Modelling Sustainability Risk in the Brazilian Cosmetics Industry

Supply chains involve several stakeholders, with different environmental, social, economic, and ethical attributes, and are exposed to various risks along all stages. One of these risks relates to conditions or events related to sustainability that have the potential to generate harmful reactions from stakeholders in the supply chain. Those risks can materialize through stakeholders’ responses, when they hold companies responsible for unfavorable conditions in the supply chain, leading to reputational damage. Understanding the supply chain’s sustainability risk factors can help companies improve supply chain resilience. This article aims to empirically identify the most influential risk factors in the Brazilian cosmetics supply chain and, additionally, analyze the interrelationships between these risks. The methodology combines interpretative structural modeling (ISM) and matrix cross-impact multiplication (MICMAC) analysis, and is grounded in the opinions of cosmetics industry experts. Firstly, the critical causes and consequences are identified, called factors. Secondly, the ISM model is built, representing the interrelationships between factors and their hierarchy. Thirdly, the MICMAC analysis is performed, unfolding the strength of the relationship among the influencing factors. Fourthly, measures are designed to act on and mitigate the factors identified in the previous steps. The results show that the Brazilian cosmetic companies analyzed do not take advantage of the opportunity to take leadership in cost reduction, differentiation, and engagement with their partners. “Financial risks” were identified as the most influential among the set of risks, while “Technology and innovation” and “Legislation and responsibility” were identified as root risk factors. This research identified measures that could be implemented to act on and mitigate the root risk factors, thus contributing to the research relating to sustainability risks in supply chains.

Prioritization of barriers in industrial symbiosis implementation in automotive industry - Using ISM and MICMAC Analysis

The continuous increase in production, lack of flexibility of organizations, and lack of knowledge on sustainability have led to the depletion of raw materials and increased waste generation. Industrial symbiosis now has become a very effective solution and an essential strategy for responsible consumption and waste utilization. This strategy helps different organizations to blend their resources, share information, logistics, and waste materials to solve their problems by forming a network to increase profits. This study was directed towards identifying the barriers towards applying Industrial Symbiosis in an organization with probable solutions to them. ISM modeling and MICMAC analysis were used to visualize the impact of different barriers for implementing Industrial symbiosis in an organization and improve efficiency in terms of eco-innovation. The results of this study give experiences and rules to practicing managers in medium and small-scale industries to effectively execute Industrial Symbiosis. The study also adds to the improvement of a basic model for examining the barriers affecting IS with regards to eco-innovation and sustainable frameworks and contributes to ongoing researches on this eco-friendly idea of Industrial Symbiosis.

Analysis of drivers for anti-food waste behaviour - TISM and MICMAC approach

Purpose This study aims to identify and analyse the interactions among drivers of anti-food waste behaviour at the consumer level. By understanding the mutual interactions among the drivers, an effort is made to identify the most driving and most dependent drivers through the total interpretive structural modelling (TISM) approach. Modelling offers inputs to propose focused interventions for reinforcing the identified drivers of anti-food waste consumer behaviour using the theoretical lens of social practices theory. Design/methodology/approach A proposed model of factors affecting anti-food waste behaviour is arrived at to suggest the most effective anti-food waste behavioural interventions. The factors were identified through an extensive literature search. A hierarchical structure of identified factors has been developed using TISM and MICMAC analysis through expert opinion. Focused marketing strategies towards promoting the identified factors for encouraging anti-food waste behaviour were suggested further. Findings This study identifies nine drivers based on extensive literature review, brainstorming and expert opinion. The TISM hierarchical model portrays the most important and least important drivers of household anti-food waste behaviour. It establishes that fundamental knowledge and socio-cultural norms are the most critical factors to drive the consumers. Marketers can focus on designing effective interventions to enhance the essential knowledge of the consumers and orient the socio-cultural norms towards anti-food waste behaviour. Practical implications This study offers implications for practitioners, policymakers and cause-driven marketing campaigns targeting anti-food waste behaviour. It provides an indicative list of critical factors relevant to household food waste behaviour, which can be used to drive effective marketing campaigns to nudge anti-food waste behaviours. Originality/value The proposed food waste behaviour management model was developed through modelling technique (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis, and relating them to marketing interventions is a novel effort in the food waste domain.

Antecedents and enablers of supply chain reconfigurability and its effect on performance

The reconfiguration of supply chain is becoming a crucial concept used to deal with market disruptions and changes such as COVID 19 pandemic, demand uncertainty, new technologies, etc. It can be defined as the ability of the supply chain to change its structure and functions in order to adapt to new changes. Its assessment requires an understanding of its quantitative factors to provide indicators that are easy to interpret. Effective reconfigurability assessment can be achieved by measuring quantitatively its six characteristics (modularity, integrability, convertibility, diagnosability, scalability and customization). This paper aims at identifying the quantitative factors of each characteristic and their inter-relationships by using Total Interpretive Structural Modelling (TISM). The structural model obtained by TISM is applied to understand the dependency quantitative factors. Based on TISM results, a classification of quantitative factors is determined using « Matrice d'Impacts Croisés, Multiplication Appliquée à un Classement » (MICMAC) analysis. This paper may be helpful to understand the previously mentioned characteristics of reconfigurable supply chain in order to facilitate the measuring and the assessment of reconfigurability.

TISM-based analysis of important factors for additive manufacturing in healthcare:a case study

Purpose This paper aims to focus on developing a theoretical framework for the analysis of factors influencing additive manufacturing (AM) in the health-care domain. Design/methodology/approach A total of 18 factors are considered through extensive literature review and the relationship between each factor is studied using total interpretive structural modeling (TISM) and the model is logically developed. TISM model is developed using appropriate expert inputs. In addition, cross-impact matrix multiplication applied to classification (MICMAC) analysis is conducted to group the factors. Findings It was found that “ease of design” and “research and development” are the two most important factors with the highest driving power and dependencies. Through MICMAC analysis, the significance of factors is studied. Practical implications The study has been done based on inputs from academic experts and industry practitioners. The inferences from the study have practical relevance. Originality/value The development of a structural model for the analysis of factors influencing AM in the health-care domain is the original contribution of the authors.

Modelling and analysis of factors for implementation of smart cities: TISM approach

Purpose The concept of “Smart Cities” is gaining prominence across the world as a solution to effectively address the issues or impediments faced by cities due to rapid urbanization. The purpose of this paper is to identify the key factors which form the primary basis for the implementation of “Smart Cities”. Particularly, this paper aims to analyse the contextual relationship and driving/dependence power of these key factors and model these using the total interpretive structural modelling (“TISM”) framework. Design/methodology/approach The key factors which form the basis for the implementation of Smart Cities were identified through an evaluation of the literature on “Smart Cities” and expert opinions. Thereon, the contextual relationship between these key factors was examined with the help of experts. Thereafter, these key factors were modelled using the total interpretive structured modelling (“TISM”) framework. Cross-impact matrix multiplication applied to classification (MICMAC) analysis was further applied to classify the factors. It is pertinent to note that the driving power and dependence of these key factors were also reviewed. Findings This paper establishes a TISM of the key factors for the implementation of “Smart Cities” which will aid in examining the interrelationship among the factors and will also identify the hierarchy among these factors. On extensive examination of the literature and expert opinions on “Smart Cities”, it can be asserted through TISM that quality of life (F1), e-services adoption (F5) and economic growth (F8) are the leading factors in establishing “Smart Cities”. Furthermore, it must be noted that the MICMAC analysis and driving-dependence graph helps in classifying the key factors as autonomous factors, drivers, linkages and outcomes, which assists in comprehending which factors possess driver power and which are exhibiting dependency. Originality/value The contribution lies in the authentic manner in which this paper attempts to use the TISM approach combined with MICMAC analysis to model key factors for the implementation of “Smart Cities”; which would aid and assist policymakers and practitioners to construct a structural framework for the implementation of “Smart Cities” through identification of drivers, linkages and outcomes.

What drives organisational performance: strategic thinking, technological change, strategic risks? A modified total interpretive structural modelling approach and MICMAC analysis

Purpose The purpose of this study is to explain the interrelationships between the elements of strategic thinking, technological change and strategic risks. The main objective of this research is to identify the hierarchy for the elements of thinking, technological change and strategic risk and also to identify the driving powers of these elements. Design/methodology/approach The methodology used in this study is modified total interpretive structural modelling and MICMAC analysis which gives the interrelationships and also the driving powers of the elements by analysing the relationships between the elements from the existing literature. This method helps us in answering/understanding the “what”, “how” and “why” of the research. Modified total Interpretive structural modeling is considered in this study, which helps in doing both the paired comparisons and transitivity checks simultaneously. A digraph is constructed at the end of the analysis, which shows the links between the elements, and a driver dependence matrix is constructed, which shows the driving powers. Findings This study gives an understanding of the role of the elements, the relationships between them and the hierarchy of addressing these elements, and also the driving and dependence power. Findings of this research give us an understanding of how strategic thinking/technological change/strategic drives the performance of the firm. Research limitations/implications This study is conducted with the help of existing literature; this can be further extended by considering the expert opinion. Practical implications The model explains the direct and transitive links of the elements and the strength of the relation between them, which helps the researchers and the practitioners to understand the driving power and importance of these constructs. It also helps us to understand the role of these elements and, if implemented in an organisation, which elements need to be prioritised for enhancing the performance of the firm. Originality/value Research done in the past has individually analysed the elements effecting strategic thinking; this study identifies the relationships between the elements of all three constructs and helps in understanding the levels of hierarchy.

Identification and Prioritization of Critical Success Factors for Off-Site Construction Using ISM and MICMAC Analysis

Many studies have been conducted to define the critical success factors (CSFs) for off-site construction (OSC) activation, but there has been a lack of identification of the relationship with the identified CSFs. However, it is necessary to clearly identify the hierarchy and relationships with the success factors in order to develop specific strategies for OSC activation. This work presents a study that was conducted to identify the CSFs for OSCs and establish the relationships of the identified CSFs for OSC. First, 20 CSFs for OSCs were identified through prior study reviews related to CSFs for OSC. Next, the interpretive structural modeling (ISM), which has advantages in developing an understanding of complex relationships, was leveraged in order to analyze the relationships between 20 CSFs for OSC to derive a hierarchical model consisting of seven levels. The CSFs for OSC were classified into four groups using MICMAC analysis, which is useful for classifying factors by the strength of the relationship with factors based on driving power and dependence power. This proposed model can be used as a basis for developing management measures for OSC project success.

Framework of Challenges Affecting Adoption of People Analytics in India Using ISM and MICMAC Analysis

People analytics has brought a paradigm shift in the processes, technologies and systems of organizations. Success is driven here through data-driven methodologies. The primary objective of this research is to identify, rank and interrelate challenges affecting the adoption of people analytics in India. The interpretative structural modelling (ISM) approach is applied to rank and interrelate these challenges in the Indian context. MICMAC analysis is conducted to reveal the driving power and dependence of these challenges of people analytics. The MICMAC analysis also indicates the relative importance and interdependence between these challenges in the Indian context. During the first phase of the research, 12 challenges are identified from the literature, and these people analytics challenges are validated based on expert opinions. During the second phase, ISM identifies ‘Leveraging existing enterprise resources’ as the most important challenge in the Indian context among the 11 validated challenges. MICMAC analysis identifies all 11 challenges as ‘linkage challenges’ with high dependence and driving power. For researchers, this methodology facilitates further carrying out exploratory studies and focusing their interactions through hierarchical structures. The study investigates the core issue among many issues faced by people analytics professionals. Second, it has methodological novelty in the context. Finally, it points to multidimensional implications for various stakeholders in people analytics in the Indian context.

Application of total interpretive structural modeling for analyzing factors of additive manufacturing and industry 4.0 integration

Purpose The purpose of this paper is to develop a model based on the total interpretive structural modeling (TISM) approach for analysis of factors of additive manufacturing (AM) and industry 4.0 (I4.0) integration. Design/methodology/approach AM integration with I4.0 is attributed due to various reasons such as developing complex shapes with good quality, real-time data analysis, augmented reality and decentralized production. To enable the integration of AM and I4.0, a structural model is to be developed. TISM technique is used as a solution methodology. TISM approach supports establishing a contextual relationship-based structural model to recognize the influential factors. Cross-impact matrix multiplication applied to classification (MICMAC) analysis has been used to validate the TISM model and to explore the driving and dependence power of each factor. Findings The derived structural model indicated the dominant factors to be focused on. Dominant factors include sensor integration (F9), resolution (F12), small build volumes (F19), internet of things and lead time (F14). MICMAC analysis showed the number of driving, dependent, linkage and autonomous factors as 3, 2, 12 and 3, respectively. Research limitations/implications In the present study, 20 factors are considered. In the future, additional factors could be considered based on advancements in I4.0 technologies. Practical implications The study has practical relevance as it had been conducted based on inputs from industry practitioners. The industry decision-makers and practitioners may use the developed TISM model to understand the inter-relationship among the factors to take appropriate measures before adoption. Originality/value The study on developing a structural model for analysis of factors influencing AM and I4.0 is the original contribution of the authors.