A Base on Fuzzy Theory to Supplier Evaluation and Selection Optimization

Lin, Chun-Tsai

doi:10.1155/2020/5241710

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…In the study by Lin [22], the author chose the three most significant criteria and established a selective supplier evaluation model through fuzzy logic inference using MATLAΒ. It increases the company's efficiency and competitiveness through suppliers' ranking and scale-adapted resource allocation [5,23].…”

Section: Supplier Selectionmentioning

confidence: 99%

Enhancing Supply Chain Resilience and Efficiency through Fuzzy Logic-based Decision-Making Automation in Volatile Environments

Berbiche,

Hlyal,

El Alami

2024

ISI

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In light of recent mutations and economic volatility stemming from unforeseen global events and increasing security concerns, supply chains are confronted with the complex challenge of fulfilling uncertain customer demands while ensuring sustained value addition. This study introduces a novel approach utilizing fuzzy logic decision-making automation to address and mitigate the impact of current disruptions. By employing if-then scenarios, this methodology facilitates the generation of more accurate predictions and smarter supply planning, enabling effective decision-making, particularly in critical areas such as semiconductor supply sourcing. The integration of Artificial Intelligence within this framework provides dynamic visibility into real-world supply chain operations, thereby aiding in more informed and effective regulatory decisions and fostering continuous improvement. The core innovation of this research lies in the development of a unique Mamdani-fuzzy logic model designed to enhance supply planning. This model extends beyond the realm of efficient inventory management to encompass safer demand forecasting and market segmentation, showcasing its superiority in versatility and effectiveness compared to conventional methods, and leveraging expert knowledge to navigate uncertainties. Through the process of fuzzification, relevant indicators such as Consumption Severity, Market Sensitivity, and Commodity Importance are identified and modeled, with input variables being assigned membership functions and categorized into varying degrees of significance. The Mamdani-inference rules are then formulated, and the output of the fuzzy logic model is defuzzified using the centroid technique to derive precise supply quantities. Simulations conducted in MATLAB demonstrate the model's capacity to convert uncertainty into optimal supply measures across diverse scenarios, thereby enhancing safety and efficiency within the supply chain by minimizing excess inventory and preventing stockouts. This hybrid approach, combining mathematical reasoning with human expertise, validates the efficacy and robustness of fuzzy logic as a potent cognitive and modeling technique for facilitating precise and responsive decision-making in the face of unexpected incidents, imprecision, and qualitative factors affecting the supply chain.

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Section: Supplier Selectionmentioning

confidence: 99%

Enhancing Supply Chain Resilience and Efficiency through Fuzzy Logic-based Decision-Making Automation in Volatile Environments

Berbiche,

Hlyal,

El Alami

2024

ISI

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Fuzzy Logic Model for the Evaluation of the Optimal Ready-Mixed Concrete Supplier Using a Fuzzy Neural Network in X-FUZZY

Guerreros,

Quijano,

Meza

2024

Communications in Computer and Information Science

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Supplier Selection Method for Complex Product Based on Grey Group Clustering and Improved Criteria Importance

Xu,

Liu,

Liu

2023

Int J Comput Intell Syst

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Supplier selection is of great significance and role, which influence the quality of major product development, and economic security and life safety. However, there exists a variety of uncertain information as a result of evaluation experts’ strong subjective consciousness and complexity of decision environment in the process of supplier selection evaluation. To deal with these problems, by exploiting gray incidence analysis, cloud models and TOPSIS, we establish a multi-attribute decision-making supplier selection method for complex product based on gray group clustering and improved criteria importance through intercriteria correlation, and then a case verifies the validity and feasibility of the proposed method. The results show that (1) the proposed model can provide a better portrayal of the uncertainty of the evaluation process in terms of both the fuzziness of the semantic concept and the randomness of the affiliation degree, while taking into account the differences between the evaluated solution and the positive and negative ideal solutions. (2) The proposed model can fully voice the decision-maker’s attitude on the basis of available information, allowing the decision-making process to be better tailored to reality by taking into account the ambiguity and randomness of the evaluation process.

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A Base on Fuzzy Theory to Supplier Evaluation and Selection Optimization

Cited by 3 publications

References 7 publications

Enhancing Supply Chain Resilience and Efficiency through Fuzzy Logic-based Decision-Making Automation in Volatile Environments

Enhancing Supply Chain Resilience and Efficiency through Fuzzy Logic-based Decision-Making Automation in Volatile Environments

Fuzzy Logic Model for the Evaluation of the Optimal Ready-Mixed Concrete Supplier Using a Fuzzy Neural Network in X-FUZZY

Supplier Selection Method for Complex Product Based on Grey Group Clustering and Improved Criteria Importance

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