1. Introduction
Outsourcing has become one of the most important strategies in business operations. Through outsourcing operations, manpower and equipment investment can be greatly reduced, thereby operating costs can be effectively controlled. The range covered by outsourcing is very wide, including component production, financial planning, accounting, logistics management, legal consulting, marketing, after-sales service, etc. [
1]. In 2018, the total amount of global companies signing outsourcing services contracts is estimated to be as high as US
$85.6 billion [
2]. This phenomenon shows that outsourcing activities have been widespread in all walks of life. An effective outsourcing evaluation system can maximize the benefits of outsourcing activities [
3,
4]. Improper selection of outsourcing providers can easily lead to the failure of outsourcing strategies, causing a decline in corporate competitiveness, and even financial risks or corporate failures [
5,
6].
The application of outsourcing strategy brings out diversified decision issues. In general, different business process owners should define not only the most appropriate conditions to gain a full compliance between in-house processes and outsourcing activities, but also require them harmonically converge towards the guidelines at the roots of decision-making [
1,
3]. However, the rise of environmental awareness has changed the concept of decision-making. It is no longer only cost-effectiveness as the ultimate consideration, but must be incorporated into green criteria to facilitate environmental protection [
7,
8]. The evaluation and selection of green outsourcing providers is an important task in supply chain management. Especially in the manufacturing industry, for highly complex products such as machine tools or ships, the number of outsourcers they have is very considerable. When an enterprise has many outsourcers, it must have a complete and systematic model to determine the weight of the evaluation criteria and the priority of outsourcing providers, otherwise the management of providers will appear very messy and difficult. [
1,
3,
4,
5,
6].
Many scholars have made significant contributions to the evaluation and selection of outsourcing providers. Some studies have pointed out that the selection of outsourcing providers can be categorized as a multiple criteria decision-making (MCDM) problem [
3,
4,
5,
9,
10,
11]. The MCDM method has excellent evaluation performance under many mutually constrained conditions. Its computing concept is different from statistics. MCDM can process expert interview data with a small sample, and can also analyze large sample data from the database. The goal of MCDM is to integrate both objective quantitative data and subjective expert judgment, and provide effective management suggestions to support decision-makers in formulating optimal strategies [
12,
13,
14]. It is suitable to establish a complete evaluation framework based on the expertise of researchers or experts and the extensive experience of practitioners [
15,
16,
17]. The evaluation and selection of MCDM projects can usually be divided into three execution stages, namely the identification of evaluation criteria, the calculation of criteria weights, and the performance analysis of alternatives [
18].
In the past, research on selecting outsourcing providers has laid the foundation for industry and academia; however, there are still some research gaps and practical application restrictions.
- (i)
Some evaluation models do not take into account criteria related to environmental protection.
Many manufacturing activities have caused various environmental pollution and destruction. Operators need to be aware of environmental protection and make products comply with the restrictions of international environmental regulations. Therefore, whether outsourcing providers have environmental awareness and green manufacturing capabilities deserves our consideration [
7].
- (ii)
Many weight-setting methods assume that the criteria are independent.
Past studies on outsourcing provider selection have often overlooked the mutually dependent relationships among criteria. For example, the analytic hierarchy process (AHP) and the best-worst method (BWM) are used to obtain criteria weights. In fact, the root causes of problems are composed of many interrelated factors [
19,
20,
21]. The decision-making trial and evaluation laboratory (DEMATEL) can overcome the assumption of independence of the criteria and determine the interdependence among the criteria [
6,
9].
- (iii)
Few studies consider both subjectivity and objectivity.
The methods of determining the importance of the criteria can be divided into two categories. Experts conduct pairwise comparisons of the criteria to evaluate their importance and call them subjective weights. Common methods are AHP, BWM, analytic network process (ANP), and DEMATEL. The other type is based on a large amount of data to estimate a set of criteria weights, called objective weights. Entropy and criteria importance through intercriteria correlation (CRITIC) belong to this type of method. If both perspectives can be included in the evaluation model, the results will be comprehensive and complete [
22].
- (iv)
When an enterprise has a large number of outsourcing providers, the ranking of outsourcing providers can no longer meet the needs of decision-makers.
For industries with a wide variety and a small amount of production (such as machinery), there would be a lot of outsourcing providers needed. However, even though the ranking of outsourcing providers is determined, it is impossible to give each outsourcing provider practical suggestions for improvement. If all outsourcing providers can be classified into different levels and given appropriate management suggestions for each level, the management efficiency of the managers can be improved. It is a good practice to classify outsourcing providers through the closeness coefficient of technique for ordering preference based on similarity to ideal solutions (TOPSIS) [
8].
Therefore, in order to tackle the aforementioned problems, this study proposes a MCDM model with a systematic green outsourcing evaluation. First, based on the existing evaluation criteria of the case company and the documentation, a complete evaluation framework for green outsourcing providers was established. The proposed framework can be divided into four main dimensions: capacity of operation, capacity of professional skills, capacity of service, and environment management. These dimensions can be divided into 15 evaluation criteria. Here, the dimension of environmental management was added to conform to the development trend of environmental awareness. Next, the DEMATEL technique was used to explore the mutually dependent relationship among the criteria, and a set of subjective weights was obtained. The DEMATEL questionnaires were obtained by interviewing eight senior managers of the case company. Furthermore, the external auditors surveyed the performance data of 165 outsourcing providers, and applied CRITIC’s algorithm to generate a set of objective weights. The proposed DEMATEL–CRITIC method can reflect the importance of mutually dependent relationships among the criteria. Finally, this study develops a classifiable TOPSIS technique, which not only introduces the concept of aspiration level, but also divides the performance of outsourcing providers into four levels. Appropriate management suggestions are given for the four levels to support outsourcing providers in formulating improvement strategies to enhance their business performance. The DEMATEL, CRITIC, and TOPSIS used in this model are all breakthrough improvements, which make the analysis ability improved and more in line with the actual needs of the industry.
To demonstrate the effectiveness of the proposed model, a Taiwanese multinational machine tool manufacturer is used as an example. Sensitivity analysis and model comparisons are also conducted in this study to demonstrate the robustness of this methodology. The proposed hybrid model is not limited to the amount of data in use. The data can be a small sample or a big data. In addition, when new outsourcing providers join, their performance levels can be quickly classified. Based on the results obtained, the decision-makers can decide whether to cooperate with a new outsourcing provider or not. In summary, the advantages and contribution of our study are described below.
- (i)
Integrating environmental protection criteria in the framework of green outsourcing providers.
- (ii)
Using the DEMATEL–CRITIC method which considers both subjectivity and objectivity. And, this method can identify the mutual influence of the criteria.
- (iii)
Proposing a classifiable TOPSIS to classify a large number of green outsourcing providers, and give appropriate suggestions for improvement according to their levels.
- (iv)
The effective and robustness of the proposed model being confirmed through the model comparisons and sensitivity analysis.
The rest of the paper is organized as follows.
Section 2 reviews the research on using MCDM to evaluate outsourcing providers.
Section 3 introduces the proposed novel model. Moreover, we improved the DEMATEL, CRITIC, and TOPSIS methods and introduced the calculation process and execution steps in detail.
Section 4 uses a real case to demonstrate the applicability of the proposed model.
Section 5 discusses management implication issues, sensitivity analysis and model comparisons. Finally, conclusions and future research directions are given in
Section 6.
2. A Brief Review of the Evaluation of Applying MCDM to Outsourcing Providers
At present, compared with the articles of suppliers, there are relatively few studies on evaluation and selection of outsourcing providers. With the rapid development of outsourcing strategies, the issue of evaluation of outsourcing providers has become increasingly important [
3,
4]. When enterprises face shortages of technology and manpower, they often increase their operational capabilities through outsourcing. From the process of finding outsourced objects to the willingness of cooperation between both parties, many details need to be coordinated and improved.
The success of the outsourcing strategies will create a lot of added values, including saving setup costs, reducing operational risks, and focusing more on core business. However, outsourcing activities will produce a certain degree of two-way information exchange and communication, and the success or failure of cooperation will involve many complicated factors [
23]. Therefore, the evaluation of outsourcing providers is a difficult and complex MCDM problem. Previous studies have used various MCDM methods to explore this issue. Research based on linear programming, for example, Li and Wan [
24] developed a method of fuzzy linear programming to address the issue of outsourcing provider selection. This method is implemented in the largest light-emitting diode (LED) production company in China. The results show that both positive and negative ideal solutions should be considered when evaluating outsourcing providers, to overcome the shortcoming that the linear programming technique for multidimensional analysis of preference (LINMAP) can only obtain local optimal solutions. In the same year, Li and Wan [
25] extended Li and Wan [
24] research and applied to a well-known information technology company in Jiangxi, China. The study shows that it is feasible to determine the weights of attributes through linear programming. In order to consider the importance of experts, Wan et al. [
8] optimized the linear programming method of Li and Wan [
25], combined with intuitionistic fuzzy preference relations (IFPRs) to determine the weights of experts to effectively integrate the group decision-making judgment.
In addition, Ji et al. [
3] proposed a comprehensive MCDM framework to solve the problem of non-compensatory criteria. The modified multi-attributive border approximation area comparison (MABAC) method is a novel weight determination method, which can explore the non-compensatory structure of the criteria. Next, the elimination et choice translating reality (ELECTRE) technique was used to rank the outsourcing providers. The study used data from Li and Wan [
24] to analyze and compare TOPSIS, weighted bonferrroni mean, and traditional MABAC methods, to explain the advantages of the proposed method. In recent years, several novel MCDM models have extended the research on outsourcing providers evaluation. Zarbakhshnia et al. [
26] combined fuzzy AHP (FAHP) and gray multi-objective optimization by ratio analysis (MOORA-G) methods to select the third-party reverse logistics providers for a car parts manufacturing company. Their research shows that the combined model can effectively deal with uncertain qualitative data. A hybrid framework was proposed by Prajapati et al. [
27], who integrated fuzzy Delphi, FAHP, and fuzzy additive ratio assessment (F-ARAS) methods to prioritize alternative outsourcing providers in energy industry. However, these studies all consider the criteria to be independent, which violates the situation in which the existing social factors depend on one another.
Taking into account factor-dependent research, for example, Liou and Chuang [
9] proposed a hybrid MCDM model to evaluate more than 50 outsourcing providers of Taiwan Airlines. The study used DEMATEL and ANP to discuss the influential relationships and influential weights of the criteria, and applied the visekriterijumska optimizacija i kompromisno resenje (VIKOR) to obtain the gap between each alternative and the ideal level. Hsu et al. [
6] improved the methodology of Liou and Chuang [
9] and integrated DEMATEL-based ANP (DANP) and modified grey relation analysis (GRA), where the DANP method puts the output values of DEMATEL into ANP to generate a set of dependent weight values. Next, modified GRA is used to determine and rank the grey correlation coefficient of each outsourcing provider. Uygun et al. [
11] combined fuzzy theory with the ANP method to evaluate the competitiveness of a Turkish communications company’s outsourcing providers. Their research focuses on the processing of uncertain information.
Table 1 summarizes the existing studies applying MCDM model to evaluate and select outsourcing providers. The studies mentioned above have made significant contributions to this topic. Unfortunately, no research has simultaneously discussed and solved the four research gaps mentioned in
Section 1.
5. Management Implications and Discussion
Due to the development trend of artificial intelligence, many machine tool equipment companies have created customized machines for customers. This also increases the research and development and manufacturing costs for the companies. Therefore, co-production through outsourcing providers becomes a good strategy. Under the Taiwan government’s “5 + 2 Industry Innovation Program” policy, the machinery industry has become one of the emerging high-tech industries, and many organizations have invested huge amounts of money to promote the industry. In order to improve the level of machine intelligence, machine tools related to smart machines have been continuously developed. Compared with other manufacturing industries, the manufacturing technology threshold for smart machinery is relatively high, and most companies will use outsourcing strategies to reduce research and development (R&D) and production costs.
According to DEMATEL–CRITIC analysis, pollution emission treatment (C43) is the most important criterion, with a weight of 0.084. The waste reduction and carbon reduction are among the most critical evaluation indicators for manufacturing. Facing the rise of environmental awareness, many international environmental protection and trade organizations have formulated many environmental protection regulations to require companies to pay attention to environmental issues. Customer relationship management and loyalty (C32) is the second most important criterion. The customer relationship management capabilities of an outsourcing provider will directly affect the willingness of the company to sign a contract, especially the coordination of design changes and the enthusiasm of after-sales service. In addition, the loyalty of outsourcing companies is particularly valued by the company’s senior management, which involves a long-term willingness to sign a contract. The weights of communication and message sharing (C33) and C32 are very close, indicating that the degree of information sharing by outsourcing providers is also highly valued. The remaining criteria can also give outsourcing providers suggestions for improvement through the weight values.
The proposed model establishes a visual rating diagram to help decision-makers to judge the performance of outsourcing providers more clearly, as shown in
Figure 2. The diagram clearly classifies all outsourcing providers into four levels, including 11 in Level A
+, 100 in Level A, 50 in Level B, and 4 in Level C. The thresholds for these classifications are determined by the decision-making team established by the company. The analysis results are verified by the case company to be both reasonable and helpful. Most of the outsourcing companies in Level A have cooperated with the company for more than 10 years, and their performance in all aspects has met the requirements of the senior management. Although the outsourcing providers of Level A have a good rating score, there are still some gaps from the aspiration level. Outsourcing companies at this level can focus on improving the criteria with greater weights first, including
C43,
C32,
C33,
C31, and
C23. Level B outsourcing providers should conduct a comprehensive review of the company’s current operating conditions and provide complete improvement measures in four major directions: operation (
D1), professional skills, (
D2), service (
D3), and environment management (
D4) to move toward Level A. Otherwise, they will face elimination in the future. Finally, the performance of the outsourcing providers at Level C does not meet the expectations of the case company at all, so the partnership of outsourcing providers at this level should be dissolved.
Next, we discuss whether the proposed DEMATEL–CRITIC method will affect the results of the classifiable TOPSIS because of the change in the ratio of subjectivity and objectivity. Therefore, the sensitivity analysis was performed nine times to test whether the priorities of outsourcing providers have changed significantly. By changing the parameters of Equation (19) from 0.1 to 0.9, all the criteria weights are changed, as shown in
Table 12.
Figure 3 shows the ranking results after the nine times of sensitivity analysis performed. Obviously, the ranking of outsourcing providers will not be changed significantly because of the excessive emphasis on the weight of subjectivity or objectivity. The sensitivity analysis shows that the proposed model is robust.
In addition, we conducted model comparisons to demonstrate the differences between this study and previous studies. Model 1 is the original SAW analysis method of the case company, and the criteria weights are directly given by the senior executives. Model 2 uses the weights of DEMATEL–CRITIC and uses SAW for performance integration. Model 3 is the proposed model.
Figure 4 shows the ranking results of all the outsourcing providers in the three models. It can be found that the ranking results of Models 1 and 2 are almost the same. There are 14 outsourcing providers in the first place in these two models. In this case, the company cannot distinguish the pros and cons of these 14 outsourcing providers. Moreover, each outsourcing provider will not be able to know what the gap is from the aspiration level. Although the SAW method is simple, it has not considered the comprehensiveness of the evaluation system, only the scores are multiplied by the weight values. The ranking result of the proposed model (Model 3) is significantly different from the other models. We determine the whole range of performance by formulating PIS and NIS, and use the concept of distance to define the relative position of each outsourcing provider. Moreover, the new index proposed by the model clearly points out the gap between the outsourcing provider and the aspiration level.
6. Conclusions
This study contributes to the research of green outsourcing evaluation. The contribution and advantages of this research include four aspects: (i) integrating environmental protection criteria in the evaluation framework of outsourcing providers, to reflect the awareness that enterprises should pay attention to environmental protection. (ii) By considering the mutual influence of the criteria, it overcomes the shortcomings of the previous studies that need to assume the criteria to be independent. (iii) Aspect three involves using the DEMATEL–CRITIC method, which considers both subjectivity and objectivity; the impact of the criteria on the evaluation system is also explored. (iv) Aspect four involves proposing a classifiable TOPSIS to classify a large number of outsourcing providers, and give appropriate suggestions for improvement according to their levels. In addition to the above contributions, our research has also discovered some findings, including the robustness of the proposed model being confirmed through the sensitivity analysis, which means that the analysis results will not be significantly affected by the changes in weights. Moreover, the model comparisons confirmed that our model is more practical and effective. In short, the research method in this paper can be copied to other MCDM evaluation and selection topics, especially the classification of information with big data.
The analysis process of this study is highly dependent on the judgment of experts, so there are several limitations on its use, including the following: (i) the selected experts are sufficiently representative; (ii) the evaluation criteria need to be repeatedly confirmed, whether it is appropriate or not; and (iii) the analysts must be able to interpret the results of each method. Moreover, the classification of TOPSIS in terms of setting the classification thresholds can be further determined by more scientific methods.
Since the methodology proposed in this study is novel, there are some suggestions for further studies in the future. The proposed model has not yet taken into consideration the uncertainty of the information and evaluation environment. Future research can combine fuzzy or grey or Z-number or neutrosophic logic theories to enhance the adaptability of the model. Finally, the proposed model can be coded and incorporated into business software to facilitate the convenient use in industry.