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Predictive Energy Load Modelling and Process Decarbonisation: AI-Driven Energy Efficiency Frameworks for Sustainable U.S. Aerospace Manufacturing
With the increasingly urgent need for sustainability, research centered on clean and productive aerospace manufacturing has gained rapid growth. As energy accounts for a significant proportion of manufacturing cost, the utilization of energy-intensive manufacturing processes favors a heavy consumption of energy, and in turn poses many environmental issues, such as air pollution, global warming, and the depletion of natural resources.
sustainable u.s. aerospace manufacturing
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
Point-of-Sale Event Streaming and Automated Replenishment Triggers: Real-Time Machine Learning Frameworks for Demand Forecasting and Inventory Optimisation
Demand forecasting and inventory replenishment are two important components of supply chain management that must evolve with the market. Accurate forecasting enables better planning decisions that help to minimize excess inventory levels as well as to meet customer demands in a timely and efficient manner. To date, traditional forecasting methods have contributed to the compiled forecasts for inventory replenishment.
Planogram Optimisation and Cross-Category Affinity Intelligence: AI-Enhanced Analytical Frameworks for Retail Merchandising Strategy
There has been much written recently about the importance of artificial intelligence (AI) to the retail industry, and we are now beginning to see the proliferation of truly AI-enhanced merchandising strategies. These strategies are designed to offer a more contextually aware, analytics-based approach to traditional merchandising retail strategies. Yet, surprisingly little has been written about the foundational principles of this emerging field.
Payer Segmentation and Reimbursement Pathway Intelligence: AI-Driven Frameworks for Pharmaceutical and Healthcare Market Access Strategy
The pharmaceutical industry is characterized by highly regulated markets, complex pricing models, and the increasing demand for personalized medicine, making market access strategies both critical and challenging. In response to these complexities, the integration of artificial intelligence (AI) and machine learning (ML) into market access frameworks is revolutionizing how pharmaceutical companies approach drug pricing and access.
Predictive Scheduling and Skill-Task Matching Intelligence: AI-Based Workforce Optimisation Frameworks in American Aerospace Manufacturing
While AI techniques have not been widely adopted in the aerospace business due to their great complexity and interdisciplinary nature, this paper aims to explore AI fields and techniques. In the context of AI techniques in the aerospace industry, the AI-based tools discussed in this review are specifically curated for workforce optimization. In general, workforce optimization can encompass not only employee scheduling and training, but also planning factory operations.
Proactive Disruption Intelligence and Inventory Hedging Models: AI-Enhanced Supply Chain Resilience Frameworks for U.S. Manufacturing Competitiveness
U.S. manufacturers are rethinking their sourcing decisions in the wake of significant disruptions to global supply chains and the realization that having everything sourced from faraway places may not be such a good idea. Policy initiatives from federal and state governments, such as the CHIPS act and a new Michigan initiative, are underway to at least partially bring offshored industries back home.
ai-enhanced supply chain resilience frameworks
Real-Time Statistical Process Control and Root Cause Intelligence: AI-Driven Quality Assurance Systems for U.S. Manufacturing Revitalisation
Laying the groundwork for understanding AI-driven quality control systems focuses on the fundamental concepts of machine learning and deep learning. There are different types of traditional machine learning models, and two successful vision-based quality control inspection systems are presented as representative examples of moving toward AI-driven systems.
Remaining Useful Life Estimation and Anomaly Detection in Industrial Assets: AI-Driven Predictive Maintenance for Operational Efficiency in General Manufacturing
Upscaling a manufacturing system consists of equipment acquisition and mounting activities that should be planned so that upgrading the capacity of the system will not disrupt the operations excessively [2].
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
Supplier Performance Intelligence and Dynamic Relationship Scoring: AI-Based Frameworks for Strategic Supplier Development in U.S. Manufacturing
The exploration of AI-based solutions for enhancing supplier relationships in U.S. manufacturing is motivated by several factors. The integration of AI technologies in manufacturing processes is seen as a long-term investment, promoting economic viability, social cohesiveness, inclusion, and environmental sustainability [2]. Additionally, the digital revolution, with AI at its core, is profoundly impacting various aspects of society, including manufacturing and supply chains [1].
Temporal Convolutional Networks and Seasonal Decomposition: AI-Based Predictive Modelling for Retail Sales Volume Forecasting
AI-based forecasting systems have caught the attention of numerous retailers in recent years. An accurate and reliable prediction of sales is an important concern among business professionals because sales predictions are crucial in enhancing the decision-making abilities of any business. AI has a remarkable influence on various areas when integrated into prediction systems. This includes marketing, consumer behavior analysis, and much more.
Vibration Signature Analysis and Fault Prognosis Through Deep Learning: AI-Driven Predictive Maintenance for Downtime Reduction in American Defense Manufacturing
Manufacturing processes have been transforming due to the next technological revolution in automation, known as industry 4.0. As such, machine manufacturers and suppliers of machine parts have been focusing on automating processes with autonomous or fully controlled smart machines [3]. This trend is also being adopted in the defense industry, where unmanned technologies in aviation, land, and underwater vehicles have been actively developed in recent years.
Yield-Maximising Process Control Through Reinforcement Learning: AI-Driven Optimisation of Semiconductor Fabrication for U.S. Manufacturing Competitiveness
Today's chips are designed by aligning billions of transistors in three dimensions. Not surprisingly, the complexity is exceeding our ability to understand them. Therefore, to handle the complexity, our best solution is simply to let the chips optimize themselves. That way, overly complicated tools and models are not needed to design and manufacture the complicated chips.
Policyholder Lifecycle Modelling and Offer Optimisation: AI-Driven Frameworks for Personalised Insurance Product Recommendation
Personalized insurance offers are increasingly gaining importance in today’s growing digital world. Insurance organizations aim to tailor their products and services to the increasing needs and requirements of their customers. The major objective is to maximize customer satisfaction and enhance customer retention and loyalty. Traditional insurance organizations operate on the premise that all clients have the same needs and requirements.
personalised insurance product recommendation
Consumer Electronics Demand Signal Processing and SKU-Level Forecasting: AI-Powered Predictive Analytics for U.S. Laptop Manufacturing Operational Efficiency
Predicting the future is a complex task, especially predicting future demand. In a digitally connected world, increasing demand complexity and constantly changing customer trends put pressure on manufacturers and service providers to determine future demand. Demand forecasting is crucial for all types of manufacturing companies and many others dealing with the public.
consumer electronics demand signal processing
u.s. laptop manufacturing operational efficiency
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
Contextual Need Assessment and Policy Matching Intelligence: Machine Learning Frameworks for Enhanced Insurance Policy Recommendation Systems
Although insurance has evolved into a proactive platform, the essence of risk relaying is yet to be encompassed in insurance frameworks. These traditional models fall short of influencing customer choices based on personalized suggestions. Subsequently, they emanate from all available products instead of a strategic attempt to leverage futuristic insights.
enhanced insurance policy recommendation systems
Cross-Functional Data Integration and Process Automation: AI-Driven Frameworks for Retail Supply Chain Efficiency Enhancement
Understanding consumer behavior is key to achieving efficiency in the modern retail supply chain. Shopping is integral to contemporary routines, and demands across this sphere have diversified across countless mediums, locations, and markets. On subsequent levels, the largest industries in the supply chain are faced with increasingly complex business offerings and operations.
retail supply chain efficiency enhancement
Operational Technology Threat Detection and Intrusion Response: AI-Enhanced Cybersecurity Frameworks for U.S. Manufacturing Infrastructure Protection
Over the past decade, there has been rapid digitalization and automation of many processes in the manufacturing industry across multiple critical infrastructures. It is expected that this trend will continue and that cyber threats will also steadily increase in frequency and complexity. The U.S. manufacturing sector is prime for cyber-attack, particularly given increasing reliance on the Internet and interconnected systems.
u.s. manufacturing infrastructure protection
Demand-Driven Staffing Intelligence and Employee Performance Analytics: AI-Based Workforce Optimisation in American Retail Management
Workforce optimization is of paramount importance in the retail sector, directly influencing the efficiency and success of retail operations. [1] emphasize the significance of understanding and predicting the impact of management practices on retail store productivity. Their approach involves a case study integrating qualitative and quantitative data collection methods, such as participant observations, staff interviews, and questionnaires.
Disruption Propagation Modelling and Recovery Optimisation: AI-Driven Strategies for Supply Chain Resilience Enhancement in U.S. Manufacturing
Supply chain resilience is a critical concept that encompasses the ability of a supply chain to withstand and recover from various disruptions. These disruptions can arise from a multitude of sources, including changes in the business environment, intentional threats, external pressures, limited resources, and risks associated with multiple tiers of customers and suppliers.
Dynamic Risk Signal Processing and Adaptive Underwriting Intelligence: Real-Time AI Frameworks for Insurance Risk Assessment and Policy Adjustment
In order to make the most accurate and informed decisions, individuals need to have the most up-to-date information relayed to them through a policy that guarantees data about the real-time environment. Stakeholders require a good understanding of the importance of discussing instantaneous data for the insurance industry, particularly with regard to timely risk management.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
End-to-End Network Flow Optimisation and Demand-Supply Synchronisation: AI-Driven Supply Chain Strategies for U.S. Manufacturing Efficiency and Competitiveness
[1]. The modern supply chain's network-based architecture and the data it generates provide a framework for the scalability of AI-driven technologies. AI algorithms excel in leveraging extensive datasets, enabling machines to derive unique insights and perform tasks more efficiently than humans.
Fabrication Process Integrity Monitoring and Insider Threat Detection: AI-Enhanced Cybersecurity for U.S. Semiconductor Manufacturing Infrastructure
Sensors and built-in self-tests have become critical for successfully scaling beyond the 5-nanometer node. As such, U.S. semiconductor manufacturing is faced with vulnerable technology that offers adversaries opportunities to access sophisticated hidden backdoors. Enhanced cybersecurity solutions will aid our nation against cyberwars ranging from mere information collection to physical destruction of our power infrastructure.
fabrication process integrity monitoring
u.s. semiconductor manufacturing infrastructure
Hierarchical Demand Signal Integration and Forecast Reconciliation: AI-Based Approaches to Demand Forecasting Accuracy in American Retail Management
Accurate demand forecasting is paramount for retail management as it directly impacts inventory optimization, resource management, and the ability to meet customer needs [3]. Particularly for weather-sensitive products, accurate forecasts with minimal errors are crucial for an efficient inventory control system.
approaches to demand forecasting accuracy
In-Line Sensor Fusion and Predictive Quality Modelling: AI-Driven Quality Control Frameworks for Revitalising U.S. Manufacturing Operations
Artificial intelligence (AI) and machine learning (ML) are changing the face of a variety of fields including agriculture, social media marketing, healthcare, and manufacturing. One of the most expected applications for ML in the future is the successful automation of the quality control (QC) process for high-volume production in manufacturing. ML could solve the challenges posed by older methods of QC and reduce cost while maintaining or improving on product quality.
revitalising u.s. manufacturing operations
Multi-Tier Supplier Risk Modelling and Logistics Contingency Planning: AI-Enhanced Supply Chain Resilience for U.S. Aerospace Manufacturing Competitiveness
The idea behind supply chain resilience could be traced back to the 1950s. It refers to a system's ability to absorb disruptions and restore itself in the face of damaged performance as a result of a disruption. Supply chain resilience can enable the supply chain to handle large-scale uncertainties due to its adaptive capacity and ambidextrous activities. Specifically, adaptive capacity is the ability of a supply chain to make rapid adjustments and recover from operational trauma.
u.s. aerospace manufacturing competitiveness
Omnichannel Sentiment Analysis and Personalised Journey Orchestration: AI-Powered Frameworks for Customer Experience Management in Retail
Today, successful retailing is all about creating exceptional customer experiences that go beyond the ordinary. Those who fail to do so will end up competing primarily on price or product features. These experiences need to be relevant, engaging, consistent, and seamless—personal, yet scalable. A positive retail customer experience exists across the complete customer journey including browsing, purchasing, contacting customer service if needed, and post-purchase.
Cross-Facility Energy Demand Forecasting and Carbon Arbitrage: AI-Driven Energy Efficiency Strategies for Sustainable U.S. Manufacturing and Logistics
The significance of AI-driven energy efficiency solutions in the U.S. manufacturing and logistics sectors lies in their potential to not only enhance economic viability but also to promote social cohesiveness, inclusion, and environmental sustainability. As highlighted by [3] , the costs associated with implementing AI applications in manufacturing should be viewed as long-term investments that contribute to the overall well-being of society and the environment.
cross-facility energy demand forecasting
Component Scarcity Forecasting and Multi-Tier Supplier Risk Modelling: AI-Driven Supply Chain Resilience for U.S. Technology Product Manufacturing
In World War II's initial days, U.S. companies dramatically altered their fields of expertise to manufacture supplies for war. Today, as the COVID-19 pandemic has disrupted "business as usual," the supply chain and component shortages contribute to federal policy discussions for revitalizing U.S. domestic tech product manufacturing.
Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/
Component-Level Power Consumption Modelling and Assembly Line Optimisation: AI-Driven Energy Efficiency Solutions for U.S. Laptop Manufacturing
The focus on AI-driven energy efficiency solutions for sustainable U.S. Laptop Manufacturing is rooted in the need to address the existing challenges in the manufacturing sector related to energy consumption and sustainability. As highlighted by [1] , the rise of highly efficient data factories, known as hyperscale facilities, has demonstrated the potential to significantly reduce energy consumption in conventional data centers.
component-level power consumption modelling
Computer Vision-Based Defect Classification and Process Deviation Detection: AI-Driven Quality Control Systems for U.S. Defense Manufacturing Revitalisation
In most of recorded history, the "quality" of goods and services was a function of the experience, care, skill, and expertise of individual craftspeople. The industrial revolution led to the development of less expensive means of manufacturing goods by replacing the highly autonomous person with machinery, tools, and process.
computer vision-based defect classification
u.s. defense manufacturing revitalisation