Integrating IoT and Blockchain for Sustainable Waste Management in Cold Chain Food Supply Chains: Enhancing Efficiency and Accountability

This study applies Actor-Network Theory (ANT), Resource-Based View (RBV), and Closed-Loop Supply Chain (CLSC) Theory to investigate the integration of Internet of Things (IoT) and blockchain technology for sustainable waste management in cold chain food supply chains. Using a case study of Company A, supported by datasets such as the Time-Temperature Data and the Google Cloud Blockchain Dataset, the research evaluates how IoT sensors and blockchain systems monitor waste and enhance operational efficiency across supply chain stages. Findings reveal that IoT-enabled real-time monitoring significantly reduces waste by addressing temperature deviations, while blockchain enhances traceability and accountability through immutable record-keeping and smart contracts. The combined application of these technologies demonstrates substantial waste reduction potential, particularly in transportation and distribution stages, aligning with sustainability objectives such as the UN Sustainable Development Goals. This research provides both theoretical and practical contributions to the adoption of digital tools in sustainable supply chain management

Sustainable Optimization of On-Demand Transportation Systems: Balancing Efficiency and Energy Concerns

The rapid growth of on-demand transportation services, driven by technological advancements and evolving urban mobility patterns, has significantly transformed urban transportation. However, this shift has highlighted a critical need to understand the interaction between on-demand services and the broader urban transportation ecosystem. This study addresses the challenge posed by the expansion of on-demand transportation, which competes with traditional public transit and raises questions about sustainability and energy efficiency. This research aims to bridge this gap through an explorative literature review. The methodology involves a systematic review of existing literature on on-demand transportation systems, focusing on themes such as operational efficiency, energy transition, and policy implications. By synthesizing and analyzing this body of literature, the research seeks to uncover insights into the current state of on-demand transportation, identify challenges and opportunities, and suggest directions for future research. Additionally, this study aims to develop operational and theoretical frameworks to support policy formulation and implementation in urban transportation planning. By integrating findings from existing case studies with insights from the literature, these frameworks will guide policymakers and urban planners in promoting sustainable, energy-efficient on-demand transportation systems. Ultimately, the research aims to contribute to evidence-based policies and practices that support the sustainable development of urban transportation networks in response to changing mobility trends. The study highlights key insights into the impact of on-demand transportation services on urban mobility, addressing challenges such as operational efficiency, energy transition, and policy implications. It also proposes operational and theoretical frameworks to guide sustainable policy formulation and implementation in urban transportation planning

Waste generation patterns and mitigation strategies in cold chains

This study explores waste generation patterns in cold chain logistics, emphasizing the interrelationships between product categories, promotional activities, and inventory inefficiencies. Using real-world data from Company A, a comprehensive methodological approach, including time-series analysis and Ordinary Least Squares (OLS) regression, was employed to identify critical drivers of waste. The findings demonstrate that promotional activities significantly increase waste levels, notably through overproduction and misaligned demand forecasting. Furthermore, clear seasonal patterns emerged, pinpointing specific periods of peak waste linked to promotions and festive demand spikes. The analysis also highlighted warehouse inefficiencies as key contributors to waste, suggesting targeted logistical optimizations as essential. The study's novelty lies in its application of the Technology-Organization-Environment (TOE) framework to structure insights into AI-driven waste reduction strategies specifically tailored for cold chain operations. Unlike existing research, this study integrates AI-powered predictive analytics, sustainable packaging solutions, and waste categorization models, offering an empirically validated, actionable framework for supply chain managers. These results contribute significantly to existing literature by moving beyond generic operational improvements, directly addressing how technological, organizational, and regulatory factors collectively influence waste mitigation. The practical implications highlight the necessity for organizations to adopt a holistic, technology-enabled, and sustainability-oriented approach, ensuring long-term economic and environmental benefits

Sustainable optimization strategies for on-demand transportation systems: Enhancing efficiency and reducing energy use

The surge in popularity of on-demand transportation services, fueled by advancements in technology and changing urban mobility patterns, has significantly reshaped urban transportation dynamics. This transformation presents challenges to traditional public transportation, raising questions about sustainability and energy efficiency. This research addresses these challenges through an explorative literature review, focusing on operational efficiency, energy transition, and policy implications. By synthesizing and analyzing existing literature, the study uncovers insights into on-demand transportation, identifies challenges and opportunities, and proposes avenues for further research. The study also develops operational and theoretical frameworks to support policy formulation and implementation in urban transportation planning, offering guidance for policymakers and urban planners. Ultimately, this research aims to contribute to developing evidence-based policies and practices that foster sustainable urban transportation networks

Inherently irrational: exploring the role of behavioural economics and organisational culture in food supply chain disruption management decisions

Understanding human behaviour in supply chain disruption management (SCDM) requires moving beyond purely rational models. While traditional decision‑making frameworks focus on empirical factors, they often overlook the role of behavioural economics and organizational culture in shaping responses to crises. This study examines how supply chain managers navigated risks and cultural shifts during the COVID‑19 pandemic, offering insights into the interplay between personal risk values, cultural cohesion, and SCDM risk levels. Using a retrospective approach, the study gathered data from 21 supply chain managers in the fast‑moving consumer goods (FMCG) and food supply chains. Questionnaires captured their attitudes towards risk, decision‑making patterns, and organizational cultural shifts before, during, and after the pandemic. Descriptive statistical analyses revealed that SCDM risk levels peaked at the height of the crisis, while cultural cohesion and personal risk values declined. Interestingly, the relationship between cultural cohesion and personal risk value intensified during the pandemic and continued to strengthen post‑pandemic. A similar trend was observed between personal risk value and SCDM risk levels, which became more pronounced over time. However, the link between cultural cohesion and SCDM risk level was strongest during the crisis but faded in pre‑ and post‑pandemic periods. These findings contribute to the growing field of behavioural operations by demonstrating the significance of psychological and cultural factors in crisis decision‑making. They underscore the need for supply chain strategies that integrate behavioural insights, recognizing that human responses to disruption are shaped by more than just rational calculations. By acknowledging the evolving dynamics of risk perception and cultural adaptation, organizations can develop more resilient and human‑centric approaches to supply chain management in times of crisis

Consumer trust in artificial intelligence in the UK and Ireland’s personal care and cosmetics sector

Consumer trust is vital in the personal care and cosmetics industry as artificial intelligence (AI) and machine learning (ML) reshape digital interactions. With the sector undergoing rapid digital transformation, understanding how AI influences trust is critical. This study explores the factors affecting consumer trust in AI-driven beauty solutions in the UK and Ireland, focusing on transparency, ethical AI governance, and personalized digital experiences. A systematic literature review was conducted across Web of Science, Scopus, PubMed, IEEE Xplore, and Google Scholar, covering studies published between 2010 and 2023. The research was guided by the Critical Realism framework, enabling the examination of both observable factors (e.g. technological functionality, data privacy) and underlying influences (e.g., social, cultural, and organizational trust dynamics). Screening followed predefined criteria based on the PRISMA framework, ensuring a transparent and structured approach to the inclusion and exclusion of studies. The results indicate that consumer trust is strongly influenced by transparency, efficiency, and the ethical handling of AI-driven technologies. Personalized digital experiences contribute to greater trust and engagement, yet privacy concerns remain a significant barrier to AI adoption. The study highlights the importance of ethical AI frameworks and regulatory measures in fostering trust and ensuring the sustainable integration of AI technologies in the cosmetics and personal care sector. For industry practitioners, this study provides strategies to enhance consumer trust in AI-driven personalization, including greater transparency in data usage, strengthened privacy protections, and ethical AI governance

Integrating learning-based solutions in intelligent transportation systems: a conceptual framework and case studies validation

Urbanization has led to significant traffic congestion, presenting challenges for traditional traffic management systems that rely on static and rule-based approaches. These systems struggle to adapt to real-time changes in traffic patterns, resulting in inefficiencies and delays. Intelligent Transportation Systems (ITS), leveraging advanced technologies such as sensors, communication networks, and data analytics, offer promising solutions. This study aims to develop and validate a conceptual framework integrating deep learning, reinforcement learning, and transfer learning into ITS for dynamic and adaptive traffic management. An explorative literature review identifies key constructs, including real-time data collection, data preprocessing, adaptive signal control, and predictive analytics. The framework is validated through case studies from Singapore, Los Angeles, and Rio de Janeiro, demonstrating practical implementation and impact. The findings highlight the potential of learning-based ITS solutions to enhance traffic flow, reduce congestion, and improve urban transportation networks, contributing to the broader vision of smart cities

Electronic supply chain practice within SMEs manufacturer in the UK.

The concepts of E-Business and Supply Chain Management (SCM) have been broadly investigated in the last 10 years. However, there have been limited insights into the integration of the two concepts - Electronic Supply Chain Management (ESCM). Also, there is limited information about the implementation of E-Business practices in the supply chain management of Small and Medium Enterprises (SMEs). Adopting an exploratory approach, this thesis investigated Information and Communication Technology (ICT) and E-Business as practical and innovative approaches towards supply chain management. Following Tornatzky and Fleischer's (1990) 'Technology-Organisation-Environment' (TOE) theory, which has recently been used by Ifinedo (2011) in investigation of E-business in organisations, this research has attempted to provide a comprehensive view towards the adoption of ESCM. Having explored and extracted key factors influencing the adoption and implementation of ESCM from a literature review, a comprehensive ESCM model was developed. The model is focused towards understanding of the significance of various technological, organisational, environmental and strategic factors on successful adoption of E-Business technologies in supply chain management. Additionally, the advantages of the application of Information Technologies (IT) in supply chain management of SMEs, and possible obstacles are investigated in depth. Using a deductive approach, a questionnaire was designed to explore the research objectives. Consequently, 6 hypotheses were proposed and tested using data from 67 manufacturing SMEs in the UK. The findings of this study will enable comprehensive understanding of the concept of ESCM in SMEs, through exploring the integration of E-Business and supply chain management, and through an investigation of key elements of ESCM adoption. It is hoped that the developed model offers a better and stronger understanding of implementation of IT in SMEs, allowing managers of SMEs to evaluate the level of success and appropriateness of E-Business capabilities and IT strategies in their supply chains. Key words: Supply Chain Management (SCM), E-Business, E-Business capabilities, Electronic Supply Chain Management (ESCM), SMEs, Information and Communication Technology (ICT)

Digital Technologies in Food Supply Chain Waste Management: A Case Study on Sustainable Practices in Smart Cities

This study explores how digital technologies and data analytics can transform urban waste management in smart cities by addressing systemic inefficiencies. Integrating perspectives from the Resource-Based View, Socio-Technical Systems Theory, Circular Economy Theory, and Institutional Theory, the research examines sustainability, operational efficiency, and resilience in extended supply chains. A case study of Company A and its demand-side supply chain with Retailer B highlights key drivers of waste, including overstocking, inventory mismanagement, and inefficiencies in transportation and promotional activities. Using a mixed-methods approach, the study combines quantitative analysis of operational data with advanced statistical techniques and machine learning models. Key data sources include inventory records, sales forecasts, promotional activities, waste logs, and IoT sensor data collected over a two-year period. Machine learning techniques were employed to uncover complex, non-linear relationships between waste drivers and waste generation. A waste-type-specific emissions framework was used to assess environmental impacts, while IoT-enabled optimization algorithms helped improve logistics efficiency and reduce waste collection costs. Our findings indicate that the adoption of IoT and AI technologies significantly reduced waste by enhancing inventory control, optimizing transportation, and improving supply chain coordination. These digital innovations also align with circular economy principles by minimizing resource consumption and emissions, contributing to broader sustainability and resilience goals in urban environments. The study underscores the importance of integrating digital solutions into waste management strategies to foster more sustainable and efficient urban supply chains. While the research is particularly relevant to the food production and retail sectors, it also provides valuable insights for policymakers, urban planners, and supply chain stakeholders. By bridging theoretical frameworks with practical applications, this study demonstrates the potential of digital technologies to drive sustainability and resilience in smart cities