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Modelling of Determinants of Logistics 4.0 Adoption: Insights from Developing Countries.

Authors :
Khan, Shahbaz
Singh, Rubee
Sá, José Carlos
Santos, Gilberto
Ferreira, Luís Pinto
Source :
Machines; Dec2022, Vol. 10 Issue 12, p1242, 18p
Publication Year :
2022

Abstract

With the emergence of industry 4.0, several elements of the supply chain are transforming through the adoption of smart technologies such as blockchain, the internet of things and cyber-physical systems. Logistics is considered one of the important elements of supply chain management and its digital transformation is crucial to the success of industry 4.0. In this circumstance, the existing logistics system needs to be upgraded with industry 4.0 technologies and emerge as logistics 4.0. However, the adoption/transformation of logistics 4.0 is dependent on several determinants that need to be explored. Therefore, this study has the prime objective of investigating the determinants of logistics 4.0 adoption in the context of a developing country, specifically, India. Initially, ten determinants of logistics 4.0 are established after a survey of the relevant literature and the input of industry experts. Further, a four-level structural model is developed among these determinants using the Interpretive Structural Modelling (ISM) approach. In addition, a fuzzy Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC) analysis is also conducted for the categorization of these determinants as per their driving and dependence power. The findings show that top management supports, information technology infrastructure and financial investment are the most significant determinants towards logistics 4.0 adoption. This study facilitates the supply chain partners to focus on these high-level determinants for the effective adoption of logistics 4.0. Moreover, the findings lead to a more in-depth insight into the determinants that influence logistics 4.0 and their significance in logistics 4.0 adoption in emerging economies. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20751702
Volume :
10
Issue :
12
Database :
Complementary Index
Journal :
Machines
Publication Type :
Academic Journal
Accession number :
161004222
Full Text :
https://doi.org/10.3390/machines10121242