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...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.展开更多
In the context of globalization and digitalization, the application of transaction cost theory in supply chain management has become increasingly important. As business environments grow more complex, enterprises face...In the context of globalization and digitalization, the application of transaction cost theory in supply chain management has become increasingly important. As business environments grow more complex, enterprises face challenges in effectively managing supply chain transaction costs. This paper systematically explores the application of transaction cost theory in supply chain management, covering key areas such as supplier selection, supply chain integration, and risk management. The research finds that supplier evaluation models based on transaction costs can help enterprises make more comprehensive selection decisions. In terms of supply chain integration, transaction cost theory provides important guidance for vertical integration decisions and the design of collaboration mechanisms. The application of digital technologies has both reduced traditional transaction costs and introduced new cost considerations. Faced with emerging risks such as cybersecurity and geopolitical issues, enterprises need to adopt dynamic transaction cost management strategies. In the future, the application of transaction cost theory in supply chain management will likely place greater emphasis on interdisciplinary integration and sustainable development, providing theoretical support for enterprises to achieve efficient, flexible, and sustainable supply chain management in the changing global business environment.展开更多
The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,a...The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.展开更多
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 manag...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.展开更多
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 statisti...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.展开更多
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 d...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.展开更多
This paper characterizes quality, budget, and demand as fuzzy variables in a fuzzy vendor selection expected value model and a fuzzy vendor selection chance-constrained programming model, to maximize the total quality...This paper characterizes quality, budget, and demand as fuzzy variables in a fuzzy vendor selection expected value model and a fuzzy vendor selection chance-constrained programming model, to maximize the total quality level. The two models have distinct advantages over existing methods for selecting vendors in fuzzy environments. A genetic algorithm based on fuzzy simulations is designed to solve these two models. Numerical examples show the effectiveness of the algorithm.展开更多
基金supported by Van Lang University,Vietnam and National Kaohsiung University of Science and Technology,Taiwan.
文摘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.
文摘In the context of globalization and digitalization, the application of transaction cost theory in supply chain management has become increasingly important. As business environments grow more complex, enterprises face challenges in effectively managing supply chain transaction costs. This paper systematically explores the application of transaction cost theory in supply chain management, covering key areas such as supplier selection, supply chain integration, and risk management. The research finds that supplier evaluation models based on transaction costs can help enterprises make more comprehensive selection decisions. In terms of supply chain integration, transaction cost theory provides important guidance for vertical integration decisions and the design of collaboration mechanisms. The application of digital technologies has both reduced traditional transaction costs and introduced new cost considerations. Faced with emerging risks such as cybersecurity and geopolitical issues, enterprises need to adopt dynamic transaction cost management strategies. In the future, the application of transaction cost theory in supply chain management will likely place greater emphasis on interdisciplinary integration and sustainable development, providing theoretical support for enterprises to achieve efficient, flexible, and sustainable supply chain management in the changing global business environment.
基金funded by the University of Jeddah,Jeddah,Saudi Arabia,under Grant No.(UJ-23-DR-26)。
文摘The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.
文摘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.
文摘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.
文摘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.
基金the National Natural Science Foundation of China (Nos.70471049 and 70571056)
文摘This paper characterizes quality, budget, and demand as fuzzy variables in a fuzzy vendor selection expected value model and a fuzzy vendor selection chance-constrained programming model, to maximize the total quality level. The two models have distinct advantages over existing methods for selecting vendors in fuzzy environments. A genetic algorithm based on fuzzy simulations is designed to solve these two models. Numerical examples show the effectiveness of the algorithm.