Search Results (3,267)

The COVID-19-induced strain on global supply chains led to significant market imbalances and unprecedented inflation, particularly affecting urban economies. Containment policies and stimulus packages resulted in unpredictable demand shifts, challenging urban supply chain planning and resource distribution. These disruptions underscored the need for robust risk management models, especially in cities where economic activity and population density exacerbate supply chain vulnerabilities. This study develops a comprehensive risk model tailored for G-7 urban economies, analyzing the causal and cointegration relationships between key economic indicators. Using Granger causality tests and a factor-augmented vector autoregression (FAVAR) approach, the study examines complex time series and high-dimensional variables, focusing on urban-specific indicators such as the composite leading indicator (CLI) and business confidence indicator (BCI). Our results indicate strong causal relationships among these indicators, validating CLI as a reliable early predictor of urban economic trends. The findings confirm the viability of this urban supply chain risk management model, offering potential pathways for strengthening urban resilience and economic sustainability in the face of future disruptions. This approach positions the study within the context of urban science, emphasizing the impacts on cities and how urban economies can benefit from the developed risk model. Full article

Food public opinion is characterized by its low ignition point, high diffusibility, persistence, and strong negativity, which significantly impact food safety and consumer trust. This paper introduces the Food Public Opinion Prevention and Control (FPOPC) model driven by deep learning and personalized recommendation algorithms, rigorously tested and analyzed through experimentation. Initially, based on an analysis of food public opinion development, a comprehensive FPOPC framework addressing all stages of food public opinion was established. Subsequently, a sentiment prediction model for food news based on user comments was developed using a Stacked Autoencoder (SAE), enabling predictions about consumer sentiments toward food news. The sentiment values of the food news were then quantified, and improvements were made in allocating Pearson correlation coefficient weights, leading to the design of a collaborative filtering-based personalized food news recommendation mechanism. Furthermore, an enhanced Bloom filter integrated with HDFS technology devised a rapid recommendation mechanism for food public opinion. Finally, the designed FPOPC model and its associated mechanisms were validated through experimental verification and simulation analysis. The results demonstrate that the FPOPC model can accurately predict and control the development of food public opinion and the entire food supply chain, providing regulatory agencies with effective tools for managing food public sentiment. Full article

Retailers depend on accurate sales forecasts to effectively plan operations and manage supply chains. These forecasts are needed across various levels of aggregation, making hierarchical forecasting methods essential for the retail industry. As competition intensifies, the use of promotions has become a widespread strategy, significantly impacting consumer purchasing behavior. This study seeks to improve forecast accuracy by incorporating promotional data into hierarchical forecasting models. Using a sales dataset from a major Portuguese retailer, base forecasts are generated for different hierarchical levels using ARIMA models and Multi-Layer Perceptron (MLP) neural networks. Reconciliation methods including bottom-up, top-down, and optimal reconciliation with OLS and WLS (struct) estimators are employed. The results show that MLPs outperform ARIMA models for forecast horizons longer than one day. While the addition of regressors enhances ARIMA’s accuracy, it does not yield similar improvements for MLP. MLPs present a compelling balance of simplicity and efficiency, outperforming ARIMA in flexibility while offering faster training times and lower computational demands compared to more complex deep learning models, making them highly suitable for practical retail forecasting applications. Full article

Ensuring the safety of oil tank farms is essential to maintaining energy security and minimizing the impact of potential accidents. This paper develops a quantitative regional risk model designed to assess both individual and societal risks in oil tank farms, with particular attention to energy-related risks such as leaks, fires, and explosions. The model integrates factors like day–night operational variations, weather conditions, and risk superposition to provide a comprehensive and accurate evaluation of regional risks. By considering the cumulative effects of multiple hazards, including those tied to energy dynamics, and the stability and validity of the model are researched through Monte Carlo simulations and case application. The results show that the model enhances the reliability of traditional risk assessment methods, making it more applicable to oil tank farm safety concerns. Furthermore, this study introduces a practical tool that simplifies the risk assessment process, allowing operators and decision-makers to evaluate risks without requiring in-depth technical expertise. The methodology improves the ability to safeguard oil tank farms, ensuring the stability of energy supply chains and contributing to broader energy security efforts. This study provides a valuable method for researchers and engineers seeking to enhance regional risk calculation efficiency, with a specific focus on energy risks. Full article

A cold-chain meat packaging project was developed for a meat product company in northwestern Mexico that moves high volumes of fresh meat into national and international markets. The objective of the present research is to evaluate the supply process for three types of thermo-shrinkable polyethylene bags to provide a technological solution for high-volume meat packaging based on a graphical user interface. A system dynamics (SD) methodology is developed in seven stages to generate a technological solution: (1) system mapping; (2) causal diagram construction; (3) stock, flow modeling, and equations; (4) model simulation; (5) model validation; (6) scenarios and multicriteria analysis; and (7) graphical user interface development. The main result for the company was a technological solution that could communicate with decision-makers and the proposed graphical user interface. Future optimistic and pessimistic scenarios were self-evaluated based on the current situation related to three thermo-shrinkable bags used for selling high volumes of fresh meat. In these solutions, previously simulated costs and savings can be implemented in a real situation. Quantitative graphical user interface data can be observed to adequately manage box and bag inventories and minimize costs. Using SD enables the development of technological solutions in complex environments with robust simulations and models that offer data to people interested in the system under study. Full article

Background: In agricultural supply chains, unequal bargaining power often leads to economic inequality, particularly for farmers. The fair profit distribution (FPD) approach offers a solution by optimizing supply chain flows (materials, information, and money) to promote economic equity among members. However, our literature review highlights a gap in applying the FPD approach to the facility location-allocation problem in supply chain network design (SCND), particularly in sugarcane-derived biofuel supply chains. Methods: Consequently, we propose a multi-period optimization model based on FPD to design a sugarcane biofuel supply chain. The methodology involves four steps: constructing a conceptual model, developing a mathematical model, designing a solution strategy, and generating insights. This model considers both investment (crop development, biorefinery construction) and operational phases over a long-term planning horizon, focusing on farm location and crop allocation. Results: By comparing the FPD model to a traditional centralized planning supply chain (CSC) approach, we examine the impact of the planning horizon, number of farms, and sugarcane prices paid by biorefineries on financial performance. While the FPD model results in lower overall system profits, it fosters a fairer economic scenario for farmers. Conclusions: This study contributes to economic justice in supply chains and offers insights to promote fair trade among stakeholders. Full article

The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationality, necessitating a robust evaluation framework. We propose a novel simulation-based model to quantify algorithmic interventions within organisational contexts, combining causal modelling and data science algorithms. To test our framework’s viability, we present a case study based on a bike-share system focusing on inventory balancing through crowdsourced user actions. Utilising New York’s Citi Bike service data, we highlight the frequent misalignment between incentives and their necessity. Our model examines the interaction dynamics between user and service provider rule-driven responses and algorithms predicting flow rates. This examination demonstrates why understanding these dynamics is essential for devising effective incentive policies. The study showcases how sophisticated machine learning models, with the ability to forecast underlying market demands unconstrained by historical supply issues, can cause imbalances that induce user behaviour, potentially spoiling plans without timely interventions. Our approach allows problems to surface during the design phase, potentially avoiding costly deployment errors in the joint performance of human and AI decision-makers. Full article

Soybeans, a vital source of protein for animal feed and an essential industrial raw material, are the most traded agricultural commodity worldwide. Accurate price forecasting is crucial for maintaining a resilient global food supply chain and has significant implications for agricultural economics and policymaking. This review examines over 100 soybean price forecast models published in the last decade, evaluating them based on the specific markets they target—futures or spot—while highlighting how differences between these markets influence critical model design decisions. The models are also classified into AI-powered and traditional categories, with an initial aim to conduct a statistical analysis comparing the performance of these two groups. This process unveiled a fundamental gap in best practices, particularly regarding the use of common benchmarks and standardised performance metrics, which limits the ability to make meaningful cross-study comparisons. Finally, this study underscores another important research gap: the lack of models forecasting soybean futures prices in Brazil, the world’s largest producer and exporter. These insights provide valuable guidance for researchers, market participants, and policymakers in agricultural economics. Full article

The automotive supply chain is one of the top eight value chains that cause 50% of global emissions. Despite its significance, limited literature has researched the role of insurance companies in addressing automotive supply chain emissions. This research explores strategies developed by insurance companies for addressing carbon emissions in the automotive supply chain in the UK. It employs a qualitative multiple case study approach and conducts in-depth analysis of main drivers, barriers, and strategies in four insurance companies in addressing automotive supply chain emissions. It finds that cost savings and competitive advantage, changing mindset, impending regulation, market changes, and increased connectedness are the main drivers. But further progress is slowed down by five main barriers: ‘the complexity of tracking and quantifying emissions’, ‘conflicts of interest in the supply chain’, ‘skill shortage’, ‘lack of accountability’, and ‘profit prioritisation’. To overcome this, the study establishes five main strategies for insurance companies to follow: ‘circular business model with green parts and repair-over-replace methodologies’, ‘supply chain collaboration’, ‘quantifying emissions and setting key performance indicators’, ‘higher weighting for ESG in tenders and policies’, and ‘education and awareness’. If followed correctly, businesses will be able to achieve ‘emission reductions’, ‘gain competitive advantage’, and ‘reduce costs in the supply chain’. Taking into account these findings and the academic literature, this study develops a framework for insurance companies to mitigate automotive supply chain emissions. This is one of the first papers to study carbon emissions in automotive supply chains from the perspective of the insurance industry. It provides practical implications for the insurance industry in developing carbon emission strategies in automotive supply chains. Full article

The mismatch between supply and demand and inefficient supply in China’s agricultural product market is quite severe, making agricultural supply-side structural reform an important topic and task for rural economic work in China. The successful implementation of agricultural supply-side reform requires a comprehensive agricultural social service platform and system to support it. However, the current agricultural social service platform system in China faces issues such as a lack of coordination among service entities and poor information communication, making it difficult to meet the demands of supply-side reform. To address this issue, under the new circumstances of supply-side reform, this paper proposes the idea of reorganizing the agricultural social service platform and system through industrial chain collaboration theory, and applying “internet +” for technological reengineering of the agricultural social service platform system. Based on this, a new agricultural social service platform system architecture is constructed, which includes service entities, service platforms, service content, and operational models. The research findings provide guidance for agricultural product producers, distributors, sellers, and related service entities along the agricultural industry chain on how to use “internet +” for collaborative decision-making. This approach is beneficial for addressing the supply-demand imbalance and low resource allocation efficiency in China’s agricultural product market, thereby advancing the structural reform of China’s agricultural supply side. Full article

This study presents the development and application of a mathematical optimization model to improve decision-making in the supply chain for high-energy-density pellet (HEDP) production and commercialization. Focused on the Metropolitan Region of Chile, the research involved a detailed analysis of key supply chain components, including identifying landfills and controlled dumps, waste volume assessments, plant location analysis, technology evaluation, and market potential exploration. The model revealed that the available raw material in the region was sufficient to meet 100% of HEDP demand, with a surplus of 2,161,952 tons remaining after satisfying maximum demand. An optimization analysis of potential plant locations identified Santa Marta as the optimal choice, resulting in annual cost savings of USD 100,000 compared to other sites. This work underscores the role of mathematical optimization in enhancing supply chain efficiency for biomass-based energy products, offering valuable insights for strategic decision-making in similar contexts. Full article

Considering the supply chain composed of a power battery supplier and a new energy vehicle manufacturer, under the carbon cap-and-trade policy, this paper studies the different cooperation modes between the manufacturer and the supplier as well as their strategies for green technology and power battery production. Three game models are constructed and solved, respectively, under the collaboration mode of wholesale purchasing, patent-licensed manufacturing, and own R&D + Wholesale purchasing. The equilibrium analysis is carried out. Finally, the influence of relevant parameters is explored through numerical simulation. It is found that (1) the manufacturer’s choice of optimal battery production strategy is influenced by the input cost of green technology, the production cost of power battery, the carbon trading price, and the free carbon quota allocated by the government; (2) the cost coefficient of technological innovation affects negatively the optimal decision-making of the supply chain members, the market demand, and the optimal profit, and it has no impact when the cost coefficient reaches a certain value; (3) carbon cap-and-trade policy can, to a certain extent, incentivize suppliers and manufacturers to carry out technological innovation to reduce carbon emissions in the production process, but we cannot ignore the negative impacts of excessively high carbon trading price on the level of emission reduction and the market demand; and (4) the government should reasonably control the carbon price and carbon quota. The above conclusion will provide reference suggestions for new energy vehicle manufacturers and related suppliers. Full article

The construction industry is shifting towards integration, digitization, and automation, necessitating an adaptive logistics system for enhanced performance. Despite this shift, poor supplier performance and lack of collaboration among stakeholders often cause delays and cost overruns. This research addresses these issues by proposing an optimization model for the planning phase of construction projects. The generalized mixed-integer linear programming (MILP) model is developed that optimizes supplier and process selection of construction projects, demonstrated through a numerical study. Results indicate that optimizing these decisions in the planning phase can significantly improve supply chain performance, enabling better cost and time management for construction projects traditional or modular. Full article

Background: This study introduces a novel supply chain management (SCM) model tailored for the hospital industry in Thailand. The model emphasises the integration of third-party logistics (3PL) providers to streamline supply-processing-distribution (SPD) functions. By outsourcing non-core activities like SPD to 3PL providers, hospitals can enhance their operational efficiency, allowing healthcare professionals to focus on core tasks and ultimately improving service delivery. Methods: This research employed a dual methodology, combining an analytic hierarchy process (AHP) with a Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). These approaches evaluated various SCM models based on multiple hospital logistics performance attributes. Results: The AHP results highlighted on-time delivery, patient safety, utilisation rate, and emergency procurement as critical criteria for selecting the optimal model. Fuzzy TOPSIS analysis identified the SCIII: W-G-H model as the most suitable for implementation in Thai hospitals. This model incorporates a centralised warehouse for negotiation leverage, a Group Purchasing Organisation (GPO) for cost efficiency, and regional SPD hubs for effective inventory management and rapid responses to demand fluctuations or emergencies. Conclusions: Adopting this SCM model is expected to significantly enhance supply chain performance, reduce operational costs, and improve the quality and safety of patient care in Thai hospitals. Full article

Endocannabinoids have attracted great interest for their ability to counteract the neuroinflammation underlying Alzheimer’s disease (AD). Our study aimed at evaluating whether this activity was also due to a rebalance of autophagic mechanisms in cellular and animal models of AD. We supplied URB597, an inhibitor of Fatty-Acid Amide Hydrolase (FAAH), the degradation enzyme of anandamide, to microglial cultures treated with Aβ_25-35, and to Tg2576 transgenic mice, thus increasing the endocannabinoid tone. The addition of URB597 did not alter cell viability and induced microglia polarization toward an anti-inflammatory phenotype, as shown by the modulation of pro- and anti-inflammatory cytokines, as well as M1 and M2 markers; moreover microglia, after URB597 treatment released higher levels of Bdnf and Nrf2, confirming the protective role underlying endocannabinoids increase, as shown by RT-PCR and immunofluorescence experiments. We assessed the number and area of amyloid plaques in animals administered with URB597 compared to untreated animals and the expression of autophagy key markers in the hippocampus and prefrontal cortex from both groups of mice, via immunohistochemistry and ELISA. After URB597 supply, we detected a reduction in the number and areas of amyloid plaques, as detected by Congo Red staining and a reshaping of microglia activation as shown by M1 and M2 markers’ modulation. URB597 administration restored autophagy in Tg2576 mice via an increase in BECN1 (Beclin1), ATG7 (Autophagy Related 7), LC3 (light chain 3) and SQSTM1/p62 (sequestrome 1) as well as via the activation of the ULK1 (Unc-51 Like Autophagy Activating Kinase 1) signaling pathway, suggesting that it targets mTOR/ULK1-dependent autophagy pathway. The potential of endocannabinoids to rebalance autophagy machinery may be considered as a new perspective for therapeutic intervention in AD. Full article