An extension of interpretive structural modeling using linguistic term sets for business decision-making

OPSEARCH ◽  
2021 ◽  
Author(s):  
Sanjay Kumar Tyagi ◽  
Sujeet Kumar Sharma ◽  
R. Krishankumar ◽  
K. S. Ravichandran
Keyword(s):  
Decision Making ◽  
Structural Modeling ◽  
Interpretive Structural Modeling ◽  
Business Decision ◽  
Linguistic Term

scholarly journals Supply Chain Finance Factors: An Interpretive Structural Modeling Approach

2021 ◽  
Vol 29 (1) ◽  
pp. 88-111
Author(s):  
Zericho Marak ◽  
◽  
Deepa Pillai ◽  
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Information Sharing ◽  
Structural Modeling ◽  
Influential Factors ◽  
Previous Literature ◽  
Critical Factors ◽  
Interpretive Structural Modeling ◽  
Modeling Methodology ◽  
Supply Chain Finance

Purpose: The present study aims to identify the critical factors of supply chain finance and the interrelationship between the factors using interpretive structural modeling. Methodology: Factors of supply chain finance were identified from the literature and experts from both industry and academia were consulted to assess the contextual relationships between the factors. Then, we applied interpretive structural modeling to examine the interrelationships between these factors and find out the critical factors. Findings: The model outcome indicates information sharing and workforce to be the most influential factors, followed by the automation of trade and financial attractiveness. Originality/value: Previous literature identified various factors that influence supply chain finance. However, studies showing interrelationships between these factors are lacking. This study is unique in the field as it applies total interpretive structural modeling for assessing the factors that affect supply chain finance. Our model will aid practitioners’ decision-making and the adoption of supply chain finance by providing a necessary framework.

Scrutinizing the Barriers That Impede Industry 4.0 Projects

2019 ◽  
pp. 294-314
Author(s):  
Ömür Yaşar Saatçioğlu ◽  
Nergis Özispa ◽  
Gökçe T. Kök
Keyword(s):  
Decision Making ◽  
Data Integration ◽  
Industry 4.0 ◽  
Implementation Process ◽  
Structural Modeling ◽  
The Other ◽  
Research Institutions ◽  
Integration Process ◽  
Interpretive Structural Modeling ◽  
Utmost Care

The concept of Industry 4.0 has recently attracted attention from academics, research institutions, and companies. In order for projects to achieve success in Industry 4.0, project specifications must be known and they must be conducted with utmost care. While Industry 4.0 projects ensure lots of advantages, they encounter many risks such as data integration, process flexibility, and security problems. Identification of barriers to Industry 4.0 is important for the success of the projects. The aim of the chapter is to determine the Industry 4.0 barriers in implementation process in Turkey's conditions investigate the interrelations among them and develop a model that can measure the interacting effects of the barriers on the other barriers in the Industry 4.0 implementation process. To reach that aim, interpretive structural modeling (ISM) and decision-making trail and evaluation laboratory (DEMATEL) are used. According to results, one of the most important findings is the lack of digital vision which found as the only affecting barrier and it affects all the other barriers.

Fuzzy Structural Models and Based Applications in Digital Marketplace

2018 ◽  
pp. 703-714 ◽  
Author(s):  
Anil Kumar ◽  
Manoj Kumar Dash
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Mathematical Formulation ◽  
Structural Models ◽  
Structural Modeling ◽  
Multi Criteria Decision Making ◽  
Interpretive Structural Modeling

One of the well-known topics of decision making is Multi-Criteria Decision Making (MCDM). Fuzzy set theory helps to provide a useful way to address a MCDM problem. Without models, MCDM methods cannot be practiced effectively, therefore, it is interesting to clarify the structure among criteria. But the shortcoming of MCDM is unable to capture imprecision or vagueness inherent in the information. Fuzzy set theory has great potential to handle such situations and fuzzy structural models have been developed. In this chapter widely used structural models i.e. Interpretive Structural Modeling (ISM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Cognition Maps (CMs) are first summarized briefly along with their mathematical formulation and then diffusion these models into fuzzy set theory is explained along with a literature review of the based applications of these models in the digital marketplace.

Establishment of Green Supply Chain Management in Steel Industries Using Interpretive Structural Modeling

2019 ◽  
Vol 18 (4) ◽  
pp. 363-374
Author(s):  
Rajesh Kumar ◽  
Shiena Shekhar
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Structural Modeling ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Multi Criteria Decision Making ◽  
Interpretive Structural Modeling ◽  
Chain Management ◽  
Steel Industries

Abstract The state of Chhattisgarh in India has a very large number of steel plants causing pollution in the region. The effect of this pollution exceeds the geographical territory of a unit, and goes much beyond it, so it becomes essential to find the reasons for the pollution and the enablers for the green supply chain management, which in turn will help in providing a cleaner environment. In this study Multi-Criteria Decision Making (MCDM) tools like Interpretive Structural Modeling and MICMAC analysis have been used.

Fuzzy Structural Models and Based Applications in Digital Marketplace

2016 ◽  
pp. 59-70
Author(s):  
Anil Kumar ◽  
Manoj Kumar Dash
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Mathematical Formulation ◽  
Structural Models ◽  
Structural Modeling ◽  
Multi Criteria Decision Making ◽  
Interpretive Structural Modeling

One of the well-known topics of decision making is Multi-Criteria Decision Making (MCDM). Fuzzy set theory helps to provide a useful way to address a MCDM problem. Without models, MCDM methods cannot be practiced effectively, therefore, it is interesting to clarify the structure among criteria. But the shortcoming of MCDM is unable to capture imprecision or vagueness inherent in the information. Fuzzy set theory has great potential to handle such situations and fuzzy structural models have been developed. In this chapter widely used structural models i.e. Interpretive Structural Modeling (ISM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Cognition Maps (CMs) are first summarized briefly along with their mathematical formulation and then diffusion these models into fuzzy set theory is explained along with a literature review of the based applications of these models in the digital marketplace.

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