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The governments of different nations implemented various policy measures in response to the COVID-19 outbreak. These policy measures had a negative impact towards freight transportation. Further, the shock and ripple effect on the other supply chain complicates the recovery process of freight transportation. The transformation of freight transportation in the post-COVID-19 world was reported to be unsustainable. Thus, emerged the requirement to formulate the recovery measures in the context of freight transportation. This study aims to identify and model the recovery measures for sustainable freight transportation (SFT).

In this study, 13 critical recovery measures are established from the literature and finalized with the help of an expert panel. An integrated Grey Decision-Making trial and evaluation laboratory is used to prioritize and establish the cause-effect relationships among the identified critical recovery measures. Further, sensitivity analysis is performed to ensure the robustness of the proposed model.

The present study reflects that Efficient traffic management (M13), sustainability focused policies (M2), sensitization of stakeholders (M10), financial support (M9) and adoption of 4 R practices (M12) are the top five critical recovery measures for SFT. The results highlight that the transport sector needs to retain the learning from the COVID-19 period to operate under low workforce availability. Further, the emerging economies are suggested to promote local manufacturing to reduce the lead time and risk of unavailability. The study findings reflect that attaining sustainability without considering the social dimension of sustainability is impossible. Also, the results shed light on the controllable and uncontrollable recovery measures.

The findings from the study would assist policymakers and practitioners in re-formulating the recovery measures for freight transportation considering the aspect of sustainability.

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.

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.

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.

The purpose of this study is to identify factors that are important for logistics organizations from the perspective of manpower readiness for digitization of logistics operations. The study also prioritizes the identified factors and also evaluates the readiness index of manpower for the digitalization of logistics processes.

The factors for manpower readiness are identified through literature review and analysis of a case study. Three major categories of factors are identified. These are organizational, behavioural and technological factors. Under these three major categories of factors, 18 sub-factors are identified. Thereafter, with experts' inputs, the factors are prioritized using Fuzzy analytic hierarchy process (AHP). Further, a case illustration of an Indian logistics company has been taken to understand the current processes, technical capabilities, manpower skills and organization culture. After the case analysis and expert inputs, the manpower readiness index has been evaluated by using graph theory matrix approach (GTMA).

The prioritization of manpower readiness factors has been done using Fuzzy AHP. Organizational factors are found to be the most important factors which require quick attention. Sub-factors that are most important for building competencies in the logistics sector are providing the right training on functional skill development (0.129), top management support and commitment for digitalization (0.117), and organizational culture for process digitalization (0.114), etc. Finally, framework for evaluation of manpower readiness index for logistics operations in the digital age has been illustrated for a case company.

Indian logistics companies can benchmark their readiness index with respect to the best in the industry. Based on the readiness index, logistics companies can analyse their position, gaps from best and worst and can also identify potential areas for improvement.

The novelty of the study lies in the development of a framework for manpower readiness for digitalization in the logistics sector. In literature, this field is very less researched and provides the scope for developing strategies for improving manpower competencies for Industry 4.0. Logistics companies can improve their performance by making their manpower ready based on results obtained for readiness index.

The purpose of this study is to analyze sustainability issues in the context of logistics service providers (LSPs) and to evaluate the sustainability index of an LSP in the Indian scenario.

The case study method is used to understand the sustainability issues of an Indian LSP. The situation–actor–process (SAP) and learning–action–performance (LAP) framework has been applied to critically examine the sustainability issues of the case organization. The information about the case study is collected from both primary and secondary sources, and a detailed analysis of different sustainability issues in the context of an Indian case has been done. The sustainability index for the case organization has been also evaluated.

It can be observed from the in-depth case analysis that LSPs have started making use of green practices for long-term sustainability, but adoption is very slow in developing countries such as India. Based on SAP-LAP analysis of an Indian LSP, suitable actions are recommended for improving the performance of LSPs.

Adoption of sustainable practices by LSPs can lead to sustainable performance. Sustainability index can assist LSPs to benchmark their sustainability performance to meet the market requirements.

The novelty of this study is to analyze the issues related to implementation of sustainable practices in an Indian LSP through SAP-LAP framework. It also illustrates the evaluation of sustainability index.

The purpose of this study is to identify sustainability practices and to propose a framework for evaluating the sustainability index of logistics service providers (LSPs).

Sustainable practices followed by LSPs are identified through literature review and analysis of a case study. Thirteen such sustainable practices are identified. Thereafter, with expert inputs, nine sustainable practices are shortlisted and considered for the evaluation of the sustainability index in the proposed framework. Graph Theory Matrix Approach has been applied to evaluate the sustainability index of an LSP.

Major practices identified for evaluating sustainability index include the use of recyclable packaging, use of renewable energy sources, green procurement, reduction in carbon emissions, use of CNG/electric vehicles, rainwater harvesting and so on. The sustainability index of an LSP is evaluated by using the proposed framework.

LSPs can benchmark their sustainability index with respect to the best in the industry. Based on it, LSPs can also identify potential areas for improvement.

Novelty of the study lies in the proposed framework for evaluation of sustainability index which can be used to develop strategies for green logistics. LSPs can also improve their performance in terms of sustainability measures by adopting green logistics.