Supplier Selection Research Articles

Multicriteria decision support under uncertainty: combining outranking methods with Bayesian networks

AbstractAssessing alternative solutions that have uncertain evaluations in conflicting multiple criteria is not straightforward. Probabilistic models such as Bayesian networks (BNs) can effectively model and represent the uncertainty in such problems, but they do not include built-in mechanisms to guide different decision makers (DMs) with varying preferences toward the final decision. We propose a systematic approach to combine outranking methods with BNs to provide decision support for solutions with multiple and conflicting criteria under uncertainty. The proposed approach is based on Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), and it offers different levels of precision and flexibility to the DMs in assessing the solutions. Our approach includes graphical tools and summary metrics to enhance the presentation of its results to the DMs. We test our approach with a case study on supplier selection where the uncertainty in supplier performances is modeled with a BN. We demonstrate that the proposed approach can enable the joint use of multiple criteria techniques with probabilistic modeling techniques like BNs to provide decision support in complex environments including uncertainty.

Analysis of coffee bean supplier selection using AHP-TOPSIS methods: case study on a ready-to-use coffee powder company

The global market competition that companies must face along with increasingly competitive market developments means that each company has its own challenges in preparing competitive strategies. PT. XYZ is a company that operates in the field of coffee bean processing. The related problems that occur are frequent delays in sending raw materials by suppliers which results in delivery delays, which means they are related to supplier selection problems. Therefore, in this research, the AHP TOPSIS method was used to conduct supplier analysis, AHP is utilized to make decisions and consider multiple factors, subsequently ranking them. Meanwhile, TOPSIS is employed to address multi-criteria problems by comparing the best alternative with the worst alternative among many options. The results of this research are that Supplier 4 is in first place with a preference value of 0.6510, while the second and third places are occupied by Supplier 1 and Supplier 3 respectively.

A decision support system for sustainable supplier selection problem: Evidence from a radiator manufacturing industry

Supplier selection is the basis of a successful supply chain. It is also a key factor in improving the competitiveness of an organization. Being a complex process, the supplier selection plays an important role in upgrading the supply chain. The purpose of this research is to present an integrated approach based on fuzzy best-worst method (BWM) and failure mode and effects analysis (FMEA) for sustainable supplier selection considering seasonal quantity discounts and supplier risk for a radiator manufacturing company. Therefore, as the aim implies, this study addresses how organizations develop a sustainable supplier selection framework by integrating linear programming and decision-making in presence of seasonal discounts. To this end, fuzzy BWM and FMEA were employed to calculate weighting factors for different selection criteria and evaluate the supplier risk, respectively. Then, suppliers were ranked using the so-called technique for order of preference by similarity to ideal solution (TOPSIS). Finally, the discount type was examined by the LP-metric method. Results of the fuzzy BWM showed that the customer satisfaction was the criterion of highest priority, followed by long-term relationship, and then pollution control, among the total of 13 criteria considered in this work. Outputs of the TOPSIS referred to the supplier 3 as the top-ranked supplier, followed by supplier 6 and then supplier 1.

An integrated best–worst method and fuzzy TOPSIS for resilient-sustainable supplier selection

Achieving a balance between economic, environmental, and social factors in supplier selection while prioritizing business continuity poses a considerable challenge. It is imperative to guarantee that selected suppliers adhere to sustainability and resilience requirements while supporting the company’s economic advancement. This study addresses this challenge through a novel approach that combines the Best–Worst Method (BWM) with the Fuzzy Technique Order of Preference by Similarity to Ideal Solution (F-TOPSIS). Integrating these methodologies reduces the burden of pairwise comparisons, a common challenge in supplier selection using multi-criteria decision-making, thereby streamlining the evaluation process. To assess the effectiveness of the proposed model, we implemented our method on an actual case study of e-commerce and conducted a sensitivity analysis of the results. The findings suggest that the proposed method offers improved consistency in rankings across criteria compared to traditional BWM. It also makes a balance between simplicity and accuracy, ensuring that selected suppliers are equipped to handle disruptions and uncertainties. This aligns practical simplicity with theoretical rigor which makes the proposed method more accessible and manageable for practitioners in real-world settings.

Supplier selection in green supply chain management using correlation-based TOPSIS in a q-rung orthopair fuzzy soft environment

Fuzzy hybrid models are efficient mathematical tools for managing unclear and vague data in real-world scenarios. This research explores the q-rung orthopair fuzzy soft set (q-ROFSS), which presents incomplete and ambiguous details in decision-making problems. The main intention of this study is to describe and evaluate the characteristics of the correlation coefficient (CC) and weighted correlation coefficient (WCC) for q-ROFSS. Also, the technique for order preference should be enhanced by similarity to the ideal solution (TOPSIS) with extended measures in q-ROFSS settings. Furthermore, we integrated mathematical formulations of correlation obstructions to confirm the consistency of the planned technique. It helps handle difficulties involving multi-attribute group decision-making (MAGDM). Moreover, a numerical illustration is presented to clarify how the advocated decision-making methodology can be implemented in evaluating suppliers in green supply chain management (GSCM). As a result, each alternative is assessed using multiple criteria, such as quality and reliability, capacity and scalability, compliance and certifications, and sustainability practices. The technique proposed in this study retains the selected research's specific structure more effectively than current techniques. A comparative analysis further substantiates the feasibility and effectiveness of the proposed approach over other decision-making techniques.

Selection of the best coffee bean supplier by using technique method for order preference by similarity to ideal solution (topsis) at CV. ORO coffee GAYO

Abstract CV. Oro Coffee Gayo is an industrial sector that operates in the field of bean processing and is also exported directly abroad choosing the right supplier. There are many factors that need to be considered when selection a supplier, including quality, price, accuracy of delivery. The company has contracts with 4 suppliers who already have certification in Aceh and 10 suppliers outside the contract. This research aims to obtain supplier priorities based on the weight of selection criteria and sub-criteria in determining the best coffee bean supplier using the TOPSIS method. The determination of criteria and sub-criteria is based on expert answers using a closed questionnaire. Assessment of the level of importance of the weight of each criterion, sub-criteria and alternative based on the AHP questionnaire and Likert Scale questionnaire. The results of the research show that there are 6 criteria with 13 sub-criteria, where the criterion that has the largest weight is K1=0,37, the sub-criterion that has the largest weight is SK4, appropriateness of price and quality, 0,8771 and supplier 4 has the largest preference value, namely 0,6315. This shows that supplier 4 is ranked first as the best or priority supplier at CV. Oro Gayo.

An Incentive Mechanism-Based Minimum Adjustment Consensus Model Under Dynamic Trust Relationship.

In traditional group decision making, the inconsistent experts are usually forced to make compromises toward the group opinion to increase the group consensus level. However, the strategy of reaching group consensus via an incentive mechanism encouraging adjustment of preferences is more effective than forcing, which is the aim of this article. Specifically, this article establishes a novel incentive mechanism to support group consensus under dynamic trust relationship. First, the supremum and infimum incentives-based rule driven by trust relationship is defined. Based on the assumption that if incentive conditions are met, then experts will be willing to adjust their preferences, the incentive behavior-driven minimum adjustment consensus model is developed to generate optimal incentive-based recommendation preferences. Thus, the proposed incentive mechanism can effectively reduce the preference adjustment cost and promote group consensus reaching. Third, the updated trust relationships between experts are shown to be strengthen by the proposed incentive-driven preference revision. Consequently, the optimization model based on trust interaction relationship is constructed to obtain the final group preference matrix. Finally, a supplier selection case of high-end medical equipment is provided to illustrate the proposed method and show the rationality and advantages of the proposed methodology with both a sensitivity analysis and a comparison analysis.

Supplier Selection Optimization to Enhance Procurement Strategies in Power Generation Operation and Maintenance Service

Supplier Selection Optimization to Enhance Procurement Strategies in Power Generation Operation and Maintenance Service

Application of the analytic network process method in the selection of raw material suppliers for yarn

Application of the analytic network process method in the selection of raw material suppliers for yarn

A Procedure for Choosing among Different Solutions to the Multi-Criteria Supplier Selection Problem along with Two Solution Methods

A Procedure for Choosing among Different Solutions to the Multi-Criteria Supplier Selection Problem along with Two Solution Methods

Green Supplier Selection Using Fuzzy AHP, Fuzzy TOSIS, and Fuzzy WASPAS: A Case Study of the Moroccan Automotive Industry

The green supplier selection presents numerous challenges, from initial assessment to final selection, which specialists in this field of supplier management often encounter. Among the techniques that aim to meet these challenges that are continually progressing is the creation and development of high-quality decision-making tools. In this study, the assessment of suppliers hinges on both traditional and environmental factors. A framework of multi-criteria decision-making (MCDM) is announced in order to appraise green supplier selection. This framework integrates Fuzzy Analytical Hierarchy Process (AHP) combined with two additional methods: WASPAS (“Weighted Aggregated Sum-Product Assessment”) and TOPSIS (“Technique for Order Preference by Similarity to Ideal Solution”). At the outset, there were five environmental criteria for green supplier selection: “Health and Safety”, “Sustainable Product Design”, “ISO 14001 Certification”, “Investment Recovery”, and “Green Packaging”, along with three conventional criteria including quality, price, and delivery, were pinpointed through a review of literature and expert input to facilitate the MCDM approach. As part of demonstrating the pertinency of the recommended framework, a practical case study of the automotive industry in Morocco is discussed. The results demonstrate that the utilized Fuzzy hybrid methods yield consistent rankings for green suppliers. Additionally, among the four green suppliers, number three obtained the best ranking, which indicates the robustness and performance of the chosen models. Furthermore, this study offers a unified platform for selecting green suppliers under a Fuzzy environment. Therefore, the chosen strategy and its analysis provide relevant data and information to decision-makers for the choice and selection of suppliers. It can also provide and help procurement departments and decision-makers to choose and select the efficient ecological supplier in the global market supply chain.

Research on Supplier Selection of Fresh Food Supermarkets under New Retail Mode

Under the new retail mode, the market competition in the fresh produce industry is fierce. Optimizing the procurement of fresh agricultural products and selecting high-quality suppliers are conducive to reducing costs and enhancing market competitiveness of fresh supermarkets under the new retail mode. This paper constructs a supplier selection index system for fresh produce supermarkets, uses hierarchical analysis and entropy weight method to determine the comprehensive weight of the indexes, and establishes the TOPSIS supplier selection model with gray correlation improvement. And the model is applied to A fresh food supermarket under the new retail mode, and the best supplier is selected for this supermarket from four strawberry suppliers.

Analisis Pemilihan Supplier pada Toko Akbar Telur dengan Menggunakan Metode AHP

Eggs are an important source of animal protein as they contain high quality protein and have high biological value. Egg consumption in Indonesia continues to increase every year. This creates opportunities and competition in the egg market, including in Bogor City. In this context, the selection of the right supplier is key in meeting market demand and increasing the existence of sellers. This research was conducted at Shop Akbar Telur in Bogor Suryakencana Market. Data was collected through direct observation and interviews with the shop owner. The AHP method was used to process qualitative and quantitative data to prioritize suppliers based on price, quality and quantity criteria. The results of the analysis using AHP show that price is the highest criterion in selecting egg suppliers at Akbar Telur, followed by quality and quantity. Suppliers with the highest value for each criterion are SJT1 (price), SJT1 (quality), and SJT1 (quantity). The consistency level of criteria comparison is also acceptable. In selecting egg suppliers, the recommended overall priorities are SJT1, SJT2, and SB. The results of this study contribute to the understanding of supplier selection strategies that can improve store efficiency and performance.

Research on Sustainable Supplier Evaluation Index System in Architectural Design Industry

Sustainable development has been popularized and emphasized in many industries. Great attention has been paid to the sustainability performance of the whole supply chain, with evaluating suppliers’ sustainability being particularly critical. However, research on sustainable development in the consulting service industry remains to be discovered, and few academic studies have analyzed the supplier selection indices derived from this industry. This paper conducted a case study in a design institute using the Delphi method to investigate the concept and characteristics of sustainable suppliers. Keywords derived from surveys and literature were collected, sorted, and established into a comprehensive hierarchy index system for sustainability evaluation. Finally, based on the improved failure mode and effects analysis (FMEA) and analytic hierarchy process (AHP), the failure risk of the indices was analyzed with weighted risk factors to obtain the high-risk key evaluation indices, which provided a simplified scheme for enterprises to streamline the evaluation process on sustainable suppliers. The results show the reliability of the improved FMEA model by differentiating the risk value and ranking. Suggestions for sustainable improvement are provided.

A novel fuzzy general best–worst method for considering diversity and inclusion in supplier selection programs

A novel fuzzy general best–worst method for considering diversity and inclusion in supplier selection programs

Sustainable supply chain decision-making in the automotive industry: A data-driven approach

The auto parts manufacturing sector faces multifaceted challenges ranging from production planning to sustainability imperatives, necessitating innovative solutions. This study presents an integrated data-driven approach tailored to address these challenges. Leveraging advanced AI techniques, including Convolutional and Recurrent Neural Networks optimized with the Moth-flame Optimization Algorithm (MFO), we accurately predict demand quantities for automotive components. Through empirical validation with Iranian auto parts manufacturers, our model achieves an impressive accuracy rate of over 90 %. Subsequently, Data Envelopment Analysis (DEA) evaluates suppliers not only based on demand quantities but also on their social, economic, and environmental impacts, with a resulting average efficiency score of 0.75. The Best-Worst Method (BWM) further refines supplier selection, leading to the identification of top-performing suppliers with an average score of 0.8. This comprehensive approach enables auto parts manufacturers to optimize production planning processes while aligning with sustainable development goals. The successful application of our model underscores the transformative potential of integrating business analytics and AI in the automotive industry towards sustainability.

Finding the right one: understanding the supplier selection process of social enterprises

PurposeThis study aims to develop an in-depth understanding of how supplier selection helps social enterprises achieve their social missions while maintaining commercial viability.Design/methodology/approachThe paper applies a multiple-case design to study the supplier selection processes of 15 Dutch social enterprises.FindingsSocial enterprises tend to build supply relationships through existing networks and evaluate suppliers based on value alignment, relationship commitment, resource complementarity, and cost. Depending on the possibility of social value creation in supplier selection, the importance of these criteria varies across different social enterprise models and between key and non-key suppliers. Moreover, suppliers’ long-term relationship commitment can help reconcile tensions between the social and commercial logic of a social enterprise and facilitate impact creation.Research limitations/implicationsData collection is limited to the perspectives of buyers – the social enterprises. Future research could collect supplier-side data to explore how they engage with social enterprises during the selection process.Practical implicationsManagers of social enterprises can use our research findings as guidance for selecting the most suitable suppliers, while organizations that want to collaborate with social enterprises should actively build network ties to be identified.Originality/valueWe contribute to the cross-sector collaboration literature by showing the underlying reasons for the preference for network reinforcing and indirect networking in supplier identification. We contribute to the social impact supply chain literature by revealing the critical role of supplier selection in shaping collaboration outcomes.

Developing sustainability, resiliency, agility, and security criteria for cloud service providers’ viability assessment: A comprehensive hierarchical structure

Cloud Service Provider (CSP) selection based on several functional attributes, such as transparency, price, latency, CPU performance, availability, and restorability, has taken much more attention in the Industry 4.0 era. On the other hand, after the COVID-19 pandemic, viability has been noticed as the crucial specification of supply chains to overcome mega crises. This paper proposes a comprehensive hierarchical structure for the viable cloud service provider assessment problem according to the reviewed literature on CSPA, viable supplier selection, and suppliers’ resiliency, sustainability, agility, and security. The proposed framework involves seven criteria, 29 sub-criteria, 181 indices, and the corresponding descriptions.

Navigating Supply Chain Resilience: A Hybrid Approach to Agri-Food Supplier Selection

Globalization and multinational commerce have increased the dynamism and complexity of supply networks, thereby increasing their susceptibility to disruptions along interconnected supply chains. This study aims to tackle the significant concern of supplier selection disruptions in the Thai agri-food industry as a response to the aforementioned challenges. A novel supplier evaluation system, PROMETHEE II, is suggested; it combines the Fuzzy Analytical Hierarchy Process (FAHP) with inferential statistical techniques. This investigation commences with the identification of critical indicators of risk in the sustainable supply chain via three phases of analysis and 315 surveys of management teams. Exploratory factor analysis (EFA) is utilized to ascertain six supply risk criteria and twenty-three sub-criteria. Following this, the parameters are prioritized by FAHP, whereas four prospective suppliers for an agricultural firm are assessed by PROMETHEE II. By integrating optimization techniques into sensitivity analysis, this hybrid approach improves supplier selection criteria by identifying dependable solutions that are customized to risk scenarios and business objectives. The iterative strategy enhances the resilience of the agri-food supply chain by enabling well-informed decision-making amidst evolving market dynamics and chain risks. In addition, this research helps agricultural and other sectors by providing a systematic approach to selecting low-risk suppliers and delineating critical supply chain risk factors. By bridging complexity and facilitating informed decision-making in supplier selection processes, the results of this study fill a significant void in the academic literature concerning sustainable supply chain risk management.

Influence of Procurement Process on Availability of Essential Drugs in Public Health Facilities in Mombasa County, Kenya

Purpose: Procurement process and practices are crucial components in the successful realization of universal healthcare and in particular the availability of essential drugs. Despite multifaceted efforts and inputs to promote the quality of the procurement process challenges still exists in the availability of essential drugs. This study aimed to establish the influence of procurement planning, supplier selection, procurement financing and needs assessment on availability of essential drugs in the public health facilities in Mombasa County, Kenya. This was a cross-sectional descriptive study design which targeted level 4 and 5 health facilities in Mombasa County. The study target population was 1216 health workers who included medical laboratory personnel, clinical officers and medical officers, nurses, pharmacists and pharmaceutical technologists. The study sample was 301. Methodology: Stratified sampling approach was used to distribute the sample among the cadre and random sampling method was used to select the respondents from each strata. Quantitative data was collected using a structured questionnaire. Findings: The study found that procurement planning, supplier selection, procurement, financing and needs assessment were significant factors in influencing the availability of essential drugs with P<0.05. Unique Contribution to Theory, Policy and Practice: The study recommended that counties conduct a comprehensive and regular need assessment of essential drugs in public health facilities, that they develop a robust procurement planning strategy that aligns with the identified needs and available financing, that there should be a transparent and competitive supplier selection process, and to establish strategic partnerships with international organizations, non-governmental organizations, or donor agencies that are committed to improving healthcare access.