Supplier Selection through Application of DEA

Sahai, Manjari; Agarwal, Prince; Mishra, Vaibhav; Bag, Monark; Singh, Vrijendra

doi:10.5815/ijem.2014.01.01

Cited by 5 publications

(3 citation statements)

References 21 publications

(20 reference statements)

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“…For solving multi-criteria decision problems in which criteria affect each other and have a non-linear correlation, the analytical network process (ANP) was adopted to select the supplier in a group decision making process [12]. The Data Enveloppent Analysis was used to measure the performance of suppliers according to the company's requirements [13]. Other researchers have used a discrete event simulation model to facilitate decision-making to choose the main suppliers for the British automotive industry [14].…”

Section: Supplier Selection Methodsmentioning

confidence: 99%

Suppliers Selection in Consideration of Risks by a Neural Network

Hiri

En-Nadi

Chafi³

2019

IJE

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Faced with the dynamic demands of a changing market, companies are facing fierce competition, which forces them to consider more and more new approaches to improve quality, reduce costs, produce on time, control their risks and remain successful in the face of any disruption. It is clear that the choice of appropriate suppliers is one of the key factors in increasing the competitiveness of companies. Thus, suppliers selection has a very important impact on the control of risks throughout the supply chain and on increase of its performance. Therefore, it is important for managers to realize the long-term impact of their supplier selection strategies on the benefits and effective functioning of the organization. To minimize supply and demand risks, this work presents a generic supplier selection model based on artificial neural networks (ANNs) to help manufacturers to choose the most efficient suppliers and monitor their performance. The results showed that ANNs are very well adapted to our problem since they have provided a very considerable efficiency in terms of the results obtained. Indeed, the application of the ANN will avoid the difficulty of desiging an algorithm to solve our problem, it is through the expertise of the managers in the purchasing department that our ANN will learn to be efficient and serve as a tool to help a decision makers to choose the best suppliers.

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Section: Supplier Selection Methodsmentioning

confidence: 99%

Suppliers Selection in Consideration of Risks by a Neural Network

Hiri

En-Nadi

Chafi³

2019

IJE

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“…Manjari Sahai, Prince Agarwal, Vaibhav Mishra, Monark Bag, Vrijendra Singh analyzed DEA by measuring the supplier performance of multinational telecommunications companies and manufacturing enterprises. The company uses this method to evaluate its suppliers based on their requirements and standards, and finds the best supplier among them [9].Its basic assumption is that all decision making units (DMUs) can reach the optimal efficiency level, but the input and output indicators of different DMUs may be different. The DEA model compares and measures the input and output indicators of different decisionmaking units to obtain the relative efficiency of each decision-making unit, and then provides reference and decision-making basis for decision makers.…”

Section: The Emergence and Development Of Data Envelopment Analysismentioning

confidence: 99%

The Efficiency of University Resource Allocation Based on DEA Model

Qin,

Jiang

2024

Advances in Transdisciplinary Engineering

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The development of universities cannot be separated from the allocation of resources. Conduct empirical research on the efficiency of resource allocation in private universities to help them identify the current situation and problems of internal resource allocation, and promote high-quality development. This paper selects the undergraduate teaching status data of eight private undergraduate universities in Guangxi from 2018 to 2022, and uses DEA model to calculate the resource allocation efficiency of eight private undergraduate universities. The results show that the overall level of resource allocation efficiency of eight private undergraduate universities in Guangxi has maintained an upward trend, and the scale efficiency is significantly higher than the pure technical efficiency, which is the main factor affecting the overall efficiency improvement. Therefore, Guangxi private undergraduate universities should optimize the technical efficiency and management efficiency of resource allocation, strengthen professional construction and improve the quality of talent training.

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“…The selection of a supplier with minimum cost criteria is not effective in advanced supply management [ 16 ]. The multi-criteria decision making (MCDM) method is now commonly used in assessing and selecting suppliers with important criteria, e.g., quality, cost, delivery time, performance, technical capability, communication and coordination, geographical location, attitude, financial position, repair services, certifications, and reputation [ 17 ]. Several methods are reported in the literature such as AHP [ 18 ], fuzzy AHP [ 19 ], VIKOR [ 20 ], fuzzy axiomatic design [ 21 ], fuzzy TOPSIS [ 22 ], TODIM [ 23 ], fuzzy grey relational analysis [ 24 ], fuzzy analytic network process (ANP) [ 25 ], and data envelopment analysis (DEA) [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Development of Best Class Discrete Production Model for Distributed Manufacturing under Industry 4.0

et al. 2022

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Global competitiveness creates a challenge for manufacturing companies to maintain their market share with dynamic customer requirements. Capital investment in machinery does not allow facility expansion to accommodate large orders from customers but to reconfigure the manufacturing enterprise. Distributed manufacturing (DM) is embraced in order to increase facility utilization by decentralizing production. An enterprise in charge of a DM network allows customers to choose the best manufacturers available for their order based on their track record, which is available through historical and online performance data. Furthermore, manufacturers as members of this network may receive orders based on their past performance. Industry 4.0 with all necessary Industrial Internet of Things (IIoT) enables the online monitoring of production key parameters of manufacturers subscribed to a DM network. We develop a new network model of manufacturers teamed under specific terms and conditions to support a group of customers who have specific needs. The proposed model, known as the continuous supervised model, is created with the ARENA simulation software. We demonstrate the effectiveness of our model by contrasting it with the standard practice approach. To ensure the best possible performance, we continuously monitor the cost, quality, delivery time, and production rate indicators of the various manufacturers and update their performance ranking for current and future orders. Furthermore, using the analytic hierarchy process (AHP) approach, a single performance measure based on the four indicators is developed. Implementing the proposed model showed an improvement in the average performance by 51.3%.

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Supplier Selection through Application of DEA

Cited by 5 publications

References 21 publications

Suppliers Selection in Consideration of Risks by a Neural Network

Suppliers Selection in Consideration of Risks by a Neural Network

The Efficiency of University Resource Allocation Based on DEA Model

Design and Development of Best Class Discrete Production Model for Distributed Manufacturing under Industry 4.0

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