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Globalization and population explosion have worsened postharvest losses (PHL) in developing countries. This study looks to identify numerous controllable variables to reduce these losses and make the fresh produce supply chain more efficient.

The study employs the interpretive structural modelling (ISM) technique to develop a hierarchical model to comprehend the intricate relationships between the variables influencing PHL. These variables are further classified based on the relative levels of importance in terms of their driving and dependence powers.

The findings of this research provide variables for enterprises operating in fresh food supply chains to understand the specific risks that that supply chain faces and how these risks interact within the system. The fuzzy MICMAC analysis also classifies and highlights critical risk factors in the supply chain to aid implementation of PHL mitigation measures. The study highlights the importance of devising policies, legislation and efforts to regulate and curtail PHL across the global food supply chain.

The efficiency of the food supply chain contributes not only to economic sustainability but also to broader goals such as food security, better utilisation of global resources and sustainability in the supply chain.

It also highlights the significance of well-informed government policies, laws and regulations in successfully controlling and reducing PHL.

This study compares factors contributing to PHL in the fresh produce supply chain and emphasises the stakeholders’ critical role in alleviating these losses. It also highlights the significance of well-informed government policies, laws and regulations in successfully controlling and reducing PHL.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

Recently, interest in sustainability has grown globally in the heavy vehicle and equipment industry (HVEI). However, this industry's complexity poses a challenge to the implementation of generic sustainable supply chain management (SSCM) practices. This study aims to identify SSCM's barriers, practices and performance (BPP) indicators in the HVEI context.

The results are derived from case studies of four multinational manufacturers. Within-case and cross-case analyses were conducted to categorise the SSCM BPP indicators that are unique to HVEI supply chains.

This study's analysis revealed that supply chain cost implications and a deficient information flow between focal firms and supply chain partners are the key barriers to SSCM in the HVEI. This analysis also revealed a set of policies, programmes and procedures that manufacturers have adopted to address SSCM barriers. The most common SSCM performance indicators included eco-portfolio sales to assess economic performance, health and safety indicators for social sustainability and carbon- and energy-related measures for environmental sustainability.

The insights can help HVEI firms understand and overcome the typical SSCM barriers in their industry and develop, deploy and optimise their SSCM strategies and practices. Managers can use this knowledge to identify appropriate mechanisms with which to accelerate their transition into a sustainable business and effectively measure performance outcomes.

The extant SSCM literature has focused on the light vehicle industry, and it has lacked a concrete examination of HVEI supply chains' sustainability BPP. This study develops a framework that simultaneously analyses SSCM BPP in the HVEI.