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Humanitarian supply chain (HSC) has been constantly challenged with multiple issues due to the complex dynamics of the disaster. These issues are inevitable and interrelated. The issues produce undesirable cascading effects that make performance measurement complicated. This paper aims to identify and model the critical barriers in the HSC.

The study identifies 17 barriers through the relevant literature and interviews with the stakeholders related to humanitarian organization (HO) in the developing economy. The barriers have been identified from strategic, technological, organizational, economic and operational point of view. Further, the barriers were ranked based on the degree of influence using the grey analytic network process (ANP) approach. The interrelationship among the influential barriers is established through modified total interpretive structural modelling (mTISM). To analyse and demonstrate the iterative consensus among the stakeholders, kappa statistics was adopted.

The barriers have been ranked to determine their extent of influence and modelled to reveal the interrelationship among them. The issues concerned with skills of personnel are the most influential barrier. Other three critical barriers identified are: chaotic operative environment, conflicting objectives of HO and lack of funding for information technology (IT) infrastructure. Appropriate funds need to be allocated towards IT infrastructure and personnel training.

Both quantitative and qualitative performance measurement frameworks had been proposed earlier for HSC. However, limited literature addresses the implementation issues with the available frameworks. This study advances the knowledge on performance measurement barriers of HSC and develops a functional description to identify the critical role of performance measurement in HOs. The study proposes a new version of the interpretive structure modelling, using mTISM technique, to determine the contextual interactions between various HSC performance measurement barriers.

The execution of product recovery strategies and the definition of an adequate system to manage its performance are crucial to move toward the employment of a successful circular economy (CE) concept. Defining strategies for the efficient management of product recovery requires product data that is difficult to obtain, making it harder to handle. However, efficient product recovery management can play a key role in shifting companies from a linear economy model to a more sustainable CE model, providing economic benefits and increasing customer satisfaction by recovering and adding value to the discarded product. Therefore, this study aims to provide better models to support decision-making and to evaluate product recovery performance.

The present study highlights a comprehensive two-stage decision approach to identify and examine the relevant key performance indicators (KPIs) for performance improvement of an information facilitated product recovery system (IFPRS) in a CE context. In the first phase, a structural equation modeling (SEM) methodology is adopted to categorize the KPIs by employing exploratory factor analysis and measurement of the model fit is obtained using the confirmatory factor analysis. Further, in the second phase, the KPIs are ranked and prioritized on the basis of expert’s recommendations adopting fuzzy-technique for order of preference by similarity to ideal solution (FTOPSIS).

Empirical investigation is conducted by compiling data from an association of six decision-makers (DMs) and two DMs from a respective prospect. The results highlight that “Technology Capacity” is ranked as the highest and is the most prominent KPI for successful employment of IFPRS practices. The results of the study would benefit policy makers and company directors in the selection of KPIs based on their importance in a context of high competition and greater pressure to adopt sustainable practices in the management of their companies.

As far as the authors know, no study has been performed till date to identify and construct a structural KPIs model for IFPRS performance improvement in the context of CE. The paper, therefore, proposes a two-phase SEM-TOPSIS technique to measure the impact of KPIs which is a new integration in the existing literature. The results of the study would benefit policy makers and company directors in the selection of KPIs based on their importance in a context of high competition and greater pressure to adopt sustainable practices in managing their organizations.

The COVID-19 has affected the whole world and forced countries to impose lockdowns and restrict travel and transportation. This was followed by different countries formulating different policies, and when transportation resumed, there were some restrictions. Such strategies forced transporters to rethink mode choice decision making for freight transportation. The purpose of this study is to identify and rank the factors affecting freight transport mode choice decisions considering the spread of COVID-19 outbreak.

Initially, the factors affecting the mode choice decisions for freight transportation amid the COVID-19 outbreak have been extracted from a literature survey and group discussion with experts. Further, this paper employs the integration of grey-Decision making trial and evaluation laboratory (DEMATEL) with fuzzy Best Worst Method to analyze the identified factors and their sub-factors. The model's robustness and feasibility were then tested using sensitivity analysis.

The findings of the study showed that Disaster Characteristics and Operations Factors are the most and least influential factors, respectively. Panic, Travel restrictions, Border restrictions, Severity, and Mortality rate are the top five ranked sub-factors. Panic, Compliance to Social distancing, and Passenger and Freight integration are among the new sub-factors proposed. In the current circumstances, these sub-factors are crucial and relevant. In addition, various recommendations are offered to improve transportation services while guaranteeing safety, such as promoting passenger and freight integration, lowering the panic level, developing dynamic rules based on region characteristics, and so on. The study's findings will help practitioners and politicians reformulate the existing transportation infrastructure in the event of disease outbreaks.

The demographic context of experts, is one of the study's limitations. Only experts from the Indian subcontinent were considered in this study. In addition, future study work can be based on a comparison of the outcomes from various Multi-Criteria Decision Making techniques.

The present research work differentiates itself through the analysis of mode choice factors considering the ongoing pandemic across the globe. The results emanated from the study can guide the concerned stakeholders to make better decisions.

This study aims to propose a comprehensive framework among Key Performance Indicators (KPIs) for analyzing the Information Facilitated Product Recovery System (IFPRS) on the basis of feedback captured from the industry experts and researchers.

Total Interpretive Structural Modeling (TISM) methodology interspersed with fuzzy MICMAC is used to extract the interrelationships and develop a hierarchical structure among the identified KPIs. Further, the Fuzzy Decision-Making Trial and Evaluation Laboratory (F-DEMATEL) method has been enforced to determine the intensity of these relationships and identify the most influential KPIs among identified KPIs from literature review and expert opinions. The outcome indicates that “information sharing,” “technology capacity” and “technology standards such as EDI, RFID” are the KPIs that have attained highest driving power.

This study has identified 15 KPIs of IFPRS and developed an integrated model using TISM and the fuzzy MICMAC approach, which is helpful to describe and organize the important KPIs and reveal the direct and indirect effects of each KPI on the IFPRS implementation. The integrated approach is developed, as the TISM model provides only binary relationship among KPIs, while fuzzy MICMAC analysis provides explicit analysis related to driving and dependence power of KPIs.

Structural Equation Modeling (SEM) analysis can be performed based on the adequate number of responses collected using structured questionnaire. More qualitative techniques like ELECTRE, TOPSIS, etc. can be used to establish the strength of relationship among the KPIs and ranking them to focus on the few critical KPIs.

The proposed modeling could empower various governmental and non-governmental regulatory bodies in formulation of policies to effectively tackle the problem related to product recovery systems. This study has strong practical implications, for both practitioners as well as academicians. The practitioners need to concentrate on identified KPIs more cautiously during IFPRS implementation in their organizations and the top management could formulate strategy for implementing these KPIs obtained.

There is a lack of studies related to the modeling of KPIs of IFPRS. As vast information is essential about the products returned during different product recovery stages, this study bridges the gap in literature by providing a framework for KPIs related to IFPRS. It is expected that the results originated will assist the experts to relevantly identify the significant and drop insignificant KPI for successful product recovery implementation and performance improvement of IFPRS.

Pharmaceutical donations are a practical approach to increase medicine availability during disasters such as disease outbreaks. However, often donated pharmaceuticals are inappropriate and unsuitable. This convergence of inappropriate pharmaceuticals is a severe operational challenge and results in environmental hazards. This study explores the pharmaceutical supply chains (PSCs) during a disease outbreak to relieve the negative impact of the material convergence problem (MCP).

This study adopts a situation-actors-process learning-action-performance (SAP-LAP) linkage framework to understand the PSC dynamics. The problem-solving component of the SAP-LAP analysis provides the strategies catering to MCP. The findings from the SAP-LAP helped to develop the causal loop diagram (CLD). This study conducts several experiments on the proposed strategies by integrating CLD into a stock and flow diagram. Later, a disease outbreak case study accessed the pharmaceutical donations effect on PSC performance.

The study synthesises and evaluates propositions and strategies to incorporate circular economy (CE) principles in PSC. This study proposed two strategies; one to sort and supply and the other to sort, supply and resell. The reuse policy improves humanitarian organisations' finances in the simulation study. This study verified the operational improvement of PSC by reducing the transport and storage burden due to MCP.

This study comprehensively approaches the issue of drug donation and uniquely produced several propositions for incorporating a CE perspective in PSC. The study also proposed a unique simulation approach to model the donation arrivals in response to a disease outbreak using susceptible, exposed, infectious and recovered modelling.

This study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.

This study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.

The results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.

The models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.

The study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.

The purpose of this study is to examine the barriers to the implementation of big data (BD) in the healthcare supply chain (HSC).

First, the barriers concerning BD adoption in the HSC were found by conducting a detailed literature survey and with the expert's opinion. Then the exploratory factor analysis (EFA) was employed to categorize the barriers. The obtained results are verified using the confirmatory factor analysis (CFA). Structural equation modeling (SEM) analysis gives the path diagram representing the interrelationship between latent variables and observed variables.

The segregation of 13 barriers into three categories, namely “data governance perspective,” “technological and expertise perspective,” and “organizational and social perspective,” is performed using EFA. Three hypotheses are tested, and all are accepted. It can be concluded that the “data governance perspective” is positively related to “technological and expertise perspective” and “organizational and social perspective” factors. Also, the “technological and expertise perspective” is positively related to “organizational and social perspective.”

In literature, very few studies have been performed on finding the barriers to BD adoption in the HSC. The systematic methodology and statistical verification applied in this study empowers the healthcare organizations and policymakers in further decision-making.

This paper is first of its kind to adopt an approach to classify barriers to BD implementation in the HSC into three distinct perspectives.

Sustainability is an integrating concept and demands strategic attention in developing countries like India. Due to strict environmental regulations and ongoing government sustainable policies such as “Namami Gange,” leather industries are extensively facing challenges to conform themselves toward these sustainable policies. The major challenge faced by leather industries is the exponentially increasing cost of adaptation to sustainable product and process. Under these operational constraints, survival of Indian leather industries has become a major challenge. In this context, this paper aims to identify and evaluate sustainable manufacturing policies. The key performance indicators (KPIs) based on triple bottom line of sustainability can assist leather industries that are about to initiate adopting sustainable practices.

This paper demonstrates the role of KPIs for evaluating sustainable manufacturing policies for leather industries in India. Initially, an in-depth literature review analysis has been carried out to identify indicators for evaluation of sustainable manufacturing policies. In this work, an integrated methodology has been developed to refine the priority map of the aforementioned KPIs based on consensus building among experts using Kappa analysis. Total interpretive structural modeling (TISM) has been used to demonstrate relationships which explain the significance of the KPIs. Further, Matriced Impact Croises Multiplication Applique analysis has been carried out to explore the relationships amongst KPIs.

Based on above analysis, identified interactive relationships among the KPIs will assist managers and decision-makers to incorporate effective sustainable policies in leather industries.

It is expected that these identified interactive interrelationships between KPIs will certainly facilitate the leather industry to achieve higher sustainable performance and competitiveness.

This study carries out an in-depth literature review analysis of sustainable manufacturing policies in leather industry. The author proposes an integrated methodology using kappa analysis, consensus building and TISM for evaluation of sustainable policies based on the literature review analysis and expert opinion.

Green human resource management (GHRM) is an arising issue for the tannery industry in the context of developing economies. As the tannery industry can be seen as one of the highest polluting industries on earth, it becomes imperative for the industry to implement GHRM practices for greening the workforce. In this context, the purpose of this paper is to focus on antecedents that will support the implementation of GHRM practices in the tannery industry supply chain.

In this study, an expanded literature review was organized to establish antecedents for implementing GHRM practices. The total interpretive structural modeling (TISM) technique is employed to explore interactions among the identified antecedents. Furthermore, Matriced Impact Croises Multiplication Applique analysis was conducted for determining the driving-dependence power of each antecedent.

The results revealed that “green selection facility,” “green recruiting facility,” “green organizational culture,” “green purchasing,” “green strategy towards ES,” “regulatory forces towards ES” and “top management commitment towards greening the workforce” are the key antecedents for the exercise of GHRM practices in the tannery industry.

The proposed model might support decision makers to understand the interactions among the antecedents of GHRM practices. This model will help managers to understand the impact of one antecedent on another prior to the implementation of GHRM practices in the tannery industry.

In this study, the author(s) propose a new version of the interpretive structural modeling approach (ISM), named the TISM technique, for determining the contextual interactions between GHRM initiative antecedents that are very new in the existing literature.

Block chain technology (BCT) has emerged as a promising solution for the co-ordination and aid mechanism issues in the context of humanitarian supply chain (HSC). However, implementation of BCT in HSC discerns several barriers. Therefore, the purpose of this study is to identify and model the block chain implementation barriers in the context of HSC.

In the present study, 14 potential barriers to BCT adoption in HSC have been identified through literature survey. The survey comprises white papers, pilot studies, conference proceedings and journal articles. Further, the identified barriers were finalised in consultation with a team of experts. The team comprised experienced stakeholders working in the humanitarian domain and BCT development. The barriers were categorised into four (technological, organisational, exogenous and economic) perspectives adopting the kappa statistics. Further, the barriers were prioritised using fuzzy best worst method (FBWM) approach. Later, sensitivity analysis was performed to check the robustness and viability of the model.

The findings from the study indicate that the barriers, such as “data privacy, ownership, and security issues” (B1), “funding issues and cost complexity” (B3) and “technological complexities” (B8), are relatively more influential. The HSC stakeholders and BCT developers are required to identify the safety mechanism against the misuse of victim’s data. The funding issues and technological complexities are interrelated and need synergetic cooperation between blockchain developers, donors, humanitarian organisations (HOs) and other HSC stakeholders. Further, “lack of awareness and understanding among stakeholders” (B6) and “interoperability, collaboration and cross-pollination among HOs” (B5) were identified as least influential barriers to BCT adoption in HSC.

In literature, limited study has been observed on determining barriers to BCT implementation. A more systematic method and statistical confirmation is necessary to establish further new confronting barriers. This study is limited to Indian context.

To the best of the authors’ knowledge, this study is first of its kind to use an FBWM approach for prioritising the barriers to BCT adoption in the context of HSC. The study provides potential barriers to BCT and categorises them into four different perspectives, along with their degree of influence.