Evaluation of strategies to manage risks in smart, sustainable Agri logistics sector: a Bayesian-based group decision-making approach

The agriculture industry is one of India's largest and most important economic contributors. This is vital to the Indian economy since it contributes around 18% to the GDP and employs over 60% of the labour force. Due to the nature of the goods, however, this sector confronts many challenges. A...

Full description

Bibliographic Details
Main Authors: Gupta, Himanshu, Kharub, Manjeet, Shreshth, Kumar, Kumar, Ashwani, Huisingh, Donald, Kumar, Anil
Format: Article
Language:English
Published: Wiley 2023
Subjects:
Online Access:https://repository.londonmet.ac.uk/8115/3/Manuscript%20R1.pdf
_version_ 1825625761279639552
author Gupta, Himanshu
Kharub, Manjeet
Shreshth, Kumar
Kumar, Ashwani
Huisingh, Donald
Kumar, Anil
author_facet Gupta, Himanshu
Kharub, Manjeet
Shreshth, Kumar
Kumar, Ashwani
Huisingh, Donald
Kumar, Anil
author_sort Gupta, Himanshu
collection LMU
description The agriculture industry is one of India's largest and most important economic contributors. This is vital to the Indian economy since it contributes around 18% to the GDP and employs over 60% of the labour force. Due to the nature of the goods, however, this sector confronts many challenges. An effective agri-logistics network can be a viable solution, but the sector must be prepared to overcome various risks. Therefore, this study aimed to identify potential risks to the smart, sustainable agri-logistics industry and strategies for mitigating those risks. Bayesian Best Worst Method (BBWM) was used to prioritise the identified risks and the mitigating strategies. Study results indicate that by obtaining the highest ratings, technological (0.351), social (0.187), and individual (0.169) are the dominating risks to the Agri-logistics sector. Further, it was discovered that combining multiple strategies is more effective than any one strategy alone in reducing the identified risks.
first_indexed 2024-07-09T04:05:53Z
format Article
id oai:repository.londonmet.ac.uk:8115
institution London Metropolitan University
language English
last_indexed 2025-02-19T01:15:10Z
publishDate 2023
publisher Wiley
record_format eprints
spelling oai:repository.londonmet.ac.uk:81152025-01-20T01:58:03Z https://repository.londonmet.ac.uk/8115/ Evaluation of strategies to manage risks in smart, sustainable Agri logistics sector: a Bayesian-based group decision-making approach Gupta, Himanshu Kharub, Manjeet Shreshth, Kumar Kumar, Ashwani Huisingh, Donald Kumar, Anil 650 Management & auxiliary services The agriculture industry is one of India's largest and most important economic contributors. This is vital to the Indian economy since it contributes around 18% to the GDP and employs over 60% of the labour force. Due to the nature of the goods, however, this sector confronts many challenges. An effective agri-logistics network can be a viable solution, but the sector must be prepared to overcome various risks. Therefore, this study aimed to identify potential risks to the smart, sustainable agri-logistics industry and strategies for mitigating those risks. Bayesian Best Worst Method (BBWM) was used to prioritise the identified risks and the mitigating strategies. Study results indicate that by obtaining the highest ratings, technological (0.351), social (0.187), and individual (0.169) are the dominating risks to the Agri-logistics sector. Further, it was discovered that combining multiple strategies is more effective than any one strategy alone in reducing the identified risks. Wiley 2023-01-20 Article PeerReviewed text en cc_by_nc_nd_4 https://repository.londonmet.ac.uk/8115/3/Manuscript%20R1.pdf Gupta, Himanshu, Kharub, Manjeet, Shreshth, Kumar, Kumar, Ashwani, Huisingh, Donald and Kumar, Anil (2023) Evaluation of strategies to manage risks in smart, sustainable Agri logistics sector: a Bayesian-based group decision-making approach. Business Strategy and the Environment, 32 (7). pp. 4335-4359. ISSN 0964-4733 https://doi.org/10.1002/bse.3368 10.1002/bse.3368 10.1002/bse.3368
spellingShingle 650 Management & auxiliary services
Gupta, Himanshu
Kharub, Manjeet
Shreshth, Kumar
Kumar, Ashwani
Huisingh, Donald
Kumar, Anil
Evaluation of strategies to manage risks in smart, sustainable Agri logistics sector: a Bayesian-based group decision-making approach
title Evaluation of strategies to manage risks in smart, sustainable Agri logistics sector: a Bayesian-based group decision-making approach
title_full Evaluation of strategies to manage risks in smart, sustainable Agri logistics sector: a Bayesian-based group decision-making approach
title_fullStr Evaluation of strategies to manage risks in smart, sustainable Agri logistics sector: a Bayesian-based group decision-making approach
title_full_unstemmed Evaluation of strategies to manage risks in smart, sustainable Agri logistics sector: a Bayesian-based group decision-making approach
title_short Evaluation of strategies to manage risks in smart, sustainable Agri logistics sector: a Bayesian-based group decision-making approach
title_sort evaluation of strategies to manage risks in smart sustainable agri logistics sector a bayesian based group decision making approach
topic 650 Management & auxiliary services
url https://repository.londonmet.ac.uk/8115/3/Manuscript%20R1.pdf
work_keys_str_mv AT guptahimanshu evaluationofstrategiestomanagerisksinsmartsustainableagrilogisticssectorabayesianbasedgroupdecisionmakingapproach
AT kharubmanjeet evaluationofstrategiestomanagerisksinsmartsustainableagrilogisticssectorabayesianbasedgroupdecisionmakingapproach
AT shreshthkumar evaluationofstrategiestomanagerisksinsmartsustainableagrilogisticssectorabayesianbasedgroupdecisionmakingapproach
AT kumarashwani evaluationofstrategiestomanagerisksinsmartsustainableagrilogisticssectorabayesianbasedgroupdecisionmakingapproach
AT huisinghdonald evaluationofstrategiestomanagerisksinsmartsustainableagrilogisticssectorabayesianbasedgroupdecisionmakingapproach
AT kumaranil evaluationofstrategiestomanagerisksinsmartsustainableagrilogisticssectorabayesianbasedgroupdecisionmakingapproach