TY - JOUR
T1 - Adoption of industry 4.0 technologies for decarbonisation in the steel industry
T2 - self-assessment framework with case illustration
AU - Mishra, Ruchi
AU - Singh, Rajesh Kr
AU - Gunasekaran, Angappa
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023
Y1 - 2023
N2 - The study aims to identify, assess, and prioritise barriers to adopting industry 4.0 technology for decarbonisation in the steel industry. It also develops a barrier intensity index of industry 4.0 technologies for decarbonisation. The barriers to industry 4.0 technologies are identified through literature review and semi-structured interviews, and then it is classified into three major categories using technological organisational and environmental theory. The fuzzy Delphi method has been used to finalise these barriers. Then, three major categories of barriers and 21 sub-barriers are prioritised using the best–worst method. The study proposes a self-assessment framework for assessing the intensity of barriers by applying the Graph Theory Matrix approach. This framework is illustrated with the help of an Indian case of steel manufacturing organisation. The findings of the study indicate that a lack of supportive infrastructure followed by a lack of real-time control system and longer learning time due to poor knowledge transfer are the significant barriers to the adoption of industry 4.0 technologies for decarbonisation in the steel industry. Using the proposed self-assessment framework, a steel manufacturing organisation can analyse its position, identify gaps, and work on potential areas of improvement. Insights from this study can help in attaining sustainable development goals (13) i.e., climate action and net zero economy goal.
AB - The study aims to identify, assess, and prioritise barriers to adopting industry 4.0 technology for decarbonisation in the steel industry. It also develops a barrier intensity index of industry 4.0 technologies for decarbonisation. The barriers to industry 4.0 technologies are identified through literature review and semi-structured interviews, and then it is classified into three major categories using technological organisational and environmental theory. The fuzzy Delphi method has been used to finalise these barriers. Then, three major categories of barriers and 21 sub-barriers are prioritised using the best–worst method. The study proposes a self-assessment framework for assessing the intensity of barriers by applying the Graph Theory Matrix approach. This framework is illustrated with the help of an Indian case of steel manufacturing organisation. The findings of the study indicate that a lack of supportive infrastructure followed by a lack of real-time control system and longer learning time due to poor knowledge transfer are the significant barriers to the adoption of industry 4.0 technologies for decarbonisation in the steel industry. Using the proposed self-assessment framework, a steel manufacturing organisation can analyse its position, identify gaps, and work on potential areas of improvement. Insights from this study can help in attaining sustainable development goals (13) i.e., climate action and net zero economy goal.
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U2 - 10.1007/s10479-023-05440-0
DO - 10.1007/s10479-023-05440-0
M3 - Article
AN - SCOPUS:85163627616
SN - 0254-5330
JO - Annals of Operations Research
JF - Annals of Operations Research
ER -