Risk Modeling Framework for Strategic and Operational Intervention to Enhance the Effectiveness of a Closed-Loop Supply Chain

In a closed-loop supply chain (CLSC), acquiring end-of-life vehicles (ELVs) and their components from both primary and secondary markets has posed a huge uncertainty and risk. Moreover, the constant supply of ELV components with minimization of cost and exploitation of natural resources is another pressing challenge. To address the issues, the present study has developed a risk simulation framework to study market uncertainty/risk in a CLSC. In the first phase of the framework, a total of 12 important variables are identified from the existing studies. The total interpretive structural model (TISM) is used to develop a causal relationship network among the variables. Then, Matriced Impacts Cruoses Multiplication Applique a un Classement is used for determining the nature of relationships (i.e., driving or dependence power). In the second phase, the relationship of TISM is used to derive a Bayesian belief network model for determining the level of risks (i.e., high, medium, and low) associated with the CLSC through the generation of conditional probabilities across 1) multi-, 2) single-, and 3) without-parent nodes. The study findings will help decision-makers in adopting strategic and operational interventions to increase the effectiveness and resiliency of the network. Furthermore, it will help practitioners to make decisions on change management implementation for stakeholders'performance audits on the attributes of the ELV recovery program and developing resilience in the CLSC network. Overall, the present study holistically contributes to a broader investigation of the implications of strategic decisions in automobile manufacturers and resellers. IEEE

Modeling of Factors Impacting Remote Workforce During COVID-19

The way an organization operates has a pattern to it. A knowledge-based way of understanding these patterns and implementing according to them retains the competitive advantage of the organizations. Thus, identifying factors is important because, if successful, it results in shared intellectual capital. Changing the core of the pattern upon which the organization works creates several problems in retaining an organization's competitiveness. This research focuses on identifying the elements which have a significant influence on an organization's operations due to the remote working of employees during situations like the COVID-19 pandemic. Further, the relationships of factors among each other have been explored from the available research. Based on the study of various organizations it has been found that not much work has been done to identify such factors even though several organizations have suddenly opted for their workforce to work remotely due to the COVID-19 pandemic. This has resulted in lost productivity and opportunities, organizational dis-balances, and a slower rate of development. The generated model may help organizations to understand the weak notes of remote working and implement structural changes accordingly to improve the productivity in remote working and tackle the productivity and opportunity loss due to remote workforce. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A model of business intelligence on cloud for managing SMEs in COVID-19 pandemic (Case: Iranian SMEs)

Purpose: The purpose of this study was to demonstrate a cloud business intelligence model for industrial SMEs. An initial model was developed to accomplish this, followed by validation and finalization of the cloud business intelligence model. Additionally, this research employs a mixed-techniques approach, including both qualitative and quantitative methods. This paper aims to achieve the following objectives: (1) Recognize the Cloud business intelligence concepts. (2) Identify the role of cloud BI in SMEs. (3) Identify the factors that affect the design and presenting a Cloud business intelligence model based on critical factors affecting SMEs during pandemic COVID-19. (4) Discuss the importance of Cloud BI in pandemic COVID-19 for SMEs. (5) Provide managerial implications for using Cloud BI effectively in Iran's SMEs. Design/methodology/approach: In the current study, an initial model was first proposed, and the cloud business intelligence model was then validated and finalized. Moreover, this study uses a mixed-methods design in which both qualitative and quantitative methods are used. The fuzzy Delphi Method has been applied for parameter validation purposes, and eventually, the Cloud business intelligence model has been presented through exploiting the interpretive structural modeling. The partial least squares method was also applied to validate the model. Data were also analyzed using the MAXQDA and Smart PLS software package. Findings: In this research, from the elimination of synonym and frequently repeated factors and classification of final factors, six main factors, 24 subfactors and 24 identifiers were discovered from the texts of the relevant papers and interviews conducted with 19 experts in the area of BI and Cloud computing. The main factors of our research include drivers, enablers, competencies, critical success factors, SME characteristics and adoption. The subfactors of included competitors pressure, decision-making time, data access, data analysis and calculations, budget, clear view, clear missions, BI tools, data infrastructure, information merging, business key sector, data owner, business process, data resource, data quality, IT skill, organizational preparedness, innovation orientation, SME characteristics, SME activity, SME structure, BI maturity, standardization, agility, balances between BI systems and business strategies. Then, the quantitative part continued with the fuzzy Delphi technique in which two factors, decision-making time and agility, were deleted in the first round, and the second round was conducted for the rest of the factors. In that step, 24 factors were assessed based on the opinions of 19 experts. In the second round, none of the factors were removed, and thus the Delphi analysis was concluded. Next, data analysis was carried out by building the structural self-interaction matrix to present the model. According to the results, adoptability is a first-level or dependent variable. Regarding the results of interpretive structural modeling (ISM), the variable of critical success factors is a second-level variable. Enablers, competencies and SME characteristics are the third-level and most effective variables of the model. Accordingly, the initial model of Cloud BI for SMEs is presented as follows: The results of ISM revealed the impact of SME characteristics on BI critical success factors and adoptability. Since this category was not an underlying category of BI;thus, it played the role of a moderating variable for the impact of critical success factors on adoptability in the final model. Research limitations/implications: Since this study is limited to about 100 SMEs in the north of Iran, results should be applied cautiously to SMEs in other countries. Generalizing the study's results to other industries and geographic regions should be done with care since management perceptions, and financial condition of a business vary significantly. Additionally, the topic of business intelligence in SMEs constrained the sample from the start since not all SMEs use business int lligence systems, and others are unaware of their advantages. BI tools enable the effective management of companies of all sizes by providing analytic data and critical performance indicators. In general, SMEs used fewer business intelligence technologies than big companies. According to studies, SMEs understand the value of simplifying their information resources to make critical business choices. Additionally, they are aware of the market's abundance of business intelligence products. However, many SMEs lack the technical knowledge necessary to choose the optimal tool combination. In light of the frequently significant investment required to implement BI approaches, a viable alternative for SMEs may be to adopt cloud computing solutions that enable organizations to strengthen their systems and information technologies on a pay-per-use basis while also providing access to cutting-edge BI technologies at a reasonable price. Practical implications: Before the implementation of Cloud BI in SMEs, condition of driver, competency and critical success factor of SMEs should also be considered. These will help to define the significant resources and skills that form the strategic edge and lead to the success of Cloud BI projects. Originality/value: Most of the previous studies have been focused on factors such as critical success factors in cloud business intelligence and cloud computing in small and medium-sized enterprises, cloud business intelligence adoption models, the services used in cloud business intelligence, the factors involved in acceptance of cloud business intelligence, the challenges and advantages of cloud business intelligence, and drivers and barriers to cloud business intelligence. None of the studied resources proposed any comprehensive model for designing and implementing cloud business intelligence in small and medium-sized enterprises;they only investigated some of the aspects of this issue. © 2021, Emerald Publishing Limited.

Lessons learned during Covid-19 from engineering asset management of dams

Public and private owners of critical infrastructures all over the world are taking high-quality standards to face the consequences of pandemics, particularly critical infrastructure such as dams that needs more attention to maintain and operate during coronavirus disease (Covid-19) pandemics. In this study, critical strategies have been identified through literature review and with the support of experts' opinions. The rough Decision-making Trial and Evaluation Laboratory and interpretive structural modelling methods were integrated to determine the most important strategies that were identified by literature review and experts' opinions. Moreover, the methodology was used to find the relationships, cause and effect between the critical strategies. Interviews were completed with professional managers and experts in the field of dam operation and maintenance to help in finding the influence degree between these critical strategies. Among 11 initial strategies, six critical strategies were selected for this study from the experts' points of view. By applying Matriced Impacts Croisés Multiplication Appliquée á un Classement analysis, driving and dependence powers were also determined and classified for these strategies. The outcomes indicate that the strategy of reviewing emergency action plans and planning for how routine and unplanned work will be implemented during pandemic staffing restrictions is the most driving among these strategies in dam asset management in Canada during pandemics. © 2021 ICE Publishing: All rights reserved.

Impacts of COVID-19 on the construction sector in the least developed countries

Purpose: Global construction has been affected by COVID-19 unprecedently. The construction sectors in the least developed countries are considered as vulnerable, but the covid made the countries experience the worst situation ever. To minimize the losses by effective measures, there needs to assess the COVID-19 impacts on the construction sector. So, the aim of this study is to investigate the most critical impacts of COVID-19 on construction in the least developed countries by considering the case study of Bangladesh. Design/methodology/approach: The authors adopted multistep research methods, including (1) literature analysis and discussion with experts to establish a comprehensive list of COVID-19 impacts;(2) through a questionnaire survey, data were collected from 217 construction professionals by email, Google Form and Skype for quantifying the significance of covid impacts;(3) reliability of the survey checked by the Cronbach Alpha test;(4) Relative Importance Index (RII) to determine the ranks of the impacts based on their significance;(5) Interpretive Structural Model (ISM) to explore the corelations and the hierarchical structure;and (6) cross-impact matrix multiplication applied to classification (MICMAC) analysis to classify the COVID-19 impacts. Findings: The study identified a total of 18 COVID-19 impacts on the construction sector. Among them, the job cuts, schedule delays, project suspension, cost overrun and effects on mental health are more influential and significant than others. Further, this study found that unpaid leave and job cuts are the two most fundamental impacts which influence other succeeding significant impacts. And ultimately all the impacts lead to hampering the national economy and development. Finally, MICMAC analysis suggested that unpaid leave and job cuts should be addressed first to resolve and effects on the national economy and development should be later. Research limitations/implications: This study does not consider all the COVID-19 impacts due to the relevant context and simplicity of the ISM method. Also, the respondent's attitude might be slightly different during the post-mass vaccination period. Practical implications: This study will help the company's management, employees and government to develop effective strategies to understand the insight of their interrelations and ultimately overcome the identified covid effects. This will must contribute to the industry, its employees, the government and society by ensuring the national economy and development, construction operations, investment, employment and social security. Originality/value: This study will contribute to the knowledge body (practitioners and researchers) by providing the list of significant covid impacts and insight into their interrelations for further deep analysis of the pandemic effects. This will also help the authorities and stakeholders in developing policies and strategies to minimize or avoid these effects and avoid future consequences due to any pandemic like covid. © 2022, Emerald Publishing Limited.

Analyzing the Interactions among the Barriers for Safe and Effective Medical Waste Management

Because of the growing number of hospitals in the country like the United Arab Emirates, huge medical wastes are generated in the hospitals, and managing this medical waste is considered a big challenge. In recent days, COVID 19 pandemic has paved the way for the generation of relatively huge amounts of infectious and hazardous waste in healthcare hospitals, and proper disposal of this heterogeneous mixture of medical waste is the biggest challenge. Improper waste management developed in health care units causes a direct impact on the workers, waste handlers, patients, caregivers, and the community. Also, it is important to manage the medical waste properly so that the environment will not get affected. In order to overcome this problem, both the manufacturer and the medical practitioner should take utmost care in managing the medical waste properly in all stages, starting from collection to the final disposal. The main aim of this research is to understand the different types of medical waste in the hospital and identify the barriers that impede the effective management of medical waste. For analyzing the interactions among the barriers, Interpretive Structural Modelling (ISM) approach is proposed as a solution methodology in this research work. By analysing the interaction among the barriers using the ISM model, we may extract the most influencing barrier that challenges both hospital management and government in managing medical waste safely and effectively. © 2021 IEEE.