Sustainable supplier selection: A ranking model based on fuzzy inference system

In these days, considering the growth of knowledge about sustainability in enterprise, the sustainable supplier selection would be the central component in the management of a sustainable supply chain. In this paper the sustainable supplier selection criteria and sub-criteria are determined and based on those criteria and sub-criteria a methodology is proposed onto evaluation and ranking of a given set of suppliers. In the evaluation process, decision makers’ opinions on the importance of deciding the criteria and sub-criteria, in addition to their preference of the suppliers’ performance with respect to sub-criteria are considered in linguistic terms. To handle the subjectivity of decision makers’ assessments, fuzzy logic has been applied and a new ranking method on the basis of fuzzy inference system (FIS) is proposed for supplier selection problem. Finally, an illustrative example is utilized to show the feasibility of the proposed method.     

供应商选择的双层规划模型及求解分析

为了促使供应商在降低产品成本的同时,提高服务质量,提出了利用双层规划建立供应商选择模型。上层规划以制造企业的采购费用最小为目标,下层以选择的供应商的服务质量最大为目标。其中,下层规划中供应商的服务质量用综合评价得分体现先计算出各影响因素的权重,乘以标准判断矩阵得出各供应商的综合得分。最后,结合模型特点设计了双层迭代的算法,并结合实例验证了模型和算法的有效性。     

供应商能力有约束的混合型多属性决策方法

研究了一种混合型多属性决策方法,用于多资源模型的供应商选择问题,并将供应商选择过程分为2个阶段.首先用熵系数模型确定评价指标的客观权重,利用逼近理想解排序法模型排定方案优劣次序;然后建立多目标规划模型确定采购数量在入选供应商之间的分配;最后给出了一个算例,算例结果表明该方法可行且有效.     

Structured methodology for supplier selection and evaluation in a supply chain

Supply chain management (SCM) is one of the most important competitive strategies used by modern enterprises. The main aim of supply chain management is to integrate various suppliers to satisfy market demand. Meanwhile, supplier selection and evaluation plays an important role in establishing an effective supply chain. Traditional supplier selection and evaluation methods focus on the requirements of single enterprises, and fail to consider the entire supply chain. Therefore, this study proposes a structured methodology for supplier selection and evaluation based on the supply chain integration architecture.In developing the methodology for supplier selection and evaluation in a supply chain, enterprise competitive strategy is first identified using strengths weaknesses opportunities threats (SWOT) analysis. Based on the competitive strategy, the criteria and indicators of supplier selection are chosen to establish the supplier selection framework. Subsequently, potential suppliers are screened through data envelopment analysis (DEA). Technique for order preference by similarity to ideal solution (TOPSIS), a multi-attribute decision-making (MADA) method is adapted to rank potential suppliers. Finally, the Taiwanese textile industry is used to illustrate the application and feasibility of the proposed methodology.This study facilitates the improvement of collaborator relationships and the management of potential suppliers to help increase product development capability and quality, reduce product lifecycle time and cost, and thus increase product marketability.     

A green supplier selection model for high-tech industry

With growing worldwide awareness of environmental protection, green production has become an important issue for almost every manufacturer and will determine the sustainability of a manufacturer in the long term. A performance evaluation system for green suppliers thus is necessary to determine the suitability of suppliers to cooperate with the firm. While the works on the evaluation and/or selection of suppliers are abundant, those that concern environmental issues are rather limited. Therefore, in this study, a model for evaluating green suppliers is proposed. The Delphi method is applied first to differentiate the criteria for evaluating traditional suppliers and green suppliers. A hierarchy is constructed next to help evaluate the importance of the selected criteria and the performance of green suppliers. Since experts may not identify the importance of factors clearly, the results of questionnaires may be biased. To consider the vagueness of experts’ opinions, the fuzzy extended analytic hierarchy process is exploited. With the proposed model, manufacturers can have a better understanding of the capabilities that a green supplier must possess and can evaluate and select the most suitable green supplier for cooperation.     

A new DEA method for supplier selection in presence of both cardinal and ordinal data

The success of a supply chain is highly dependent on selection of best suppliers. These decisions are an important component of production and logistics management for many firms. Little attention is given in the literature to the simultaneous consideration of cardinal and ordinal data in supplier selection process. This paper proposes a new integrated data envelopment analysis (DEA) model which is able to identify most efficient supplier in presence of both cardinal and ordinal data. Then, utilizing this model, an innovative method for prioritizing suppliers by considering multiple criteria is proposed. As an advantage, our method identifies best supplier by solving only one mixed integer linear programming (MILP). Applicability of proposed method is indicated by using data set includes specifications of 18 suppliers.     

A stochastic cross‐efficiency data envelopment analysis approach for supplier selection under uncertainty

This paper addresses one of the key objectives of the supply chain strategic design phase, that is, the optimal selection of suppliers. A methodology for supplier selection under uncertainty is proposed, integrating the cross‐efficiency data envelopment analysis (DEA) and Monte Carlo approach. The combination of these two techniques allows overcoming the deterministic feature of the classical cross‐efficiency DEA approach. Moreover, we define an indicator of the robustness of the determined supplier ranking. The technique is able to manage the supplier selection problem considering nondeterministic input and output data. It allows the evaluation of suppliers under uncertainty, a particularly significant circumstance for the assessment of potential suppliers. The novel approach helps buyers in choosing the right partners under uncertainty and ranking suppliers upon a multiple sourcing strategy, even when considering complex evaluations with a high number of suppliers and many input and output criteria.     

基于双层规划的供应商选择模型及求解分析

从降低整车厂采购费用和提高零部件供应商服务质量两个效益背反的因素出发,利用双层规划的博弈特点建立模型对汽车零部件供应商选择问题进行定量分析,其中上层规划以整车厂采购总费用最小为目标,下层规划以供应商的服务质量最大为目标,引入“综合表现度”的概念,运用ANP（网络层次分析法）法计算影响因素的权重,并乘以供应商各影响因素的取值,从而确定了供应商的综合表现度。最后,结合模型的特点设计了遗传算法,并应用实例验证了模型和算法的有效性,为汽车零部件供应商的选择提供有益的参考。     

供应商选择的组合多属性群决策

为解决图书供应商选择问题,建立同时考虑多个属性,由多人进行决策的组合多属性群决策模型。先用TOPSIS法求出单个决策者对各供应商的综合评价值,再用三种不同的社会选择函数分别对个体偏好进行集结,然后将三种结果进行组合,得出决策群体对供应商的最终排序结果。最后以一个实例说明模型的应用。该方法可提高供应商选择的有效性。     

A data-driven decision support system for sustainable supplier evaluation in the Industry 5.0 era: A case study for medical equipment manufacturing

Effective supplier management is critical for an enterprise’s success, as supplier procurement accounts for up to approximately 70% to 80% of total manufacturing costs. Correct supplier selection can ensure the competitiveness of the enterprise and contribute to supply chain integration and innovation. In recent years, supplier evaluation frameworks based on Industry 4.0 concepts have contributed to the development of the industry. However, the novel concepts of Industry 5.0 require examination from a people-oriented and sustainable perspective. Unfortunately, at the present time, supplier evaluation frameworks based on Industry 5.0 are lacking. Therefore, the primary task of this study is to develop a novel and comprehensive supplier evaluation framework for the Industry 5.0 era. This study proposes a data-driven decision support system to execute the supplier evaluation process. First, variable precision- dominance-based rough set approach (VC-DRSA) is applied to extract the core criteria, to remove the noise factors and to generate decision rules for the decision-makers’ reference. Second, the criterion importance through intercriteria correlation (CRITIC) approach is adopted to obtain the dependency weights of the core criteria and their ranking. Finally, a modified classifiable technique for order preference by similarity to ideal solution (CTOPSIS) is used to integrate the final performance values of suppliers when new alternative suppliers are added. The research concept is in line with the conception of data-driven decision support in business intelligence and does not rely on the subjective judgments and opinions of experts. Data provided by a multinational medical equipment manufacturer are used as an example to demonstrate the proposed model. VC-DRSA retains nine core criteria from the original twenty criteria, which greatly reduces the labor and cost of supplier audits. In addition, the CRITIC results show that digital transformation, real-time information sharing, and organizational culture transformation are the three main factors affecting the development of enterprises towards Industry 5.0. The results show that CTOPSIS can be used to quickly assess the ratings of new alternative suppliers are listed.     

Simulation based fuzzy TOPSIS approach for group multi-criteria supplier selection problem

Supplier selection is nowadays one of the critical topics in supply chain management. This paper presents a new decision making approach for group multi-criteria supplier selection problem, which clubs supplier selection process with order allocation for dynamic supply chains to cope market variations. More specifically, the developed approach imitates the knowledge acquisition and manipulation in a manner similar to the decision makers who have gathered considerable knowledge and expertise in procurement domain. Nevertheless, under many conditions, exact data are inadequate to model real-life situation and fuzzy logic can be incorporated to handle the vagueness of the decision makers. As per this concept, fuzzy-AHP method is used first for supplier selection through four classes (CLASS I: Performance strategy, CLASS II: Quality of service, CLASS III: Innovation and CLASS IV: Risk), which are qualitatively meaningful. Thereafter, using simulation based fuzzy TOPSIS technique, the criteria application is quantitatively evaluated for order allocation among the selected suppliers. As a result, the approach generates decision-making knowledge, and thereafter, the developed combination of rules order allocation can easily be interpreted, adopted and at the same time if necessary, modified by decision makers. To demonstrate the applicability of the proposed approach, an illustrative example is presented and the results analyzed.     

Using data mining synergies for evaluating criteria at pre-qualification stage of supplier selection

A company must purchase a lot of diverse components and raw material from different upstream suppliers to manufacture or assemble its products. Under this situation the supplier selection has become a critical issue for the purchasing department.The selection of suppliers depends on number of criteria and the challenge is to optimize selection process based on critical criteria and select the best supplier(s). During supplier selection process initial screening of potential suppliers from a large set is vital and the determination of prospective supplier is largely dependent on the criteria chosen of such pre-qualification. In the literature, many judgments based methods are proposed and derived criteria selection from the opinion of either the customers or the experts. All these techniques use the knowledge and experience of the decision makers. These methods inherit certain degree of uncertainty due to complex supply chain structure. The extraction of hidden knowledge is one of the most important tools to address such uncertainty and data mining is one such concept to account for such uncertainty and it has been found applicable in many scenarios. The proposed research aims to introduce a data mining approach, to discover the hidden relationships among the supplier’s pre-qualification data with the overall supplier rating that have been derived after observation of previously executed work for a period of time. It provides an overview that how supplier’s initial strength influence its final work performance.     

A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context

Sustainable supply chain management (SSCM) has received much consideration from corporate and academic over the past decade. Sustainable supplier performance evaluation and selection plays a significant role in establishing an effective SSCM. One of the techniques that can be used for sustainable supplier performance evaluation and selection is data envelopment analysis (DEA). In real world problems, the inputs and outputs might be imprecise. This paper develops an integrated DEA enhanced Russell measure (ERM) model in fuzzy context to select the best sustainable suppliers. A case study is presented to exhibit the efficacy of the proposed method for sustainable supplier selection problem in a resin production company. The case study demonstrates that the proposed model can measure effectiveness, efficiency, and productivity in uncertain environment with different α levels. Also, it shows that the proposed model aids decision makers to deal with economic, social, and environmental factors when selecting sustainable suppliers.     

A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection

Supplier selection has become a very critical activity to the performance of organizations and supply chains. Studies presented in the literature propose the use of the methods Fuzzy TOPSIS (Fuzzy Technique for Order of Preference by Similarity to Ideal Solution) and Fuzzy AHP (Fuzzy Analytic Hierarchy Process) to aid the supplier selection decision process. However, there are no comparative studies of these two methods when applied to the problem of supplier selection. Thus, this paper presents a comparative analysis of these two methods in the context of supplier selection decision making. The comparison was made based on the factors: adequacy to changes of alternatives or criteria; agility in the decision process; computational complexity; adequacy to support group decision making; the number of alternative suppliers and criteria; and modeling of uncertainty. As an illustrative example, both methods were applied to the selection of suppliers of a company in the automotive production chain. In addition, computational tests were performed considering several scenarios of supplier selection. The results have shown that both methods are suitable for the problem of supplier selection, particularly to supporting group decision making and modeling of uncertainty. However, the comparative analysis has shown that the Fuzzy TOPSIS method is better suited to the problem of supplier selection in regard to changes of alternatives and criteria, agility and number of criteria and alternative suppliers. Thus, this comparative study contributes to helping researchers and practitioners to choose more effective approaches for supplier selection. Suggestions of further work are also proposed so as to make these methods more adequate to the problem of supplier selection.     

Supplier behavior modeling and winner determination using parallel MDP

Behavioral uncertainty of a supplier is a major challenge to a buyer operating in e-procurement setting. Modeling suppliers’ behavior from past transactions, estimation of possible future performance and integrating this knowledge with the winner determination process can bring a new dimension to procurement process automation. We propose a states-space model to capture the uncertainty involved in long-term supplier behavior. The states represent the performance level of a supplier. This behavioral aspect is then integrated with the winner determination process of a multi-attribute reverse auction for efficient supplier selection using parallel MDP. We also propose an implementation framework to collect the feedback on supplier, generate an aggregate performance score and integrate it with the winner determination process. The performance aggregation and winner determination with help of Markov decision process effectively uses the past performance information. In addition, it updates performance information in regular invervals and allevates the problem of maintaining a long history. We compare the MDP-based selection with that of performance score-based selection through a simulation experiment. It is observed that our scheme gives better buyer utility, selects best suppliers and fetches better quality product. The benefits realized through these attributes to the buyer increases the efficiency of the MDP-based selection process.     

A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach

This study presents a strategy-aligned fuzzy simple multiattribute rating technique (SMART) approach for solving the supplier/vendor selection problem from the perspective of strategic management of the supply chain (SC). The majority of supplier rating systems obtained their optimal solutions without considering firm operations management (OM)/SC strategy. The proposed system utilizes OM/SC strategy to identify supplier selection criteria. A fuzzy SMART is applied to evaluate the alternative suppliers, and deals with the ratings of both qualitative and quantitative criteria. The final decision-maker incorporates the supply risks of individual suppliers into final decision making. Finally, an empirical study is conducted to demonstrate the procedure of the proposed system and identify the suitable supplier(s).     

基于数据挖掘的油田企业供应商选择

选择合适的供应商,有效控制油田企业成本,是油田企业提升盈利水平、持续健康发展的重中之重。在大数据时代下,伴随着供应链全球化的趋势日益增强,供应商的数量愈加巨大,以往对供应商的选择方式已经不适用于企业科学有效地选择供应商的需求。文章基于数据挖掘的理念,研究某油田企业现有的某一领域供应商评价指标分值,并以此作为数据样本,应用聚类分析中的K-means算法对供应商评价指标分值进行聚类分析。将供应商进行分类,挖掘供应商评价指标分值的分布特点及规律,构建适合油田企业的科学、规范、高效的供应商选择体系,摒弃传统选择方式无法避免的主观性、随机性和偶然性等弊端,为油田企业决策者挑选最合适的供应商作为战略合作伙伴提供了科学、有效的依据。     

Supplier evaluation based on Nash bargaining game model

Traditional DEA method is improper for supplier evaluation and selection, as it adopts varying weights in evaluation, and fails to consider competition among the suppliers. In order to solve these two problems, Nash bargaining game DEA model is applied to supplier evaluation in present paper. However, there is a non-uniqueness problem with Nash bargaining game efficiency of supplier in existing Nash bargaining game DEA model. The existing Nash bargaining game DEA model is improved in present paper on this issue, then the improved model is applied to the third party logistics service provider evaluation. The result of supplier evaluation based on the improved model is more persuasive compared with the existing research achievement, owing to adopting common weights in evaluation, and the game between suppliers being taken account.     

Extracting performance rules of suppliers in the manufacturing industry: an empirical study

Performance evaluation of suppliers is increasingly recognized as a critical indicator in supply chain cooperation. Traditional performance evaluation methods have the problems of a simple buy/sell relation and in one’s subjective views between manufacturers and suppliers, and they lack objective automatic evaluation processes in the supply chain considered. Statistical techniques used for evaluation rely on the restrictive assumptions of linear separability, multivariate normality, and independence of the predictive variables. Unfortunately, many of the common models of performance evaluation of suppliers violate these assumptions. The study proposes an integrated model by combining K-means clustering, feature selection, and the decision tree method into a single evaluation model to assess the performance of suppliers and simultaneously tackles the above-mentioned shortcomings. The integrated model is illustrated with an empirical case study of a manufacturer for an original design manufacturer (ODM) to demonstrate the model performance. The experimental results indicate that the proposed method outperforms listed methods in terms of accuracy, and three redundant attributes can be eliminated from the empirical case. Furthermore, the extracted rules by the decision tree C4.5 algorithm form an automatic knowledge system for supplier performance evaluation.     

A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method

Supplier selection, the process of finding the right suppliers who are able to provide the buyer with the right quality products and/or services at the right price, at the right time and in the right quantities, is one of the most critical activities for establishing an effective supply chain. On the other hand, it is a hard problem since supplier selection is typically a multi criteria group decision-making problem involving several conflicting criteria on which decision maker’s knowledge is usually vague and imprecise. In this study, TOPSIS method combined with intuitionistic fuzzy set is proposed to select appropriate supplier in group decision making environment. Intuitionistic fuzzy weighted averaging (IFWA) operator is utilized to aggregate individual opinions of decision makers for rating the importance of criteria and alternatives. Finally, a numerical example for supplier selection is given to illustrate application of intuitionistic fuzzy TOPSIS method.