Supplier Selection Research Articles

A multi-objective optimization approach for green supply chain network design for the sea cucumber (Apostichopus japonicus) industry

In China, aquatic supply chain network design does not include the green concept or the coordination of environmental and economic performance. Sea cucumber (Apostichopus japonicus) is an aquatic product of high economic value; however, studies on sea cucumber supply chain network optimization are lacking. This study is the first to design the sea cucumber supply chain and construct an optimization model. Considering the characteristics of the sea cucumber industry, LCA for Experts software and the CML-IA-Aug. 2016-world method were used to assess each aquaculture model's global warming potential (GWP), as the environmental performance indicator. In addition, multi-objective genetic algorithm (MOGA) coupled with Modified Technique for Order of Preference by Similarity to Ideal Solution (M-TOPSIS) integrates yield production, economic benefits, and environmental performance. The results demonstrated that cage seed rearing (CSR) combined bottom sowing aquaculture (BSA) represents the best production strategy upstream of the sea cucumber supply chain. In the downstream, the best proportion of sales channels in supermarkets, boutique stores and online shops accounted for 14.79 %, 58.02 % and 27.19 % of the production, respectively. The proposed optimization scenario 4 (S4) can increase product profit by 27.88 % and reduce GWP by 56.89 %. The following improvement measures are proposed: using sea cucumber aquaculture industry standards (cleaner production and green supplier selection) to regulate the behavior of enterprises, adopting an ecological and green production strategy, eliminating high-energy consumption and high emission production practices, and promoting widespread adoption of green consumption concepts. Finally, these measures may improve the sea cucumber supply chain, achieve coordinated environmental and economic performance development in the sea cucumber industry, and provide guidance for green optimization of other aquatic product supply chains in China.

Evaluation of strawberry supply chain efficiency level with data envelopment analysis (DEA) model approach in Lumbung Stroberi, Pandanrejo Village, Batu City

Supply chain activities are one of the considerations for business actors. However, several problems within these activities lead to inefficiencies in the performance of supply chain actors. This study aimed to describe the strawberry supply chain system and analyze its efficiency level at Lumbung Stroberi, and evaluate supplier selection based on the supply chain performance efficiency results. The respondents of this study were five partner farmers who supplied Lumbung Stroberi and were interviewed directly using questionnaires. Data analysis was conducted using two methods, namely descriptive analysis and quantitative analysis using the Data Envelopment Analysis (DEA) model approach. The study’s findings revealed the occurrence of supply chain activities from suppliers to Lumbung Stroberi and the efficiency level of the strawberry supply chain, with three suppliers operating efficiently. This supplier selection based on supply chain performance efficiency results is crucial evaluation for maintaining the efficient suppliers and improving the inefficient ones.

SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN KUALITAS DEPOT AIR MINERAL ISI ULANG MENGGUNAKAN METODE TOPSIS (TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION)

The Decision Support System (DSS) for determining the quality of mineral water depots using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is an application designed to assist mineral water depot managers in selecting the best mineral water supplier based on certain criteria. The TOPSIS method is used to solve multi-criteria problems by considering the relative proximity to ideal solutions and anti-ideal solutions. First, relevant criteria for assessing the quality of mineral water are selected, including physical, chemical, and microbiological parameters. Then, mineral water quality data from various suppliers is processed and normalized. Next, the normalized decision matrix is used to calculate the ideal solution and anti-ideal solution matrices. After that, a relative closeness score for each supplier is calculated based on the Euclidean distance to the ideal and anti-ideal solutions. The results of the TOPSIS analysis are used to provide recommendations for the best mineral water suppliers. By using this system, mineral water depot managers can optimize supplier selection based on predetermined quality criteria, thereby increasing customer satisfaction and maintaining the reputation of the mineral water depot in the market.

Employing fuzzy DEA for Green-resilient supplier selection in an electronic industry of household appliances: a case study (Snowa)

Employing fuzzy DEA for Green-resilient supplier selection in an electronic industry of household appliances: a case study (Snowa)

Multi-CODP decision models for supplier selection and order allocation in customized logistics service supply chain

Multi-CODP decision models for supplier selection and order allocation in customized logistics service supply chain

Two-stage consensus reaching process in social network large group decision-making with application to battery supplier selection

Trust relationships play an important role in social network large group decision making (SN-LGDM). Extant studies overlook the self-adjustment stage of decision makers (DMs) and the binding force of subgroup on preference adjustment in consensus reaching process (CRP). This article proposes a new two-stage CRP for SN-LGDM considering self-adjustment and binding force of subgroup. A new trust propagation approach is presented based on individual trust level. An improved grey clustering algorithm is devised based on the fusion of preference similarity and trust relationship. The preference stability and the awareness of big picture are defined to determine the weights of DMs. The subgroup size, cohesion and influence are integrated to determine the weights of subgroups comprehensively. For the new two-stage CRP, the first stage is self-adjustment stage. DMs may proactively choose to adjust the preference or update the trust relationship according to the degree of matching between the trust relationship and preference difference. In the second stage, four scenarios are identified and the corresponding adjustment strategies are given considering the intention of DM to modify and the binding force of subgroup, which makes the consensus more consistent with reality. A battery supplier selection case is offered to illustrate the proposed method.

Quintic fuzzy sets: A new class of fuzzy sets for solving multi-criteria decision-making problems under uncertainty

This study proposes a new class of fuzzy sets called Quintic Fuzzy Set (QuFS), where the sum of the fifth power of the membership degree and non-membership degree is less than or equal to one. We introduce the complement operator and basic operations on QuFS and further develop various theorems based on these fundamental set operations. We also present the standard modal operators on QuFS and discuss its elementary properties. We then define a novel score and accuracy function for ranking QuFS, as well as the Euclidean and Hamming distances between two QuFSs. A supplier selection example is presented in detail to illustrate the viability and utility of the proposed QuF-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. We also present a multi-criteria decision-making (MCDM) problem on COVID-19 lab selection to perform a comparative analysis of QuFS with Fermatean Fuzzy Set (FFS) and Pythagorean Fuzzy Set (PFS). Furthermore, we demonstrate an existing real case study on selection of Health care waste treatment (HCWT) technique in India and perform a comparative analysis with TOPSIS, SAW (Simple Additive Weighting) and WASPAS (Weighted Aggregated Sum Product Assessment) MCDM methods in QuF framework. With the advent of QuFS, a decision-maker can now employ the fuzzy notion to manage complicated and uncertain human judgements. The comparative results indicate that the suggested QuF measures and QuF MCDM technique outperform other fuzzy concepts regarding effectiveness and validity. Due to the restrictive nature of the current fuzzy set theories, the decision-makers in some complex, uncertain real-life problems are not free to choose the degree of membership and non-membership of their choice. Compared to the Intuitionistic Fuzzy Set (IFS), PFS, and FFS, the QuFS developed in this study has a bigger space grade space. This feature of QuFS enables decision-makers to structure complex, uncertain circumstances with greater flexibility in terms of membership and non-membership degrees.

Decision Support System for Single-Valued Neutrosophic Aczel–Alsina Aggregation Operators Based on Known Weights

Multiattribute decision making (MADM) approach is a well-known decision-making process utilized in a variety of fields such as solid waste management, renewable energy resources, air quality assurance, hotel location decision, sustainable supplier selection, partner recognition, green supplier enterprises, game theory, construction development authority, and weapon group target estimation. The aggregation operators (AOs) are essential components of the decision-making process and have a great capability to deal with ambiguous and unpredictable information in the different fields of fuzzy environments. In this article, we expressed the theory concepts of single-valued neutrosophic (SVN) sets (SVNS) and also characterized their basic operations. The power aggregation tools are allowed to input arguments to support each other among different arguments. Recently, Aczel–Alisna aggregation tools conquered great attention from several research scholars. We also exposed some reliable operations of Aczel–Alsina aggregation models under the consideration of SVN information. We established a series of new approaches, including the “single-valued neutrosophic Aczel–Alsina power weighted average” (SVNAAPWA) operator and “single-valued neutrosophic Aczel–Alsina power weighted geometric” (SVNAAPWG) operators. To show the effectiveness and compatibility of derived approaches, some prominent characteristics are also established. We constructed a MADM technique to solve an application of engineering and construction materials under consideration of our derived methodologies. An experimental case study is also presented to determine a suitable optimal option from a group of options. To find the flexibility of our proposed work, we provided a comparative study that compares the results of existing AOs with our proposed work. A comprehensive overview is also presented here.

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

Green supplier selection under supply risks using novel integrated fuzzy multi-criteria decision making techniques

Green supplier selection is one of the core business processes in the practice of sustainable supply chains, which enables firms to improve their performance to reduce harmful environmental impacts in all inbound activities. In this challenging and more dynamic business environment, materials supply can suffer from disruptions. This study aims to select suppliers based on environmental aspects by considering supply risks under an uncertain environment. Combined multi-criteria decision-making techniques are developed to address such complexity. Integrated fuzzy Analytical Hierarchy Process-Combined Compromise Solution (Fuzzy AHP-CoCoSo) and Fuzzy Stepwise Weight Assessment Ratio Analysis-Failure Mode and Effects Analysis (Fuzzy SWARA-FMEA) are applied to evaluate suppliers based on green criteria and supply risks. Data Envelopment Analysis (DEA) is used to aggregate the overall score for the final selection. A case study of green supplier selection in a manufacturing company is presented. Extensive analysis is performed to investigate the dependencies of supply risks based on the supplier selection criteria.

A novel neutrosophic cubic MADM method based on Aczel-Alsina operator and MEREC and its application for supplier selection

Neutrosophic cubic set (NCS) can process complex information by choosing both interval value and single value membership and indeterminacy and falsehood components. The aggregation operators based on Aczel-Alsina t-norm and t-corm are quite effective for evaluating the interrelationship among attributes. The purpose of this paper is to diagnose the interrelationship among attributes with neutrosophic cubic information, and propose a multi-attribute decision-making(MADM) method for supplier selection problem with unknown weight under neutrosophic cubic environment. We defined neutrosophic cubic Aczel-Alsina (NC-AA) operator and neutrosophic cubic Aczel–Alsina weighted arithmetic average (NCAAWAA) operator, then we discussed various important results and some properties of the proposed operators. Additionally, we proposed a MADM method under the presence of the NC-AAWAA operator. When the weights of attributes are unknown, we use the MEREC method to determine the weights. Later, the NC-AAWAA operator and MEREC method are applied to address the supplier selection problem. Finally, a sensitivity analysis and a comparative analysis are conducted to illustrate the stability and superiority of the proposed method. The results show the NC-AAWAA operator can handle the interrelationship among complex information more effectively, and MEREC method can weight the attributes based on the removal effect of a neutrosophic cubic attribute.

A two-stage decision model for sustainable-resilient supplier selection and order allocation under uncertain environment

PurposeThe paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.Design/methodology/approachFirst, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.FindingsThe paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.Originality/valueThe paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

Developing a hybrid methodology for green-based supplier selection: Application in the automotive industry

The green performance values of businesses are of great importance in terms of sustainability, which includes long-term economic, social, and environmental effects. Thus, today, enterprises and managers are increasingly interested in this issue, and related topics, including supplier selection, have been inserted into decision-making procedures. However, how to predict the effects of green performance criteria, which represent environmental sustainability, on social and economic sustainability remains unclear. In this regard, the main purpose of this research is to develop a supplier selection methodology considering green performance criteria by applying multiple regression analysis and the Evidential Fuzzy Multi-Criteria Decision Making (F-MCDM) method based on Dempster-Shafer Theory (DST), which are both powerful methods in statistical analysis and decision-making under uncertainty. In the first phase of the research, variables that significantly affect green performance have been determined by testing the eight generated hypotheses with multiple regression analysis. Then, the best supplier was determined using those green supplier selection criteria in the Evidential F-MCDM method. Since using environmentally hazardous paints in the production process continues, the automobile paint production sector has been chosen as the application area of this green-based supplier selection methodology. In this respect, green dynamic capacity, green purchasing, eco-design, investment recovery, and green product innovation variables have been inserted into the Evidential F-MCDM method as the determinant variables of green performance. This research reveals that integrating multiple regression and Evidential F-MCDM methods can be a hybrid methodology in supplier selection. Thus, a different perspective is introduced into the green supplier selection decision-making process by considering the effects of criteria in the MCDM model on green performance. This innovation enhances the criteria determination and selection processes in classical MCDM approaches. In addition, green dynamic capacity is the most critical criterion in supplier selection based on their green performance, especially in the scope of this research.

A new energy vehicle battery supplier selection using SWARA-MEREC-MARCOS approach under probabilistic triangular intuitionistic hesitant fuzzy environment

In this manuscript, we construct a Multi-Criteria Decision-Making (MCDM) model to study the new energy vehicle (NEV) battery supplier selection problem. Firstly, we select criteria to build an evaluation index system. Secondly, SAWARA and MEREC methods are used to calculate subjective and objective weights in the ranking process, respectively, and PTIHFS (Probabilistic Triangular Intuitionistic Hesitant Fuzzy Set) is employed to describe the decision maker’s accurate preferences in performing the calculation of subjective weights. Then, the game theory is used to find the satisfactory weights. We use TFNs to describe the original information in the MARCOS method to obtain the optimal alternative. Finally, a correlation calculation using Spearman coefficients is carried out to compare with existing methods and prove the model’s validity.

The Effect of Procurement Practices on the Public Project Performance: The Case of North Showa Zone, Oromiya Region, Ethiopia

The overall objective of this study was to assess the effects of procurement practices on the performance of public projects in the case of North Shoa Zone. The specific objectives of the study were, to find out the effects of procurement planning, supplier selection process, procurement contract management and procurement staff competency on the performance of projects. The study took a quantitative research approach and explanatory research design. A census sampling technique was employed to select 114 employees related to project procurement practices and project management as respondents. All of the questionnaires were submitted back for analysis, representing a response rate of 100%. A structured questionnaire was administered to collect data from the respondents. The data was finally analyzed using Statistical Package for Social Sciences (SPSS) V.28 to compute descriptive statistics such as the mean and standard deviation and inferential statistics like Pearson’s correlation analysis to obtain findings vital for the research study. In addition, regression analyses were conducted to estimate the average effects of procurement practices on public project performance. Majority of the respondents indicated that procurement practices affect project performance to a large extent. The regression analysis result indicated that procurement planning, supplier selection process, procurement contract management, and procurement staff competency had significant positive influence on performance of projects. The analysis further revealed that the four independent variables in this study accounted for around 68 % of change in project performance. It therefore recommended that implementing procurement practices effectively helps to improves projects performance.

Green Supplier Selection using MCDM: A Comprehensive Review of Recent Studies

Green Supplier Selection using MCDM: A Comprehensive Review of Recent Studies

Consistency Improvement in the Analytic Hierarchy Process

Consistency checking is one of the reasons for the Analytic Hierarchy Process (AHP) leadership in publications on multiple criteria decision-making (MCDM). Consistency is a measure of the quality of data input in the AHP. The theory of AHP provides indicators for the consistency of data. When an indicator is out of the desired interval, the data must be reviewed. This article presents a method for improving the consistency of reviewing the data input in an AHP application. First, a conventional literature review is presented on the theme. Then, an innovative tool of artificial intelligence is shown to confirm the main result of the conventional review: this topic is still attracting interest from AHP and MCDM researchers. Finally, a simple technique for consistency improvement is presented and illustrated with a practical case of MCDM: supplier selection by a company.

Enhancing sustainability in supply chain management using softmax Schweizer-Sklar information aggregation

Selecting sustainable suppliers is crucial to achieving sustainability objectives within the supply chain. However, the inherent complexity and uncertainty associated with evaluating supplier performance necessitate the use of fuzzy decision-making tools. This paper proposes the use of cubic intuitionistic fuzzy sets (CIFS) with four aggregation operators (AOs) based on Schweizer-Sklar (SS) t-norm and t-conorm: cubic intuitionistic fuzzy softmax Schweizer-Sklar weighted average (CIFSSSWA), cubic intuitionistic fuzzy softmax Schweizer-Sklar ordered weighted average (CIFSSSOWA), cubic intuitionistic fuzzy softmax Schweizer-Sklar weighted geometric (CIFSSSWG), and cubic intuitionistic fuzzy softmax Schweizer-Sklar ordered weighted geometric (CIFSSSOWG). The inclusion of pseudocodes increases practical application. An exhaustive sensitivity analysis assesses the influence of different parameters on the effectiveness of these techniques. The advantages of precision, efficiency, and execution time are underscored in the comparative analysis, which offers valuable insights that can inform more informed decision-making regarding the selection of green suppliers. The results demonstrate the efficacy of all the proposed approaches in providing a comprehensive and accurate evaluation of green suppliers. This study helps green supplier selection practices get better and gives practical advice to supply chain professionals who want to make their operations more sustainable.

Cooking Up a Sustainable Future: insights of Circular Economy in the Restaurants

Restaurants increasingly prioritize promoting sustainability within their kitchen’s operations, focusing their offers on valuing raw materials as a central point of gastronomy, thus highlighting the importance of local, responsible consumption and respect for nature and the environment. The Circular Economy (CE) emerges as a new sustainability paradigm, uncovering valuable opportunities for organizations and customers, in harmony with the environment. This study aims to characterize good sustainable and circular practices in the restaurant sector, understanding how endogenous products can enhance food waste reduction. Through a qualitative methodology, nine semi-structured exploratory interviews were carried out with restaurant experts, to gain in-depth knowledge into sustainable and CE practices used in restaurants. By conducting a thematic analysis, three main themes emerged 'Positioning and Branding,' 'Circular Economy,' and 'Product and Food Waste'. 'Positioning and Branding' covers the evolution of an eco-conscious mindset in organizations, from the inception of sustainable thinking to the imperative for change. It emerges from the categories: ´Organization, actions, and partnerships’, ‘Challenges and success factors’, and ‘Awareness’. The theme 'Circular Economy,' presents circular practices within daily restaurant operations, their understanding of circular concepts, and the progress made in adopting these behaviors. It arises from three categories: ‘Circularity’, ‘Resource Management’ and ‘Transition’. The 'Product and Food Waste' theme addresses concerns related to product and food waste, including supplier selection, innovative gastronomic offerings, and food waste minimization strategies. It is based on the categories: ‘Suppliers and Raw Materials’, ‘Food Waste Minimization”, and ‘Gastronomic Offer’.The findings reveal a growing awareness of the need for sustainability and circular practices in the restaurant sector. Organizations are actively seeking ways to reduce food waste, optimize sourcing, best operating procedures and offer more sustainable menus representing significant progress toward a more sustainable future in the restaurant business.

Supplier Selection Modeling and Analysis in the Metal Casting Industry Using Analytical Hierarchy Process

This study presents a supplier selection problem of a raw material using an analytical hierarchy process. In the existing process, the delivery of raw materials experienced delays and impacted the production process. Therefore, this paper aims to determine the criteria for supplier selection and provide recommendations for the best vendor to be selected from the last request for quotation document. Analytical Hierarchy Process (AHP), as a multi-criteria approach, was utilized in this research, starting from determining criteria, the weight of criteria, and the final score for each supplier. Through discussions with the company’s expert, procurement department, and users, as well as a review of the previous studies, this research defined three criteria, each consisting of three sub-criteria. The AHP approach was utilized to evaluate and determine the weights for the three criteria, yielding the following results: quality (62%), price (28%), and delivery (10%). The identified criteria, sub-criteria, and respective weights are subsequently utilized in a supplier selection scenario. Three suppliers of mild steel raw materials were evaluated using the weights of the criteria and sub-criteria obtained. Supplier 1 was selected because of having the higher alternative value of 0.602. The use of AHP in supplier selection is often impractical and contains subjectivity. Therefore, further research can be performed by integrating AHP with other methods, such as weighted scoring, to facilitate further the vendor selection process and integration with other methods, such as fuzzy logic, to reduce subjectivity.