An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0

Background: This paper explores the potential of Industry 5.0 in driving societal transition to a circular economy. We focus on the strategic role of reverse logistics in this context, underlining its significance in optimizing resource use, reducing waste, and enhancing sustainable production and consumption patterns. Adopting sustainable industrial practices is critical to addressing global environmental challenges. Industry 5.0 offers opportunities for achieving these goals, particularly through the enhancement of reverse logistics processes. Methods: We propose an integrated methodology that combines binary logistic regression and decision trees to predict and optimize reverse logistics flows and networks within the Industry 5.0 framework. Results: The methodology demonstrates effective quantitative modeling of influential predictors in reverse logistics and provides a structured framework for understanding their interrelations. It yields actionable insights that enhance decision-making processes in supply chain management. Conclusions: The methodology supports the integration of advanced technologies and human-centered approaches into industrial reverse logistics, thereby improving resource sustainability, systemic innovation, and contributing to the broader goals of a circular economy. Future research should explore the scalability of this methodology across different industrial sectors and its integration with other Industry 5.0 technologies. Continuous refinement and adaptation of the methodology will be necessary to keep pace with the evolving landscape of industrial sustainability.<br/

Organisational factors in RFID adoption, implementation, and benefits

This study investigates the impact of organisational and technological factors within pre-adoption, implementation, and post-implementation phases of RFID system deployment. In the pre-adoption phase, the study examines factors that drive and hinder organisations’ decision to adopt RFID. In the implementation phase, the study investigates the impact of organisational factors (business size, strength of culture, and business process re-engineering) on influencing the implementation processes of RFID. In the post-implementation phase, the study investigates how the benefits derived from RFID implementation interact with organisational factors (business size, strength of culture, and business process re-engineering) and RFID-related factors (product unit level of tagging, RFID implementation stage, and organisational pedigree in RFID). This study was motivated by the lack of (i) an advisory framework which considers quantifiable firm characteristics and the costs and benefits of implementing RFID, in yielding advice to guide decisions on RFID adoption, and (ii) a framework that covers the complete processes of RFID project deployment (from adoption decision to benefits derived) in yielding advice to guide decisions on RFID adoption. This study is achieved using a two-phase research approach: questionnaire survey of organisations that have adopted or plan to adopt RFID and case studies of organisations that have integrated RFID into their business processes. In addition, a thorough review of existing literature on RFID in different industrial settings was conducted. The key findings from the study indicate that RFID adoption is driven by factors from technological, organisational and environmental contexts and that the adoption, implementation and benefits of RFID are influenced by organisational culture strength, business size, and BPR. It was found that strong cultures, organisational size and BPR are all positively correlated with RFID adoption decisions, implementation and benefits. Potential contribution towards the existing body of knowledge is through highlighting the significance of organisational culture strength, business size, and BPR in providing a platform in which RFID will be accepted and implemented successfully to achieve maximum derivable benefits

A Review of RFID in Supply Chain Management: 2000–2015

This paper presents a systematic literature review of papers that were published in academic journals on the applications of radio frequency identification (RFID) in supply chain management between the years 2000 and 2015. As the literature on RFID is not confined to specific disciplines or repositories, this paper proposes a discipline-based framework for classifying RFID literature. Five main classification categories are used in this paper: technology, supply chain management, research methodology, application industries, and social aspects. The paper then focuses on the category of supply chain management and reviews 1187 articles that were published between 2000 and 2015 in rated journals. All the papers reviewed are further classified into eight subclasses under this category of supply chain management. The review yields useful insights into the anatomy of RFID literature in supply chain management, enhances evidence-based knowledge, and contributes to informing practice, policymaking and future research. The review reveals that even presently, despite technical and cost challenges, enormous potential exists for the application of RFID in several areas of supply chain management and the prospects are likely to grow into the future. Since RFID solutions have emerged primarily over only the past 20 years, significant research opportunities exist and would need to be addressed to continue to support the technology’s maturation, evaluation, adoption, implementation, and diffusion

Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. Findings: In 2021, there were 529 million (95% uncertainty interval [UI] 500-564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8-6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7-9·9]) and, at the regional level, in Oceania (12·3% [11·5-13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1-79·5) in individuals aged 75-79 years. Total diabetes prevalence-especially among older adults-primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1-96·8) of diabetes cases and 95·4% (94·9-95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5-71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5-30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22-1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1-17·6) in north Africa and the Middle East and 11·3% (10·8-11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%. Interpretation: Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers. Funding: Bill & Melinda Gates Foundation