An Integrated Kano Model, Fuzzy Analytical Hierarchy Process, and Decision Matrix for Sustainable Supplier Selection in Palm Oil Industries Indonesia, a Case Study

Clustering sustainable suppliers in the plastics industry: A fuzzy equivalence relation approach

Nowadays, pure economic supply chain management is not commonly contemplated among companies (especially buyers), as recently novel dimensions of supply chains, e.g., environmental, sustainability, and risk, play significant roles. In addition, since companies prefer buying their needs from a group of suppliers, the problem of supplier selection is not solely choosing or qualifying a supplier from among others. Buyers, hence, commonly assemble a portfolio of suppliers by looking at the multi-dimensional pre-determined selection criteria. Since sustainable supplier selection criteria are often assessed by linguistic terms, an appropriate clustering approach is required. This paper presents an innovative way to implement fuzzy equivalence relation to clustering sustainable suppliers through developing a comprehensive taxonomy of sustainable supplier selection criteria, including supply chain risk. Fifteen experts participated in this study to evaluate 20 suppliers and cluster them in the plastics industry. Findings reveal that the best partitioning occurs when the suppliers are divided into two clusters, with 4 (20%) and 16 (80%) suppliers, respectively. The four suppliers in cluster one are performing better in terms of the capability of supplier/delivery, service, risk, and sustainability criteria such as environment protection/management, and green innovation. These factors are critical in clustering and selecting sustainable suppliers. The originality of this study lies in developing an all-inclusive set of criteria for clustering sustainable suppliers and adding risk factors to the conventional supplier selection criteria. In addition to partitioning the suppliers and determining the best-performing ones, this study also highlights the most influential factors by analysing the suppliers in the best cluster.

A Hybrid OPA and Fuzzy MARCOS Methodology for Sustainable Supplier Selection with Technology 4.0 Evaluation

The concern of sustainable supplier selection has been raised recently in organizations’ decision making to enhance their competitiveness. Many tools have been developed to support supplier evaluation, yet the factors of Industry 4.0 (I4.0) have been ignored despite their impact on sustainable performance. Hence, this paper aims to include the technology of I4.0 as the criteria to evaluate the competence of suppliers in sustainability. Multiple-criteria decision making (MCDM) has been used to build decision-making systems; thus, this study employed two advanced methods of MCDM, the ordinal priority approach (OPA) and measurement of alternatives and ranking according to compromise solution (MARCOS) in a fuzzy environment. To test the feasibility of the proposal, five manufacturers of Vietnam’s leather and footwear industry were hypothetically assigned. Firstly, the evaluation criteria were weighted by OPA. Then, the ranking of alternatives was determined by fuzzy MARCOS. The results show that “green image”, “green product innovation”, “cloud computing”, “service level”, and “blockchain” are the topmost significant criteria in evaluating sustainable practices in the supply chain from the I4.0 perspective. Furthermore, sensitivity and comparison analyses were carried out to verify the robustness of the methodology. The outcomes of this paper contribute a new model of decision making with respect to the involvement of sustainability and I4.0.

Construction and Application of the Talent Training System in Colleges and Universities Based on the Fuzzy Analytic Hierarchy Process

At present, the competition among talents to seize jobs is becoming more and fiercer. How to stand out in the fierce competition for talents and seize market resources is a problem that every graduate must think deeply. Domestic research on the composition of the implementation elements of talent training in colleges and universities from the perspective of excellent school talent training has achieved fruitful results. Their measures and implementation ideas mainly include the following aspects: first, analyze the system of talent training in colleges and universities; second, analyze the teaching methods of college teachers; third, analyze the future development direction of talents. Taking a university as a pilot unit, this paper constructs a talent training system based on the combination method of fuzzy hierarchy, determines the training goal according to students' own ability, formulates the talent training scheme according to the goal, determines the rectangular array relationship between the training system and students' own ability requirements and attaches importance to practical teaching. The algorithm used in this paper firstly establishes a hierarchical structure model and then combines the relevant measures of AHP to sort the weights of indicators and finally calculates the entropy value by quoting entropy weight. We use the fuzzy method combined with hierarchical analysis to evaluate the five indexes of students' own influencing factors, such as learning attitude, basic knowledge, cooperation ability, development ability, and professional ability. It can be seen that the algorithm model is more accurate and the error value is the smallest. Compared with their combination method, the fuzzy evaluation method and the hierarchical analysis method are practical, and the fuzzy method combined with the hierarchical analysis combination method has more practical significance. Finally, the employment situation of graduates is compared and analyzed to further highlight the effectiveness of the model.

Double hierarchy hesitant fuzzy linguistic information based framework for personalized ranking of sustainable suppliers

With the growing appetite for reducing carbon footprint, organizations are tirelessly working towards green practices and one such crucial practice is purchasing raw materials from sustainable suppliers (SSs). Inspired by the drift in purchase habits, several sustainable suppliers emerged in the market and a rational selection of a suitable sustainable supplier is a complex decision problem. There are many criteria associated with the evaluation of sustainable suppliers, and double hierarchy hesitant fuzzy linguistic (DHHFL) structure is a popular preference style that accepts complex linguistic expressions in the natural language form. Earlier studies on sustainable supplier selection infer that (i) complex linguistic expressions are not properly modeled, (ii) interrelationship among criteria must be considered during importance assessment, (iii) direct assignment of attitudinal values of experts causes bias and subjectivity, and (iv) nature of criteria play a crucial role in ranking SSs. To overcome these limitations, a novel MCMD framework is proposed in this study in which the attitudinal characteristic values of experts are calculated by using a variance approach. Besides, importance of diverse sustainable criteria is calculated by proposing novel attitude-CRITIC approach that supports proper capturing of interrelationship among criteria along with experts' attitude values. Later, weighted distance approximation algorithm is presented to DHHFL setting for personalized and cumulative ranking of SSs by properly considering nature of criteria. These methods are integrated to form a framework under DHHFL setting, and its usefulness is exemplified by using a case study of SS selection in an automotive firm. A comprehensive sensitivity analysis as well performed to test the validity of the proposed model approves the applicability, validity, and robustness of the model. Lastly, comparison is done with other methods to understand the merits and shortcomings of the proposal.

An Empirical Investigation of Green Product Design and Development Strategies for Eco Industries Using Kano Model and Fuzzy AHP

Collaboration in green product design and development is becoming more significant to ensure a brighter future for eco industries, and research into such innovation has increased in recent years. So, it has been emphasized by practitioners that green thinking should be adopted from the design stage through the development stage and into the disposal stage of a product. However, it is challenging to identify the severity of strategies that mainly hampers the growth of green product design and development (GPDD). The current research aimed to identify and rank various strategies based on their significant impact on the development of green product design. The study contains three segments: (1) Multiple strategies were identified based on the published literature, project reports, and interactions with academics and industry experts. Then, 22 strategies were selected for GPDD that could be divided into five clusters: cooperation and commitment from top management commitment (TMC), design for environment (DFE), utilization of green technologies (UGT), green external supply chain management (GESCM), and green internal supply chain management (GISCM). (2) The Kano model was used to identify user requirements and satisfaction levels. (3) The priority weight and rank among selected strategies were determined using the fuzzy AHP approach. The results show that the “DFE” ranks highest among the main categories, and “design for disassembly” has been highlighted as a necessary sub-criteria in the emerging eco industries. Various recommendations are suggested to adopt these techniques, which augment the growth of green product design and development for eco industries.