysis, presented on figure 5, remains superficial, as the amount of analyzed data is not sufficient enough to guarantee the robustness of the results. Nevertheless the global qualitative substitution that can be seen on figure 5 confirms the feeling of the marketing about the market. But as we explained at the beginning of this paragraph, these data represents settlement of the system substitution and not technology substitution. Another interesting result, based on the systemic description of the customers is the elicitation of more relevant set of Evaluation Parameters (EP). The customers study and other participants to the value chain pointed out five families of parameters but the standards only provided three measurable EP shared among two of them. The final set was composed of seventeen EP, as illustrated on figure 6. Some parameters like acoustical comfort have been added due to the evolution noise analysis in other domains. Others like uptake measurable parameters have been defined by the working group. The new set of assessment parameters allowed a better understanding of the state of the art and TRL of potential solutions related to PE families. We did not seek, through lack of resources, to carry out a quantitative analysis of the evolution, i.e. logistic curves of the new EPs. Nevertheless we did evaluate the actual performance of each component either with data from literature or from simulations and experiments. We have also established, through this technical and scientific state of the art, to what extent some performance tradeoffs observed on current systems can be potentially removed. It was proposed to address the remaining performance dilemma with TRIZ. The motivation for using TRIZ was to complete the state of the art and see whether it was possible with available knowledge to overcome the remaining contradiction or at least get a better understanding of the problem. The decision how to go further was done by the stake holders based on the produced knowledge. Discussion and perspectives

The study performed with this company aimed at identifying future technological trends but also at determining the innovation projects that will be launched in the next coming years. The results are very satisfying as several prior problems have been formulated and gave birth to development projects that are still in progress. Moreover it helped in building a better understanding of the considered system, its evolution and the main performance criterion that have to be considered. The proposed method also gave new development opportunities for the company, as, for example, the systematization of modelization and simulation in development projects. The system modeling and later validating allowed getting information about system that was not available before. This new knowledge may change the way to address the competition.

Concerning the FORMAT method, some of the conclusions that have been pointed out during the application are:

 All the proposed steps in FORMAT are necessary to be performed, but it cannot be complied as a linear process, and some answers of further steps are required to well assess the answers of first steps, especially between FOR and M steps. Perhaps should FOR, M, A, T sequence be considered as a functional model, and consistencies checking between the sequences be added.  TRIZ based models have to be completed with other systems' description models to have a more global understanding of the system  One of the main benefits, but also one of the main difficulties in FORMAT method is the use of logistic and substitution curves. It gives very interesting information about the remaining years to improve an existing system, and the necessity to develop new generation one. But the collect of reliable data is really hard, and these data are not necessarily available. There is still research work to do in order to pro-vide companies with this data. One way to address the question could be to provide companies with ways to collect relevant information progressively so that it is available when needed.

If a new project on long-term prospective of a system will have to be considered, the first concern will then be to consider which data are available and how reliable they are. Then the more performant system will be considered, modelled, and optimization of this system will be looked for by simulation. The use of Design of Experiments to guide the simulation will also enable to point out the limits of the considered working principle and to identify both innovation directions, and contradictions to be solved, as presented in [START_REF] Lin | Optimization Methods for Inventive Design[END_REF][START_REF] Dubois | From simulation to contradictions, different ways to formulate innovation directions[END_REF][START_REF] Rasovska | Using dominance relation to identify relevant generalized technical contradictions in innovative design[END_REF]. Depending on the specific situation the latest approach have advantage but it might also have some limitations. Indeed the use of modeling and simulation can fasten the knowledge acquisition when a good model is available quickly and when the system is too complex for human to catch relations between the design parameters and evaluation parameters or when experiments on real system are impossible, too much time consuming or too expensive. But, when the model accuracy is not known, the process to build an accurate and credible model can limit the efficiency of the method. Independently from the resource consuming of this process the efficacy of the use of simulation might be improved by finding a way to evaluate beforehand the relevant degrees of accuracy and credibility that will allow the stake holders to use the results when deciding.

Fig. 1 .

 1 Fig. 1. ARIZ-85A process

Fig. 3 .

 3 Fig. 3. Questions and duties of FORMAT methods' contributions on the 4 questions

Fig. 4 .Fig. 5 .

 45 Fig. 4. Detailed process of Analysis of Initial Situation in the case study

Fig. 6 .

 6 Fig. 6. Initial and final set of Evaluation Parameters

4

 4

Table 1 .

 1 Format stages[START_REF] Consortium | FORMAT -the handbook[END_REF]

	Stage	Main Functions	Questions and duties
	FOR: Diagnose ques-	Prepare and make decisions	WHY do we need to know
	tions and plan project	about forecasting project	the future?
		Define boundaries/resources of	WHAT do we need to know
		forecasting project	about the future?
			HOW do we plan to learn
			about future?
	M: Define the system	Review existing knowledge	WHAT the System To Fore-
	for forecast and study	about system	cast (STF) is for? (WHY we
	contexts		need the STF?)
			WHICH systems allow to get
			the same results?
			HOW to measure the perfor-

http://www.format-project.eu/about

http://www.format-project.eu/about

http://www.idef.com

http://www.lsis.org/dea/M6optionD/Exp-GL41-SADT.pdf


Slim Rabih

Houssin Rémy

Coulibaly Amadou

Convergence and contradiction between Lean and Industry 4.0 for inventive design of smart production systems

Keywords: Industry 4.0, Lean, design process, production systems, contradiction. 2

Due to the globalization, we have witnessed the emergence of new challenges, driven by a sharp drop in industrial production costs and a great ability to produce high quality products at competitive prices. To cope with these growing challenges, Industry 4.0 were launched in 2011. This concept represents the digitalization of the industry, integrating resources into production: people, machines and processes. These networked resources can interact with each other. Industry 4.0 optimizes the production system. The plant becomes agile, with flexible production methods, reconfigurable tools, and more efficient. Considering the Lean requirements during the early stages of production system design could facilitate the development of Industry 4.0. This paper mainly focuses on both Lean and Industry 4.0 by considering their requirements during the early stages of production system design. In this study, we analyze the convergence and contradictions for the implementation of these two concepts.

Introduction

Industry 4.0 will be able to produce a customized product more quickly, while limiting costs, better use of resources and with new solutions to reduce the difficulty of work and increase human ability to operate there [START_REF] Bidet-Mayer | L'industrie du futur : une compétition mondiale[END_REF].

Recent studies on both the Lean and Industry 4.0 concepts have proven the possibility of linking these two approaches [START_REF] Dombrowski | Interdependencies of Industrie 4.0 & Lean Production Systems: A Use Cases Analysis[END_REF][START_REF] Kolberg | Lean Automation enabled by Industry 4.0 Technologies[END_REF][START_REF] Mrugalska | Towards Lean Production in Industry 4.0[END_REF][START_REF] Wagner | Industry 4.0 Impacts on Lean Production Systems[END_REF]. In addition, concrete examples of the combination of these two concepts have been mentioned in literatures.

Our study makes an analysis of convergences and contradictions for the implementation of these two concepts.

After the introduction, we present a state of the art on industry 4.0 and Lean, their definitions, their evolution, their principles and their appropriate technologies. The third section deals with previous studies related to their combination and the next section outlines our study. In the two last sections we discuss our proposition and make a conclusion.

3. The emergence of analytic and intelligence capabilities and new forms of human-machine interaction such as tactile interfaces and augmented reality systems. 4. The improvements in the transfer of digital instructions to the physical world.

The current work allows to emerge several concepts and need to define the relations between them. (see Fig. 1). In the literature, the main components of Industry 4.0 are Cyber-Physical system (CPS), Internet of Things (IoT), Internet of Services (IoS) and Smart factory.

Through the Cyber-Physical systems (CPS), Industry 4.0 can react in an autonomous way, it is self-adaptable and agile. The use of such systems in production is then often described as Cyber Physical Production Systems (CPPS). The Internet of Things (IoT) is an information network of physical objects (sensors, machines, ...) that allows devices to communicate and interact both and connect using standard technologies with centralized controllers [START_REF] Kiel | The influence of the Industrial Internet of Things on business models of established manufacturing companies -A business level perspective[END_REF]. The (IoT) involves the integration of both (CPS), which connect the physical and the virtual worlds, and the (IoT) into industrial processes. It also decentralizes analytics and decision making, enabling real-time responses [START_REF] Rüßmann | Industry 4.0[END_REF].

In this environment, (CPS)s communicate and cooperate with each other and with humans in real-time and via the Internet of Services (IoS), that allows' service providers to offer their services via the Internet.

Based on (CPS) and (IoT), Smart Factory can be defined as a factory where (CPS) communicate over the (IoT) and assist users and machines in the execution of their tasks [START_REF] Hermann | Working Paper, A Literature Review[END_REF].

Hoffmann [START_REF] Hofmann | Industry 4.0 and the current status as well as future prospects on logistics[END_REF] illustrates that the smart factory is defined as follows:

 Products and services are flexibly connected via the internet.  The digital connectivity enables an automated and self-optimized production of services.  The value networks are decentralized. Therefore, a Smart Factory contains technologies that provide the optimum methods and techniques for production system [START_REF] Jeschke | Industrial Internet of Things Cybermanufacturing Systems[END_REF] (see Fig. 2). Fig. 2. Key terms in advanced manufacturing. [START_REF] Jeschke | Industrial Internet of Things Cybermanufacturing Systems[END_REF] In order to make this Smart Factory, industry 4.0 builds on six main principles. Moreover, these design principles make the factory autonomous, flexible and adaptable to a changing of the production system (see Fig. 3). Interoperability is a characteristic of a production system in which its components are able to exchange information among themselves [START_REF] Liao | The Role of Interoperability in The Fourth Industrial Revolution Era[END_REF]. Smart Factory is virtualized in order to be able to simulate and follow in 3D products and production process. Decisions are decentralized via cyber-physical systems that can make decisions autonomous and in real time. Recent advances in service-oriented computing and cloud computing including computational power, storage, and networking offer exciting opportunities for solving complex problems [START_REF] Bessis | Service-Oriented System Engineering[END_REF]. The Modularity lies in the plant's ability to adapt quickly to a changing demand, which increases the flexibility of production system.

Modularity

In this environment, smart Factories need new technologies to enhance productive processes and to aim for value-adds to the concept of Industry 4.0. To this moment, there are nine appropriate technologies and considering as the pillars of this concept. These technologies are integrated along these three dimensions:

1. The technical system: processes and tools.

2. The management system: organization, IT, performance management.

3. The people system: capabilities and behaviors.

To support the data needs in these smart factories, a Big data is necessary to integrate several layers and components for the collection, storage, processing, analysis and distribution of data [START_REF] Santos | A Big Data system supporting Bosch Braga Industry 4.0 strategy[END_REF]. With the use of cloud computing that enables to store Big Data, company will be more agile and flexible [START_REF] Ooi | Cloud computing in manufacturing: The next industrial revolution in Malaysia?[END_REF]. Virtual models of manufactured products are essential to bridge the gap between design and manufacturing [START_REF] Schleich | Shaping the digital twin for design and production engineering[END_REF], enabling simulation, testing and optimization in a virtual environment. Integration and agility in industrial automation need to improve to connect the company with the outside (suppliers and customers). Augmented Reality (AR) allows to give a virtual image in real time on all the information necessary for the operator, to help him perform these tasks in an optimal way. Thanks to mobiles and tablets, 3D glasses, other (AR) devices…, flow information, production monitoring, breakdowns, etc… are in the hands of operators in real time; enabling them to improve decision making, self-maintenance and quality.

(AR) connects operators with each other and with their managers, quality teams, maintenance teams ...The future of manufacturing with 3D printing, which allows real objects to be created using a computer-aided design (CAD) tool are in the integration automation of the production processes with Additive Manufacturing technologies [START_REF] Yeong | Quality Control, and Measurement Sciences in 3D printing and additive manufacturing[END_REF]. In order to enhance factory automation, sensors and software capabilities will make the new manufacturing equipment smarter [START_REF] Witkowski | Internet of Things, Big Data, Industry 4.0 -Innovative Solutions in Logistics and Supply Chains Management[END_REF].

In order to increase productivity, the new generation of robots are becoming autonomous, flexible and cooperative. they work side by side with humans do it for him the complex tasks, or by automating repetitive tasks in an ergonomic, user-friendly environment, while simultaneously ensuring the highest possible levels of security and safety.

Lean Production System

The Toyota Production System (TPS) is a concept developed by Toyota engineers to eliminate waste of the entire industrial process from design to distribution. They have identified a seven forms of waste (Muda) [START_REF] Takeda | The synchronized Production System: Going beyond Just-in-Time through Kaizen[END_REF]: by overproduction, waiting time, transport. Processing, storage, through movement and producing bad parts. Lean's goal is customer satisfaction in terms of cost, quality and delivery time; which requires the optimization of production systems in its three dimensions (technical, management and users). In order to achieve the goals laid down, lean is based on five principles (see Fig. 4). The first principle is to link the value principle to the customer. In principle, it means that everything that brings value to the customer is essential, but anything else is waste and must be eliminated. The second principle introduces the notion of flow. This notion is crucial because it brings the visualization of the process, this makes it possible to visualize flows of materials and information and will facilitate waste identifications.

Value Stream Mapping (VSM) provides an overview of activities, inputs, outputs and connections to detect wastes and plan their elimination. The elimination of waste allows for a continuous flow in a process where the operations are linked without disturbances and without interruptions. The Pull system is the technique of planning the production according to the customer's request, which makes it possible to reduce the stock. It is also about offering the customer a product whose added value is aligned according to their needs. The fifth and final fundamental principle is perfection, by examining and improving processes continuously. It is a process of permanent progress to bring new ideas and eliminate new wastes.

Fig. 4. Lean Principles

Lean relies on tools developed over several decades and proven methods. Lean means the application of various lean tools or techniques including value stream mapping (VSM), 5S, visual management, for cost reduction. The famous Lean house contains Lean tools (see Fig. 5) [START_REF] Dombrowski | Ganzheitliche Produktionssysteme Aktueller[END_REF]. The House contains (Jidoka) that is one of the two pillars of the Toyota Production System with Just in Time (JIT). (Jidoka) is introducing a culture of adapted systems that automatically detect deviations from normal operation. Thus, the necessary measures can be taken immediately to avoid the propagation of errors or machine breakdowns. The second pillar is Just in Time (JIT), it is the synchronization of the production process by using (Kanban), which are a signboard for the management of production via a pull system under a production with zero defects.

The main tools that are related to (Jidoka) are:  Man machine Separation which allows operator to work on several machines at the same time.  (Poka-yoke) which serves to improve the visibility of operators and limit these choices to perform a task in a way that the right choice is the only possible.  (Andon) to alert operators in case of breakdown. And the main tools that are related to Just in Time are:

 (Heijunka) is the smoothing of production.  (Takt) time which allows to rhythm the production according to customer's demand.  Pull flow according to customer demand which guarantees the variety of production.  (SMED) (Single-Minute Exchange of Dies) is to reduce the changeover times less than 10 minutes, which increases the flexibility of the system.

Lean Principles

Specify Value

Identify the Value Stream Flow Pull Perfection Fig. 5. Methods and tools proposed by Lean In addition, to involve users in an orderly and organized environment, tools such as standardization serve to ensure the stability of the entire process and to facilitate the work of operators and reduce complexity. (5S) serves to increase the quality in the work area, based on lean thinking, all that is useless is to eliminate. Visual management is used to display information about the current status of production. Total Productive Maintenance (TPM) is used to achieve the highest possible plant performance the availability of production equipment in order to minimize maintenance costs. The value stream serves to clarify and visualize the process including material flow and information flow for all the plant. In this way, the potential for improvement can be identified and the waste in the process can be reduced. Therefore, lean's idea serves to create an organized environment in which the process is optimized, tasks are simplified and people are involved in order to reduce waste, increase performance and satisfy customer. All these Lean tools could be integrated and taken into account from the early phases of the production systems design. This could lead to a more optimal production system from the beginning, which requires fewer improvements during the utilization. Dekogel in [START_REF] De Kogel | Development of Design Support Tool for New Lean Production Systems[END_REF] proposes a tool to integrate Lean philosophy into the design of new production systems. They consist of:

1. Describe the steps of the production systems design. 2. Illustrate the flow of different types of information during the design process. 3. Establish guidelines for taking Lean into account during design phase.

Convergence and contradiction between Lean & Industry 4.0

The elimination of waste requires new technologies. The combination of Lean and Industry 4.0 has already been mentioned in recent studies, and most of the Engineering Department of large international companies have developed a concrete solutions for a good implementation of Industry 4.0 in a manner consistent with principles and concepts of Lean on the improvement side of the plant through the integration of recent technology in the field of industry, and the consequent in terms of organization and the human machine interaction, in order to satisfy the requirements of customer and to enhance the performance. In this context, many questions may be raised:  Are Lean and I4.0 compatible?  Lean should it be combined with I4.0?  Is industry I4.0 a contradiction of lean?  Lean tools are they applicable in the concept of smart factory?  Will we go towards complete automation?  Where is the role of human in this environment? Dombrowski (2017) [START_REF] Dombrowski | Interdependencies of Industrie 4.0 & Lean Production Systems: A Use Cases Analysis[END_REF] tries to answer some of these questions by asking 260 German industry with the aim of making a detailed analysis of the interdependencies between Lean and Industry 4.0. His study shows that the application of modern information and communication technologies (ICT) in Lean Manufacturing, can improve the performance of production systems by obtaining more efficient production and logistics processes.

Many concrete examples of such convergence to link Lean tools with the Industry 4.0 technologies have been mentioned in literature. Via sensors that makes the product intelligent and connects operator to products and machines, operator can be alerted in case of failure or defects by detecting the anomaly (self-maintenance). In addition, an electronic Kanban can be added to product, it contains the necessary information for the production which allows to receive the requirements of customers or suppliers in real time [START_REF] Kolberg | Lean Automation enabled by Industry 4.0 Technologies[END_REF]. Accompanied by robots that works in an autonomous way next to human, add to that the technology of augmented reality, operator will be smart, he can obtain all the information on the process and product in real time. It is connected to the production systems and to the outside (customers, supplier). Andon systems are integrated to alert human in real time in case of malfunction. Even though the lean's philosophy that put people at the center of these objectives is preserved. The risk is that human missions may be limited to surveillance. The smart machine is reconfigurable, flexible, able to work autonomously via the (RFID) technology, it can warn operator in real time in case of failure (Poka-yoke), it is possible also to reduce the changeover times (set up times) less than 10 minutes by using the Plug'n Produce technology, which increases the flexibility and reduce the unexpected stop [START_REF] Mrugalska | Towards Lean Production in Industry 4.0[END_REF]. Wagner (2017) [START_REF] Wagner | Industry 4.0 Impacts on Lean Production Systems[END_REF] propose to develop a cyber-physic system, that replace the Kanban cards by a vertical integrated of machine to machine communication to link the all the flow information from the supplier between manufacturing order, machines to the customer, that guarantee the just in time delivery.

To more understand the convergence from examples in literatures, the table below is used to link lean tools combined with industry 4.0 technologies to obtain Smart concepts for successful implementation of both concepts in the of the new optimal production systems design. The ladder shows the possibility of linking these two concepts and that these can support each other (see Fig. 6). The connections were made according to concrete examples mentioned in the literature. Smart Planner

Lean

1 1 1 1 1 1 Smart Machine 1 1 1 1 Smart Operator 1 1 1 1 1 1 1 1 1
Fig. 6. Linking Lean to Industry 4.0

The table (see Fig. 7) illustrates the limits of the implementation of industry 4.0 and lean concepts, pointing out the convergences and contradictions according to different criteria, based on the three dimensions: human, process and organization. It derived from the views and examples mentioned in the literature, which was explicitly stated above in the state of the art. The code "+" presents the effect of the implementation of Lean and Industry 4.0 on production systems according to criteria that influence the aspect of the production system and their ability to respond market requirements and the integration of recent technologies. "+" shows that there can be a low positive effect. "++" shows that there can be a high effect. "+++" shows that there can be the highest effect.

I4.0 Lean

Convergence Contradiction

Technical System

Applicability +++ + I4.0 is applicable In production systems design, while Lean is a continuous improvement on existing system.

Productivity

+++ ++

The digital connectivity enables an automated and self-optimized production

Flexibility

Discussion

From examples mentioned in recent studies, lean and industry 4.0 can combine together to design an optimal and modern production system in a way of satisfaction of market requirements. The concept of Industry 4.0 covers all phases of system life cycle. In order to design more efficient production systems, it seeks to make smart not only the use of systems but also their logistics, their maintenance and their end of life. Nor would it be in contradiction with Lean principles in terms of customer satisfaction, reducing costs and eliminating waste with a view to designing an optimal production system. But the risk which we think they could be quite real nowadays engendered from making the mistake of full automation that limit the role of human and almost can be completely cancelled, which is one of the basic elements of Lean philosophy and which is all about it. In this paper, we analyze to what extent the two concept can be corresponded or clashed with each other within the process of design of production systems, following a several criteria listed above in the table. We see that I4.0 is applicable in production systems design, while Lean is a continuous improvement on existing system. In addition, integrating I4.0 in the early design stage allows to develop a flexible production system, which can produce a variety of products without the need for major changes, more agile that adapts quickly to a changing demand, reconfigurable who can adjust quickly to adapt to new products. In contrast, Lean don't have a significant impact in regards to these criteria. The complexity of system in I4.0 will be increased, accompanying by more automation that makes the system more autonomous. As we have previously stated, that's against the Lean principles which aims to simplify all tasks and procedures done by the system and its users. Lean is a set of methods and tools that can be applied on existing system, but I4.0 is a set of principles and objectives to consider in the design of new systems. Despite these some contradictions, the combination of the two concepts remains possible; That analyses should enable us to how to overcome these contradictions in order to integrate the two concepts Lean and Industry 4.0 in the early design stage of production system.

Conclusion and perspectives

Studying the convergences and contradictions of the implementation of Lean concepts and Industry 4.0 in production systems may strengthens the premise of considering the Lean and Industry 4.0 requirements during the early stages of production system design, which could facilitate the development of Industry 4.0. What is suggested was an overall approach aimed at a synergy between the two concepts Lean and Industry 4.0. And that could prove for future work to consider their requirements during the early stages of production system design.

Fig. 1 .

 1 Fig. 1. Concepts of Industry 4.0. [11]

Fig. 3 .

 3 Fig. 3. Industry 4.0 Design Principles

Fig. 7 .

 7 Fig. 7. Convergence and contradiction between Lean and Industry 4.0