Ranking Of Suppliers Research Articles

A large-scale supplier evaluation approach for circular economy in the presence of circular criteria interactions and weight consistency

Circular economy (CE) is an economic model in which we achieve sustainable development through resource recovery, reuse, and remanufacturing. Large-scale supplier evaluation is crucial for ensuring the sustainability of the CE supply chain and efficient utilization of resources. Especially, criterion interaction and consistency of criterion weights are two key factors of large suppliers’ evaluation in CE. However, current research often overlooks these key factors, this study proposes a large-scale supplier evaluation approach in CE to address these issues. The novel aspects of the proposed approach come with considering the interaction between circular and non-circular criteria, as well as eliminating the impact of large-scale complex information on the consistency of criterion weights. Firstly, large-scale hesitant evaluation information is fused by evidence reasoning, and interaction rules between circular and non-circular criteria are established. Then, the interaction weights of the criteria are obtained based on the interaction of circular criteria, and the comprehensive criteria weights are determined by an improved best-worst method. Next, the decision makers’ weights are generated by the utility value of the circular criterion. Finally, the results of numerical example and analysis show that 86% of the supplier ranking results are stable, and the consistency of the proposed approach is 0.9757, indicating that the proposed approach exhibits high validity and reliability. It emphasizes the significance of the supplier’s circular ability in CE and reduces the interference of large-scale complex information on the consistency of evaluation results.

Pemilihan Supplier Kedelai dengan Metode AHP Dan TOPSIS (Studi Kasus UKM Tahu Pak Wit Di Desa Ledok Kulon, Bojonegoro)

Raw materials as the most important factors in production activities, are obtained from suppliers. Suppliers are partners who ensure the availability of raw materials for the continuity of the company's production. Therefore, it is necessary for companies to choose the right supplier. The problem that occurs in Pak Wit's Tofu SME is that SMEs often change suppliers in procuring soybean raw materials because the SMEs do not have a fixed soybean supplier. This fact results in no certainty of raw materials from the supplier so that SMEs often experience a shortage of quality raw materials. This study aims to select the best supplier according to the criteria set by SMEs. There are 4 suppliers who are candidates with 4 criterias (price, quality, service and flexibility in delivery) used in choosing the best supplier. This research uses AHP to weight the criteria and TOPSIS to rank suppliers. The results of calculations with AHP found that the quality criteria were the first priority, with a value of 0.58. While with the TOPSIS method it was found that supplier 2 was the selected supplier with the highest preference of 0.941.

A Hybrid method of SWARA and ARAS for ranking of supplier: A case study at PT.Adi Satria Abadi (PT.ASA)

The development of a suggested model for rating suppliers is the goal of this research. In this study, suppliers are rated and ranked using Additive Ratio Assessment (ARAS) and Stepwise Weight Assessment Ratio Analysis (SWARA). The suggested approach incorporates both objective and subjective criteria while applying Delphi to a subset of the criteria. In order to test the proposed model in a leather company for actual use, a case study is provided. The model is able to capture all objective and arbitrary criteria in the dynamics of the group of decision-makers when rating suppliers. To help in decision-making, this proposed model includes criteria selection. Because the results of the ranking of suppliers will depend on how accurately the criteria are chosen. The suggested approach produces a workable resolution because it is not sensitive to changes in the parameters

Embracing Sustainability in Green Supplier Evaluation: A Novel Integrated Multi-Criteria Decision-Making Framework

Nowadays, industrial production is a critical issue for companies as public consciousness is rising because of environmental impacts. Like most other industries, the ready-made garment (RMG) industry faces immense pressure to be more competitive in this increasingly competitive market and proactively reduce waste and ecological footprint impacts. Besides, suppliers’ environmental performance and image have far more effect on a corporation’s environmental sustainability than the corporation’s internal environmental efforts. Moreover, there is a shortage of environmental consciousness in emerging economies; thus, green supply chain practices are lagging. Therefore, the green supplier evaluation criteria should be identified and focused on during sustainable procurement. This study analyses quantitative and qualitative factors and explores the relationship between green supplier preference principles using the combination of the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method and the Fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS). Initially, the supplier’s green performance was measured by identifying the criteria from relevant literature. After that, the Fuzzy DEMATEL technique measures the related criteria weights, and the Fuzzy TOPSIS method utilizes these criteria weights to rank suppliers among alternative suppliers. The findings will guide policymakers to improve supplier quality, increase profitability through improved brand recognition, and attract consumers who prioritize environmentally friendly products.

A STUDY ON SUPPLIER EVALUATION PROCESS IN FOOTWEAR MANUFACTURING INDUSTRY

This study introduces an innovative supplier evaluation approach for footwear manufacturing by integrating the SCOR 4.0 model with machine learning techniques. The SCOR 4.0 model, combined with the Best Worst Method (BWM), provides a structured framework for evaluating suppliers across various dimensions. A Random Forest (RF) machine learning model is then used to classify and rank suppliers based on performance ratings. The results show that the RF algorithm effectively identifies suitable suppliers, with lower rejection scores indicating superior performance. This integration enhances supplier evaluation processes and offers valuable insights for supply chain management in the footwear industry. Key Words: Supplier evaluation, SCOR 4.0 model, Best-worst method (BWM), Random Forest (RF), Supplier Rejection score

Analisis Pemilihan Supplier Suku Cadang dengan Menggunakan Metode AHP dan TOPSIS di Perusahaan Forwarding

The forwarding company that will be studied is a subsidiary operating in the logistics services sector of a State-Owned Enterprise (BUMN) company that provides forwarding and warehousing services. The company has 30 trailer trucks in operation so it needs a competent spare parts supplier to meet the company's needs. Currently, companies only pay attention to price as the main factor in choosing suppliers, which ultimately causes late deliveries from these suppliers. In addition to looking for the best suppliers that companies should choose, this research aims to determine the main criteria and main sub-criteria for businesses in the process of selecting spare parts providers. In determining the weight of each criterion and ranking of suppliers that companies should prioritize, the TOPSIS approach and AHP method are used in this research. There are 5 criteria and 15 sub-criteria as well as 4 alternative suppliers that are compared. The supplier with the highest weight after using Expert Choice 11 software and the AHP technique is supplier 1 with a weight of 0.421, supplier 2 with a weight of 0.164, supplier 3 with a weight of 0.12, and supplier 4 with a weight of 0.294. By using the TOPSIS approach, supplier priority is obtained, namely supplier 1 has a preference value of 0.9256, supplier 4 has a value of 0.5217, supplier 2 has a value of 0.1829, and supplier 3 has a value of 0.0180.

Optimizing Supplier Selection in Indonesia Through Analytical Hierarchy Process (AHP)

This study introduces a structured multi-criteria decision-making framework using the Analytical Hierarchy Process (AHP), applied to evaluate suppliers based on quality, price, quantity of supply, and plantation area. Effective supplier selection is crucial for maintaining supply chain efficiency. By employing the Expert Choice 11 software, the AHP method quantitatively assessed each criterion, resulting in a definitive ranking of suppliers. The analysis highlighted the paramount importance of quality in supplier selection, assigning it the highest weight (0.565). The outcomes reveal that a meticulous application of AHP significantly enhances decision-making in supplier selection, suggesting its broader applicability in global supply chain management contexts to improve operational efficacy and resilience. Highlights: Structured Evaluation: The AHP method provides a systematic approach to supplier assessment. Emphasis on Quality: Quality is the dominant criterion in supplier selection with the highest weight. Operational Improvement: Implementing AHP enhances supply chain efficiency and decision accuracy. Keywords: Supplier Selection, Analytical Hierarchy Process, Expert Choice

Analisis Pemilihan Supplier Bahan Baku Tas Gitar dengan Metode Fuzzy Analytical Hierarchy Process (FAHP) di PT. True Indonesia

The use of high-quality raw materials will affect the quality of products produced by a company. Companies really need suppliers who can supply quality raw materials. This can be realized by selecting the most appropriate supplier so that the company can minimize problems that can occur regarding raw material suppliers. PT True Indonesia is a manufacturing company that produces music equipment bags. So far, the company has made decisions in choosing suppliers only with the intuition of the parts related to the procurement of these raw materials. The decision chosen by the company in determining this supplier is often not right. The problems experienced by the company are the quality of raw materials that do not meet the company's criteria and the supplier's delay in sending raw material orders. In this study, the Fuzzy Analytical Hierarchy Process (FAHP) method was used. There are 7 main criteria in choosing a polyester fabric supplier, namely quality, delivery, price, communication system, warranties and claim policies, performance history, and geographic location. From the calculations carried out, the best polyester fabric supplier ranking for PT True Indonesia in order, Tongxiang has a total value of 0,3395, Hantron has a total value of 0,3350, PT. Lambang Jaya has a total value of 0,3340, PT. Shinta Indah Jaya has a total value of 0,3174, and CV. Agung has a total value of 0.3116.

Strategi Pemilihan Supplier Mesin Turbocharger Menggunakan Metode Analytical Hierarchy Process dan Multi-Objective Optimization Method By Ratio Analysis

The right supplier selection is an important factor for PT. Central Turbo, which is currently facing problems related to the supplier selection system that is not accurate. The study aims to develop a decision support system for the selection of turbocharger suppliers. The method used is AHP-MOORA to measure the consistency of criteria and sub-criteria in determining the highest supplier ranking. The research procedure begins with the creation of a hierarchical structure, followed by the creation of an AHP matrix to determine the weight of the criteria, the calculation of the consistency index and ratio, the normalization of the matrix, and the determination of Yi/preference value in the MOORA analysis. The decision support system developed can help PT. Central Turbo in selecting the right turbocharger supplier. This system can evaluate supplier performance more objectively and efficiently, so that it can help the company to improve its competitiveness and customer satisfaction, by considering 3 criteria, namely product performance, supplier, and service. As a result, Fencheng Xindongli Turbocharger Co., Ltd was selected as the best supplier.

An integrated GBWM-PROMETHEE-CLOUD & MCGP model for green supplier selection and order allocation (GSSOA) in an oil refinery

With the increase of environmental concerns in recent years and the prevalence of such issues, which originate mainly from large industries and manufacturing plants, green supply chain management (GSCM) issues have been given special attention among researchers. GSCM is a group of management applications integrating supply chain management with criteria related to environment and supplier selection with the purpose of reducing environmental pollution caused by processes involved in a company supply chain. In this research the main contributions is to draw a decision-making structure addressing the applicability of the green supplier selection (GSS) and order allocation (OA) problem in an Iranian Oil refinery company. A combination of Cloud-PROMETHEE along with using GBWM was taken into account for GSS as the first phase and MCGP method for OA as the second phase, in order to solve the GSSOA problem. The proposed technique lets the vagueness and uncertainty of experts' judgments be obtained by assisting uncertain linguistic variable derived from the cloud model. Furthermore, the group best-worst method (GBWM) obtains the optimal criteria weights by forming a nonlinear programing model and the prioritizing of nominated suppliers is established using the PROMETHEE method with the possibility to pick a distinct preference function for each criterion and draw a graph of supplier preferences. At last, MCGP method was applied considering with the possibility of multi-choice aspiration levels (MCAL) for each goal. The suggested model illustrated with actual figures in an oil refinery to provide insight to decision-makers of green supplier selection and order allocation. To do so, initially nine criteria have been derived after vast research on literature and experts’ opinions. Then the suppliers was successfully evaluated with considering uncertainties and randomness in a group of decision maker judgments. The order allocation was also completed by minimizing total cost and delays and maximizing purchase using results of supplier ranking in the first phase. Results have shown that the supplier with better ranking in GSS is preferred in allocating orders. A sensitivity analysis then performed and confirmed that the suggested method is highly stable against different changes in the data. The proposed method was also compared with the basic MCGDM cloud model and the method is approved. Finally, managerial implications and conclusions for additional researches are demonstrated.

Efficient picture fuzzy soft CRITIC-CoCoSo framework for supplier selection under uncertainties in Industry 4.0

<abstract><p>The picture fuzzy soft set (PiFSS) is a new hybrid model to address complex and uncertain information in Industry 4.0. Topological structure on PiFSS develops an innovative approach for topological data analysis to seek an optimal and unanimous decision in decision-making processes. This conception combines the advantages of a picture fuzzy set (PiFS) and a soft set (SS), allowing for a more comprehensive representation of the ambiguity in the supplier selection. Moreover, the criteria importance through intercriteria correlation (CRITIC) and the combined compromise solution (CoCoSo) technique is applied to the proposed framework to determine the relative importance of the evaluation parameter and to select the most suitable supplier in the context of sustainable development. The suggested technique was implemented and evaluated by applying it to a manufacturing company as a case study. The outcomes reveal that the approach is practical, efficient and produces favorable results when used for decision-making purposes. Evaluating and ranking of efficient suppliers based on their sustainability performance can be effectively accomplished through the use of PiFS-topology, thus facilitating the decision-making process in the CE and Industry 4.0 era.</p></abstract>

An integrated model for rice supplier selection strategies and a comparative analysis of fuzzy multicriteria decision-making approaches based on the fuzzy entropy weight method for evaluating rice suppliers.

Rice, being a staple food in many countries, necessitates the identification of reliable suppliers to ensure a steady supply. Consequently, it is vital to establish trustworthy vendors for various types of this essential grain who can meet stringent product quality standards. This study aims to identify, analyze, rank, and select primary rice suppliers. The study emphasizes the importance of selecting and managing suitable providers to meet customer demands, proposes a ranking model for rice suppliers, and introduces developed fuzzy MCDM techniques. It proposes an integrated model for selecting rice suppliers, considering factors related to the processes before, during, and after selecting providers within a defined framework. The outcomes shows that rice supplier selection strategy can efficiently identify reliable rice suppliers, improve buyer value, reduce procurement risk, enhance efficiency, and establish strong supply chain relationships in complex decision-making processes. To assess suppliers, the study introduces two advanced integrated approaches and compares them. The fuzzy entropy weight method (EWM) was used to determine the criteria weights. The ranking of rice suppliers was achieved using a fuzzy multi-objective optimization based on ratio analysis (MOORA), fuzzy complex proportional assessment (COPRAS), and combinations of these two methods in different approaches. The methodology supports decision-makers in a rapidly evolving global environment by assisting importers, traders, suppliers, procurement, and logistics management, particularly for non-rice-cultivating countries in rice importation and supplier selection. The numerical analysis is grounded in a real-world case study of selecting rice suppliers in Jordan. The findings reveal that the various strategies yield both similar and different results. Furthermore, the integrated method is considered the most accurate for evaluating rice imports and suppliers, aligning closely with the reality of the current situation.

EVALUASI SUPPLIER OBAT DI PUSKESMAS XYZ DENGAN MENGGUNAKAN METODE ANALYTICAL HIERARCHY PROCESS (AHP)

The problem with the XYZ Health Center is the slow delivery of goods and claims that are not handled properly and quickly, the number of items that are not in accordance with orders causes the availability of drug stock to become empty. Therefore, it is necessary to set standards in the selection of suppliers by determining the criteria and sub-criteria according to the policy of the Puskesmas in selecting suppliers to supply drug stock availability at the XYZ Puskesmas. The main objective of this research is to find the best supplier ranking according to the criteria and sub-criteria. The research uses the Analytical Hierarchy Process (AHP) method in decision making and expert choice software in processing the data from the questionnaire results. The research results obtained 5 criteria with 15 sub-criteria in selecting suppliers. The quality criteria with a weight of 0,2998 and the drug reliability sub-criteria with a weight of 0,1227 are the criteria and sub-criteria with the highest weighting results. Based on the ranking using the AHP method, PT A is an alternative supplier with the highest weight value, namely 0,2042 and a percentage of 20%, it can be concluded that PT A is the best supplier to be maintained.

Innovative Multi-Criteria Decision-Making Approach for Supplier Evaluation: Combining TLF, Fuzzy BWM, and VIKOR

When confronted with underperforming suppliers, the need to evaluate and improve supplier performance becomes apparent. However, the inherent inaccuracies in information introduce complexity, especially when subjective human judgment is involved in the supplier evaluation process. Associated with such problem, this study presents a novel methodology for supplier performance evaluation in the crumb rubber industry, integrating the Taguchi Loss Function (TLF), fuzzy Best-Worst Method (BWM), and VIKOR technique in group decision-making environment. Aimed at addressing the challenges in industries with variable supplier quality and performance, such as the crumb rubber industry in Indonesia, the methodology was empirically tested to demonstrate its practical utility. The process involved identifying evaluation criteria through literature review tailored to the needs of decision makers (DMs), applying TLF to quantify losses from supplier performance deviations, using fuzzy BWM to determine criteria weights based on the DMs judgment, and employing the VIKOR technique for comprehensive supplier ranking. The findings underscore the methodology's effectiveness in enhancing decision-making, offering a unified metric that accommodates diverse criteria and balances precise data with subjective assessments. This approach simplifies the evaluation process, particularly in situations with conflicting interests among decision-makers. Demonstrating its practical application in the crumb rubber industry, the study highlights the methodology's potential for broader industrial applicability. Future research could explore comparative analyses with other analytical methods, further establishing the methodology's robustness and adaptability in different management contexts.

Fuzzy Multiple Attribute Decision Making and Simple Additive Weighting for Supplier Measurement In Furniture Business

This study delves into the complexities of supplier selection in the furniture industry, where Decision Support Systems play a pivotal role in achieving data-driven, sustainable supplier choices. It underscores the Fuzzy Multiple Attribute Decision Making and Simple Additive Weighting approach, particularly emphasizing Price, response time, and delivery fees as critical factors. The overarching objective is to elevate supplier selection in alignment with furniture companies' specific requirements and strategic goals. Additionally, the Supplier Ranking System leverages Fuzzy Multiple Attribute Decision Making and Simple Additive Weighting techniques, ranking the third Supplier as the top Supplier with a high preference score of 0.90 and the fourth Supplier as the lowest-ranked Supplier with a score of 0.50. Notably, User Acceptance Tests affirm the System's outstanding performance and intense user satisfaction.

Analysis of 220 ml cup supplier selection with AHP method and rating scale: case study PT. sabina tirta utama

PT. Sabina Tirta Utama is a company that produces Bottled Drinking Water (AMDK) in 220 ml cup packaging. The company has problems related to 220 ml cup suppliers such as found in cup packaging with holes when the goods arrive, the quantity does not match the demand so the company needs suppliers who match the company's criteria and produce good quality packaging through supplier selection and assisted with the selection method. decision. The method used in supplier selection is AHP and rating scale. The AHP method is used to obtain global weight values which are then used to assist in ranking with the rating scale method. The purpose of this study was to determine the criteria and sub-criteria for a 220 ml cup and to identify priority suppliers. Supplier selection using 5 criteria and 11 sub-criteria. AHP weighting results on quality criteria is 0.197, price with a weight result of 0.296 delivery with a weight result of 0.401, communication system with a weight result of 0.035, and packaging capability of 0.07. The results of supplier ranking with a rating scale obtained the priority of the first supplier with a final assessment result of 3.551, namely supplier A with a high-performance category. Meanwhile, another alternative priority is supplier B with a final result of 2.641 in the performance category according to standards.

Selection of suppliers using crisp gradual means integral in conjunction with clustering algorithms

It is important for companies to select the right suppliers to deal with in order to avoid any disruptions. Considerable research has been conducted to find the solution for this problem. Previous attempts were made to account for uncertainties and disruptions in decision-making in order to rank suppliers based on different criteria of interest and select the desired resources as needed. This study addresses this issue by grouping suppliers in groups based on their similarities in terms of decision-making standards (criteria). Crisp Gradual Means Integral used in fuzzy analytic hierarchy process is used in conjunction with the clustering technique. By grouping suppliers into clusters, several advantages can be gained by the decisions’ maker firms. For example, a firm can determine the level of similarity between different suppliers when taking into consideration all variables of interest to its decision-makers. When computing this similarity, the decision-makers can substitute any of the suppliers with other supplier from the same group in the event of the occurrence of a negative event that prevents the original selected supplier from fulfilling their obligations.•The crisp gradual means integral is a well know approach used in the fuzzy analytics hierarchy processes.•The proposed methodology adapts the crisp means integral and clustering algorithms.•The resulted outcomes group suppliers into groups to help the decision makers select the best supplier.

A user review data-driven supplier ranking model using aspect-based sentiment analysis and fuzzy theory

Background:The supplier selection problem is a sophisticated decision-making process that involves evaluating multiple factors. While previous research has primarily focused on objective attributes, such as supplier qualifications, product quality, and price, the subjective opinions of users have often been overlooked. However, with the growing importance of user reviews and sentiment analysis in e-commerce, incorporating users’ opinions on supplier products can provide valuable insights. Purpose:This study aims to address the limitations of existing supplier selection approaches by proposing a comprehensive framework that integrates aspect-level sentiment analysis and a fuzzy multi-attribute decision model. The goal is to enhance the decision-making process by considering both objective attributes and subjective opinions. Methods:To achieve this, we develop a novel convolutional neural network (CNN) model with a gating mechanism to perform aspect-level sentiment analysis. Furthermore, we propose a fuzzy multi-attribute decision model that combines the predefined sentiment aspects with traditional evaluation criteria. The model is applied to a dataset specifically designed for automotive component supplier selection. Results:Experimental results demonstrate the superior performance of our approach compared to existing methods and datasets. A case study demonstrates the combination of aspect-level sentiment analysis and the fuzzy decision model allows for a more comprehensive evaluation of suppliers. Conclusion:By integrating aspect-level sentiment analysis and the fuzzy multi-attribute decision model, our proposed framework offers a novel perspective on supplier selection problems. The results highlight the feasibility and superiority of our approach, providing valuable insights for management in making informed decisions. This research contributes to the fields of supplier selection, sentiment analysis, and decision-making, with potential applications in various industries beyond the automotive sector.

A multi-criteria decision-making model for analyzing a project-driven supply chain under interval type-2 fuzzy sets

Reviewing and integrating decisions in the supply chain and project management is crucial. This problem is known as a project-driven supply chain problem. By the growing dependency of project-driven companies on suppliers since they prefer to concentrate on their core and subcontracting processes, the project-based organization's dependence on suppliers has increased. A project-driven supply chain (PDSC) focuses on integrating project management activities into the supply chain. In this paper, a new model is presented for the integration of supply chain and project management decisions. Firstly, a new method is proposed to determine the resilience score of suppliers. This new model consists of three parts: the weighting of criteria, the weighting of experts, and the ranking of suppliers. To give weight to the experts, a new version of the combinative distance-based assessment (CODAS) method is presented based on the average and positive ideal concepts. Also, the importance of the criteria is specified by an expanded best-worst method (BWM). Computing the resilience score of suppliers is done through a developed CODAS method that is extended by the average and positive concepts. Furthermore, in order to consider the uncertainty of project-driven supply chain problems, interval type-2 fuzzy sets (IT2FSs) are used. Secondly, a new subtraction operation is defined to avoid producing negative in recursive critical path method (CPM) calculations in the IT2F environment. Also, the criticality score of the project's activities is defined. According to the criticality and resilience indexes of activities, suggestions are made to project management to prevent delays. Finally, a case study of hospital construction is solved to illustrate the strengths of the proposed model.

The effect of resilience for selection of suppliers with hybrid fuzzy mcdm

The purpose of this study is to investigate the effect of resilience for the selection as well as ranking suppliers with Fuzzy DEMATEL (Case study: Tabriz Compressor Manufacturing Company). The industrial engineers and production planners of Company were included in this study. For information gathering the questionnaire designed by the researchers was used. The Fuzzy DEMATEL method has been used to investigate the factors affecting resilience in the selection of suppliers. Also, Fuzzy VIKOR and COPRAS-G method has been used to compare and to represent ranking suppliers. Since COPRAS-G approach is used to analyze the different alternatives, and to estimate the alternatives based on their significance, degree of utility and the decision makers’ support to make more accurate decisions. In this regard, thirteen criteria have been selected by industrial experts. Based on the results obtained from Fuzzy DEMATEL, cost, quality, delivery time, and risk factor are ranked respectively. Additionally, after comparing Fuzzy Vikor and COPPRAS-G, the results obtained from COPRAS-G showed the best ranked amounts throughout different alternatives. Finally, the results obtained from COPRAS-G showed the traditional criteria in resilience supplier selection ranked the first and supplier selection from the stakeholders' perspectives placed in second order. Furthermore, utility values and ranking candidate alternatives showed that the green supplier (A5) ranked the first, selection of suppliers (A3) was the next, following the traditional criteria (A1), supplier criteria(A4), supplier selection from the stakeholders' perspectives (A2), as the remaining ranks, respectively.