Readiness assessment of leagility supply chain based on fuzzy cognitive maps and interpretive structural modeling: a case study

2018 ◽  
Vol 33 (4) ◽  
pp. 442-456 ◽  
Author(s):  
Taher Kalantari ◽  
Farid Khoshalhan
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Cognitive Maps ◽  
Structural Modeling ◽  
Study Data ◽  
Fuzzy Cognitive Maps ◽  
Readiness Assessment ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Functional Areas

Purpose The evaluation of readiness provides insight into the readiness of its individual components for successful accomplishment of tasks. This study aims to evaluate readiness in leagility of supply chains based on the design and analysis of fuzzy cognitive maps (FCM) and interpretive structural modeling (ISM). Design/methodology/approach On the basis of the purpose of this study, data are gathered via the Delphi method. Moreover, FCM and ISM are also used to evaluate readiness. Findings Findings initially demonstrate a categorization of factors influencing leagility into static and dynamic variables according to the degree of their influence derived from the resultant behavior of FCM and ISM. It is also found that evaluating readiness in leagility of supply chains with ISM and FCM was done with respect to the type and role of the study variables, which were determined within the minimum and maximum ranges of 20 to 100 per cent, respectively. Originality/value The evaluation of the readiness using the FCM and ISM is proved to be more efficient than other classical methods. Experimental results of the study contribute to improve readiness of leagility of supply chain as well as develop functional areas of business.

Modeling reconfigurability in supply chains using total interpretive structural modeling

2017 ◽  
Vol 14 (2) ◽  
pp. 194-221 ◽  
Author(s):  
Pallab Biswas
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
New Product Development ◽  
Structural Changes ◽  
Structural Modeling ◽  
Global Market ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis ◽  
Expert Opinions

Purpose The purpose of this paper is to identify, analyze, and categorize the major enablers of reconfigurability that can facilitate structural changes within a supply chain in a global scenario. The paper also addresses five reconfigurability dimensions in the perspective of supply chains and the major enablers to attain them. The paper further aims to understand the mutual interactions among these enablers through the identification of hierarchical relationships among them. Design/methodology/approach A framework that holistically considers all the major enablers of reconfigurability has been developed. The hierarchical interrelationships between major enablers have been presented and interpreted using a novel qualitative modeling technique, i.e., total interpretive structural modeling (TISM), which is an extension of ISM. SPSS 22.0 is employed to carry out a one-tailed one-sample t-test further to test the hypotheses for validating the results of TISM. Impact matrix cross-reference multiplication applied to a classification (MICMAC) analysis has been employed to identify the driving and dependence powers of these reconfigurability enablers. Findings In this paper, 15 enablers for reconfigurability paradigm have been identified through literature review and expert opinions. The authors established interrelationships and interdependencies among these enablers and categorized them as enablers of each dimension. New product development and customer satisfaction come at the highest level of priority. The levels of these enablers were obtained using TISM. The authors compared the results with the clusters derived from MICMAC analysis, and the results are found to be well within the acceptable range. Research limitations/implications The study has implications for both practitioners and academia. The work provides a comprehensive list of enablers that are relevant to reconfigure supply chains in today’s volatile global market. This research will also help decision makers to strategically focus on the top-level enablers and their concerned dimensions. The research is based on an automobile company case study and can be extended to products with volatile and changing demands. Originality/value The proposed model for reconfigurability enablers using TISM is a new effort altogether in the area of supply chain management. The novelty of this research lies in its identification of specific enablers to reconfigure a supply chain through different dimensions.

An integrated interpretive structural modeling and a graph-theoretic approach for measuring the supply chain complexity in the Indian automotive industry

2018 ◽  
Vol 29 (3) ◽  
pp. 478-514 ◽  
Author(s):  
Kavilal E.G. ◽  
Shanmugam Prasanna Venkatesan ◽  
Joshi Sanket
Keyword(s):  
Supply Chain ◽  
Automotive Industry ◽  
Structural Modeling ◽  
Theoretic Approach ◽  
Passenger Vehicle ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Graph Theoretic ◽  
Graph Theoretic Approach ◽  
Numerical Index

Purpose Easily employable quantitative supply chain complexity (SCC) measures considering the significant dimensions of complexity as well as the drivers that represent those dimensions are limited in the literature. The purpose of this paper is to propose an integrated interpretive structural modeling (ISM) and a graph-theoretic approach to quantify SCC by a single numerical index considering the interdependence and the inheritance of the SCC drivers. Design/methodology/approach In total, 18 SCC drivers identified from the literature are clustered according to the significant dimensions of complexity. The interdependencies established through ISM and inheritance values of SCC drivers are mapped into a Variable Permanent Matrix (VPM). The permanent function of this VPM is then computed and the resulting single numerical index is the measure of SCC. Findings A scale is proposed by computing the minimum and maximum threshold values of SCC with the help of expert opinions of the Indian automotive industry. The complexity of commercial and passenger vehicle sectors within the automotive industry is measured and compared using the proposed scale. From the results, it is identified that the number of suppliers, increase in spare-parts due to shortened product life-cycle and demand uncertainties increase the SCC of the passenger vehicle sector, while number of parts, products and processes, variety of products and process and unreliability of suppliers increase the complexity of the commercial vehicle sector. The result indicates that various SCC drivers have a different impact on determining the SCC level of these two sectors. Originality/value The authors propose an integrated method that can be readily applied to measure and quantify SCC considering the significant dimensions of complexity as well as the interdependence and the inheritance of the SCC drivers that contribute to those dimensions. This index further helps to compare the complexity of the supply chain which varies between industries.

scholarly journals Modelling the inhibitors of cold supply chain using fuzzy interpretive structural modeling and fuzzy MICMAC analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249046
Author(s):  
Anshuman Sharma ◽  
Haidar Abbas ◽  
Muhammad Qutubuddin Siddiqui
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
United Arab Emirates ◽  
Hierarchical Model ◽  
Structural Modeling ◽  
Structured Interviews ◽  
Interpretive Structural Modeling ◽  
Inadequate Information ◽  
Fuzzy Micmac ◽  
Lack Of Commitment

The Cold Supply Chain (CSC) is an integral part of the supply chain of perishable products. The aim of this research is to examine the inhibitors that have a major impact on the performance of CSC operations in the United Arab Emirates (UAE). This study provides a synthesis and suggests a hierarchical model among CSC inhibitors and their respective relevance. The hierarchical synthesis of twelve (12) primary CSC inhibitors is achieved through a comprehensive literature review and consultation with academics and CSC professionals. This study used semi-structured interviews, a fuzzy interpretive structural modeling (FISM) and a Fuzzy-MICMAC (FMICMAC) analysis to explore and establish the relationship between and among identified inhibitors. FISM is used to examine the interaction between inhibitors, while FMICMAC analysis is used to examine the nature of inhibitors on the basis of their dependence and driving power. The results of the FISM and FMICMAC analysis show the inter-relationships and relative dominance of identified inhibitors. The results show that some inhibitors are of high strategic importance due to their high driving power and low dependence. These inhibitors seek more management attention in order to improve their effectiveness. The result of a hierarchical model helps to understand the influence of a particular inhibitor on others. ‘Higher capital and operating costs’ occupy the highest level in the FISM model. The ‘fragmented cold supply chains’, ‘lack of skilled labor’, ‘inadequate information system infrastructure’ and ‘lack of commitment by top level management’ had strong driving power but weak dependence, which characterizes them as independent inhibitors. Management should be extra careful when dealing with these inhibitors as they influence the effects of other variables at the top of the FISM hierarchy in the overall management of the cold supply chain. The study also suggests a number of recommendations for addressing these inhibitors in cold supply chains operating in the UAE. With due attention and care for these inhibitors, the operation of the cold supply chains is likely to be even more successful.

Modeling big data enablers for operations and supply chain management

2018 ◽  
Vol 29 (2) ◽  
pp. 629-658 ◽  
Author(s):  
Kuldeep Lamba ◽  
Surya Prakash Singh
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Science ◽  
Structural Modeling ◽  
Security And Privacy ◽  
Interpretive Structural Modeling ◽  
Chain Management ◽  
Content Type

Purpose The purpose of this paper is to identify and analyse the interactions among various enablers which are critical to the success of big data initiatives in operations and supply chain management (OSCM). Design/methodology/approach Fourteen enablers of big data in OSCM have been selected from literature and consequent deliberations with experts from industry. Three different multi criteria decision-making (MCDM) techniques, namely, interpretive structural modeling (ISM), fuzzy total interpretive structural modeling (fuzzy-TISM) and decision-making trial and evaluation laboratory (DEMATEL) have been used to identify driving enablers. Further, common enablers from each technique, their hierarchies and inter-relationships have been established. Findings The enabler modelings using ISM, Fuzzy-TISM and DEMATEL shows that the top management commitment, financial support for big data initiatives, big data/data science skills, organizational structure and change management program are the most influential/driving enablers. Across all three different techniques, these five different enablers has been identified as the most promising ones to implement big data in OSCM. On the other hand, interpretability of analysis, big data quality management, data capture and storage and data security and privacy have been commonly identified across all three different modeling techniques as the most dependent big data enablers for OSCM. Research limitations/implications The MCDM models of big data enablers have been formulated based on the inputs from few domain experts and may not reflect the opinion of whole practitioners community. Practical implications The findings enable the decision makers to appropriately choose the desired and drop undesired enablers in implementing the big data initiatives to improve the performance of OSCM. The most common driving big data enablers can be given high priority over others and can significantly enhance the performance of OSCM. Originality/value MCDM-based hierarchical models and causal diagram for big data enablers depicting contextual inter-relationships has been proposed which is a new effort for implementation of big data in OSCM.

Barriers to sustainable supply chain management implementation in Egyptian industries: an interpretive structural modeling (ISM) approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Esraa Osama Zayed ◽  
Ehab A. Yaseen
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Structural Modeling ◽  
Equation Modeling ◽  
Sustainable Supply Chain ◽  
Interpretive Structural Modeling ◽  
Structured Model ◽  
Chain Management ◽  
Content Type ◽  
Sustainable Supply Chain Management

PurposeRecently, sustainability aspects are gaining importance among supply chain management (SCM) research field, hence this study aims to explore barriers to sustainable supply chain management (SSCM) implementation in Egyptian industries and the interrelationships among these barriers to provide a structured detailed model for barriers and suggest recommendations to deal with these barriers.Design/methodology/approachThe paper is an empirical study with a descriptive research approach using qualitative methodology. Data were collected through interviewing experts involved in sustainability implementation within supply chain functions. Afterward interpretive structural modeling (ISM) for barriers was conducted to develop a structured model representing possible interrelationships between barriers.FindingsFindings have reported slight differences among barriers to SSCM implementation in Egyptian industries other than those stated previously. ISM analysis helped in shaping barriers into a detailed structured model where interrelationships among barriers can be clearly defined. Additionally, based on the data collected and the ISM model, this study managed to offer recommendations to deal with these barriers.Research limitations/implicationsFuture researches might consider developing ISM analysis for a smaller number of barriers, or focus on each of internal and external barriers individually to minimize ISM analysis complexity and enhance its accuracy. As ISM analysis technique is highly dependent on experts' opinions and experience, validation is highly recommended either by structural equation modeling (SEM) or linear structural relationship approach.Practical implicationsThis study provides insights for managers about internal and external barriers to SSCM implementation in Egyptian industries, a detailed structured model for interrelationships among these barriers and recommendations to deal with these barriers.Originality/valueThis study is one of the very first studies to implement ISM for barriers to SSCM on data collected from Egyptian industries. Consequently, it will direct further research focusing on developing strategies or recommendations to overcome these barriers.

An interpretive structural modeling of drivers and barriers of sustainable supply chain management

2020 ◽  
Vol 31 (5) ◽  
pp. 1071-1090 ◽  
Author(s):  
Gunjan Soni ◽  
Surya Prakash ◽  
Himanshu Kumar ◽  
Surya Prakash Singh ◽  
Vipul Jain ◽  
...  
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Management Practices ◽  
Structural Modeling ◽  
Sustainable Supply Chain ◽  
Interpretive Structural Modeling ◽  
Chain Management ◽  
Content Type ◽  
Sustainable Supply Chain Management ◽  
Stone Industry

PurposeThe Indian marble and stone industry has got the potential to contribute well to the development of the emerging economy. However, unlike the other Indian industries, stone and marble industries are highly underrated sectors, which may become a critical factor for development. This paper analyses the sustainability factors in supply chain management practices.Design/methodology/approachA literature review is used to identify the barriers and drivers in sustainable supply chain management practices. Interpretive structural modeling has been used to obtain a hierarchy of barriers and drivers along with driving power and dependence power analysis. Further, MICMAC analysis is used for segregating the barriers and drivers in terms of their impact on sustainability.FindingsThe findings of the work of this research are that the attention of society, government, and commercial banks should be more toward the unorganized condition of stone and marble sector. There should be an increase in the commitment of stakeholders to reduce pollution and install safety, by enforcing more relevant laws and regulations and creating the importance of environmental awareness.Originality/valueThe main contribution of this research is to identify the barriers and drivers of sustainable supply chain management in a stone and marble industry. The paper proposes a sound mathematical model to prioritize the critical factors for responsible production and consumption of resources from sustainability perspectives of stone industry.

Propagation of risks and their impact on performance in fresh food retail

2015 ◽  
Vol 26 (3) ◽  
pp. 568-602 ◽  
Author(s):  
Samir K Srivastava ◽  
Atanu Chaudhuri ◽  
Rajiv K. Srivastava
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Performance Measures ◽  
Theory And Practice ◽  
Food Retail ◽  
Content Type ◽  
Fresh Food ◽  
Retail Supply Chains ◽  
And Performance ◽  
Supply Chain Risks

Purpose – The purpose of this paper is to carry out structural analysis of potential supply chain risks and performance measures in fresh food retail by applying interpretive structural modeling (ISM). Design/methodology/approach – Inputs were taken from industry experts in identifying and understanding interdependencies among food retail supply chain risks on different levels (sourcing and logistics outside the retail stores; storage and customer interface at the stores). Interdependencies among risks and their impact on performance measures are structured into a hierarchy in order to derive subsystems of interdependent elements to derive useful insights for theory and practice. Findings – Using the ISM approach the risks and performance measures were clustered according to their driving power and dependence power. Change in/inadequate government regulations’ are at the bottom level of the hierarchy implying highest driving power and require higher attention and focussed mitigation strategies. Risks like lack of traceability, transport delays/breakdowns and temperature abuse, cross-contamination in transport and storage have medium driver and dependence powers. Research limitations/implications – The approach is focussed on food retail supply chains in the Indian context and thereby limits the ability to generalize the findings. The academics and experts were selected on convenience and availability. Practical implications – It gives managers a better understanding of the risks and performance measures that have most influence on others (driving performance measures) and those measures which are most influenced by others (dependent performance measures) in fresh food retail and also a tool to prioritize them. This kind of information is strategic for managers who can use it to identify which performance measures they should concentrate on managing the trade-offs between measures. The findings and the applicability for practical use have been validated by both experts and practicing managers in food retail supply chains. Originality/value – The work is perhaps the first to link supply chain risks with performance and explains the propagation of risks in food retail supply chains. It contributes to theory by addressing a few research gaps and provides relevant managerial insights for practitioners.

AI technologies and their impact on supply chain resilience during COVID-19

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sachin Modgil ◽  
Shivam Gupta ◽  
Rébecca Stekelorum ◽  
Issam Laguir
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Information Processing ◽  
Supply Chains ◽  
Failure Modes ◽  
Stress Test ◽  
Weak Links ◽  
Content Type ◽  
Supply Chain Resilience

PurposeCOVID-19 has pushed many supply chains to re-think and strengthen their resilience and how it can help organisations survive in difficult times. Considering the availability of data and the huge number of supply chains that had their weak links exposed during COVID-19, the objective of the study is to employ artificial intelligence to develop supply chain resilience to withstand extreme disruptions such as COVID-19.Design/methodology/approachWe adopted a qualitative approach for interviewing respondents using a semi-structured interview schedule through the lens of organisational information processing theory. A total of 31 respondents from the supply chain and information systems field shared their views on employing artificial intelligence (AI) for supply chain resilience during COVID-19. We used a process of open, axial and selective coding to extract interrelated themes and proposals that resulted in the establishment of our framework.FindingsAn AI-facilitated supply chain helps systematically develop resilience in its structure and network. Resilient supply chains in dynamic settings and during extreme disruption scenarios are capable of recognising (sensing risks, degree of localisation, failure modes and data trends), analysing (what-if scenarios, realistic customer demand, stress test simulation and constraints), reconfiguring (automation, re-alignment of a network, tracking effort, physical security threats and control) and activating (establishing operating rules, contingency management, managing demand volatility and mitigating supply chain shock) operations quickly.Research limitations/implicationsAs the present research was conducted through semi-structured qualitative interviews to understand the role of AI in supply chain resilience during COVID-19, the respondents may have an inclination towards a specific role of AI due to their limited exposure.Practical implicationsSupply chain managers can utilise data to embed the required degree of resilience in their supply chains by considering the proposed framework elements and phases.Originality/valueThe present research contributes a framework that presents a four-phased, structured and systematic platform considering the required information processing capabilities to recognise, analyse, reconfigure and activate phases to ensure supply chain resilience.

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