Sanjoy Kumar Paul

Australia is committed to achieving net-zero emissions by 2050, a goal that may require a major transformation of the household energy sector. Hydrogen can, however, be deployed as a complementary energy source to electricity by displacing natural gas. But the potential for hydrogen to make this transition is dependent on building a credible Australian household hydrogen supply chain (HHSC), which includes national distribution centres (NDCs), regional distribution centres (RDCs) and local distribution centres (LDCs). The HHSC is particularly vulnerable to operational disruptions under rapid adoption pathways and in perfect-competition market conditions, where infrastructure, supply, and pricing decisions are decentralised. Hydrogen flows may be disrupted at the NDCs and RDCs, leading to failure to meet demand and monetary losses across the HHSC. While many studies have assessed vulnerabilities within hydrogen supply chains, there is little attention paid to the consequences of distribution-level failures. This research aims to quantify the impacts associated with distribution centre (DC) disruptions in the HHSC using a multi-period network optimisation model to assess three operational situations: ideal situations, disrupted-DC situations without mitigation strategies, and disrupted-DC situations with suitable mitigation strategies. The results indicate that without mitigation strategies, demand fulfilment could potentially drop to zero, penalty costs could increase drastically, and profitability could decrease due to not meeting demand. In contrast, the implications of suitable mitigation strategies, including rerouting hydrogen through alternate, unaffected NDCs or RDCs, using spare capacity by increasing operating hours, and maintaining safety stock at RDCs, significantly increase HHSC performance. In these situations, demand fulfilment increases to up to 95%, and profitability improves substantially. This study contributes to the hydrogen supply chain literature by demonstrating how HHSCs can be planned and replanned to manage disruptions in DCs. The study also provides practical insights for policymakers and managers for a sustainable HHSC.

To ensure efficient delivery of perishable food products, food supply chains (FSCs) have advanced the usage of recent technologies and started integrating them into logistical systems. This study examines the interplay between digital capabilities, sustainable practices, logistical networks, and customer satisfaction in perishable FSCs through a cross‐sectional survey of 416 Indian consumers. It draws on a comprehensive literature review that highlights the potential variables and their impacts on the perishable FSCs. The data was collected using a five‐point Likert‐scale questionnaire, analyzed using partial least squares‐structural equation modeling (PLS‐SEM), and robustness was ensured by Harman's test. The study integrates value percept theory (VPT) to develop a comprehensive conceptual framework that explains how digital capabilities and sustainable practices enhance product quality and customer satisfaction in perishable FSCs. The findings from the study explicitly support the positive moderating role of logistical networks in the relationship between digital capabilities and product quality. The findings can assist management professionals operating in the perishable food sector in enhancing their theoretical and practical comprehension of the profound influence exerted by digital capabilities, sustainable practices, and logistical networks on the crucial nexus between product quality and customer satisfaction.

This paper investigates awareness‐motivation‐capability (AMC) related factors and their configurations for improving sustainable firm performance. The study is two‐phased. In the first phase, factors influencing sustainable firm performance were identified through a review of the literature. Then, multiple case studies were conducted to validate the factors, identify new factors in the context of the study, and categorize them under the AMC framework. In the second stage, a survey was conducted with 63 practitioners in the pharmaceutical industry to explore alternative configurations of factors leading to sustainable firm performance using fuzzy set qualitative comparative analysis (fsQCA). In the first phase, seven factors were found in the literature, and the case study revealed two more factors influencing sustainable firm performance, which were categorized under the AMC framework. The results of the second phase identified six distinct configurations of factors that contribute to sustainable firm performance. Among the configurations, those emphasizing government support, competitive advantage, firm's sustainability commitment, organizational slack, top management support, and skilled workforce were central. While prior studies have employed the AMC framework in various business domains, this study is among the first to apply it to configure factors for sustainable firm performance using a configurational approach. The insights from the configurations can assist practitioners and policymakers in developing policies to address issues with sustainable firm performance.

This study comprehensively explores the pivotal position that Artificial Intelligence (AI) enables on the advancement of decarbonization efforts, mainly in the context of Circular Supply Chains (CSCs). Employing a two-stage methodology, this study delves into identifying and analyzing the enablers essential for leveraging AI in the pursuit of decarbonization objectives. In the first stage, a literature review and an exploratory factor analysis are performed to discern the key enablers of AI for decarbonization initiatives. This process resulted in the identification of 15 significant enablers and categorization of enablers into environmental, organizational, institutional, and technological categories. Building upon the findings from the first stage, this study progresses to its second stage, wherein the Grey-Ordinal Priority Approach (G-OPA) is applied to analyze the identified enablers. The results indicate that adopting recyclable materials to enhance the efficiency of supply chains, emphasizing local production for recovery practices through advanced technology, and managing product life-cycle through intelligent and additive manufacturing technologies are the top three enablers. The application of the G-OPA enriches the robustness and comprehensiveness of the analysis, enabling an understanding of the complex interplay among the enablers. By clarifying the key enablers, business planners and designers can migrate from traditional linear supply chains to more sustainable CSCs through the careful implementation of enablers for decarbonization.

This study seeks to identify and analyze the major factors that contribute to an inclusive workforce for women in the area of logistics and supply chain. It further addresses the need for gender diversity and inclusivity in a traditionally male‐dominated field by adopting a human‐centric approach. This study employs a combination of Fuzzy Delphi Method (FDM) and Fuzzy Best Worst Method (FBWM) for methodically identifying and prioritizing factors that influence inclusiveness for women in the logistics and supply chain industry. FDM gathers experts' opinions and achieves a consensus on the identified relevant factors. Subsequently, FBWM is used to analyze the factors, providing a clear priority ranking based on their relative significance. The analysis identified potential factors that are crucial for fostering an inclusive workforce in the logistics and supply chain industry for women. The factors were classified into three main categories: employee growth and culture, inclusive business ecosystems, and accessibility and diversity factors. Based on the global weights, the top three ranked factors are: gender‐inclusive supply chain practices, skill development workshops, and supporting women‐owned businesses. This study is original in terms of gender inclusiveness in the logistics and supply chain industry. The innovative combination of multiple methods stipulates a robust methodology for identifying and analyzing the factors that impact inclusiveness, offering a novel contribution to the literature and practical applications in this field.

In the rapidly evolving landscape of global trade, understanding the transformation of production networks and business models within Global Value Chains (GVCs) is crucial. This chapter explores their evolution, tracing their historical development, contemporary changes, and the impact of regional integration and technological advancements. Key factors include modularization, outsourcing, digitalization, and automation. Historically, production networks evolved from simple, localized systems to complex global structures. The Industrial Revolution introduced mechanization and mass production, expanding production networks. Improved transportation infrastructure integrated regional markets, paving the way for global trade. The latter 20th century saw the rise of GVCs, distributing production stages across multiple countries to exploit regional comparative advantages, enhancing efficiency and cost-effectiveness. Technological advancements have significantly reshaped production networks. Digitalization and automation enable real-time data sharing, seamless communication, and improved coordination. Technologies such as the usage of the Internet of Things, the implementation of artificial intelligence, and the integration of robotic process automation streamline operations, increase productivity, and reduce errors. These innovations support new business models like mass customization and digital platforms, allowing companies to offer personalized products at scale and connect with global consumers. Regional integration through trade agreements and economic partnerships facilitates the flow of goods, services, and capital, enhancing GVC efficiency and competitiveness.

This study identifies Critical Success Factors (CSFs) for implementing Big Data Analytics (BDA) for sustainable logistics practices in the context of an emerging economy. Through a combination of literature review and experts' opinions, the study identifies 18 CSFs essential for the effective application of BDA in the logistics sector. The identified CSFs are further classified into four major categories: Organizational Efforts (OE), Technological Capabilities (TC), Environmental Practices (EP), and Social Factors (SF) using TOE and stakeholders theory. With the help of experts, the identified CSFs are later ranked using the Best‐Worst Method (BWM). A real‐life Indian logistics company is studied to comprehend its existing operations, technological abilities, workforce competencies, and organizational environment. Further, the Graph Theory Matrix Approach (GTMA) is used to develop a sustainability index for analyzing the case study and expert remarks. The prioritization of CSFs under different categories can guide logistics companies in implementing BDA to achieve sustainability in logistics. The findings from the study reflect that OE and TC are the most important CSFs. The sustainability index value guides the evaluation of the current sustainability of the case company and assists in improving performance by benchmarking the best index values of the same industry. Logistics companies can learn from benchmarked companies and can adopt their strategies for achieving goals, simultaneously considering the ranking of identified CSFs for implementing BDA.