Sustainable municipal solid waste management (MSWM) in the smart cities in Indian context

PurposeSmart cities in India are going to be a reality very soon by turning challenges into opportunities for the society. However, due to rapid increase in population burden, fast urbanization and growing demand of advanced services in the smart cities, the quantity of per capita municipal solid waste (MSW) has escalated. Moreover, the COVID-19 pandemic has further challenged the municipal solid waste management (MSWM) system with the increasing amount of infectious wastes coming from households (HHs), quarantine centers, healthcare facilities, vaccination centers, etc. Therefore, the present study attempts to explore and analyze the various dimensions of sustainable MSWM system in the smart cities.Design/methodology/approachThe study identifies 13 factors of sustainable MSWM system from the literature, field surveys and stakeholders' opinions. Thereafter, stakeholders' opinions are collected and analyzed using total interpretive structural modeling (TISM) approach to explore the interrelationships among the factors of sustainable MSWM system. These relationships are further validated through the empirical investigation of the real-life case study of Rourkela Municipal Corporation (RMC), Odisha, India.FindingsThe TISM approach places all 13 factors into six levels in the hierarchical digraph depending upon the inputs received from the various stakeholders on their interrelationships. Study also validates the proposed TISM model by collecting the data of RMC, Odisha, on the development of MSWM system over the period of 2015–2021.Practical implicationsThe study also highlights various implications for the other developing cities and stakeholders to set up the roadmap for developing the sustainable MSWM system. Study defines “IT platform” and “awareness among citizens” as the base of the sustainable MSWM system in any smart city.Originality/valueThe present study is the first of its kind to explore the interrelationships among the factors of sustainable MSWM system by using TISM approach. Moreover, the proposed TISM framework is further validated through the empirical journey of one of the smart cities in India.

Risk Analysis of Tunnel Water and Mud Inrush Using Interpretive Structural Modeling and Fault Tree

Water and mud inrush is a common geological hazard in tunnel construction. Risk analysis of tunnel water and mud inrush has always been an important subject. In order to avoid the geological hazard, this paper presents a risk analysis model of tunnel water and mud inrush. The model combines the interpretive structural modeling method (ISM) and fault tree analysis (FTA). Relying on the Qinyu tunnel in the Weiwu expressway project, water and mud inrush risk factors are obtained by using ISM. Fundamental risk factors include formation lithology, attitude of stratum, strata combination, topography and geomorphology, geological structure and weather. ISM core risk factors are used as FTA basic events. Fuzzy importance of FTA basic events is obtained by using fuzzy interval calculation. The results show that geological structure is the primary risk factor causing Qinyu tunnel water and mud inrush. The model achieves qualitative and quantitative analysis of tunnel water and mud inrush. It accurately determines the main factors affecting the tunnel water and mud inrush, which is conducive to accident prevention.

Energy Market in the Post-COVID-19 World

The objective of this chapter was to structurally model the high priority factors in the face of the impact of severe acute respiratory syndrome COVID-19 on the energy market. The method was based on interpretive structural modeling, and the matrix of crossed impacts multiplication was applied to classification. A model of 12 factors structured hierarchically in six levels is proposed in which consumption preferences, regulatory and normative modifications, political restrictions, and planning strategies have the greatest influence on the energy market from the perspective of China. As a result of this, it is suggested to move towards greater participation of public and private actors in renewable energy vectors.

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

Modeling Factors Affecting Extended Producer Responsibility An ELV Case Study in Saudi Arabia

ELVs have become a worldwide concern as vehicles have become so common around the globe. Under the present circumstances where automobile usage is under rapid rise, ELV management is of high significance regarding the resource conservation, and waste management. This research paper manages to identify significant hindrances in realizing extended producer responsibility towards their product in the case of end of life vehicles recycling in Saudi Arabia utilizing interpretive structural modeling approach. By breaking down the current hindrances, one can look forward how to put ELV orders in Saudi Arabia later on and to position ELV management at a superior level along the lines of development prompting a better tomorrow.

Investigating the Drivers and Barriers of Reverse Logistics Practices in the Pharmaceuticals Supply Chain

This empirical research investigates the reverse logistics practices adopted by a leading pharmaceutical company in Egypt, the drivers behind the applied reverse logistics activities, and the barriers affecting the application of reverse logistics. The methodological approach of interpretive structural modeling (ISM) is applied to study the mutual influences across barriers listed by a preliminary case analysis, and to identify the “driving” barriers which may worsen other barriers, and “dependent” barriers influenced by the driving barriers. A key finding of the analysis is that lack of regulation enforcement and lack of public awareness regarding the importance of reverse logistics are the most driving barriers influencing the rest of the identified barriers.

A Customized Quality Model for Software Quality Assurance in Agile Environment

The agile approach grew dramatically over traditional approaches. The methodology focuses more on rapid development, quick evaluation, quantifiable progress and continuous delivery satisfying the customer desire. In view of this, there is a need for measurement of the agile development process. In this respect, the present research work investigates the inter-relationships and inter-dependencies between the identified quality factors (QF), thereby outlining which of these QF have high driving power and dependence power, working indirectly towards the success of agile development process. This paper proposes a new agile quality model, utilizing an interpretive structural modeling (ISM) approach and the identified factors are classifies using Matriced' Impacts Croise's Multiplication Applique´e a UN Classement (MICMAC) approach. The research findings can significantly impact agile development process by understanding how these QF related to each other and how they can be adopted.

Prioritizing Different Types of Barriers to Knowledge Sharing: A Cause-and-Effect Analysis of the Views of Academics in Turkey

This study aims to identify different types of barriers to knowledge sharing among academics in Turkey and to investigate the relationships between the barriers and their degree of impact. Accordingly, it implements qualitative and quantitative approaches in two phases. In the first phase, the knowledge sharing barriers are identified through the literature review and categorized under organizational, individual, and technological dimensions via expert opinions so as to determine current barriers for the Turkish academics. In the second phase, the identified barriers and their interactions are more deeply investigated by using the Interpretive Structural Modeling (ISM) and Decision Making Trial and Evaluation Laboratory Method (DEMATEL) methods. The findings of the study reveal that organizational and individual knowledge sharing barriers have a stronger effect than technological barriers. According to ISM and DEMATEL findings, “corporate structure”, “power relations"”, and “supportive corporate culture” are the driving forces for the knowledge sharing among the academics in Turkey. This study provides a hierarchical and causal relation model that may enable both performing the actions needed to promote academic knowledge sharing and advancing university performances. The findings offer useful insights on what the key barriers are and how these interrelate, so that they can be overcome. Thus, the findings hold significant potential to contribute both to the academic field and to the policymakers who are in charge of taking regulatory actions.

SOCIAL MEDIA AND SUSTAINABLE BEHAVIOR: A DECISION MAKING FRAMEWORK USING INTERPRETIVE STRUCTURAL MODELING(ISM)

The growing population and consumer income have led to more consumption activity globally. The increased consumption requires further extraction of resources and pressure on existing resources. The damage effects are reflected in global warming, environmental pollution, and decreased flora fauna. In addition, human activities are perennially damaging the environment. High resource extraction and consumption activity require a more prudent use of resources and product choices. This study attempts to identify and prioritize the potential drivers for sustainable consumption through the Interpretive Structural Modeling (ISM) approach. ISM helps to obtain qualitative data instead of quantifiable factors resulting from modeling. Thirteen drivers of sustainable consumption were finalized after extensive literature review and expert’s consultation. Thirty respondents were sampled to collect data. The relationship between drivers was further established. Incentives, Labeling and Values Norms were the most important drivers of sustainable consumption. The study's findings would help policymakers to develop strategies to promote sustainable consumer practices among millennials. Adopting sustainable consumption would promote green consumption, improve the environment and save valuable resources.