TY - JOUR
T1 - An Integrated Decision-Making Approach for Cause-And-Effect Analysis of Sustainable Manufacturing Indicators
AU - Bhanot, Neeraj
AU - Qaiser, Fahham Hasan
AU - Alkahtani, Mohammed
AU - Rehman, Ateekh Ur
PY - 2020/2/18
Y1 - 2020/2/18
N2 - Sustainability is a growing concern for manufacturing companies, as they are major contributors to pollution and consume a substantial portion of the world´s natural resources. Sustainable manufacturing can reduce waste, conserve energy and increase resource efficiency. However, one of the main challenges facing manufacturing organisations to put sustainability into practice is the lack of understanding of the cause-and-effect relationships between critical indicators of sustainable manufacturing. To overcome this challenge, a novel, rigorous and integrated framework, composed of four quantitative methods, is proposed to analyse critical indicators of sustainable manufacturing. The analysis is based on responses from both academic and industry experts. These four methods including DEMATEL (decision-making trial and evaluation laboratory), the MMDE (maximum mean de-entropy) algorithm, ISM (interpretive structural modelling) and SEM (structural equation modelling) are uniquely integrated to present statistically validated relationships between critical indicators using information on varying degrees of relationship between them. The final cause-and-effect models for the respondent groups (i.e., researchers and industry experts) are further validated through gathering the viewpoints of a researcher and an industry practitioner for its robustness. The novelty of our research lies in: (1) proposing a novel and integrated rigorous quantitative framework combined with qualitative research method; (2) applying the proposed framework to analyse contextual relationships between critical indicators of implementing sustainability, in the manufacturing sector as a whole, which to the best of authors’ knowledge is the first of its kind; and (3) comparing and contrasting results of researchers and industry practitioners’ groups along with a check of their validation and robustness.
AB - Sustainability is a growing concern for manufacturing companies, as they are major contributors to pollution and consume a substantial portion of the world´s natural resources. Sustainable manufacturing can reduce waste, conserve energy and increase resource efficiency. However, one of the main challenges facing manufacturing organisations to put sustainability into practice is the lack of understanding of the cause-and-effect relationships between critical indicators of sustainable manufacturing. To overcome this challenge, a novel, rigorous and integrated framework, composed of four quantitative methods, is proposed to analyse critical indicators of sustainable manufacturing. The analysis is based on responses from both academic and industry experts. These four methods including DEMATEL (decision-making trial and evaluation laboratory), the MMDE (maximum mean de-entropy) algorithm, ISM (interpretive structural modelling) and SEM (structural equation modelling) are uniquely integrated to present statistically validated relationships between critical indicators using information on varying degrees of relationship between them. The final cause-and-effect models for the respondent groups (i.e., researchers and industry experts) are further validated through gathering the viewpoints of a researcher and an industry practitioner for its robustness. The novelty of our research lies in: (1) proposing a novel and integrated rigorous quantitative framework combined with qualitative research method; (2) applying the proposed framework to analyse contextual relationships between critical indicators of implementing sustainability, in the manufacturing sector as a whole, which to the best of authors’ knowledge is the first of its kind; and (3) comparing and contrasting results of researchers and industry practitioners’ groups along with a check of their validation and robustness.
KW - Sustainable manufacturing
KW - Critical indicators
KW - Interpretive structural modeling
KW - Decision making trial and evaluation laboratory
KW - Structural equation modeling
KW - Max-mean de entropy algorithm
KW - Cause-and-effect analysis
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081283973&doi=10.3390%2fsu12041517&partnerID=40&md5=05ae3c507e9a691e5adc3d13864a08e9
U2 - 10.3390/su12041517
DO - 10.3390/su12041517
M3 - Article
VL - 12
JO - Sustainability
JF - Sustainability
SN - 2071-1050
IS - 4
M1 - 1517
ER -