Digital transformation of warehousing is revolutionizing operations by integrating advanced technologies. Automated Guided Vehicles (AGVs) optimize the movement of goods, while Internet of Things (IoT) enhances connectivity and data collection for improved decision-making. Robots automate tasks like picking and packing, increasing efficiency and reducing errors. Blockchain technology enhances transparency and traceability in the supply chain, building trust and reducing fraud. Digital twins create virtual replicas of warehouses for simulation and optimization, enabling proactive problem-solving and improved resource allocation. Such transformation addresses the increasing complexity and dynamic nature of warehousing in the modern marketplace. This systematic literature review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, investigates the role of optimization models in this digital shift from 2010 to 2023. The findings highlight the crucial enhancements and practical implications brought about by these technologies, showcasing their impact on streamlining operations. These improvements include advancements in path planning, task allocation, inventory management and storage assignment which significantly enhance operational effectiveness within warehousing. The results demonstrate a 25-30% increase in perishable food freshness, a 10% reduction in picking time, a 14.8% increase in space utilization, and a 9.95% decrease in total costs. Moreover, the findings call attention to the insufficient analysis on the economic implications of digital transformations in warehousing, notably in cost reductions and Return on Investment (ROI) evaluations which are crucial metrics for evaluating the financial success and wider benefits of digital transformation initiatives, such as customer satisfaction and revenue growth. The review also identifies critical research gaps, including the need to incorporate sustainability into optimization models, strategies for adapting to dynamic environments, the exploration of collaborative robots (cobots), and the optimization of reverse logistics and returns. This review provides a comprehensive foundation for future research and emphasizes the potential of digitalization to revolutionize warehousing practices through innovative optimization strategies and real-world applications.

In response to the growing need for environmental stewardship, Green Human Resource Management (GHRM) has emerged to incorporate sustainability in organizational practices, with Artificial Intelligence (AI) offering transformative potential. However, a comprehensive synthesis of the intersection between AI and GHRM is notably absent, prompting this review to systematically map the existing knowledge base and identify key trends. To bride this research gap, a systematic literature review was undertaken following the PRISMA framework, employing content and bibliometric analysis on 53 relevant articles published between 2018 and 2025. The analysis revealed five primary research themes, highlighting AI’s significant role in enhancing green recruitment, training, and performance management, while also underscoring critical challenges related to ethical considerations and organizational readiness. This review offers a structured synthesis of the AI-GHRM landscape, concluding with key interpretations that guide future research toward areas including adaptive systems, big data analytics, and the development of robust ethical frameworks, thereby serving as a valuable resource for advancing sustainable HRM practices. Moreover, this study directly contributes to the United Nations Sustainable Development Goals (SDGs), particularly SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production), through emphasizing the strategic significance of AI-enabled Green HRM in fostering organizational sustainability.

Digital transformation (DT) is reshaping cold chain operations through technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and digital twins. However, evidence remains fragmented, and a systematic synthesis focused on how these technologies affect cold chain performance, sustainability, and cost-efficiency is limited. This PRISMA-based systematic literature review analyzes 107 studies published between 2009 and 2025 to examine enabling technologies and application areas, operational and sustainability impacts, and the main adoption challenges. The reviewed evidence suggests that digitalization can improve real-time visibility, temperature control, traceability, and energy management, supporting waste reduction and improved quality assurance. Key challenges include high implementation costs and uncertain returns on investment, interoperability constraints, data governance and cybersecurity concerns, and organizational readiness gaps. The paper concludes with implications for managers and policymakers and a future research agenda emphasizing integrated multi-technology solutions, standardized sustainability assessment, and rigorous validation through pilots, testbeds, and real-world deployments to enable scalable and resilient cold chain digitalization.

Background: Selecting enterprise resource planning (ERP) systems for complex port environments is a significant challenge. This study addresses a key research gap by identifying and prioritizing the critical factors for ERP selection within the strategic context of United Arab Emirates (UAE) ports, which function as vital hubs in global trade. Methods: A hybrid methodology was employed, first using the Fuzzy Delphi Method (FDM) to validate thirteen challenges with five industry experts. Subsequently, the Relative Importance Index (RII) was used to rank these challenges based on survey data from 48 UAE port professionals. Results: The analysis revealed “Cybersecurity concerns” as the highest-ranked challenge (RII = 0.896), followed by “Engagement with external stakeholders” (RII = 0.842), and both “Process optimization” and “Technical capabilities” (RII = 0.808). Notably, factors traditionally seen as critical in other sectors, such as “Organizational readiness” (RII = 0.746), were ranked significantly lower. Conclusions: The findings indicate a strategic shift in ERP selection priorities toward digital resilience and external integration rather than internal organizational factors. This research provides a sector-specific decision-support framework and offers actionable insights for port authorities, vendors, and policymakers to enhance ERP implementation in the maritime industry.

Purpose: This research explores how integrating Lean Six Sigma (LSS) methodologies with Circular Economy (CE) principles can enhance operational efficiency and sustainability in wastewater treatment (WWT) systems. Design/methodology/approach: Using a case study approach, this research evaluates a municipal WWT plant as a bounded system. It applies established tools, such as Failure Mode and Effects Analysis (FMEA), regression analysis and hypothesis testing, to assess process performance. While these tools are widely recognized, their domain-specific adaptation to climate resilience and CE challenges in WWT systems remains limited, particularly within a structured, single-case empirical framework. Findings: The results show that traditional quality improvement tools can be effectively adapted to achieve environmental goals. The proposed framework achieved a 6.5% reduction in energy use in the secondary treatment stage, corresponding to annual savings of 575,665 kWh and approximately $48,891. It also contributed to reduced greenhouse gas (GHG) emissions by recovering methane (CH4) and supported long-term resilience through hydraulic capacity forecasting. Research limitations/implications: Although based on a single case, the study offers insights that can guide broader application across WWT sectors and geographies, laying the groundwork for future empirical validation. Practical implications: This research provides a flexible framework for industry professionals to integrate LSS and CE strategies into infrastructure operations. Originality/value: The study bridges a research gap by introducing a novel LSS–CE framework tailored to sustainability and climate resilience in WWT systems.