Abstract

Evaluation of modular suppliers is a crucial step towards building an effective modular production network. However, few studies focus on the salient features of modular production and discuss the selection and evaluation of modular suppliers. In this paper, the fuzzy evaluation method is used to make the evaluation. Regarding the method, this paper applies mathematical analysis to further discuss the stability of the method by introducing a dispersion degree and modifying the evaluation vector. Then, based on modularization theory, this paper adopts a factor analysis to identify the criteria for a modular supplier. This paper has two main findings: constructing an index system to evaluate modular suppliers and providing a way to test the stability of the method used. As such, the results of the paper contribute to helping enterprises involved in modular production to identify qualified modular suppliers and make reliable decision with regard to modular supplier evaluation.

1. Introduction

Since the 1970s and 1980s, the global manufacturing sector has undergone an extensive and profound change. In the past ten years, manufacturing companies tended to take the vertical integration strategy to produce. Namely, a company assumes control over several production or distribution steps involved in the creation of its product or service [1]. Vertical integration can help companies reduce costs and improve efficiencies by decreasing transportation expenses and reducing turnaround time, among other advantages [1–5]. Nevertheless, with the development of information technology and diversification of personal needs, manufacturing companies gradually disintegrate many activities and take the focus strategy. Big MNCs such as IBM, GM, GE, Toyota, Apple, P&G, Unilever, HP, and Philips now focus on a small number of core businesses (or areas) and outsource the noncore businesses through outsourcing and global sourcing and even sell their productive branches at home or abroad. The purpose of doing so is to reduce the operation risks, respond to the diversified market demands and adapt to the rapid development of information technology [6–9]. The vertical disintegration of industrial organizations does not mean that they simply return the integrated bureaucracy to the market. In fact by focusing on its core businesses and connected by the noncore businesses, these firms form network organizations which help the member firms to cooperate and obtain benefit through win-win activities. And it is called the networking of industrial organizations production network paradigm [10].

Furthermore, with the upsurge of modularity, namely, the use of exchangeable parts or options in the fabrication of an object, the internal production chain is decomposed and reorganized, which leads to the fragmentation of production process and the relocation of these fragments around the globe. Different procedures and processes of production are split up and assigned to different countries to produce. Then around a final product a series of companies are organized and connected and form the interorganizational network. Such new industrial organizing mode is called modular production network (MPN). Within functionally specialized value chain nodes activities tend to remain tightly integrated and based on tacit linkages [10]. Between these nodes, however, linkages are formed by the transfer of codified information. From such linkages the network architecture arises.

In a modular production network, the modular suppliers are always flexible and specialized. They can produce “the capacity pooling effect”. That is, brand manufacturers can easily integrate with the necessary modules and service capabilities and quickly turn their own ideas and designs into real products. According to the view of system economics, modular production network is actually an organic economic system which consists of system integrators and general/private module suppliers. Generally modular suppliers have the ability to independently develop and produce modules in accordance with system design rules. In some cases, they can even influence the system integrators who act as system designers in formulating and modifying the system design rules.

Therefore, in a modular production network the modular suppliers appear to have strong ability. The relationship between modular suppliers and system integrators is no longer a simple attached and dependent relationship. Instead, in order to achieve the optimality of modular production network, they have carried out extensive cooperation. So for a modular production network, it has dual subjects. One is production integrator and another is modular supplier. A production integrator usually acts as a convener and leader in the modular production network whereas a modular supplier is also indispensable and to some extent it is even of the same importance of production integrator. Both production integrator and modular supplier have relatively equal strong ability and this is the distinctive characteristic of modular production network that differentiates it from other production cooperation modes. Hence, for a production integrator, to build an effective modular production network the selection and evaluation of modular suppliers are of great importance.

However, few studies are carried out to discuss the selection of modular suppliers. Although there are already abundant researches of supplier evaluation and selection, very few studies discuss the selection of modular suppliers in the new modular production network. There lacks systematic study regarding the construction of the index system of modular supplier selection and the use of evaluation methods.

In fact, as analyzed above, there is a big difference between modular suppliers and common suppliers and such difference is mainly caused by the characteristics of modular production. On the premise of modularization, modular production is a production organizing mode that links module production enterprises and module assembly enterprises through relationships. In the context of such production cooperation, first of all the modular suppliers should possess powerful module production and R&D capability and can provide product modules which the production integrators need. At the same time, the compatibility of module suppliers will enable them to better communicate, cooperate, and innovate with production integrators under the guidance of “visible design rules”. Nevertheless, these characteristics have not been fully considered in the evaluation of common suppliers.

To fill the research gap, first in this paper the fuzzy evaluation method is adopted and modified to evaluate the modular suppliers. Based on it, the stability of the method is discussed. Then based on the method, this paper conducts an empirical study on the index system of modular supplier selection. The index system can provide a comprehensive lens to study and select the potential modular suppliers. And the method as a whole is supposed to provide references for managers to make better selection decisions.

This paper is structured as follows: the second section is a literature review, in which relevant studies are reviewed and discussed; the third section is an introduction to the method we propose; the fourth section is an empirical study of the index system. By investigation and expert consultation, the questionnaires are designed and an empirical study is conducted; the fifth section is a case study. Based on the index system we built and by applying the fuzzy evaluation method, a modular supplier of JMC company is evaluated and the stability of the result is discussed.

2. Literature Review

2.1. An Overview on Indexes of Supplier Selection

The study on supplier selection can be traced to the 1960s. It was first examined in the studies about the selection of vendors in the US [11]. And he found that three factors are crucial in the choice of a vendor and they are the ability to meet quality standards, the ability to deliver the product on time, and performance history. Then many scholars respectively conducted studies on selection and evaluation of suppliers from different perspectives. For example, an empirical study was conducted on US auto industry and 8 indexes out of 26 were chosen to evaluate suppliers [12]. The indexes include quality, delivery, reliability, relationship, flexibility, price, and service. A study established an objective-orientation driven supplier customer satisfaction performance rating system [13]. In the study, the authors found four factors that are especially important for supplier selection and they are incoming inspection, line reject performance, supplier service quality, and product reliability. A study discussed the importance of suppliers’ roles in new product development and suggested that supplier selection should be extended to consider the influence of supplier configuration [14]. A study examined the extension of the vendor evaluation methods with environmental, green issues [15]. By dividing the criteria into the traditional (managerial) and environmental (green) factors, they apply data envelopment analysis (DEA) with the common weights analysis (CWA) method to design a weight system, which helps to determine the environmental factors, as important decision factors. Until now the discussion of factors of supplier selection involves a wide range. We try to make a summary of the factors mentioned in previous studies and for the summary please see Table 1.

From the above research on the index of supplier selection and evaluation, we can see the early research of supplier selection index mostly focuses on factors such as cost, quality, price, delivery, and service. With the changes of environment and extension of supplier roles and capability, the former indexes can hardly completely evaluate the capability of suppliers. In such circumstances, some scholars begin to pay close attention to other factors, such as market agility, innovation ability, information reception and processing ability, and environmental management ability. The supplier selection index is gradually systematized and the evaluation criteria have been diversified and comprehensive.

2.2. An Overview on Methodologies of Supplier Selection

Regarding the methodology of supplier selection, both domestic and foreign scholars apply almost the same methods. And the main methods they use are analytic hierarchy process method (AHP), activity-based costing method (ABC), fuzzy theory, linear program method (LP), neural networks, and data envelopment analysis method (DEA).

For example, a study used Taguchi loss functions to measure performance of each supplier candidate with respect to the risks and benefits and adopted AHP method to determine the relative importance of these factors to the decision-maker, which provides a comprehensive decision tool for supplier selection [43]. Based on the analysis of the weakness of the existing ABC method, a study established a total cost of ownership matrix, which defined and classified all kinds of cost by extending the traditional ABC method [44]. Based on a case study, a study applied the fuzzy theory to design an integrated fuzzy model to discuss the supplier management issues [45]. By considering material preparation for outsourcing firms, technological transition, quality, lead time, and their interactions, a study adopted a linear programming model with fuzzy multiple goals for analyzing cost effectiveness during vendor selection [25]. Another study constructed a supplier quality evaluation index system under SCM environment [46]. On the basis of it, the BP neural network is used to conduct an empirical study on the supplier quality space. A study presented a chance-constrained data envelopment analysis (CCDEA) approach in the presence of multiple performance measures that are uncertain and the effectiveness of application of CCDEA in the area of purchasing were demonstrated [28].

Regarding the methodology of supplier selection, in early research, some simple methods such as activity-based costing method (ABC) are mainly used. With the development of technology, some more advanced evaluation methods such as data envelopment analysis method (DEA) and neural networks method are applied to evaluate suppliers. One trend for the use of supplier selection method is the combination and synthesis of more than two methods. Another trend is the continuous modification of these methods. By overcoming the shortcomings of the methods, the essence of today’s methodologies is to make the evaluation of a supplier have better relevance and veracity.

2.3. An Overview on Selection of Modular Supplier

Generally speaking modular production refers to such network organization that connects the module production enterprises and module assembly enterprises with the premise of modularization. Generally modular production incorporates two aspects: the decomposition of modules, namely, according to certain connection rules to decompose a complex system or process into self-disciplined subsystems; the integration of modules, namely, to connect the different independent subsystems into a complete system or process [47].

The main participants of a modular production network are system integrators, modular suppliers, and system designers. Usually the system integrator is the core enterprise in modular production. It has strong comprehensive strength and can satisfy the customers’ needs according to the market demands by integrating different modules. System designers are responsible for formulating the rules. They decompose the complicated products, determine the configuration, interface and standard of the product system, and guarantee the independent submodules can be reorganized into an organic system. In this sense, system designers can also serve as the system integrators.

Modular suppliers are important participants in modular production network. According to Masahiko Aoki, the effective operation of a modular system lies in the “visible design rules” and the “implicit design rules”. The visible rules mainly refer to the interfaces and standards while the invisible rules indicate the design and production rules embedded in a module. The visible rules enable the producers to decompose the complicated modular products into different modules and therefore design, purchase, and produce independently. So in a modular production network, a modular supplier has higher discretion than common supplier to produce as long as the module they produce can well connect other modules based on the standardized interfaces. On the other hand, a module is comprised of many components and is a complicated product too. According to the invisible rules, a modular supplier should have the capability to do R&D independently and decide how to organize these components to produce a module. As such, generally modular suppliers have relatively higher core competence than common suppliers. And such core competence mainly incorporates the R&D capability, design capability, and modularization capability. Besides, the capability of a modular supplier to manage a number of component suppliers also differentiates a modular supplier from a common supplier. In addition, the strong ability of modular suppliers also alters the relationship between modular suppliers and system integrators. It is no longer a mere dependent relationship but a mutually beneficial cooperation relationship built based on reciprocity [48]. In some cases, the modular suppliers can even influence the system integrators by participating in the R&D activities of a modular product and as a result formulating and modifying the system design rules [49].

So as an important actor in a modular production system, modular suppliers are quite different from common suppliers in a number of perspectives. At present, there are still rare studies on the selection of modular suppliers. A study carried out a fuzzy collaborative selection of modular suppliers from the perspective of the whole process of an enterprise [50]. Because the evaluation is built based on the whole process of enterprise production, it focuses on the evaluation of production and technical capabilities but fails to take full account of the modular and production cooperation capabilities of modular suppliers. In fact, these capabilities are very crucial capabilities for modular suppliers. As one of the very few studies on the selection of modular suppliers, this study provides a way of thinking about modular supplier selection issues but it lacks to provide a comprehensive picture of modular suppliers.

In summary, we can find at present the research on supplier selection is more systematic and the design of the evaluation index is more comprehensive. The use of the evaluation methods is more inclined to overcome the judgment errors and other shortcomings as well. Under this research background, we reviewed related studies on modular supplier selection and found very few studies were conducted. To fill this research void, we endeavor to provide a systematic methodology from method test to index design to evaluate modular suppliers. To do that, first we adopt a modified fuzzy evaluation method and based on mathematical analysis we conduct a stability analysis to further discuss the modular supplier selection issue. Then we adopt a factor analysis method to identify the criteria for modular suppliers to construct an evaluation index system. And lastly by a practical case, the method is applied and tested.

3. Fuzzy Evaluation Method and the Stability Analysis

To select and evaluate modular suppliers we need to consider different factors. For the factor system please see Section 4. However, many of these factors are qualitative and this qualitative information is in nature ambiguous. Based on fuzzy sets, the fuzzy evaluation method can solve this problem by giving a comprehensive evaluation of the levels of the evaluated [51, 52]. By dividing the intervals, this method can deal with the ambiguity of the evaluation standards and consider the different levels of the objects. Besides, taking the advantages of experts’ experiences makes the results more objective and adaptable to the reality. So this paper adopts the fuzzy evaluation method to evaluate and select the modular suppliers.

However, this method depends much on experts’ subjective judgment. If an expert underestimates or overestimates a factor, it may influence the final result. So in this paper we further discuss the stability of the method. That is, if an expert underestimates or overestimates a factor, whether the result is stable. In the stability analysis, a dispersion degree is introduced, which helps to further identify the differences of experts’ subjective judgment. Based on it, mathematical analysis is adopted to discuss the influence of such difference on the final results. Generally mathematical analysis is a logical demonstration with strictness and continuity and therefore it can provide reliable argument for the topic discussed. As such, in the stability analysis, a mathematical analysis is adopted to provide a clear logic to judge the degree of reliability of the results.

3.1. Determining the Weights

Suppose , where denotes an expert consulted. , where denotes a factor of modular supplier selection. , where denotes a criterion of a factor of modular supplier selection. , where respectively denote the “good”, “general”, “fairly weak”, and “weak” comment of each criterion.

Each expert makes a series of judgments based on pairwise comparisons of the criteria of a factor. For two criteria of a factor, the relative important one is given 1 while the less important one is given 0. If the importance is considered as the same, then 0.5 is given to each index. So for any expert, we can have the following pairwise comparison table (see Table 2).

Then for each criterion we sum the values of all the experts (see Table 3).

Then we can calculate the weight of :

Using the pairwise comparison method, we can also calculate the weight of each factor.

Hence, the final weight of each criterion is

3.2. Establishing Membership and Conducting Comprehensive Evaluation

Although we can get definite comments on each criterion, the “boundary” is relatively ambiguous. Therefore, the membership degree of each criterion to the evaluation set is calculated. In doing so, we need to grade each criterion based on specialist consultancy. We can get the membership vector of criterion to evaluation set . , is the evaluation value of . And we have . . We can get the evaluation membership matrix of the criteria of modular supplier selection.

Suppose , where is the weight of . Then we can calculate the comprehensive evaluation vector .

3.3. Calculating the Dispersion Degree

According to the above method, we can get the comprehensive evaluation of a supplier. But if there are two suppliers and their comprehensive evaluation are similar. That is, more than 50% evaluation is general and good. But for one supplier, the evaluations of all the criteria are above general while for another supplier some criteria evaluations are good. The evaluations of some other criteria are below fairly weak. So we can easily find the first supplier is better than the second one because the second one does not develop evenly and therefore its risk is bigger than the first one.

Considering such circumstance, we introduce a dispersion degree to measure it. Based on probability theory, referring to the definition of variance in statistics, we use to reflect the dispersion degree of criterion , where . If is big, then the comprehensive evaluation should move down. We construct , where denotes the parameter which decision-maker can control. It is designed to reflect the adjustment that the dispersion causes to the evaluation judgment.

We suppose if the accumulative evaluation of level is bigger than 0.5, then the comprehensive evaluation of the supplier is in level. And it should satisfy . Based on , we can modify the judgment criterion as follows:where . By the modification, the comprehensive evaluation of a supplier has both considered the membership matrix and the dispersion degree. It is more objective and comprehensive.

3.4. Stability Analysis

Experts’ erroneous judgment may occur in adjacent levels or across levels. In this paper, to initiate the discussion on these situations and to simplify the problem, we mainly focus on the former circumstance. So we suppose the erroneous judgment of one criterion occurs in adjacent evaluation levels of the criterion. That is, suppose the positive error occurs in and the negative error occurs in . Correspondingly, there are the following circumstances:(1)Both positive and negative errors have impact on formula (2) and formula (3). Namely, , .(2)The positive error has impact on formula (2); both positive and negative errors have impact on formula (3). Namely, , .(3)The negative error has impact on formula (2); both positive and negative error have impact on formula (3). Namely, , .(4)Both positive and negative errors do not have impact on formula (2); the negative error has impact on formula (3). Namely, , .(5)Both positive and negative errors do not have impact on formula (2); the positive error has impact on formula (3). Namely, , .(6)Both positive and negative errors do not have impact on the two formulas. Namely, , .

In this paper, denotes the dispersion degree; denotes the membership; denotes the initial judgment matrix; denotes the weight of a criterion; denotes the evaluation level that the accumulative evaluation of a criterion is bigger than 0.5; denotes a level lower than .

Then we make

Then we conduct stability analysis as follows.

(1) Both positive and negative errors have impact on formula (2) and formula (3). Namely, , .

Because , by rearranging the formula we can get We make .

Then we can get the following.

If , then the above inequality always holds.

If ,Because , by rearranging the formula, we can get :Therefore, if If , (2) The positive error has impact on formula (2); both positive and negative errors have impact on formula (3). Namely, , .

Because , by rearranging the formula, we can get We make .

Then we can get the following:

If , then the above inequality always holds.

If , If ,Because both positive and negative errors have impact on formula (3), and according to the deduction of circumstance (1), to satisfy formula (3), it should have Therefore, if ,If ,If ,(3) The negative error has impact on formula (2); both positive and negative error have impact on formula (3). Namely, , .

Because , by rearranging the formula, we can get We make .

Then we can get the following: if ,

then the above inequality always holds.

If , If ,Because both positive and negative errors have impact on formula (3), and according to the deduction of circumstance (1), to satisfy formula (3), it should have Therefore, if ,If , If ,(4) Both positive and negative errors do not have impact on formula (2); the negative error has impact on formula (3). Namely, , .

Because , by rearranging the formula, we can get Then we can get the following: if , then the above inequality always holds.

If , If ,For formula (2), by rearranging the formula, we can get Therefore, if ,If ,If , (5) Both positive and negative errors do not have impact on formula (2); the positive error has impact on formula (3). Namely, , .