Advancing predictive analytics models for supply chain optimization in global trade systems

The last-mile delivery problem is one of the most complex and resource-intensive aspects of modern logistics, especially within the growing e-commerce sector. As online shopping continues to expand, companies are under immense pressure to deliver goods more quickly, efficiently, and at lower costs, all while meeting the demands of increasingly time-sensitive customers. This has created a need for innovative solutions that can tackle challenges related to dynamic traffic patterns, fluctuating customer preferences, and operational constraints such as vehicle capacities and delivery windows. In response to these challenges, this paper explores the application of predictive analytics as a tool for optimizing last-mile delivery routes in real-time.The study begins by identifying the core challenges inherent in last-mile logistics, particularly in the U.S. e-commerce landscape, where the cost of last-mile delivery can represent up to 53% of total shipping costs. With traffic congestion, unpredictable customer availability, and delivery time constraints posing significant hurdles, conventional static route planning models often fall short. In this paper, predictive analytics is proposed as a solution to these challenges, utilizing real-time data to inform more efficient routing decisions. By processing vast amounts of real-time traffic data, customer preferences, and delivery constraints, predictive models can offer a more flexible and responsive approach to last-mile delivery.The research then presents a comprehensive literature review of existing route optimization methods, such as the traditional Vehicle Routing Problem (VRP) and its extensions, including VRP with Time Windows (VRPTW), Dynamic VRP (DVRP), and Capacitated VRP (CVRP). While these models have proven useful, their limitations are exposed when faced with real-time operational complexities in the e-commerce sector. Therefore, this study introduces an advanced dynamic routing model that integrates machine learning algorithms—such as decision trees and neural networks—with traditional VRP frameworks. These machine learning models, trained on historical data, are capable of predicting future traffic patterns, customer behavior, and delivery time windows.A case study is conducted using data from U.S.-based e-commerce companies to demonstrate the practical application of predictive analytics in optimizing last-mile delivery. The case study outlines how predictive models are used to dynamically adjust delivery routes based on real-time conditions, leading to significant improvements in efficiency, cost savings, and customer satisfaction. Key performance indicators such as delivery times, fuel consumption, and vehicle utilization are examined before and after the implementation of the predictive models, with the results showing a reduction in delivery time by 20% and fuel costs by 15%, alongside improved on-time delivery rates.The paper concludes by presenting the proposed dynamic route optimization model as a solution that combines the flexibility and responsiveness of predictive analytics with the robust framework of traditional VRP models. Through the integration of machine learning, real-time data processing, and dynamic routing, the model is shown to significantly improve last-mile delivery efficiency. This study's findings highlight the potential for predictive analytics to revolutionize the logistics industry, particularly in the high-demand e-commerce sector, where quick and reliable delivery is paramount. The research suggests that as e-commerce continues to grow, predictive analytics will play an increasingly critical role in ensuring that last-mile delivery is both cost-effective and responsive to the evolving needs of consumers.

A Road Map for More Inclusive Canadian Trade Policy (Conclusion)

Analytics has become an essential tool in our search for long-lasting and forward- looking solutions in a world where information is constantly shared, and data is being made at a rate that has never been seen before. Welcome to the ”Sustain- able and Predictive Analytics Model,” an in-depth look at how sustainability and predictive analytics work together. This writing gives people a complete plan for using data-driven ideas to help society and business. Analysis methods for the long term and their ability to predict The book starts by looking at the basic concepts behind sustainable and predictive analytics. This creative work details why and how these two fields must work together to solve the world’s most pressing problems. In this study’s second part, we discuss the analyti- cal ideas and tools we used. The fundamental analytics ideas are looked at in depth to start with a solid base. In the next part, you’ll learn basic facts to help you analyse and understand data more effectively. In the third part, the basic ideas of sustainability are looked at. The main goal of this effort is to bring about sustainable growth. Through a thorough look at sustainability ideas, you will learn what you need to know to make sound economic, environmen- tal, and societal decisions. Part 4 of ”Approaches to Predictive Modelling For the book to make accurate predictions, it must look closely at historical facts. This part will overview the different predictive modelling methods and explain their advantages and disadvantages. Collecting information for a full look at long-term sustainability For statistics to work well, data must be used. This chapter gets into the book’s main topic, which is how to collect and prepare data for a study on sustainability. Building a model that can predict sustain- ability By using the information and tools in this book, we can start the process of building predictive sustainability frameworks, which will help us make well- informed choices about the future. Case studies on how to use statistics that are good for the environment Using real-world examples and case studies help show how important sustainable statistics are in many fields. Explores from a social and moral point of view. In the information age we live in, ethics are becoming increasingly important. The writers of this book look into the ethical and social aspects of sustainable and predictive analytics to ensure they align with responsible practices. We are checking and analysing how accurate and useful the model is. To ensure predictions are accurate throughout the process, you must know much about model validation and performance review. In this part, we’ll talk about how to set up and connect a certain system or piece of technology. The most important thing is that analytics work well with existing systems. This chapter looks at the different ways release and integration can be done. Chapter 11: What the future holds for sustainable and predictive analytics Analytics is a field that is constantly growing and improving. The book looks into the future by discussing upcoming technological changes for sustainable and predictive analytics. Final Thoughts and an Immediate Call for Change A call to action demonstrates the book’s turning point. This article ex- amines the many effects of sustainable and predictive analytics and encourages readers to use this changing method for a better future. This academic study digs deep into the ”Sustainable and Predictive Analytics Model” literature and looks at the complicated link between data, sustainability, and prediction. The joint projects described in the book hold hope for future generations because they show new ways to do things and help make the world more sustainable.

Climate change induced extreme precipitation poses a significant threat to agricultural production. Such impacts extend beyond local agricultural production regions, generating remote and cross-sector impacts that disrupt global supply chains (GSCs). While the direct impacts of extreme precipitation on agricultural production have been widely studied, how such local impacts cascade through supply chain networks to remote places remains elusive, partly because of the complex interdependencies within the global trade systems. To address this, we propose a Resilience Enhancement in Supply Chains Under Environmental Shocks (RESCUES) framework. RESCUES couples an agricultural production loss model with a dynamic recursive economic network model. It allows us to identify channels through which the impacts of climate change on agricultural production propagate along GSCs to interconnected sectors and regions. We design nine climate shock scenarios (i.e., dry, wet, and compound precipitation anomalies with extreme, severe, and moderate levels of severity) using the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSPs) scenarios (SSP126 and SSP585). We then use RESCUES to simulate the GSCs dynamics over 2016-2050 under these nine scenarios. We find that direct agricultural losses driven by local precipitation anomalies can spread through GSCs to a wider range of countries and regions across the globe, creating large spatial spillover effects with direct and indirect economic losses. We estimate that the averaged per event total value-added (VA) losses caused by compound extremes is around $20.4/22.6 billion under SSP126/585, followed by dry extremes ($16.4/15.0 billion) and wet extremes ($8.7/11.6 billion). Moreover, the global distribution of direct and indirect losses exhibits high spatial heterogeneity. Countries with large agricultural outputs tend to have both high direct and indirect VA losses, especially in China, India, the United States, Russia, and Brazil. In contrast, poorer countries, such as Tanzania, Sudan, Myanmar, Yemen, Afghanistan, and Nepal, experience relatively larger direct losses, while rich regions heavily dependent on agricultural imports, including Hong Kong, Qatar, and Singapore, suffer relatively larger indirect losses. Considering that nations frequently implement export restrictions to ensure food self-sufficiency, we further design a hypothetical scenario to assess the global trade and economic impacts of near-term (2025-2030) agricultural export restrictions in four key food production regions (China, India, the United States, and Indonesia) under extreme precipitation anomalies. Our study highlights the importance of an integrated and comprehensive assessment of the risk footprint of climate change-related shocks, encompassing both direct and indirect impacts on GSCs. 

There has been a paradigm shift in the functioning of global supply chains, particularly in thepost-pandemic era. This shift is characterized by the convergence of technology. deployment, geopolitical dynamics, and an increased awareness of environmental and social responsibilities. These transformative changes compel businesses to navigate a complex. web of challenges while simultaneously creating many opportunities for global supply chains. This includes delegating responsibilities to the supplier side, empowering suppliers, and promoting digital supply chain networks. Ongoing trade tensions between major economies, such as the United States and China, have resulted in fluctuations in global logistics patterns. Alterations in tariffs and trade agreements impact shipping routes, transportation costs, and the overall flow of goods. Consequently, supply chains must focus on end-to-end supply chains originating from low-cost sources to withstand unforeseen events like black swan occurrences and geopolitical and trade wars. The pandemic has underscored the critical importance of building resilient supply chains. Many firms reevaluate their supply chain strategies, emphasizing diversification to mitigate risk. This involves reassessing suppliers, exploring alternative sourcing options, and increasing safety stock levels. Geopolitical uncertainties and disruptions have prompted reconsidering global sourcing strategies, with reshoring and nearshoring gaining prominence. Companies seek to mitigate risks associated with distant and complex supply chains by bringing production closer to target markets, enhancing responsiveness, reducing lead times, and fortifying supply chain resilience amid uncertain business conditions. From the traditional supply chain model, there is a continuous effort to integrate information technology to transform value streams and increase processes' transparency. Blockchain is playing a significant role, from tracking the provenance of raw materials to reducing transaction fraud. This technology fosters a new level of trust among supply chain partners, safeguarding against potential disruptions and reinforcing the integrity of the entire supply chain ecosystem. These rapid transformations amid uncertain conditions have prompted a reevaluation of supply chain resiliency with a greater focus on risk mitigation strategies. In global logistics, owning ships and manufacturing/leasing containers for extended periods have become the guiding principles. The recent Brexit has also impacted global transport's European and UK legs. The European industry is grappling with significant shortages of resources, including drivers and trained supply chain manpower, affecting last-mile delivery and land transportation in numerous geographies. The growth of e-commerce has significantly influenced supply chains, leading to innovations in last-mile delivery solutions. Companies are investing in warehouse automation, drone deliveries, and other technologies to meet the demands of the rapidly expanding e-commerce market. x Furthermore, a seismic shift toward sustainability reshapes the global supply chain landscape. Businesses recognize the imperative to address environmental, social, and governance (ESG) concerns. Initiatives to reduce carbon footprints, optimize transportation routes, and embrace ethical sourcing practices are becoming integral components of supply chain strategies. Sustainability is no longer merely a corporate buzzword but a critical factor in ensuring long-term viability and meeting the expectations of socially conscious consumers. Overall, global supply chains are undergoing significant transformation driven by technological innovation, environmental consciousness, and a commitment to building resilient networks. Businesses that embrace these trends are not only adapting to the challenges of today but are also positioning themselves to thrive in the dynamic landscape of tomorrow. The key to success is navigating change, leveraging technology, and building sustainable businesses.

Aim: Recent innovations in artificial intelligence (AI) transform the supply chain management system and allow organizations to predict demand, regulate the stock, reduce risk, and optimize the logistics process, becoming more specific and quicker. The purpose of this study is to examine how end-to-end supply chain optimization through artificial intelligence (AI)-based predictive analytics can improve the accuracy of the forecast, inventory management, and logistics effectiveness. Methods: Based on modern-day case evidence and measurements, the analysis will be directed towards quantifiable goals: 10 30% reductions in MAPE; 5-15% reductions in days-in-inventory; 10-20% reductions in logistics cost per order; and 4-8% reduction percentage payback in on-time-in-very-full and ROI of over 20%. The study adopted a comparative analytical design using secondary data from multiple industries to assess the effectiveness of AI-driven predictive models (gradient boosting, random forests, and LSTM) against classical time-series forecasting approaches. Results: The findings show that companies with unified AI platforms, feature stores, MLOps pipelines, and balanced data models are binding significantly more adoption and return compared to the competitors, which use individual instruments, since the mutual data resource, lineage, and governance decelerate friction and aid learning speed. Conclusion: The study concludes that predictive analytics, when integrated into unified AI platforms, enhances supply chain resilience and sustainability by converting real-time data into actionable insights for cost and service optimization. Recommendation: The study proposes that organizations should embrace unified AI structures that have controlled data models to achieve optimal predictive analytics performance and scale across supply chain operations.

Agriculture, food and global value chains: issues, methods and challenges

In the contemporary healthcare landscape, escalating costs and the imperative to enhance patient outcomes have catalyzed the integration of advanced data-driven methodologies into clinical practice. This paper presents a comprehensive conceptual framework for a predictive analytics model to optimize healthcare delivery through early disease detection, personalized treatment, and strategic resource allocation. Grounded in robust theoretical underpinnings—including data-driven decision-making, systems theory, health informatics, and machine learning—the study critically examines existing predictive models to identify their strengths and limitations, thereby highlighting the necessity for a more integrated and scalable approach. The proposed framework leverages diverse datasets, such as electronic health records, wearable device metrics, and socioeconomic indicators, to generate actionable insights that inform clinical and administrative decision-making. By incorporating advanced predictive algorithms and seamlessly integrating these insights into clinical workflows and operational dashboards, the model aims to shift healthcare from a reactive to a proactive paradigm. Through a mixed-methods research design, this study employs rigorous quantitative analyses and qualitative evaluations to validate the model’s predictive accuracy and practical applicability across diverse healthcare settings. Key hypotheses developed within this framework address the potential of predictive analytics to reduce unnecessary hospital readmissions, optimize resource utilization, and improve overall patient care quality. The discussion further explores data privacy challenges, ethical considerations, and algorithmic bias, offering strategic recommendations for mitigating these issues. Policy implications are also discussed, emphasizing the need for regulatory frameworks that balance innovation with equity and security. Ultimately, this paper contributes a robust, scalable model that advances the academic discourse on predictive analytics in healthcare and provides a practical blueprint for enhancing operational efficiency and patient outcomes cost-effectively.

Local water scarcity risk (LWSR, meaning potential economic output losses in water-using sectors due to physical water scarcity) can be transmitted to downstream economies through the globalized supply chains. To understand the vulnerability of the global economy to water scarcity, we examine the impacts of local water scarcity risk on the global trade system from 1995 to 2009. We observe increasingly intensified geographical separation between physical water scarcity and production losses due to water scarcity. We identify top nation-sectors in virtual water scarcity risk (VWSR) exports (indicating local water scarcity risk in each nation transmitted to foreign nations through its exports), including agriculture and utilities in major economies such as China, India, Spain, France, and Turkey. These nation-sectors are critical to the resilience of the global economy to water scarcity. We also identify top nation-sectors in virtual water scarcity risk imports (indicating each nation's vulnerability to foreign water scarcity risk through the global trade system), highlighting their vulnerability to distant water scarcity. Our findings reveal the need for nations to collaboratively manage and conserve water resources, and lay the foundation for firms in high VWSR-importing sectors to develop strategies to mitigate such risk.

In March 2021, Egypt's Suez Canal experienced a temporary disruption due to the grounding of the cargo ship “Ever Given,” resulting in billions of dollars in economic losses and severe shocks to the supply chains of key industries such as oil, natural gas, manufacturing, and food. This incident highlighted the global trade system's heavy reliance on specific geographical hubs, revealing the vulnerabilities of global supply chains and increasing concerns among nations about the risks of supply chain disruptions. This paper explores the connection between the Suez Canal blockage and national risk management, analyzing how disruptions to such global trade hubs threaten national economies and strategic security. Through this study, the causes of global supply chain risks and strategies to address them will be examined. Finally, the paper proposes practical national risk prevention strategies to ensure the stability of the global trade system.

The global supply chain landscape is experiencing transformative change due to the escalating impacts of climate change, which disrupt traditional models and pose risks to supply chain resilience. In response, Sustainable Supply Chain Management (SSCM) has emerged as a key strategy that integrates environmental, social, and economic considerations into supply chain practices. This paper explores the interdisciplinary convergence of environmental science and business logistics, highlighting how such integration can enhance supply chain adaptability and long-term sustainability. Using a comprehensive literature review, secondary data analysis, and relevant case studies, the study investigates the effects of climate disruptions on global supply chains and examines innovative SSCM practices such as green procurement, circular supply chains, and renewable energy integration. Findings demonstrate that companies adopting SSCM approaches especially those investing in clean technologies and collaborative environmental strategies are better equipped to mitigate risks and gain competitive advantages. Key case studies from firms like Unilever, IKEA, and Walmart underscore the practical benefits of sustainability-focused supply chains. Additionally, the role of digital transformation technologies such as blockchain, IoT, and predictive analytics is discussed as an enabler of traceability and transparency. This study concludes by providing actionable recommendations for both business practitioners and policymakers, while also identifying areas for future interdisciplinary research in SSCM, such as emerging technologies and regional adaptation strategies. The proposed framework emphasizes the need for systemic shifts in supply chain thinking to align with climate resilience and sustainability imperatives.

A precise prediction of the health status of industrial equipment is of significant importance to determine its reliability and lifespan. This prediction provides users information that is useful in determining when to service, repair, or replace the unhealthy equipment’s components. In the last decades, many works have been conducted on data-driven prognostic models to estimate the asset’s remaining useful life. These models require updates on the novel happenings from regular diagnostics, otherwise, failure may happen before the estimated time due to different facts that may oblige rapid maintenance actions, including unexpected replacement. Adding to offline prognostic models, the continuous monitoring and prediction of remaining useful life can prevent failures, increase the useful lifespan through on-time maintenance actions, and reduce the unnecessary preventive maintenance and associated costs. This paper presents the ability of the two real-time tiny predictive analytics models: tiny long short-term memory (TinyLSTM) and sequential dense neural network (DNN). The model (TinyModel) from Edge Impulse is used to predict the remaining useful life of the equipment by considering the status of its different components. The equipment degradation insights were assessed through the real-time data gathered from operating equipment. To label our dataset, fuzzy logic based on the maintainer’s expertise is used to generate maintenance priorities, which are later used to compute the actual remaining useful life. The predictive analytic models were developed and performed well, with an evaluation loss of 0.01 and 0.11, respectively, for the LSTM and model from Edge Impulse. Both models were converted into TinyModels for on-device deployment. Unseen data were used to simulate the deployment of both TinyModels. Conferring to the evaluation and deployment results, both TinyLSTM and TinyModel from Edge Impulse are powerful in real-time predictive maintenance, but the model from Edge Impulse is much easier in terms of development, conversion to Tiny version, and deployment.

Machine learning (ML) is revolutionizing industries by providing innovative solutions that drive sustainable economic growth and market efficiency. The integration of ML techniques in various sectors, including finance, healthcare, manufacturing, and energy, offers significant potential for enhancing productivity, optimizing resource allocation, and fostering innovation. This review explores the transformative impact of ML on economic growth and market efficiency, emphasizing its role in predictive analytics, decision-making, and automation. In finance, ML algorithms are employed to predict market trends, manage risks, and detect fraudulent activities. By analyzing vast amounts of data in real-time, ML models enable financial institutions to make informed decisions, reduce operational costs, and improve customer experiences. In healthcare, ML applications range from diagnosing diseases and predicting patient outcomes to optimizing treatment plans and managing healthcare resources, thereby improving patient care and reducing costs. The manufacturing sector benefits from ML through predictive maintenance, quality control, and supply chain optimization. ML models analyze data from sensors and production lines to predict equipment failures, ensuring timely maintenance and minimizing downtime. This leads to increased productivity and reduced waste, contributing to economic sustainability. Energy management is another critical area where ML drives efficiency. ML algorithms optimize energy consumption, forecast renewable energy production, and enhance grid management. By integrating ML into energy systems, organizations can reduce their carbon footprint and operational costs while promoting sustainable energy practices. Moreover, ML enhances market efficiency by enabling businesses to understand consumer behavior and preferences. Retailers use ML to personalize marketing strategies, optimize pricing, and manage inventory effectively. This not only boosts sales but also improves customer satisfaction and loyalty. Despite the significant benefits, the adoption of ML presents challenges such as data privacy concerns, algorithmic bias, and the need for skilled professionals. Addressing these challenges requires robust regulatory frameworks, ethical guidelines, and investment in education and training. In conclusion, harnessing ML techniques is pivotal for driving sustainable economic growth and market efficiency. By leveraging ML's capabilities in predictive analytics, decision-making, and automation, industries can achieve higher productivity, optimized resource utilization, and innovative solutions that contribute to long-term economic sustainability and competitiveness. As ML continues to evolve, its potential to transform various sectors and promote sustainable development remains immense.

Predictive analytics has become a crucial tool in modern business decision-making, particularly in demand forecasting. By leveraging historical data and statistical modeling techniques, businesses can enhance resource allocation, optimize inventory management, and improve overall operational efficiency. Among various predictive analytics methods, time series models are widely employed due to their ability to capture trends, seasonality, and patterns in demand fluctuations. This explores the application of time series models, including Moving Average (MA), Autoregressive (AR), Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Exponential Smoothing (Holt-Winters method), and advanced deep learning models such as Long Short-Term Memory (LSTM) networks. These models enable businesses to make data-driven decisions by accurately predicting future demand, thereby reducing costs and improving supply chain responsiveness. Furthermore, the integration of predictive analytics in business resource allocation extends beyond inventory management. It influences workforce planning, financial forecasting, and supply chain optimization, ensuring that organizations align their operations with anticipated market trends. However, despite the benefits, challenges such as data quality issues, model interpretability, external disruptions, and scalability concerns remain key obstacles in implementing time series forecasting. Emerging advancements in artificial intelligence (AI), big data analytics, and cloud-based forecasting solutions are shaping the future of predictive analytics. The integration of real-time data and machine learning models offers new opportunities for businesses to enhance forecasting accuracy and agility. As demand forecasting continues to evolve, organizations must adopt a strategic approach to model selection and implementation to fully capitalize on the advantages of predictive analytics. This study provides a comprehensive overview of predictive analytics for demand forecasting, highlighting the role of time series models in optimizing business resource allocation and discussing future trends that will drive further innovation in this field.

Using predictive analytics is a key part of making the computer supply chain safer because it helps companies find and stop threats before they happen. Predictive analytics uses complex algorithms and machine learning to look through huge amounts of data for trends and outliers that could point to cyber dangers. Businesses can stay ahead of cyber attackers and keep their digital assets safe with this method. In the online supply chain, one of the best things about prediction analytics is that it can find new threats before they become full-blown attacks. Predictive analytics looks at past data, current trends, and other factors to be able to guess possible computer dangers and weak spots. In turn, this lets businesses take strategic steps to lower these risks, like fixing security holes or adding more protections. Predictive analytics can also improve the cyber supply chain's ability to respond to incidents. Predictive analytics looks at data from many places, like network logs, endpoint devices, and security monitors, to help find and rank possible security events. This lets companies react quickly and effectively to cyberattacks to lessen their effects. The prediction analytics is a great way to make the online supply chain safer. Using advanced algorithms and machine learning, businesses can find and stop cyber dangers before they happen, keep their digital assets safe, and boost their total security.