Salah Zouggar

Marc Zolghadri

Xin Zhang

Philippe Girard

Network compatibility blocs as basis of partner's selection

Keywords: Network design, product architecture, partner selection

The aim of this paper is to contribute to partner's network design beyond the common partner's selection process usually made in the literature. The originality of our approach lies in compatibility concept that comes to consolidate the ordinary approach of partner's selection. We suggest the use of product architecture to extract its related network of partners that would be analyzed with paying attention not only to the efficiency of each required partner within the network, but also to its compatibility with other actors. The gBOMO (generalised Bill Of Materials and Operations) concept becomes significant tool that we intensively use in order to detect the imperatives of realization phase of manufactured product. We will develop exploratory ideas about the network compatibility blocs. These ideas allow a better understanding of partner's compatibility requirements within network.

Introduction

The partners' selection was extensively studied in the scientific literature since several decades see for instance [START_REF] Huang | Comprehensive and configurable metrics for supplier selection[END_REF]. However, still remains the need for those firms, called Focal Company (FC), which initiate a product development project to improve their understanding of partnership and the relevancy of their partner selection procedures.

The partner's selection process for any company requires much more attention and know how than what was required in the past [START_REF] Petersen | Supplier integration into new product development: coordinating product, process and supply chain design[END_REF]. Indeed, the complexity of products and their components is growing everyday. This induces companies to be careful about their partners [START_REF] Junkkari | Higher product complexity and shorter development time continuous challenge to design and test environment[END_REF]. Nevertheless, partner's selection is highly risky because of their weaknesses could be propagated to the whole network.

The architecture of the partners' network is not independent from the product that it aims to manufacture. Starting from this observation, we claim that simultaneous analysis of product architecture and network architecture is of upmost importance for the selection process of relevant partners. This could be done by considering partners potential exchanges and/or dependencies.

The paper is organized as follows. In section 2 a brief state-of-the-art reviews some existing works in the field of product and network modularity. In section 3, we evoke the product architecture and explore its links with the associated partners' network architecture. Section 4 treats the partners' selection process. In section 5, network compatibility blocs approach is developed. Finally, some conclusions and perspectives end the paper.

Related works

The architecture of any product can be more or less modular. In recent years, several author's publications see benefits in adopting modular product design [START_REF] Gershenson | Product modularity: definitions and benefits[END_REF], [START_REF] Kusiak | Integrated product and process design: a modularity perspective[END_REF], [START_REF] Ro | Modularity as a strategy for supply chain coordination: the case of US auto[END_REF]. In fact, in modular product architecture, components are interchangeable, autonomous, loosely coupled, and individually upgradeable thanks to standardized interfaces [START_REF] Fine | Clockspeed: Winning Industry Control in the Age of Temporary Advantage[END_REF]. In this case, mapping between functions and components becomes easier. For a modular product, two major factors are: dependence of components and their interfaces. The dependence refers to the way that one component needs some inputs coming form another while the interface defines precisely how this exchange is performed. Obviously, exchanges between components could be and are, quite frequently bilateral.

Focusing on the network, the modularity is measured through proximity concept defined by [START_REF] Fine | Clockspeed: Winning Industry Control in the Age of Temporary Advantage[END_REF] along the following dimensions: 1-geographic proximity that can be measured by physical distance; 2-organizational proximity which deals with ownership, managerial control and interpersonal and inter-team interdependencies, 3cultural proximity that captures commonality of language, business mores, ethical standards and laws, among other things, and finally, 4-electronic proximity that can be captured by e-mail, EDI, intranets, video conferencing, etc. These measures are of great indicators for network modularity. But, here authors consider hat they are not enough to characterise exactly the network. The complementarity and dependence of partners are considered to be of high importance.

Network architecture extracted from product architecture

To clearly define these concepts, we use the gBOMO, generalised Bill Of Materials and Operations, developed in [START_REF] Zolghadri | Modelling mutual dependencies between products architecture and network of partners[END_REF], adapted from Jiao BOMO [START_REF] Jiao | Generic Bill-of-Materials-and-Operations for high variety production management[END_REF]. This representation gathers jointly technical data of BOO (Bill Of Operations) and BOM (Bill Of Materials) of a considered product. gBOMO looks for perceiving the connection between the focal company and a subset of its major partners (cf. Fig 1).

Partner's selection in the network

Our analysis on partner's selection process is based on information got from gBOMO, we quote the number of partners, the adjacent partners and the kind of relationship (subcontractor, supplier, service providers….) gBOMO allows a visual understanding of executive operations of manufacturing product during realization phase to perceive those partners that are directly involved in the same workflow. Therefore, hereafter we talk about dyadic relationships to start gradually our analysis on neighbor partners and extend the analysis to effectively and really linked partners in the network by highlighting hidden links.

Dyadic relationship

Each couple of adjacent partners detected within the gBOMO might be dependent on their work contributions. As shown in figure 2, we consider a couple of actors S1 and R. They operate in a sequential way with objective of adding value to the same workflow; the upstream or downstream position is relative to the workflow direction. Downstream Partner R has expectations and requirements on the work provided by upstream partner S1. As the upstream partner S1 is expected to add value to the workflow, its contribution can be evaluated according to his ability to satisfy the requirements of R. Ideally, these requirements and expectations must be fulfilled by contributions of upstream partner S1 at 100%. If we consider the couple S1 and S2, they operate in parallel way, there is no direct dependency of their works, but the junction point K gathers their flows and generate a relative dependency between S1 and S2.

In the following points, we will focus on existing exchanges between partners during the realization phase, and suggest improvements to the selection process.

Partner's effectiveness

Different criteria and indicators for partner's evaluation have been suggested throughout the literature. According to mostly known library of indicators of Huang and Keskar in [START_REF] Huang | Comprehensive and configurable metrics for supplier selection[END_REF], we achieve to distinguish two evaluation dimensions of partners: 1. Product-related effectiveness: that we define as the technical capability of a partner to design or realize given product parts. 2. Process-related effectiveness: it reflects the organizational capability of the partner and its ability to perform activities following the network dynamics. The distinction made between the two suggested dimensions is that the partner process-related effectiveness underlines mainly the processing abilities of partners while the product-related effectiveness focuses on the technical aspects of the partner's abilities. In [START_REF] Zouggar | Performance improvement in supply chains through better partners' selection[END_REF], we argue that overall performance of the partners' network is not only linked to product-related effectiveness and process-related effectiveness but it depends also closely to partner-partner compatibility. (see Figure . 3)

The compatibility is defined as an indicator that qualifies the collaboration efficiency between two actors and directly reliant on the nature of interactions and exchanges between partners.

The objective is the enhancement of compatibility of each couple of linked partners in the network. Once the technical and technological aspects of the product are guaranteed and the networking dynamics are ensured, it seems obvious that higher is the compatibility better could be the added value to the workflow and as a consequence better would be the performance recorded by the network.

Hereafter a categorization of collaborative situations corresponding to the effective networking is proposed.

Categorization of collaborative situations

Let us take two partners A and B belonging to the same workflow. Their contributions are considered as an added value expected by the focal company. To classify their coupled contributions, we suggest considering two dimensions. In one hand, partners' effectiveness (product and process related) and on the other hand their compatibility (exchanges and interactions quality). Potential collaboration situations are classified in Fig. 4. Four collaboration categories maybe highlighted. They are ranked from the "ideal" collaboration situation (1) to the worst one (4): 1. Ideal. The category (1) corresponds to the ideal case, because the concerned partners have the best expected effectiveness and their compatibility is high. This situation has necessary but not sufficient conditions for optimal performance. 2. Limited. In category (2), partners have a low-level effectiveness but their compatibility is high. The use of this collaboration situation is limited and might be suitable only for non critical tasks. 3. Risky. In category (3), high-level effectiveness of partners is ensured but their ability to work together is not satisfying. This case is called risky because the highlevel of technicality could not guarantee by its own the success of the project.

4.

Uninteresting. In category (4), partners have limited effectiveness and they are not efficient in working together. This case is uninteresting because dysfunctions could appear in both product and partners interactions.

As the expected objective of each company is to achieve the ideal case, the adaptation of categories ( 2) and ( 3) allows achieving that.

Category (2) requires the improvement of the partners' effectiveness, this represents product and process oriented improvements which are often expensive to acquire, long to understand and difficult to implement.

For the third category (3), the effectiveness is proven; the only improvement to make is to enhance the compatibility which can be achieved, either by a better adaptation of the partners to the needs of each other, or by looking for new partners with expected effectiveness and a better compatibility.

In order to exploit these improvements based on compatibility, partners selection process has to be adapted and include a third dimension treating of partners links. These links are not always obvious and some of them could be hidden. Hereafter we focus on partners' dependencies to indentify the real links between all the partners of the network.

Network compatibility blocs

The study of the relationship between two adjacent partners as it was done until now seems obvious. In this section, the idea is to investigate relationships beyond not directly linked partners.

In [START_REF] Zouggar | Modelling product and partners network architectures to identify hidden dependencies[END_REF] a dependency modeling approach has been proposed, the studied case concerns product made up five modules (a,b,c,d,e) and five partners within gBOMO (S1, S2, S3, S4, S5). This approach leads to linkage situations categorization by comparing product components linkage matrix and suppliers' strengths dependency matrix, (table 1).

Components links Supplier links

Links types Category

∞ S5 1//2 ∞ S4 1/2 1/2 ∞ S3 1/2 1/2 1/2 ∞ S2 1/2 0 0 1 ∞ S1 S5 S4 S3 S2 S1 ∞ S5 1//2 ∞ S4 1/2 1/2 ∞ S3 1/2 1/2 1/2 ∞ S2 1/2 0 0 1 ∞ S1 S5 S4 S3 S2 S1 -

Suppliers strengths dependency matrix

Components linkage matrix

Based on the linkage situations, the first remark that we can make is the rising of two main situations: linked partners (directly or not) and not linked partners. According to this observation and the previous collaboration categorization of section 4.3, we can deduce that categories 3 & 4 of partners linkage (linked partners) require considering the compatibility of corresponding partners while for categories 1 & 2, no compatibility is required among the unlinked partners.

Blocs extraction

From supplier strength dependency matrix, we can identify for each partner the imperative compatible partners based on their link (table 2). We distinguish two kinds of links: the strong link (equals 1) and the weak one (less than 1).

S5 1//2 ? S4 1/2 1/2 ? S3 1/2 1/2 1/2 ? S2 1/2 0 0 1 S1 S5 S4 S3 S2 S1 - S5 1//2 - S4 1/2 1/2 - S3 1/2 1/2 1/2 - S2 1/2 0 0 1 S1 S5 S4 S3 S2 S1 - Partner Strong link Weak link S1 S2 S5 S2 S1 S3, S4, S5 S3 S2, S4, S5 S4 S2, S3, S5 S5 S1, S2, S3, S4
Each group of partners where all the partners are linked constitutes a bloc (fig. 5). It is possible to identify subsets of partners, belonging to the network, which form network compatibility blocs because of their internal imperative synergy. Once all the blocs identified, partners' selection process can be improved by evaluating the compatibility in each bloc. This evaluation has to be done according to the kind of the links between partners and based on measures and criteria to be defined.

Conclusion

In a high versatile environment, the focal company needs more partnership flexibility to adapt its activities without creating costly dysfunctions. Thanks to suggested approach, for each product module and each new project, the FC would be able to select the suitable sub-contractors and suppliers, looking at the existing network. Modularity of product once correctly understood and coherently handled can brings significant benefits to the focal company, since it serves as support and basis to developing different groups of partners to carry out the most effective partnership and most flexible as possible.

Our analysis focused on the partners' compatibility improvements and partners' dependency links. As a result network compatibility blocs suggest new developments to adapt selection process and enlarge the scope to a third dimension of partners exchanges and relative dependencies that have to be considered specifically beyond the usual partners' selection process based on Product/Process related effectiveness.

Compatibility evaluation based on measures and criteria require a deep analysis of the linkage situations and necessitate further developments.

Fig. 1 .

 1 Fig.1. gBOMO of product gBOMO allows representing the realization phase as workflows involving partners at various stages. This highlights the exchanges which should take place from one partner to another directly (if they should collaborate together) or indirectly (their collaboration is structured through the FC).

Fig. 2 .

 2 Fig. 2. Dyadic partners' relationship

FCFig. 3 .

 3 Fig. 3. Framework of compatibility analysisThe distinction made between the two suggested dimensions is that the partner process-related effectiveness underlines mainly the processing abilities of partners while the product-related effectiveness focuses on the technical aspects of the partner's abilities. In[START_REF] Zouggar | Performance improvement in supply chains through better partners' selection[END_REF], we argue that overall performance of the partners' network

Fig. 4 .

 4 Fig. 4. Categorization of collaborative situations

Fig. 5 .

 5 Fig. 5. Network compatibility blocs

Table 1 :

 1 linkage situations

	Links types	Supplier links	Components links
	Category
	Category 1 Category 1	Don't exist Don't exist	Don't exist Don't exist
	Category 2 Category 2	Don't exist Don't exist	Exist Exist
	Category 3 Category 3	Exist Exist	Don't exist Don't exist
	Category 4 Category 4	Exist Exist	Exist Exist

Table 2 .

 2 Compatible partners' identification

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Xiaohua Wang

Shudong Wang

Zhenyu Cai

Jianli Ding

Ding Jianli

The Research on Natural Vegetation's Response to Agriculture in Tarim River Basin in Recent 50 Years Using Multi-Source Remote Sensing Data

Keywords:

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

INTRODUCTION

Tarim river is called mother"s river, which is the main source of farm field and ecology. However, unrestricted water consumption causes negative influences such as considerable deforestation, desertification, and increased soil salinity [1][2][3][START_REF] Asrar | EOS Science Strategy for the Earth Observing System[END_REF][5] . The ecological environment in the Tarim River basin is extremely vulnerable [6][7][8][9] . The local and center government have paid great attention to deterioration of the ecology and environment. In an attempt to restore the ecological system, some significant improvements have been made in regulating the stream flow of the Tarim River for flood control and ecological water releases from Boston Lake through Daxihaizi Reservoir were carried out several times from 2000. It is very important to deep study on the relation between area of farm field and natural vegetation in recent 50 years. Collecting and processing various decades of remote sensing data, we got area of farm field, grass, forest, town and non-serviceable field using multi-source remote sensing data such as MSS, Landsat-TM, aerial photograph, time series MODIS products NDVI (250m) [10][11][12][13][14] .

2.HISTORICAL DATA PROCESSING AND ANALYSIS

Data processing

To study the relation between farm field and area of vegetation, it is hard to analyze the tendency using short time series of data. We select the time from 1950s to 2000s and the remote sensing data of various sources which include Land sat MSS, Landsat TM and so forth, and the data processing methods include the transition from raster to vector data, supervise classification and so forth. For example, time series of MODIS data were used to extract farm field information using variance method (Fig. 1). Finally, we got statistical data of various land use and cover (Fig. 2).

Data analysis

The analysis from Land use and cover change figure of Tarim river basin shows that the land use and cover always changes from 1950s:

(1) From 1950 to 1972, area of farm field has a tiny increasing rate of 2.35-2.73% (percentage of statistical area), at the same time, forest area had a little increase of 22.12%-25.13%, but area of grassland had a little decrease of 4.51% -3.13%. (2) From 1972 to 1990, area of farm field still has a little increase rate of 2.73-2.92% compared with the changes from 1950 to 1972. At the same time, forest area decrease to about 22% again, but area of grassland has a rapid decrease from 3.13% to 7.02%. (3) From 1990 to 2000, area of farm field has a rapid increase rate of 2.92-3.82% compared with the changes from 1970 to 1990, but area of forest and grassland has a sharp increase trend, and that of forest rapidly decreases from about 23% to about 10% and that of grassland from 7.02% to 4.48%. ( 4) From 2000 to 2006, area of the farm field keeps stable, but the area of forest and grassland increase obviously using method of variance of time serious MODIS data products. As for area of town and City, there is a continued increase from 1970, and which becomes obvious from 1972 to 2000.

Comprehensive analysis indicts that agriculture control implemented by government is necessary to recovery of ecology, or farm field also would be lost eventually with destruction of natural ecology. According to historical material and above results, because of the high flow period between 1950 and 1972, a part of grassland converts forest. From 1972 to 1990, because of increase of farm field, limited water isn"t enough to grassland and forest, and a fraction of forest area converts grassland again. With rapid increase of area of farm field, more water need from the river and underground, which causes water level depression, and natural ecology becomes deteriorative from 1990 to 2000. Water releases from Boston lake for restoration of the ecosystem agriculture control implemented by government help recovery of natural vegetation after 2000.

3.THE RELATION ANALYSIS BETWEEN FARM FIELD AND NATURAL VEGETATION

We analyzed the relation curve of farm field and vegetation from 1950s to 2000s, and the result indicts that the correlation coefficient between natural vegetation and farm field be 0.8775. It is higher than that of vegetation (natural vegetation and farm field) and farm field. It indicted that more water need from the river and underground, which causes water level depression, and natural ecology become deteriorative , with rapid increase of area of farm field.

4.CONCLUSION

Currently, Some significant improvements to restore the ecological system have been made in regulating the stream flow of the Tarim River for flood control and ecological water releases from Boston Lake through Daxihaizi Reservoir were carried out several times from 2000. Water releases from Boston lake for restoration of the ecosystem agriculture control implemented by government help recovery of natural vegetation after 2000. As a result, the natural vegetation begin to restore.

Fig. 1

 1 Fig.1 Farm field area in 2000 and 2006

Fig. 3

 3 Fig.3 Changes of farm field and vegetation

*The paper is supported by the project : the National Key Technology R&D Program of China (2006BAB07) and Open foundation of Key Laboratory of Oasis Ecology (Xinjiang University) Ministry of Education