Total Interpretive Structural Modelling of Critical Factors of Sustainable-Oriented Innovation for Indian Manufacturing MSMEs

2020 ◽  
pp. 95-106 ◽  
Author(s):  
Sonal Khurana ◽  
Bisma Mannan ◽  
Abid Haleem
Keyword(s):  
Structural Modelling ◽  
Critical Factors ◽  
Interpretive Structural Modelling ◽  
Total Interpretive Structural Modelling

Retailer responsiveness: a total interpretive structural modelling approach

2020 ◽  
Vol 13 (3) ◽  
pp. 336 ◽  
Author(s):  
Deepika Sharma ◽  
Rashi Taggar ◽  
Sunali Bindra ◽  
Sanjay Dhir
Keyword(s):  
Structural Modelling ◽  
Interpretive Structural Modelling ◽  
Total Interpretive Structural Modelling ◽  
Modelling Approach

Analysis of supply chain resilience barriers in Indian automotive company using total interpretive structural modelling

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nishtha Agarwal ◽  
Nitin Seth
Keyword(s):  
Supply Chain ◽  
Second Phase ◽  
Structural Modelling ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Interpretive Structural Modelling ◽  
Micmac Analysis ◽  
Root Barrier ◽  
Definition Of ◽  
Total Interpretive Structural Modelling

PurposeThe study tries to identify the barriers influencing supply chain resilience and examine the inter-relationships between them. These relationships are built on the basis of how one barrier drives or is driven by the changes in another barriers.Design/methodology/approachIn the first phase, literature review and with due discussion with experts, the barriers have been identified and shortlisted for an Indian automotive case company. In the second phase, total interpretive structural modelling (TISM) has been applied to examine inter-relationships between the barriers for an Indian automobile case company. Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis has also been performed to analyse the driving and dependence power of the barriers.FindingsIn total, 11 barriers are identified from the first phase of the study. In the second phase, the TISM digraph is created which qualitatively explains the reason behind how one barrier leads to another. MICMAC analysis classifies these variables in four clusters namely autonomous, linkage, dependent and independent. These clusters characterise the barriers based on their driving and dependent power which helps managers in strategically tackling them while taking understanding from the TISM digraph.Research limitations/implicationsThree research implications can be made from the study. First, a comprehensive definition of supply chain which helps in understanding of resilience based on disruption phases and recovery. Second, 11 barriers are identified which hinder resilience in automotive sector. Their relationships are modelled using TISM which also gives why a particular relationship exists. Last, MICMAC analysis classifies barriers based on how high or low the driving and dependence power exists.Practical implicationsThe study offers significant implications for supply chain managers helping them in building resilience by identifying barriers and reducing their effect. Barriers are identified for case company which might help managers to tackle them during disruptions. The final TISM digraph depicts the “why” between the inter-relationships between the barriers to resilient supply chains. TISM shows that non-commitment of top management is the major root barrier which has been causing the other problems. MICMAC analysis is also performed along with discussion as to how autonomous, linkage, dependent and independent barriers can be tackled to build resilience.Originality/valueTISM is considered as an effective methodology for conceptual framework development as it also explains “why” between the relationships besides explaining the “what” as against ISM. Identification and understanding of barriers and their interrelationship will help supply chain managers to analyse the influence and inter-dependence of barriers on the resilience of the supply chain. Such understanding will help in mitigating/averting these barriers hence improving the resilience capability. It also adds to the knowledge base in the area of supply chain resilience where several authors have pointed the lack of research.

Retailer responsiveness: a total interpretive structural modelling approach

2020 ◽  
Vol 13 (3) ◽  
pp. 336
Author(s):  
Sanjay Dhir ◽  
Rashi Taggar ◽  
Sunali Bindra ◽  
Deepika Sharma
Keyword(s):  
Structural Modelling ◽  
Interpretive Structural Modelling ◽  
Total Interpretive Structural Modelling ◽  
Modelling Approach

Modelling of Critical Factors for Improving Logistics Performance of India Using Interpretive Structural Modelling

2018 ◽  
Vol 5 (1) ◽  
pp. 29-51
Author(s):  
Amrita Jhawar ◽  
S. K. Garg
Keyword(s):  
Performance Improvement ◽  
Service Providers ◽  
Trade Integration ◽  
Structural Modelling ◽  
Critical Factors ◽  
Interpretive Structural Modelling ◽  
Long Run ◽  
Logistics Service Providers ◽  
Two Factors ◽  
Power And Dependence

Improving logistics performance is at the essential of policies to strengthen competitiveness and to boost trade integration. The purpose of this article is to identify the factors which will lead to improvement in the logistics performance for a developing country like India, using interpretive structural modelling (ISM). This research presents a framework and the mutual relationships among the factors identified for improving the logistics performance. The identified factors were clustered according to their driving power and dependence power. Investment by government and investment by logistics service providers are the two factors at the bottom level of the hierarchy, implying that developing countries need to focus on high investments for improving logistics performance. Improvement in logistics performance will improve the competitiveness of organizations and higher profits, leading to improvement in foreign direct investment and economic growth in the long run.

Towards understanding key enablers to green humanitarian supply chain management practices

2020 ◽  
Vol 31 (5) ◽  
pp. 1111-1145
Author(s):  
Surajit Bag ◽  
Sunil Luthra ◽  
V.G. Venkatesh ◽  
Gunjan Yadav
Keyword(s):  
Decision Making ◽  
Supply Chains ◽  
Research Team ◽  
Management Practices ◽  
Structural Modelling ◽  
Content Type ◽  
Interpretive Structural Modelling ◽  
Humanitarian Supply Chains ◽  
Total Interpretive Structural Modelling ◽  
High Influence

PurposeHumanitarian supply chains (HSCs) by their very nature require urgent reaction to unforeseeable needs, making it difficult to properly plan for the support of actual demands. As such, integrating sustainability into traditional HSC practices continues to present a challenge to governments, nongovernmental organizations (NGOs) and other humanitarian-related agencies. This study focuses on identifying and categorizing the leading enablers to green humanitarian supply chains (GHSCs) and proposes a model for improving the responsiveness based upon a fuzzy total interpretive structural modelling approach.Design/methodology/approachTotal interpretive structural modelling (TISM) uses group decision-making to identify contextual relationships among each pair of enablers and elucidates the nature of each underlying relationship. The fuzzy TISM shows the level of strength (very high influence, high influence, low influence and very low influence) of each enabler in relation to other enablers, which can help to inform management decision-making.FindingsGHSC management requires strategic planning of inventory and logistics management. The importance of collaborative relationship building with HSC partners for developing capability and the effective use of available resources are keys to success. These improved relationships also help to promote postponement and similar speculation-based logistics strategies, as well as advanced purchasing and pre-positioning strategies. Finally, the speed and quality of response is found to be the top enabler in GHSC management.Research limitations/implicationsOne noted shortcoming of the chosen research method is its reliance on subjective expert judgement. However, collecting judgements is at the basis of many research methods, and the research team took utmost care throughout the research process to allay biases. Future empirical research can further examine the relationships suggested herein. Managers can use the model developed in this research to consider impactful ways to design and execute sustainable HSCs.Originality/valueTo the best of the authors' knowledge, this is a novel attempt to identify enablers to GHSC management. Secondly, the research team has used an advanced methodology (fuzzy TISM) to develop the contextual inter-relationships among the enablers which has not been used earlier in this direction before and thus advances the GHSC literature.

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