Interpretive structural modelling to assess third party logistics providers

Gupta, Aman; Walton, Robert O.

doi:10.1504/writr.2016.078155

Cited by 9 publications

(13 citation statements)

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“…ISM enables individuals or groups to develop a map of the complex relationships between the many elements involved in a complex situation [16]. For example, in [17], ISM was used in evaluating and selecting 3PLs based on twelve criteria. The results indicated that cost was the most dependent and least driving force, while the largest and least dependent driving force was financial stability.…”

Section: Literature Reviewmentioning

confidence: 99%

A Proposed Approach for Selecting Third Party Logistic Alternatives

Mostafa

Galal

Elawady

2021

The Egyptian International Journal of Engineering Sciences and

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At present, the demand for a third-party logistics service provider has become an increasingly important issue for companies to improve customer service and reduce logistics costs. Third-party logistics is using another company to perform some tasks. There are many ways to select a third-party logistic provider, like Multicriteria decision making, statistical approaches, mathematical programming, artificial intelligence, and hybrid method (or integrated method). This paper presents an ambiguous integrated approach for assessing and selecting third-party logistics service providers. This method consists of two phases, in phase one, Fuzzy Analytical Hierarchy Process (FAHP) is used to determine the weights of the evaluation criteria. In phase two, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to evaluate alternatives, sequence them, and make the final selection. The objective is to minimize the effects of inaccuracies such as human judgment and preferences while searching for the best decision. Two methods are used to find the fuzzy set: extent analysis method and the geometric mean technique. In this paper, a survey was conducted on papers from 2014 to 2018 to find the most frequently used criteria, seven criteria were identified:

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Section: Literature Reviewmentioning

confidence: 99%

A Proposed Approach for Selecting Third Party Logistic Alternatives

Mostafa

Galal

Elawady

2021

The Egyptian International Journal of Engineering Sciences and

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“…Zavadskas, Turskis, Vilutien ė, and Lepkova (2017) verified the role of analytical MCDM tools including ARAS, TOPSIS to the problem of facility management strategy selection. Gupta and Walton (2016) explored the applicability of interpretive structural modeling (ISM) in the selection of third party logistic providers by twelve main criteria (cost, reputation, quality, locations, collaboration and range of services etc.). Dweiri, Kumar, Khan, and Jain (2016) designed an AHP model to assist the automotive industry in the supplier selection problem.…”

Section: Supply Chain and Logistics Management Via Dsssmentioning

confidence: 99%

A group decision making support system in logistics and supply chain management

Yazdani

Zaraté

Coulibaly

et al. 2017

Expert Systems with Applications

132

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The paper proposes a decision support system for selecting logistics providers based on the quality function deployment (QFD) and the technique for order preference by the similarity to ideal solution (TOPSIS) for agricultural supply chain in France. The research provides a platform for group decision making to facilitate decision process and check the consistency of the outcomes. Methodology: The proposed model looks at the decision problem from two points of view considering both technical and customer perspectives. The main customer criteria are confidence in a safe and durable product, emission of pollutants and hazardous materials, social responsibility, etc. The main technical factors are financial stability, quality, delivery condition, services, etc. based on the literature review. The second stage in the adopted methodology is the combination of quality function deployment and the technique for order preference by similarity to ideal solution to effectively analyze the decision problem. In final section we structure a group decision system called GRoUp System (GRUS) which has been developed by Institut de Recherche en Informatique de Toulouse (IRIT) in the Toulouse University. Results: This paper designs a group decision making system to interface decision makers and customer values in order to aid agricultural partners and investors in the selection of third party logistic providers. Moreover, we have figured out a decision support system under fuzzy linguistic variables is able to assist agricultural parties in uncertain situations. This integrated and efficient decision support system enhances quality and reliability of the decision making. Novelty/Originality: The novelty of this paper is reflected by several items. The integration of group multicriteria decision tools enables decision makers to obtain a comprehensive understanding of customer needs and technical requirements of the logistic process. In addition, this investigation is carried out under a European commission project called Risk and Uncertain Conditions for Agriculture Production Systems (RUC-APS) which models risk reduction and elimination from the agricultural supply chain. Ultimately, we have implemented the decision support tool to select the best logistic provider among France logistics and transportation companies.

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“…The ISM method helps to establish relationships between criteria. ISM advises researchers to use expert opinions based on various management techniques such as brainstorming in developing contextual relationships between criteria (Gupta & Walton, 2016). For this purpose, researchers should consult with industry experts and academics in identifying contextual relationships between criteria.…”

Section: Interpretive Structural Modelingmentioning

confidence: 99%

“…The questionnaire results in the form of symbols to indicate the direction of the relationship between two criteria, i.e., i and j. The symbols are V (criterion i influences criterion j), A (criterion i influenced criteria j), X (criterion i and j influence each other), or O (criteria i and j do not affect each other) (Gupta & Walton, 2016).…”

Section: Interpretive Structural Modelingmentioning

confidence: 99%

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A literature review of criteria selection in supplier

Ristono

Pratikto

Santoso

et al. 2018

JIEM

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Purpose: This paper proposes a new model for further research on how to select criteria in supplier selection, through a literature review and analysis of the advantages and disadvantages of previously used methods.Design/methodology/approach: The methods used to select criteria in supplier selection were extracted from various online academic databases. The weaknesses and advantages of these methods were then analyzed. Based on these findings, several opportunities for improvement are proposed for further research. Finally, criteria design methods for the selection of suppliers are proposed using statistical multi-criteria decision making (S-MCDM) methods.Findings: Direction and guidance for subsequent research to select the criteria used in supplier selection, based on the advantages and disadvantages of the decision methods used.Research limitations/implications: Limitations of this study are that it is focused on the methods of criteria design in supplier selection.Practical implications: This study can provide a research direction on the selection of criteria for supplier selection.Social implications: This study provides ongoing guidance and avenues for further research.Originality/value: New ideas for working out the developmental strategy for criteria selection are provided by statistical MCDM methods in supplier selection.

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Interpretive structural modelling to assess third party logistics providers

Cited by 9 publications

References 0 publications

A Proposed Approach for Selecting Third Party Logistic Alternatives

A Proposed Approach for Selecting Third Party Logistic Alternatives

A group decision making support system in logistics and supply chain management

A literature review of criteria selection in supplier

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