Application of Intuitionistic Z-Numbers in Supplier Selection

Intuitionistic fuzzy numbers incorporate the membership and non-membership degrees.In contrast,Z-numbers consist of restriction components,with the existence of a reliability component describing the degree of certainty for the restriction.The combination of intuitionistic fuzzy numbers and Z-numbers produce a new type of fuzzy numbers,namely intuitionistic Z-numbers(IZN).The strength of IZN is their capability of better handling the uncertainty compared to Zadeh's Z-numbers since both components of Z-numbers are charac-terized by the membership and non-membership functions,exhibiting the degree of the hesitancy of decision-makers.This paper presents the application of such numbers in fuzzy multi-criteria decision-making problems.A decision-making model is proposed using the trapezoidal intuitionistic fuzzy power ordered weighted average as the aggregation function and the ranking function to rank the alternatives.The proposed model is then implemented in a supplier selection problem.The obtained ranking is compared to the existing models based on Z-numbers.The results show that the ranking order is slightly different from the existing models.Sensitivity analysis is performed to validate the obtained ranking.The sensitivity analysis result shows that the best supplier is obtained using the proposed model with 80%to 100%consistency despite the drastic change of criteria weights.Intuitionistic Z-numbers play a very important role in describing the uncertainty in the decision makers’opinions in solving decision-making problems.

Selecting suitable key supplier for core components during smart complex equipment central-private enterprises collaborative development process:from two different forms of evaluation information and matching perspective

With the development of central-private enterprises integration,selecting suitable key suppliers are able to provide core components for smart complex equipment.We consider selecting suitable key suppliers from matching perspective,for it not only satisfies natural development of smart complex equipment,it is also a good implementation of equipment project in central-private enterprises integration context.In in this paper,we carry out two parts of research,one is evaluation attributes based on comprehensive analysis,and the other is matching process between key suppliers and core components based on the matching attribute.In practical analysis process,we employ comprehensive evaluated analysis methods to acquire relevant attributes for the matching process that follows.In the analysis process,we adopt entropy-maximum deviation method(MDM)-decision-making trial and evaluation laboratory(DEMATEL)-technique for order preference by similarity to an ideal solution(TOPSIS)to obtain a comprehensive analysis.The entropy-MDM is applied to get weight value,DEMATEL is utilized to obtain internal relations,and TOPSIS is adopted to get ideal evaluated solution.We consider aggregating two types of evaluation information according to similarities of smart complex equipment based on the combination between geometric mean and arithmetic mean.Moreover,based on the aforementioned attributes and generalized power Heronian mean operator,we aggregate preference information to acquire relevant satisfaction degree,then combine the constructed matching model to get suitable key supplier.Through comprehensive analysis of selecting suitable suppliers,we know that two-sided matching and information aggregation can provide more research perspectives for smart complex equipment.Through analysis for relevant factors,we find that leading role and service level are also significant for the smart complex equipment development process.

Supplier Selection Fuzzy Model in Supply Chain Management:Vietnamese Cafe Industry Case

Supplier selection is a vital part of the supply chain and is also a current issue that concerns businesses today as supplier quality directly affects the operations of the organization.Choosing the right supplier can help businesses increase productivity,competitiveness in the market,and profits without having to lower the quality of the products.However,choosing a supplier is not a simple matter,it requires businesses to consider many aspects about their suppliers.Therefore,the goal of this study is to propose an integrated model consisting of two models:Fuzzy Analytics Network Process(Fuzzy-ANP)model and Weighted Aggregated Sum Product Assessment(WASPAS)to solve the problem above.The Fuzzy-ANP model was developed to evaluate the weightings of the supplier selection criteria,and the WASPAS Model was used to rank the suppliers.An example of supplier selection in the coffee industry in Vietnam was studied to validate the model,namely 5 main criteria,with 16 sub-criteria,and 7 suppliers.The model test results show that the Fuzzy ANP and WASPAS integration model was suitable.In future,these developing models can apply to other industries or integrate with other models.

China Procurement-Leveraging Supplier Selection and Supplier Management for Success

Successful China pro- curement is the result of a comprehensive set of complex and high-ly-specialized processes. Dis- tilling this down to a ＂magical recipe＂ for success is no simple task, and there are no shortcuts. However, as a useful reference, two fundamental ingredients for procurement success are： the selection of the right suppliers： and the effective management of chosen suppliers to optimize their performance.

Supplier selection and order splitting modeling in the pr esence of supplier capacity and resilience

To solve the problem of a supplier＇s failure to deliver thus impacting supply chain system performance in the supply chain operating process, a model of supplier selection and order splitting in the context of a multiple sourcing setting is proposed. First, by the analysis of the elements of expected total costs of the buyer firm, namely, expected loss costs, resilience effort costs, supplier maintenance costs, and cycle purchase costs, the expected total costs function is obtained. And then, the effects of supplier characters on the supplier selection and order splitting decisionmaking are investigated by numerical examples. The results show that the maximum delivery capacity, the probability of failure to deliver and the resilience parameters are crucial elements in determining which suppliers should be selected and how to do order splitting between suppliers. Finally, current analyses focus only on the expected total costs of the buyer firm but ignore the suppliers＇ costs： thus, it is more interesting to examine the supplier decisions from both parties＇ points of view.

Comparison on neural networks and support vector machines in suppliers' selection

Suppliers＇ selection in supply chain management （SCM） has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statistical methods. However, neural networks have inherent drawbacks, such as local optimization solution, lack generalization, and uncontrolled convergence. A relatively new machine learning technique, support vector machine （SVM）, which overcomes the drawbacks of neural networks, is introduced to provide a model with better explanatory power to select ideal supplier partners. Meanwhile, in practice, the suppliers＇ samples are very insufficient. SVMs are adaptive to deal with small samples＇ training and testing. The prediction accuracies for BPNN and SVM methods are compared to choose the appreciating suppliers. The actual examples illustrate that SVM methods are superior to BPNN.

Combining TOPSIS and GRA for supplier selection problem with interval numbers

Supplier selection can be regarded as a typical multiple attribute decision-making problem. In real-world situation, the values of the alternative attributes and their weights are always being nondeterministic, and as a result of this, the values are considered interval numbers. In addition, the common approach to measure the similarity between alternatives through their distance suffers from some minor shortcomings. To address these problems, this study develops a novel hybrid decision-making method by combining the technique for order preference by similarity to an ideal solution （TOPSIS） with grey relational analysis （GRA） for supplier selection with interval numbers. By introducing the intervals theory, the extensions of Euclidean distance and grey relational grade are defined. And then a new comprehensive closeness coefficient is constituted for supplier alternatives evaluation based on the interval Euclidean distance and the interval grey relational grade, which could indicate the distance-based similarity and the shape-based similarity simultaneously. A mtmerical example is taken to validate the flexibility of the proposed method, and result shows that this method can tackle the uncertainty in real-world supplier selection and also help decision makers to effectively select optimal suppliers.

An integrated model for supplier selection process

In today’s highly competitive manufacturing environment, the supplier selection process becomes one of crucial activities in supply chain management. In order to select the best supplier(s) it is not only necessary to continuously tracking and benchmarking performance of suppliers but also to make a tradeoff between tangible and intangible factors some of which may conflict. In this paper an integration of case based reasoning (CBR), analytical network process (ANP) and linear programming (LP) is proposed to solve the supplier selection problem.

A Novel Green Supplier Selection Method Based on the Interval Type-2 Fuzzy Prioritized Choquet Bonferroni Means

In view of the environment competencies,selecting the optimal green supplier is one of the crucial issues for enterprises,and multi-criteria decision-making(MCDM)methodologies can more easily solve this green supplier selection(GSS)problem.In addition,prioritized aggregation(PA)operator can focus on the prioritization relationship over the criteria,Choquet integral(CI)operator can fully take account of the importance of criteria and the interactions among them,and Bonferroni mean(BM)operator can capture the interrelationships of criteria.However,most existing researches cannot simultaneously consider the interactions,interrelationships and prioritizations over the criteria,which are involved in the GSS process.Moreover,the interval type-2 fuzzy set(IT2FS)is a more effective tool to represent the fuzziness.Therefore,based on the advantages of PA,CI,BM and IT2FS,in this paper,the interval type-2 fuzzy prioritized Choquet normalized weighted BM operators with fuzzy measure and generalized prioritized measure are proposed,and some properties are discussed.Then,a novel MCDM approach for GSS based upon the presented operators is developed,and detailed decision steps are given.Finally,the applicability and practicability of the proposed methodology are demonstrated by its application in the shared-bike GSS and by comparisons with other methods.The advantages of the proposed method are that it can consider interactions,interrelationships and prioritizations over the criteria simultaneously.

Fuzzy Multi-Objective Decision Model of Supplier Selection with Preference Information

Supplier selection is a multi-objective decision problem, which must be considered many objectives, some objectives are qualitative, and others are quantitative. Meanwhile, manufacturer has preference for different suppliers. In this paper, a new multi-objective decision model with preference information of supplier is established. A practical example of supplier selection problem utilizing this model is studied. The result demonstrates the feasibility and effectiveness of the methods proposed in the paper.

Supplier Evaluation Model Based on Fuzzy Measures and Choquet Integral

To identify interactions among evaluation criteria and describe their importance,a new identification method making use of a fuzzy measure is presented.The relative weight and interaction degree of every evaluation criteria pair are obtained by using the diamond pairwise comparison method.Based on comparison results,the maximum eigenvector method of analytic hierarchy process （AHP）,the hierarchical clustering method,and the phi（s） transformation are utilized to generate values of the fuzzy measure for each subset of the evaluation criterion set.Overall evaluation on each supplier is aggregated by Choquet integral with respect to the fuzzy measure.Finally,an illustrative example demonstrates the practical feasibility and validity of the proposed method.

Study on Evaluation of Supplier's Quality Assurance System

To be a worldwide competitor, enterprise needs to e va luate and select its supplier carefully. Generally, to evaluate a supplier, the evaluating effort is focused on the purchase price, delivery time, product quali ty, etc. The vendors’ quality assurance is seldom considered. However, it reflec ts the ability that a vendor can provide high quality but low cost products cont inuously and stably. In this paper, with the study on evaluation of supplier’s q uality assurance system, a set of methods and indices to supplier’s quality assu rance evaluation is introduced. The indices construct an index system, which is based on the ISO9000 series standards. According to the problem’s character and the requirement of evaluation, all the evaluation indices are set off to three k inds: general index, functional index and protective index. And the evaluation m ethod combines quantitative analysis with qualitative analysis. Firstly, a sensi tive factor model is constructed to estimate the contribution of factors that ha ve key effect on synthetic evaluation in supplier’s quality system. Then, those suppliers having low evaluating value are rejected. Secondly, fuzzy logic is int roduced to evaluate other suppliers synthetically. The rest suppliers are compar able. So, supplier’s quality assurance system can be evaluated in quantity. Afte r unification works, the evaluated suppliers can be ranked. And the best vendor can be selected out intuitively.

A New Decision-Making Model Based on Plithogenic Set for Supplier Selection

Supplier selection is a common and relevant phase to initialize the supply chain processes and ensure its sustainability.The choice of supplier is a multicriteria decision making(MCDM)to obtain the optimal decision based on a group of criteria.The health care sector faces several types of problems,and one of the most important is selecting an appropriate supplier that fits the desired performance level.The development of service/product quality in health care facilities in a country will improve the quality of the life of its population.This paper proposes an integrated multi-attribute border approximation area comparison(MABAC)based on the best-worst method(BWM),plithogenic set,and rough numbers.BWM is applied to regulate the weight vector of the measures in group decision-making problems with a high level of consistency.For the treatment of uncertainty,a plithogenic set and rough number(RN)are used to improve the accuracy of results.Plithogenic set operations are used to deal with information in the desired manner that handles uncertainty and vagueness.Then,based on the plithogenic aggregation and the results of BWM evaluation,we use MABAC to find the optimal alternative according to defined criteria.To examine the proposed integrated algorithm,an empirical example is produced to select an optimal supplier within five options in the healthcare industry.

Improving Supply Chain Performance Through Supplier Selection and Order Allocation Problem

Suppliers play the vital role of ensuring the continuous supply of goods to themarket for businesses.If businesses do not maintain a strong bond with their suppliers,they may not be able to secure a steady supply of goods and products for their customers.As a result of failure to deliver products,the production and business activities of the business can be delayed which leads to the loss of customers.Normally,each trading enterprise will have a variety of commodity supply chains withmultiple suppliers.Suppliers play an important role and contribute to the value of the entire supply chain.Should any supplier encounters a problem,the whole supply chain of businesses will be affected and could lead to not guaranteeing the stable supply to the market.Thus,suppliers can be seen as a threat to businesses where they have the ability to increase input prices or decrease the quality of the required products and services they provide.The quantity of the business,and the supply lead time directly affect the operations and reduce the profitability of the business.The paper mainly focuses on the supplier selection problemunder a variety of price level and product families when using a two-phase fuzzy multi-objective linear programming.The objectives of the proposed model are to minimize the total purchasing and ordering cost in order to reduce the quantity of defective materials and the late-delivery components from suppliers.Moreover,the piecewise linear membership function is applied in themodel to determine an optimal solution which is based on the requirement of decision makers under their fuzzy environment.The results of this study can be applied in various business environment and provide a reliable decision tool for choosing potential suppliers relating to these objectives.Based on the results,the company canmake a good decision on supplier selection;therefore,the company can improve the quality and quantity of their final product.This is because,the best supplier can supply raw material using just-in-time application and reduce production risk on the manufacturing process.

TOCO total assessment tool for decision-making on IP supplier selection

According to the Total Cost of Ownership concept (TOCO), the selection criterion of international procurement suppliers can be classified into two levels, namely Macroeconomic decision-making level and Microeconomic decision-making level. In this paper, a new quantitative method is put forward to accomplish the task of total assessment on the Microeconomic level which analyses all of the quantitative and qualitative factors with regard to the supplier selection. A Microsoft Excel based new application kit named TOCO Total Assessment Tool is introduced. It can calculate the direct cost and the indirect cost conveniently and can help to evaluate the performance of candidate suppliers. To use the tool, the first module called Total Cost Analysis Module is introduced to calculate the total cost of supplier selection, and then the second module named Supplier Evaluation Module is used to evaluate the performance of each supplier. Finally, the results from these two modules are transferred to the Final Comparison Module to get the final decision-making results. In this paper, the supplier selection related factors are discussed; the method of using the tool is illustrated in detail. It is shown that scientific usage of the TOCO Total Assessment Tool can make the decision-making processes of supplier selection in international procurement transparent, easily calculated, and objective. At the end, a practical case is given to clarify the procedure of using the tool.

Measuring the influences of supplier selection and supply chain tasks on firm performance among manufacturing firms in India

The primary objective of this research is to empirically probe the various aspects and variables that have been already addressed in the previous literature related to supplier selection criterion, supply effort management and firm performance. Further, this research aims to develop a measurement framework and pragmatically prove the framework through a measurement model. First, a factor structure for various constructs is made and the initial validity is determined from practicing managers and academicians. This research employs survey method and the data is collected from 358 supply chain professionals working in manufacturing firms in India. A measurement model is developed and proved with various tests of reliability and validity. Finally, three major latent constructs were formulated, namely, criterion of supplier selection, supply effort management and firm performance. The factor scores of these latent variables were used for further analysis. A six-stage approach was followed in the analysis of data. Firm performance was regressed against supplier selection criterion and supply effort management. The results indicate that the predictive variable has positive and significant effect on firm performance and they do not have any interaction and multicollinearity effects.

Combining permissioned blockchain and Bayesian best-worst method for transparent supplier selection in supply chain management

Supplier selection is an important business activity in order to realize the purchasing function in supply chain management.The supplier selection process includes four stages,i.e.,bidding inviting,bidding,group decision-making,and results disclosure,involving the participation of manufacturing service demanders(MSDs),manufacturing service suppliers(MSSs),and decisionmakers.Nowadays,all the participants have raised concerns about the increased transparency in supplier selection.Therefore,this study proposes a transparent supplier selection method by considering the engagement of suppliers.In this method,the Bayesian best-worst method(Bayesian BWM)is used to aggregate decision-makers'preferences into the overall optimal weights of the alternative MSSs,and the MSS with the largest weight is considered the suitable MSS for MSDs.Furthermore,blockchain is introduced to record the decision-making process information about supplier selection through a customized smart contract,where MSSs act as supervisors to supervise the decision-making process through the distributed consensus mechanism rather than directly participate in the decision-making process.Finally,a case study of supplier selection in purchasing vibration acceleration sensors is presented.The result shows that the proposed method can support MSDs in selecting suitable MSS from alternative MSSs by aggregating decision-makers'preferences,and blockchain can provide credible information about the supplier selection process for MSSs,MSDs,and decision-makers.In this way,the transparency of supplier selection is enhanced.

A Decision Model for Supplier Selection Considering Trust

A best selection of suppliers＇ combination can reduce a buyer＇s cost efficiently in SCM. Tt＇ust between suppliers and buyers influences the final choice. In this article a supplier selection model is established considering trust, using integrated analytic hierarchy process （AHP） and linear programming （LP）. This model is applied to supplier selection in Tianjin Electric Construction Company.

Research on Supplier Relationship Management of the Main Manufacturer of Large Aircraft Project:A Psychological Contract Perspective

The main manufacturer and its suppliers＇ relationship management is very important for success of the large aircraft project. The study concentrates on analysis of the relationship management on a psychological con- tract perspective, shows that constructing good psychological contract is useful to promote mutual trust, reduce the transaction cost caused by incompleteness of formal contract, avoid the risk of supplier management between the main manufacturer and suppliers of the large aircraft project. And with further understanding of the dynamic contracting process as well as formation framework of psychological contract between the main manufacturer and suppliers, we can better understand the relationship black-box and provide a new perspective for the study of the relationship management, then given some constructive suggestions to supplier relationship management.

A Model for Selecting a Biomass Furnace Supplier Based on Qualitative and Quantitative Factors

In developing countries,solar energy is the largest source of energy,accounting for 35%–45%of the total energy supply.This energy resource plays a vital role in meeting the energy needs of the world,especially in Vietnam.Vietnam has favorable natural conditions for this energy production.Because it is hot and humid,and it has much rainfall and fertile soil,biomass develops very quickly.Therefore,byproducts from agriculture and forestry are abundant and continuously increasing.However,byproducts that are considered natural waste have become the cause of environmental pollution;these include burning forests,straw,and sawdust in the North;and rice husks dumped into rivers and canals in the Mekong Delta region.Biomass energy is provided in a short cycle,is environmentally safe to use and is encouraged by organizations that support sustainable development.Taking advantage of this energy source provides energy for economic development and ensures environmental protection.Due to the abovementioned favorable conditions,many biomass energy plants are being built in Vietnam.Like other renewable energy investment projects,the selection of the construction contractor,the selection of equipment for the installation of the power plant,and the choice of construction site are complex multi-criteria decisions.In this case,decisionmakers must evaluate many qualitative and quantitative factors.These factors interact with each other and it is difficult to use personal experience to choose the optimal solution for such complex decision-making problems,especially in a fuzzy decision-making environment.Therefore,in this study,the authors use a Multi-Criteria Decision-Making(MCDM)model that uses a Fuzzy Analytic Hierarchy Process(FAHP)model and the Combined Compromise Solution(CoCoSo)algorithm to select biomass furnace suppliers utilizing both qualitative and quantitative factors.Furthermore,the results of this work will provide the first look at a hybrid CoCoSo/FAHP method that decision-makers in other fields can use to find the best supplier.