Search Results (3,479)

The building sector accounts for one-third of global greenhouse gas emissions, representing a significant environmental challenge in the 21st century. Green supply chain management is considered an effective approach to achieving green transformation in the construction industry. However, the green building supply chain (GBSC) involves multiple stakeholders, necessitating integrated consideration of various participants to ensure efficient GBSC implementation. In this context, and accounting for consumer green preferences, this paper identifies the government, enterprises, and consumers as key stakeholders. A tripartite evolutionary game model is established, and the influence of the participants’ strategic choices on the system equilibrium is analyzed. The model’s validity was assessed through sensitivity analysis and by comparing its outputs with findings from the existing literature. The findings show that: (1) Significant interdependence exists among GBSC participants. (2) The system will eventually tend toward an equilibrium characterized by active enterprise implementation and consumer green consumption, reducing the need for government intervention. (3) The sensitivity analysis shows that green consumption is significantly affected by the extra cost and perceived environmental benefits. These conclusions suggest that governments should build a collaborative governance system, implement dynamic and precise supervision of enterprises in stages, and optimize the incentive design for consumers to promote the implementation of the green building supply chain. Full article

With economic development and shifting consumption trends, branding has become an important way to improve the efficiency of poverty alleviation supply chains (PASCs) in practice. However, academic research on this topic is limited. To fill this gap in the literature, we constructed a differential game of a PASC that examines how to build a sustainable poverty reduction model through branding, considering government subsidies and supplier’s Nash bargaining fairness concerns. Our findings show the following: (1) Government subsidies can improve the decision-making level and channel efficiency of leading enterprises (E) and poor suppliers (F). Government subsidies are necessary for a PASC to establish a sustainable poverty alleviation mechanism. (2) F’s Nash bargaining fairness concerns only reduce their level of production effort but do not affect the brand construction and corporate social responsibility levels of E. (3) As F’s bargaining power increases, Nash bargaining fairness concerns have a more significant effect on the PASC’s performance. While F’s fairness concerns can enhance their utility to some extent, it ultimately leads to more significant profit losses for both parties. (4) The proposed mixed cost-sharing and revenue-sharing contract can effectively align members’ incentives, enhancing profitability for both parties. Full article

In recent years, Out-of-Stock (OOS) occurrences have posed a persistent challenge for both retailers and manufacturers. In the context of grocery retail, an OOS event represents a situation where customers are unable to locate a specific product when attempting to make a purchase. This study analyzes the issue from the manufacturer’s perspective. The proposed system, named the “Multi-modal yield eSTimation System of in-prOmotion Commercial Key-ProductS” (MySTOCKS) platform, is a sophisticated multi-modal yield estimation system designed to optimize inventory forecasting for the agrifood and large-scale retail sectors, particularly during promotional periods. MySTOCKS addresses the complexities of inventory management in settings where Out-of-Stock (OOS) and Surplus-of-Stock (SOS) situations frequently arise, offering predictive insights into final stock levels across defined forecasting intervals to support sustainable resource management. Unlike traditional approaches, MySTOCKS leverages an advanced deep learning framework that incorporates transformer models with self-attention mechanisms and domain adaptation capabilities, enabling accurate temporal and spatial modeling tailored to the dynamic requirements of the agrifood supply chain. The system includes two distinct forecasting modules: TR1, designed for standard stock-level estimation, and TR2, which focuses on elevated demand periods during promotions. Additionally, MySTOCKS integrates Elastic Weight Consolidation (EWC) to mitigate the effects of catastrophic forgetting, thus enhancing predictive accuracy amidst changing data patterns. Preliminary results indicate high system performance, with test accuracy, sensitivity, and specificity rates approximating 93.8%. This paper provides an in-depth examination of the MySTOCKS platform’s modular structure, data-processing workflow, and its broader implications for sustainable and economically efficient inventory management within agrifood and large-scale retail environments. Full article

According to data from the Food and Agricultural Organization of the United Nations (FAO), Ukraine was the fifth and ninth global producer and exporter of corn and wheat, respectively, before Russia’s invasion. The disruption of the supply chain in Ukraine in these food sectors due to the Russian–Ukrainian conflict can hamper global food security. Very little is known about how the war has impacted these important food sectors globally. This paper examines the global impact of the war on the corn and wheat sectors in the first year of the war and investigates the relationship between market vulnerability and trade ties with the conflicting regions. Analysis of FAO data shows that Ukraine suffered a 12.87% and 17.45% production decline in the corn and wheat sectors, respectively, compared to the 2012–2021 base years. Using the Acclimate economic network model, these shocks are applied to Ukraine’s corn and wheat network nodes to analyze their global impact. The production value and total losses are calculated and compared to the base year, both regionally and in some major global economic blocs. The results show that the corn sectors in Germany, Ukraine, Poland, and Belgium suffered the largest production value losses, while Ukraine, China, Venezuela, and Korea suffered the largest overall losses. In the wheat sector, Russia, Germany, Ukraine, and Canada suffered the largest production value losses, while Ukraine, Kazakhstan, Uzbekistan, and China suffered the largest overall losses. Overall, the corn sector was the hardest hit globally compared to the wheat sector, with the EU, the US, China, South America, and Africa being the hardest hit in the corn sector, while BRICS and Oceania were the hardest hit in the wheat sector. The study equally finds a strong correlation between increased regional food market vulnerability and Ukraine’s trade relations. These findings can contribute to better investment decisions, regional and sectoral emergency management planning, and the development of regulatory frameworks. Full article

Background/Objectives: The integration of Artificial Intelligence (AI) and Machine Learning (ML) in pharmaceutical research and development is transforming the industry by improving efficiency and effectiveness across drug discovery, development, and healthcare delivery. This review explores the diverse applications of AI and ML, emphasizing their role in predictive modeling, drug repurposing, lead optimization, and clinical trials. Additionally, the review highlights AI’s contributions to regulatory compliance, pharmacovigilance, and personalized medicine while addressing ethical and regulatory considerations. Methods: A comprehensive literature review was conducted to assess the impact of AI and ML in various pharmaceutical domains. Research articles, case studies, and industry reports were analyzed to examine AI-driven advancements in predictive modeling, computational chemistry, clinical trials, drug safety, and supply chain management. Results: AI and ML have demonstrated significant advancements in pharmaceutical research, including improved target identification, accelerated drug discovery through generative models, and enhanced structure-based drug design via molecular docking and QSAR modeling. In clinical trials, AI streamlines patient recruitment, predicts trial outcomes, and enables real-time monitoring. AI-driven predictive maintenance, process optimization, and inventory management have enhanced efficiency in pharmaceutical manufacturing and supply chains. Furthermore, AI has revolutionized personalized medicine by enabling precise treatment strategies through genomic data analysis, biomarker discovery, and AI-driven diagnostics. Conclusions: AI and ML are reshaping pharmaceutical research, offering innovative solutions across drug discovery, regulatory compliance, and patient care. The integration of AI enhances treatment outcomes and operational efficiencies while raising ethical and regulatory challenges that require transparent, accountable applications. Future advancements in AI will rely on collaborative efforts to ensure its responsible implementation, ultimately driving the continued transformation of the pharmaceutical sector. Full article

A reliable, accessible, and high-quality blood supply is critical for the sustainment of any healthcare system. World events such as the COVID-19 pandemic have proven that maintaining the supply of blood presents a logistical challenge. The current blood supply is overseen by extensive donor programs around the world. In the United States, as in other countries, the need for blood has increased, with a decline in blood donations and increasing exclusions for blood donor qualification. While there is a need to improve blood donation participation, there is also need for new alternatives to traditional donation to ensure readiness to treat hemorrhagic shock common in the setting of trauma, as often occurs during a natural disaster or conflict. These operational medicine scenarios require significant blood availability which may tax the current blood supply chain. Aside from a walking blood bank (WBB) model for blood collection in suboptimal conditions, researchers have proposed alternatives for blood that include the manufacturing of blood from stem cell sources. Other alternatives include synthetic liquids that can carry oxygen such as Perfluoro-Chemicals (PFCs) and hemoglobin-based oxygen-carrying systems (HBCOs). Here, we review some of these alternatives to the traditional donor blood model. Researchers now have the technology that makes it feasible to develop blood alternatives that one day may supplement and help alleviate the limitations in blood supply. Full article

China’s retail industry faces unique challenges in supply chain financing, particularly for small and medium-sized enterprises (SMEs) that often struggle to secure loans due to insufficient credit ratings and collateral in the business environment of China. This paper presents a groundbreaking approach that integrates real-time data elements into financing models, addressing the critical issue of information asymmetry between financial institutions and retail SMEs. By leveraging dynamic data such as orders, receivables, and project progress, our novel framework moves beyond the limitations of traditional asset-based lending, employing advanced data analytics for enhanced credit assessment and risk management. Applying the Stackelberg game theory, we explore the strategic interactions between suppliers and purchasers in the retail supply chain, identifying optimal financing strategies that improve capital flow efficiency and reduce overall costs. Our comprehensive data-driven model incorporates various scenarios, including the traditional supply chain financing model (Model T) and the innovative data-element secured financing model (Model G). The latter further considers risk assessment, risk appetite, volume, and schedule factors, providing a holistic approach to financial decision-making. Through rigorous mathematical modeling and numerical analysis, we demonstrate the effectiveness of our proposed framework in optimizing supply chain financing strategies. The results highlight the potential for data-driven approaches to unlock new financing opportunities for SMEs, fostering a more collaborative and efficient ecosystem within the retail industry. This study presents comprehensive data-driven strategies that unlock new financing opportunities for SMEs, providing a practical roadmap for stakeholders to foster a more collaborative and efficient supply chain financing ecosystem. The significance of studying supply chain finance for small and medium-sized enterprises (SMEs) lies in optimizing financing models to address the financing difficulties faced by SMEs. This helps improve their market competitiveness and promotes resource sharing and collaboration among all parties in the supply chain, thereby achieving sustainable economic development. Full article

The increasing global emphasis on sustainability necessitates the integration of environmentally responsible practices within supply chains. This study explores the impact of green supply chain management practices (GSCMPs) on firm sustainable performance in Pakistan’s manufacturing and service industries. Unlike prior research, which primarily focuses on the direct impact of GSCMPs, this study advances knowledge by incorporating green technological innovation (GTI) and green managerial innovation (GMI) as mediators and green organizational culture (GOC) as a moderator. The study looks at survey data from 480 industry professionals and uses partial least squares structural equation modeling (PLS-SEM) and multi-group analysis (MGA). It discovers that GSCMPs greatly enhance sustainability outcomes, especially when green innovations are used. Furthermore, the impact of GSCMPs is more pronounced in the manufacturing sector, emphasizing the role of regulatory pressures and technological advancements. This study makes a significant contribution to the literature by integrating post-pandemic sustainability challenges, highlighting industry-specific dynamics, and providing actionable strategies to enhance green supply chain adoption in emerging markets. The study provides applicable strategies for managers and policymakers to embed sustainability deeper into corporate strategies, ensuring resilience and competitive advantages in evolving global markets. Full article

This study aims to explore the impact of key drivers on the integration of blockchain technology implementation and green innovation practices within green supply chains. This study combines the TOE and TAM frameworks to identify six key driving factors that in the proposed model. A survey was conducted with Vietnamese enterprises, resulting in 328 valid responses from senior managers across various sectors. The PLS-SEM approach was conducted to analyze the relationships between the variables and to gain deeper insights into their interactions. The research findings highlight the significant potential of adopting blockchain and green innovation programs to enhance organizational performance. Six essential factors act as key drivers for implementing these initiatives, exerting a positive influence. Among them, Perceived Usefulness, Organizational Readiness, and Partnerships emerge as the three most influential variables within this research framework. Our research offers several valuable implications, both theoretical and practical. The structural framework model provides empirical evidence demonstrating the feasibility of achieving expected benefits for green supply chains, particularly in emerging economies such as Vietnam. Thus, these results serve as valuable references for senior managers and policy makers. Full article

Irrational use of antimicrobials in humans, livestock, and poultry is often cited as the key driver of the accelerated emergence of antimicrobial resistance (AMR) in humans and animals. In Pakistan, the use of antimicrobials in livestock and poultry is not well-regulated, owing to limited and/or ineffective implementation of existing legislations and lack of coordination amongst the key stakeholders. To gather data and information pertinent to the supply chain mapping and the usage of antibiotics in the livestock and poultry sectors of Pakistan, a national workshop for selected influential stakeholders was held in Islamabad, Pakistan during March 2020 to map the supply chain and the usage of antibiotics in the country. Participants from all relevant organizations identified the supply chain and discussed the status, challenges, and the way forward to enhance data collection and monitoring of antimicrobial usage (AMU) in livestock and poultry sectors to contain the emergence of AMR. The pre-workshop questionnaire was designed to gather relevant information on AMU and its distribution among diverse markets and users from the workshop participants, utilizing open-ended questions. A chart depicting the relative magnitude of an antimicrobial use (AMU) supply chain was constructed to illustrate the flow of antimicrobials from import and production to end-use at the farm level. This chart was shared with participants to gather their professional opinions and potential corrections. It also presented a list of agencies and their roles in regulating AMU, along with the types of AMU data available at each level. Specific recommendations were made at the end of the workshop to review and update legislation to cover the entire AMU supply chain, enhance regulations to restrict the use of antimicrobial growth promoters, build an integrated national AMU database system, and raise awareness about the responsible use of antimicrobials in the livestock and poultry sectors. It was concluded that the AMU supply chain in the veterinary sector of Pakistan is fragmented and is co-regulated by various federal and provincial stakeholders. There are some drugs, such as antibiotic growth promoters, which are not regulated at all. The approach and findings from this study can serve as a model to validate the use and management of antibiotics in other, similar countries currently grappling with serious antimicrobial resistance (AMR) crises. Full article

Biomass ash is a byproduct of renewable energy generation that can be used in the cement and concrete industries as a supplementary cementitious material (SCM) to reduce their environmental impact. However, using biomass ashes as an SCM presents challenges, such as the distant location of crops and processing plants from cement and concrete plants, the absence of a supply chain to connect the biomass ash and cement/concrete producers, and the lack of a mechanism to set the price of the ash. We adopted a supply chain perspective to evaluate the environmental and economic impact of incorporating biomass ashes as an SCM in the cement and concrete industries. We developed a bilevel optimization model considering the strategic behavior of the two stakeholders of the supply chain: the biomass ash generator, which maximizes its profits by setting the price of the ash, and the cement/concrete manufacturer and minimizes its total operating costs, including the processes necessary to adapt its supply chain for the use of new raw material. We validated the model using data from the Colombian context at a nationwide industrial level. Our results indicate that introducing SCMs can potentially reduce CO₂ emissions without increasing the cost of the supply chain. Full article

This study investigates the effects of the mediations of economic utility and moderation of perceived risk on supply chain performance as determined by the organization factors (innovation, organizational culture, and employee motivation) in the Egyptian Fast-Moving Consumer Goods (FMCG) Industry. Although previous research has identified an excess of factors affecting supply chain performance, limited were the studies trying to establish relationships among those factors, especially in emerging economies. Thus, a quantitative approach was adopted, using a structured questionnaire, which was distributed to 382 FMCG supply chain professionals in Egypt. Data were analyzed using structural equation modeling (SEM) to test relations between the hypothesized variables. The results provide evidence that innovation and employee motivation have a positive impact on supply chain performance; organizational culture, however, has no significant impact. Economic utility partially mediates this relationship, of which time utility is by far the strongest mediator. Furthermore, perceived risk moderates the effects of innovation and organizational culture on supply chain performance, thus creating a necessity for risk management. Such information will benefit supply chain managers through considering organizational agility, time-efficient utility drivers, and risk reduction strategies. This research adds to the literature by providing a broad framework integrating organizational factors, economic utility, and perceived risk within supply chain performance systems in developing markets. Full article

Cost overruns are prevalent in global construction industries, propelled by the swift expansion of the construction sector. To tackle this difficulty, it is essential to comprehend the primary elements leading to these overruns. This research examines the correlation between systematic elements affecting construction costs via a literature review, expert interviews, exploratory factor analysis (EFA), and structural equation modeling (SEM). A theoretical framework analyzes contract management, resource distribution, and external environmental factors affecting cost overruns. Twenty-four criteria were selected via literature review and expert consultation, and data from 212 questionnaires were analyzed. The results identify 22 critical elements classified into project management, resources, supply chain, and external and environmental influences. Project management and resources, together with supply chain, exerted the most significant influence. SEM study indicates that management inadequacies intensify resource problems, whereas proficient internal management alleviates external difficulties. This research offers practical recommendations for mitigating budget excesses and enhancing project cost-effectiveness. Full article

Background: Within the solar ultraviolet (UV) spectrum, ultraviolet A rays (UVA, 320–400 nm), although less energetic than ultraviolet B rays (UVB, 280–320 nm), constitute at least 95% of solar UV radiation that penetrates deep into the skin The UV rays are associated with both epidermal and dermal damage resulting from the generation of reactive oxygen species (ROS). Among them, the longest UVA wavelengths (UVA1, 340–400 nm) can represent up to 75% of the total UV energy. Therefore, UVA radiation is linked to various acute and chronic conditions, including increased skin pigmentation and photoaging. Despite many advances in the skin photoprotection category, there is still a growing demand for natural daily photoprotection active ingredients that offer broad protection against skin damage caused by UVA exposure. In our quest to discover new, disruptive, next generation of photoprotective ingredients, we were drawn to pomegranate, based on its diverse polyphenolic profile. We investigated the pericarp of the fruit, so far considered as byproducts of the pomegranate supply chain, to design a novel patented extract “POMAOX” with a desired spectrum of phenolic components comprising of αβ-punicalagins, αβ-punicalins and ellagic acid. Methods: Antioxidant properties of POMAOX were measured using in-tubo standard tests capable of revealing a battery of radical oxygen species (ROS): peroxyl radical (ORAC), singlet oxygen (SOAC), superoxide anion (SORAC), peroxynitrite (NORAC), and hydroxyl radical (HORAC). In vitro, confirmation of antioxidant properties was first performed by evaluating protection against UVA-induced lipid peroxidation in human dermal fibroblasts (HDF), via the release of 8 iso-prostanes. The protection offered by POMAOX was further validated in a 3D in vitro reconstructed T-Skin^TM model, by analyzing tissue viability/morphology and measuring the release of Matrix Metallopeptidase 1 (MMP-1) & pro-inflammatory mediators (IL-1α, IL-1ra, IL-6, IL-8, GM-CSF, and TNF-α) after UVA1 exposure. Results: POMAOX displayed strong antioxidant activity against peroxynitrite (NORAC) at 1.0–3.0 ppm, comparable to the reference vitaminC, as well as singlet oxygen (SOAC) at 220 ppm, and superoxide radicals with a SORAC value of 500 ppm. Additionally, POMAOX demonstrated strong photoprotection benefit at 0.001% concentration, offering up to 74% protection against UVA-induced lipid peroxidation on HDF, in a similar range as the positive reference, Vitamin E at 0.002% (50 µM), and with higher efficacy than ellagic acid alone at 5 µM. Moreover, our pomegranate-derived extract delivered photoprotection at 0.001%, mitigating dermal damages induced by UVA1, through inhibition of MMP-1 and significant inhibition of pro-inflammatory mediators release (including IL-1α, IL-1ra, IL-6, IL-8, GM-CSF, and TNFα) on an in vitro reconstructed full-thickness human skin model with a similar level of protection to that of Vitamin C tested at 0.035% (200 µM). Conclusions: Overall, the novel pomegranate-derived extract “POMAOX” significantly reduced the impact of UVA on human skin, due to its broad-spectrum antioxidant profile. These findings suggest that POMAOX could offer enhanced protection against the detrimental effects of UV exposure, addressing the growing consumer demand for strong photoprotection with skincare benefits. Full article

Accurate demand forecasting is essential for retail operations as it directly impacts supply chain efficiency, inventory management, and financial performance. However, forecasting retail time series presents significant challenges due to their irregular patterns, hierarchical structures, and strong dependence on external factors such as promotions, pricing strategies, and socio-economic conditions. This study evaluates the effectiveness of Transformer-based architectures, specifically Vanilla Transformer, Informer, Autoformer, ETSformer, NSTransformer, and Reformer, for probabilistic time series forecasting in retail. A key focus is the integration of explanatory variables, such as calendar-related indicators, selling prices, and socio-economic factors, which play a crucial role in capturing demand fluctuations. This study assesses how incorporating these variables enhances forecast accuracy, addressing a research gap in the comprehensive evaluation of explanatory variables within multiple Transformer-based models. Empirical results, based on the M5 dataset, show that incorporating explanatory variables generally improves forecasting performance. Models leveraging these variables achieve up to 12.4% reduction in Normalized Root Mean Squared Error (NRMSE) and 2.9% improvement in Mean Absolute Scaled Error (MASE) compared to models that rely solely on past sales. Furthermore, probabilistic forecasting enhances decision making by quantifying uncertainty, providing more reliable demand predictions for risk management. These findings underscore the effectiveness of Transformer-based models in retail forecasting and emphasize the importance of integrating domain-specific explanatory variables to achieve more accurate, context-aware predictions in dynamic retail environments. Full article