Interpretive Structural Modeling Methodology Research Articles

Analysis of Barriers of Sustainable Supply Chain Management in Electronics Industry: An Interpretive Structural Modelling Approach

In the past, industries were solely concerned with the financial aspects of a company's operations. In order to address the social and environmental impact of their supply chain, many organisations are implementing sustainability principles in their operations. Their supply chain is an example of this. There will be some obstacles to overcome in the process of transforming to a sustainable supply chain. It is necessary to eliminate or mitigate these risks. It is critical to understand and research these obstacles in order to successfully manage them. Stumbling blocks in a specific context The purpose of this research is to identify and analyse the most significant obstacles to long-term sustainability. In the case of the Indian electronics industry, this is how it is implemented. Eleven experts' opinions and a review of the literature were obtained. Identification and classification of barriers into categories of policy, human resource, and technology are carried out. The hierarchical structure and interrelationship among these barriers is established using Interpretive Structural Modeling (ISM) methodology.

Lean Enhancement by Application of Total Interpretive Structural Modelling

Prioritizing of factors for effective lean manufacturing poses a challenge to management due to complexities in interrelationships. Diligent understanding of measures of lean manufacturing assumes great importance. Essential manufacturing flexibilities take care of uncertainties driven by dynamics of the market. Interrelationship between factors of manufacturing flexibility and lean manufacturing adds to complexity. Judicious analysis of these factors is imperative to understand their effect on lean manufacturing. Total interpretive structural modeling methodology is used for establishing relationships among the factors affecting lean performance. Case studies have been carried out and TISM is applied to understand the dynamism of factors. Study brings out how the organization of the companies and level of automation help in understating the driving and dependence power. The study helps in understanding the influence of hierarchy and level of factors identified by TISM technique on lean performance as also the factors which merit attention of top management to achieve better results

Enabling the adoption of machine learning in clinical decision support: A Total Interpretive Structural Modeling Approach

It has been reported that the healthcare industry is the slowest adopter of artificial intelligence methods, particularly machine learning (ML), compared to other industries. However, ML can provide unprecedented opportunities for clinical decision-making aid that help improve treatment outcomes and enhance cost-effectiveness. This method paper aims to identify the enablers for adopting ML in supporting clinical decision-making and propose a strategic road map toward boosting the clinicians' intentions to adopt ML as a clinical decision support tool. This paper utilizes the Total Interpretive Structural Modeling (TISM) methodology and the Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis to investigate the relationships and the interaction between the identified enablers and to develop a hierarchical model that helps policymakers and the other key stakeholders devise the necessary strategies to enhance the adoption of ML in supporting clinical decision-making. The paper concludes that building an academic foundation, raising awareness among the clinicians and patients, building trust in machine learning, and enhancing the perceived normative congruence are among the most important enablers for boosting the clinicians' intentions to adopt machine learning in supporting clinical decision-making.

Analysing the impact of artificial intelligence on the competitiveness of tourism firms: a modified total interpretive structural modeling (m-TISM) approach

PurposeThis study explores the variables that drive the impact of artificial intelligence (AI) on the competitiveness of a tourism firm. The relationship between the variables is established using the modified total interpretive structural modelling (m-TISM) methodology. The factors are identified through literature review and expert opinion. This study investigates the hierarchical relationship between these variables.Design/methodology/approachThe modified total interpretive structural modelling (m-TISM) method is used to develop a hierarchical interrelationship among variables that display direct and indirect impact. The competitiveness of a tourism firm is measured by investigating the effect of variables on the firm's financial performance.FindingsThe study identifies ten key factors essential for analysing the impact of AI on a firm's competitiveness. The m-TISM methodology gave us the hierarchical relationship between the factors and their interpretation. A theoretical TISM model has been constructed based on the hierarchy and relationship of the elements. The elements that fall in Level V are “AI Skilled Workforce”, “Infrastructure” and “Policies and Regulations”. Level IV includes the elements “AI Readiness”, “AI-Enabled Technologies” and “Digital Platforms”. Elements that fall under Level III are “Productivity” and “AI Innovation”. Level II and Level I comprise “Tourist Satisfaction” and “Financial Performance”, respectively. The levels indicate the elements' hierarchical level, with Level I the highest and Level V the lowest.Research limitations/implicationsTourism and AI scholars can analyse the given variables by including the transitive links and incorporate new variables depending upon future research. The m-TISM model constructed from literature review and expert opinion can act as a theoretical base for future studies to be conducted by researchers.Practical implicationsManagement/Practitioners can focus on the available characteristics and capitalise on them while working on the factors lacking in their organisation to enhance their competitiveness. Entrepreneurs starting their own business can utilise the elements in understanding the ecosystem of strengthening a firm's competitiveness. They can work to improve on the aspects which are crucial and trigger the impact on competitiveness. The government and management can devise policies and strategies that encompass the essential factors that positively impact the competitiveness of the firms. The approach can then be looked at with a holistic approach to cater to the other related components of the tourism industry.Originality/valueThis study is the first of its kind to use the modified TISM methodology to understand the impact of AI on the competitiveness of tourism firms.

Analysis of barriers of sustainable supply chain management in electronics industry: An interpretive structural modelling approach

In the past, industries were concentrating only on the economic aspects of a business. Many organizations are now addressing the social and environment impact of their supply chain for which they are adopting sustainability in their supply chain. The process of transformation to a sustainable supply chain will encounter some barriers which need to be eliminated or mitigated. To successfully manage these obstacles, it is crucial to know and study these barriers in specific context. This research aims to identify and analyse major barriers hampering sustainability implementation in the case of electronics industry in India. Through literature review and experts’ opinion eleven barriers are identified and classified into categories of Policy, Human resource and Technology. The hierarchical structure and interrelationship among these barriers is established using Interpretive Structural Modeling (ISM) methodology. A major finding in this research is that lack of awareness on benefits of sustainability, lack of regulations and enforcement of environment standards and lack of commitment from top management are significant barriers in implementation of a sustainable supply chain. The driving and driven powers of these barriers and their interdependence is determined. Using MICMAC analysis, five barriers were recognized to be the driver variables, three barriers as dependent variables and three as linkage variables. The barrier lack of performance metrics/evaluation standards on sustainability is having strong dependence power and least driving power implying that it is highly influenced by other variables. It is found that most of the barriers in the policy category are independent with high driving and influencing power. The research draws attention to formulate targeted policies at both government and organizational level in the electronics industry for successful implementation of sustainable supply chain management. The modeling gives an insight to organizations in confronting barriers and to manage their resources in an efficient and effective way while making their supply chains sustainable. The paper concludes with findings and future scope of research being discussed. • Identified key barriers of sustainable supply chain in Indian electronics industry. • The barriers are classified into categories of Policy, Human resource and Technology. • Interpretive Structural Modeling is employed for identifying the interrelationship among the barriers. • MICMAC analysis has been carried out to classify barriers of sustainability into four types, viz., autonomous, dependent, linkage and independent. • Lack of awareness of benefits of sustainability is root causal barrier affecting sustainability operations. • Most of the barriers in policy category are independent with high driving and influencing power.

Innovation Trajectories for a Society 5.0

Big Data, the Internet of Things, and robotic and augmented realities are just some of the technologies that belong to Industry 4.0. These technologies improve working conditions and increase productivity and the quality of industry production. However, they can also improve life and society as a whole. A new perspective is oriented towards social well-being and it is called Society 5.0. Industry 4.0 supports the transition to the new society, but other drivers are also needed. To guide the transition, it is necessary to identify the enabling factors that integrate Industry 4.0. A conceptual framework was developed in which these factors were identified through a literature review and the analytical hierarchy process (AHP) methodology. Furthermore, the way in which they relate was evaluated with the help of the interpretive structural modeling (ISM) methodology. The proposed framework fills a research gap, which has not yet consolidated a strategy that includes all aspects of Society 5.0. As a result, the main driver, in addition to technology, is international politics.

Structural modeling of lean supply chain enablers: a hybrid AHP and ISM-MICMAC based approach

PurposeToday the role of industry 4.0 plays a very important role in enhancing any supply chain network, as the industry 4.0 supply chain uses Big Data and advanced analytics to inform the complete visibility. Latest data are available to bring clarity and support real-time decision-making in the entire supply chain that’s why adopting optimization techniques such as lean manufacturing and lean supply chain concept for enhancing the supply chain network of the organizations is a good idea and would benefit them in increasing their cost efficiency and productivity. The purpose of this work is to develop a technique, which may be useful for future researchers and managers to identify and classification of the significant lean supply chain enablers.Design/methodology/approachIn this paper, the authors considered hybrid analytical hierarchy process to find the ranking of the identified lean supply chain enablers by calculating their weightage. Interpretive structural modeling (ISM) is applied to develop the structural interrelationship among various lean supply chain management enablers. Considering the results obtained from ISM the Matrices d'Impacts Croises Multiplication Appliqué a un Classement (MICMAC) analysis is done to identify the driving and dependence power of Lean Supply Chain Management Enablers (LSCMEs).FindingsFurther, the best results applying these methodologies could be used to analyze their inter-relationships for successful Lean supply chain management implementation in an organization. The authors developed an integrated model after the identification of 20 key LSCMEs, which is very helpful to identify and classify the important enablers by ISM methodology and explore the direct and indirect effects of each enabler by MICMAC analysis on the LSCM implementation. This will help organizations optimize their supply chain by selective control of lean enablers.Practical implicationsFor lean manufacturing practitioners, the result of the study can be beneficial where the manufacturer is required to increase efficiency and reduce cost and wastage of resources in the lean manufacturing process, as well as in enhancing the supply chain.Originality/valueThis paper is the first research paper that considered firstly deep literature review of identified lean supply chain enablers and second developed structured modeling of various lean enablers of supply chain with the help of various methodologies.

ISM–ANP hybrid approach to prioritize the barriers in green lean Six Sigma implementation in construction sector

PurposeThe purpose of this paper is to prioritize and establish relationships among the barriers that affect green, lean and Six Sigma (GLSS) implementation in the Indian construction sector.Design/methodology/approachA hierarchal model consisting of several levels is generated by the interpretive structural modelling (ISM) methodology. For establishing the priority weights and the ranking of the barriers, the relationships among barriers from the model in ISM are used to provide an output from the analytic network process (ANP). The 12 vital barriers that affect implementation of GLSS adoption were shortlisted from literature and then finalized in consultation with experts belonging from both industry and academia.FindingsBased on the ISM model “Lack of awareness for green products, Lack of top management commitment and involvement as well as Lack of funds along with an improper estimation” are at the highest level. Similar results were found while ranking the barriers through ISM–ANP integration.Originality/valueThis study identified and prioritized the barriers that affect GLSS implementation using ISM–ANP approach, such a study has not been attempted previously for the construction sector. The ISM model and ANP ranks are based on the inputs gathered from experts and academicians so as to ensure practical validity. This approach is assists decision-makers to focus on the key barriers priority basis and enables them to implement GLSS smoothly.

Analysis of Enablers to Implement Ergonomic Interventions in Indian Manufacturing Industry: A Multi-criteria Decision-making Approach

The implementation of ergonomics intervention practices is the need of time in a highly global competitive working environment for cutting down musculoskeletal health issues and improving the productivity of workers. Thus far, no research has developed practical methodological approaches and techniques to look into the dynamic nature of ergonomics intervention practices, specifically in the glass artware industry. Therefore, the objective of this article is to identify and prioritize the enablers to implement ergonomics intervention practices in Indian glass artware industries. Nineteen ergonomics intervention (EI) practices enablers were identified from the literature review and 16 were screened from identified enablers with the help of fuzzy Delphi method (FDM). The screened important enablers were then examined using a hybrid Interpretive Structural Modeling- Matrix Cross-Reference Multiplication Applied to a Classification-based framework. The interpretive structural modelling (ISM) methodology helps in evaluating contextual interactions among the enablers while classifying them based on their driving and dependence power. The results revealed that stringent legal requirements from the government, along with management support and commitment, are the main drivers and play a significant role over other enablers. Further, this study attempts to explore the critical enablers that are most prevalent for implementing EI practices. The results would be helpful for industries in implementing EI practices within their production systems by introducing and boosting the dominant enabler. This research offers key insight for practitioners, researchers and policy-makers by identifying interrelationships among the enablers.

Modelling Critical Innovation Factors in Rural Agrifood Industries: A Case Study in Cuenca, Spain

The agrifood industry contributes to sustaining the population and the economic growth in rural areas of Spain. Innovation in the agrifood sector has therefore become a necessity as a means of improving the competitiveness of companies and the territory, thus promoting sustainable rural development in areas currently characterised by social issues such as depopulation. Meeting this need requires the generation of specific knowledge on innovation in the rural agrifood industry to strategically steer the business management of innovation. This study aims to contribute to further improving the competitiveness of the agrifood industry through the interrelation of critical innovation factors in small and medium-sized agrifood enterprises, thus shedding light on the innovation environment of differentiated local products in depopulated rural regions. The qualitative Interpretive Structural Modelling (ISM) methodology was used with the participation of entrepreneurs and experts from the sector. The ISM was applied to a case study in Alcarria Conquense, a Spanish region that embodies the current problems of many rural territories. The results show four factors (cooperation, managerial skills, absorptive capacity, and market orientation) are binding variables with a high power of influence and dependence, and a fifth factor, funding, is the most dependent on the others. The work contributes to the literature by revealing the needs and opportunities for a potential strategic planning of rural development that can positively influence the problems of the region through innovation management in this industry.

Exploring the paths to big data analytics implementation success in banking and financial service: an integrated approach

PurposeBig data analytics (BDA) is recognized as a recent breakthrough technology with potential business impact, however, the roadmap for its successful implementation and the path to exploiting its essential value remains unclear. This study aims to provide a deeper understanding of the enablers facilitating BDA implementation in the banking and financial service sector from the perspective of interdependencies and interrelations.Design/methodology/approachWe use an integrated approach that incorporates Delphi study, interpretive structural modelling (ISM) and fuzzy MICMAC methodology to identify the interactions among enablers that determine the success of BDA implementation. Our integrated approach utilizes experts' domain knowledge and gains a novel insight into the underlying causal relations associated with enablers, linguistic evaluation of the mutual impacts among variables and incorporating two innovative ways for visualizing the results.FindingsOur findings highlight the key role of enabling factors, including technical and skilled workforce, financial support, infrastructure readiness and selecting appropriate big data technologies, that have significant driving impacts on other enablers in a hierarchical model. The results provide reliable, robust and easy to understand insights about the dynamics of BDA implementation in banking and financial service as a whole system while demonstrating potential influences of all interconnected influential factors.Originality/valueThis study explores the key enablers leading to successful BDA implementation in the banking and financial service sector. More importantly, it reveals the interrelationships of factors by calculating driving and dependence degrees. This exploration provides managers with a clear strategic path towards effective BDA implementation.

Determinants of alliance productivity and performance: evidence from the automobile industry

PurposeThis study aims to identify critical factors and examine their impact on alliance performance from an organizational learning point of view.Design/methodology/approachA modified total interpretive structural modeling (M-TISM) methodology was used in this study. The different paths/links in the developed M-TISM model were further validated by using the Mahindra-Ford alliance case study.FindingsIn this study, a total of seven critical factors were identified using an extensive literature review, and a hierarchical model was developed. Results show that prior alliance experience, inter-partner learning, knowledge transfer, absorptive capacity and knowledge internalization have a positive on the alliance productivity and performance. Furthermore, the findings indicate that prior alliance experience remains essential for alliance productivity and performance, while knowledge transfer and absorptive capacity can contribute to inter-partner learning and knowledge internalization in strategic alliances.Research limitations/implicationsThis study can help managers and policymakers to understand the identified critical factors from an organizational learning perspective and understand their impact on the alliance performance in a competitive environment. The managers should know that previous alliance experience, learning from partner firms, building an absorptive capacity, etc., are necessary to achieve superior alliance productivity and performance. For academicians, the M-TISM methodology used in this study can provide a mechanism to perform exploratory research and build a hierarchical model in different management research fields.Originality/valueThe study fills research gaps by identifying key factors, developing a hierarchical model, and examining their impact on the performance of strategic alliances in the Indian automotive industry.

Performance indicators for technology business incubators in Indian higher educational institutes

PurposeThe paper aims to review existing performance indicators in technology business incubators (TBIs) and propose some new indicators with a focus on incubation activities in higher educational institutes (HEIs) of India.Design/methodology/approachPerformance indicators of various types of incubators were identified from research papers followed by interview, consultation and suggestion from experts of the subject. Nature of interrelationship between the identified indicators has been established with the help of Interpretive Structural Modelling methodology and Matrice d’impacts croisés multiplication appliquée á un classment analysis.FindingsNumber of ideas came for screening and number of ideas converted to start-ups, survival rate of incubatees is the indicators which have the highest driving power followed by time taken in screening an idea and number of failed or rejected ideas returned back into incubation. Few indicators (driving indicators) are affecting performance of other indicators as well.Research limitations/implicationsSome performance indicators are proposed which can be used for measuring performance of technology incubators in India. The actual implications will be known when these findings are used to assess performance of some technology incubator. This also is the limitation of the study that some cases can be included to validate the findings of this research.Practical implicationsA total of 15 performance indicators for measuring performance of TBIs in Indian HEIs have been proposed. The proposed indicators will help incubator management to prioritize the efforts and resource allocation.Social implicationsTBIs are looked upon as mechanism for promoting entrepreneurial culture in Indian HEIs. Their success is well linked to growth of society. This research will help technology incubators to identify the most important factors in incubation process. Performance improvement will directly affect society in whole. Culture of IEE (Innovation, Entrepreneurship and Employment ) can be achieved through technology incubatorsOriginality/valueIdentification of new indicators for performance measurement of incubators in Indian HEIs is the novelty of this research. This has a lot of value due to multilevel hierarchy model.

Corporate social responsibility motives of Indian firms

PurposeThe purpose of this paper is to explore the motives of Indian firms for engaging with corporate social responsibility (CSR) practices and their interplay by using interpretive structural modelling methodology (ISM) and Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis.Design/methodology/approachThe research uses ISM and Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis to find the structural relationship among the CSR motives of the Indian firms identified from the past literature and agreed upon by the experts.FindingsThe ISM model indicates that firms primarily engage in CSR either because of top management commitment to certain values, to meet the legal mandate or of the pressure from the NGOs. The top management commitment gives a strategic orientation to CSR, which results in community engagement by the firm as one of the important components of the strategy. The community engagement helps in engaging with its employees and investors along with finding sources of innovations, which, in turn, help the firm in engaging its customers, managing corporate reputation and getting a cost advantage. Collectively, these help them in improving their financial performance. However, the model highlights two autonomous sources, meeting legal mandate and pressure from NGOs also motivate firms to engage in CSR without having any strategic thought or engagement with its strategic system.Originality/valueThe study provides a comprehensive listing of CSR motives of Indian firms along with the structural relationships among the identified CSR motives. The model developed provides CSR professionals and policymakers an understanding of the primary CSR motives along with their driving power and dependence. This insight will help them in manipulating these motives for better CSR engagement by the Indian firms.

Development of ecosystem for effective supply chains in 3D printing industry - an ISM approach

3D printing (3DP) is an important technology to manufacture the objects of intricate designs that are otherwise not possible for traditional manufacturing. This technology has witnessed growth in research papers in the last decade that shows the increasing interest of researchers and users in this technology. Many researchers have predicted and written about the unimaginable new supply chain configurations after the large-scale adoption of 3D printing. A supply chain is a lifeline for any business because it drives the business by satisfying customers’ demands economically. This study was undertaken to explore the factors that are critical for developing a supply chain ecosystem for the 3DP industry. The study used the literature of the relevant research papers, conference articles, additive manufacturing business reports, and supply chain management (SCM)research papers & articles on 3D printing technology. This study highlights factors that are critical for the supply chain ecosystem for the 3DP industry. Interpretive Structural Modelling (ISM) methodology identified interactive association among those factors. Impact Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC analysis) has classified those factors into four categories for better understanding and strategy making. The stakeholders in the 3D printing industry can draw insights from the output of this study. Researchers can do further research on these factors for the benefit of sustainable supply chains in the 3DP industry.

Application of multi‐criteria decision‐making approach in healthcare surgical management

AbstractThe decision‐making process in healthcare systems, especially in the area of surgical management is generally considered to have a low level of transparency; hence, effective decision‐making plays a vital role in the case sector. Non‐union of a fracture is dependent on many critical causative factors and in the past healthcare management studies, several research activities highlighted the influence of various causative factors on non‐union. However, none of the studies neither tried to prioritise the factors nor identified the interrelationship between them. In the present research, through literature review and opinions of seven surgeons' 10 causative factors of non‐union were shortlisted and they were analysed and modelled by a multi‐criteria decision‐making tool. The total interpretive structural modelling (TISM) methodology was used for identifying the mutual relationships among the causative factors and for developing a hierarchical structure. Also, the MICMAC approach was employed to identify the significant/dominant factors. The results of the TISM approach and MICMAC analysis revealed that ‘Alcohol abuse’ was found to be the most significant causative factor followed by ‘use of tobacco’, and ‘inappropriate dynamisation’ was found to be the least important. This study intends to guide the medical practitioners and healthcare managers in understanding the mutual influence of the causative factors and prioritisation of the same. Further, it helps them to carry out the surgical management effectively.

Drivers of implementing Big Data Analytics in food supply chains for transition to a circular economy and sustainable operations management

Purpose The aim of this study is to evaluate Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM). Design/methodology/approach Ten different BDA drivers in FSC are examined for transition to CE; these are Supply Chains (SC) Visibility, Operations Efficiency, Information Management and Technology, Collaborations between SC partners, Data-driven innovation, Demand management and Production Planning, Talent Management, Organizational Commitment, Management Team Capability and Governmental Incentive. An interpretive structural modelling (ISM) methodology is used to indicate the relationships between identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with a number of experts from industry and academia. Findings The results show that Information Management and Technology, Governmental Incentive and Management Team Capability drivers are classified as independent factors; Organizational Commitment and Operations Efficiency are categorized as dependent factors. SC Visibility, Data-driven innovation, Demand management and Production Planning, Talent Management and Collaborations between SC partners can be classified as linkage factors. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSC transition from linearity to CE and SOM. In addition, Operations Efficiency, Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM. Research limitations/implications The interactions between these drivers will provide benefits to both industry and academia in prioritizing and understanding these drivers more thoroughly when implementing BDA based on a range of factors. This study will provide valuable insights. The results from this study will help in drawing up regulations to prevent food fraud, implementing laws concerning government incentives, reducing food loss and waste, increasing tracing and traceability, providing training activities to improve knowledge about BDA and focusing more on data analytics. Originality/value The main contribution of the study is to analyze BDA drivers in the context of FSC for transition to CE and SOM. This study is unique in examining these BDA drivers based on FSC. We hope to find sustainable solutions to minimize losses or other negative impacts on these SC.

Analysing the critical success factors and the risks associated with third-party logistics in the food supply chain: a case of coffee industry

PurposeThe purpose of this study is to analyse various risks associated with third-party logistics (3PL) in the coffee supply chain and to present a framework that computes the influence of these risks on the critical success factors of the coffee supply chain.Design/methodology/approachThe risks have been identified through a comprehensive literature review and validation by industry experts. The paper utilises an interpretive structural modelling (ISM) methodology for developing a hierarchical relationship among the CSFs. Furthermore, fuzzy MICMAC analysis is carried out to categorise these CSFs based on their driving power and dependence value. The fuzzy technique for order preferences by the similarity of an ideal solution (fuzzy-TOPSIS) approach has been applied to prioritise the risks associated with 3PL based on their ability to influence the CSFs of the coffee SC. Furthermore, we performed a sensitivity analysis to analyse the stability of the results obtained in this study.FindingsThis study illustrates ten risks associated with 3PL and five CSFs in the coffee supply chain. The analysis revealed that coffee enterprises need to develop a balanced pricing strategy to ensure a sustainable competitive advantage, whereas the lack of direct customer communication is the most dominant 3PL risk affecting the CSFs.Practical implicationsThis research provides coffee enterprises with a generalised framework with set parameters that can be used to attain a successful coffee supply chain in any developing nation.Originality/valueThe study contributes to the literature by being the first kind of study, which has used fuzzy ISM-MICMAC to analyse the CSFs of the coffee supply chain and fuzzy-TOPSIS for analysing the impact of various risks associated with the 3PL in the coffee supply chain. Thus, this work can be considered a benchmark for future research and advancement in the coffee business field.

Evaluating Industry 4.0 Implementation Challenges Using Interpretive Structural Modeling and Fuzzy Analytic Hierarchy Process

The fourth industrial revolution known as Industry 4.0 is reshaping and evolving the way industries produce products and individuals live and work therefore, gaining massive attraction from academia, business, and politics. The manufacturing industries are optimistic regarding the opportunities that Industry 4.0 may offer such as improved efficiency, productivity and customization. The present research contributes to the Industry 4.0 literature by identifying, modeling, analyzing, and prioritizing the challenges in implementing Industry 4.0 in manufacturing industries. In doing so, the article first introduces the interpretive structural modeling (ISM) to develop the hierarchical relationships among the challenges and analyzes their mutual interactions. Further, “Matrice d’Impacts Croises Multiplication Appliquee aun Classement” (MICMAC) analysis is used to categorize the challenges into four categories, namely autonomous, driver, dependent, and linkage based on their driving power and dependence power. Moreover, fuzzy analytic hierarchy process (F-AHP) methodology is used to prioritize the challenges based on three criteria: driving power, dependence power, and change management. The hierarchical model developed through ISM methodology shows that “lack of vision and leadership from top management (C12), lack of skills training program and education (C2), and uncertainty of return on investment (C9)” are the major challenges in implementing Industry 4.0 in manufacturing industries. The findings of F-AHP analysis suggest that “lack of vision and leadership from top management (C12), lack of skilled workforce (C3), lack of skills training program and education (C2), and uncertainty of return on investment (C9)” are some of the major challenges of implementing Industry 4.0. Finally, the obtained results show how challenges affect other so that to uncover the root cause triggering the other challenges. The industrial practitioners and managers can then take advantage of these analyses to know which challenge acts as the main barrier in implementing Industry 4.0 and to be focused first in order to reach a solution.

Interpretive structural modelling approach to assess financial attributes of the air cargo industry

Air cargo represents about one-third of the total value of global trade carried and is therefore an essential player in the world economy. Monitoring the financial stability of air cargo carriers is vital to the industry, governments, and stakeholders. To detect financial difficulties within the aviation industry numerous prediction models have been developed, but most focus on the passenger airlines. In addition, these prediction models typically do not take into account essential non-statistical factors. This paper identified qualitative factors that could influence the financial health of all-cargo airlines and attempted to understand the interaction among them. The factors were identified based on previous research and validated by expert opinions. Interpretive structural modelling (ISM) was used to determine any mutual influences among the factors. Further, the ISM methodology was able to establish the level of the factors and determine their driving power and dependence. The results are very useful for air cargo management in that they can use the findings of this study to overcome dominant factors.