Global Journal of Multidisciplinary Sciences and Arts
The Global Journal of Multidisciplinary Sciences and Arts is an internationally recognized Pakistan-based journal that is published by a well-known publisher of Pakistan GLOBAL ALLIANCE FOR SCIENTIFIC STUDIES. Global Journal of Multidisciplinary Sciences and Arts operates under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) It welcomes submissions from various disciplines. It is a peer-reviewed and open-access publication that aims to showcase high-quality original research, reviews, letters, and case reports in natural sciences, social sciences, applied sciences, formal sciences, arts, and related areas. Our goal is to improve the efficient dissemination of new research ideas and findings, enabling researchers to generate new knowledge, studies, and inventions that will serve as a valuable reference for the future. The Journal is affiliated with Global Alliance for Scientific Studies (SMC-Private) Limited Multan, Pakistan. Journal Description Journal Name Global Journal of Multidisciplinary Sciences and Arts ISSN Number 3078-2724 Editor in Chief Abdul Samad | Department of Animal Science Gyeongsang National University 501, Jinju-daero, Jinju-si, Gyeongsangnam-do Frequency a Half Year| June and Decemeber Research Scope Computer Science | Management | Veterinary | Mathematics | Agriculture | Engineering | Health
Sustainable Manufacturing and Process Innovation in Medical Devices
The merging of additive manufacturing (AM) and precision engineering are providing a new paradigm in medical device development, where patient-specific, high precision and complex structures are being realized. This review discusses the basics of AM, such as the main technologies and biomaterials, and examines how they have been combined with precision engineering to enhance accuracy, surface quality and performance. Applications include implants, prosthetics, dental devices, surgical tools and tissue scaffolds. While there are numerous advantages to this including customizability, minimization of waste and quick prototyping, there are still issues with accuracy, materials, cost and regulation. New technologies such as AI, bioprinting and 4D manufacturing hold the potential of further innovations. In sum, this synergy is transforming healthcare with innovative, efficient and personalized medical solutions.
Copyright (c) 2026 Salman Kunnipurayil, Mannan Mohammed (Author) https://creativecommons.org/licenses/by-nc/4.0
Machine Learning Models for Predictive Workforce Planning in Public Sector Logistics
This review explores machine learning models for predictive workforce planning in public sector logistics, emphasizing their role in improving efficiency, accuracy, and resource allocation. The traditional planning approach tends to make assumptions that are unchangeable, and is not suitable for logistics systems with fluctuating demands. Machine learning methods such as regression, tree models, time series forecasting, and deep learning help predict the workforce requirements based on operational, workforce, and external data sources. Additionally, the study focuses on the feature engineering, evaluation metrics, applications, challenges, and ethics of the research. It also covers trends to watch, including real-time analytics and AI-powered automation in intelligent workforce management.
Copyright (c) 2026 Mussavir Mustafa Shaikh (Author) https://creativecommons.org/licenses/by-nc/4.0
A Systematic Review of Project Management Methodologies in Manufacturing
In this systematic review we have put the project management methodologies of the manufacturing sector under the microscope, with an eye on Waterfall, Agile, Lean and Six Sigma as well as any hybrid variants. Our aim is to see how they are put to use in everything from production planning and product development to supply chain and process improvement. When you compare them on such measures as cost control, quality, risk and efficiency, it becomes clear that there is no one-size-fits-all solution. You will find that Lean and Six Sigma do their job of driving up quality and efficiency, whereas Agile lends itself to greater adaptability; hybrid models, for their part, provide a more even-handed approach. Of course, there are obstacles to contend with, be it a lack of skills or technology, or simply resistance to change. We also look at how new developments like Industry 4.0 and the use of AI and other digital tools are changing the way things are done. Ultimately, our review points to some gaps in the current research and makes the case for more adaptive and sustainable, data-driven frameworks in manufacturing.
Copyright (c) 2026 Muhammad Saad (Author) https://creativecommons.org/licenses/by-nc/4.0
Machine Learning and Deep Learning for AI-Driven Data Analytics: A Review
You will find that Machine Learning (ML) and Deep Learning (DL) are now indispensable to AI-powered data analytics, what with their ability to pull advanced insights from even the most voluminous and intricate datasets. In this review we look at the underpinnings of ML and DL, covering the principal methodologies and how they are put to use in fields as diverse as healthcare, finance, cybersecurity, IoT and business intelligence. We delve into the core learning paradigms – supervised, unsupervised and reinforcement – as well as deep learning architectures you would expect to see, from CNNs and RNNs to Transformers. The scope of our study extends to the tools and frameworks of the data processing pipeline that make for scalable analytics. We also take up some of the more pressing issues, namely computational complexity, interpretability and the matter of data quality, while touching on where things are headed with trends in AutoML, XAI, Edge AI and Federated Learning. Put simply, modern data-driven systems owe much of their automation, decision-making and predictive power to these technologies.
Copyright (c) 2025 Hassan Raza (Author) https://creativecommons.org/licenses/by-nc/4.0
From Raw Data to Actionable Insight: Innovations in Machine Learning and Predictive Modeling
This review will examine how machine learning and predictive modeling can transform raw data into actionable insights. It outlines the main steps such as data preprocessing, feature engineering, algorithm development, and model evaluation. The paper analyzes fundamental and developed algorithms like regression, classification, deep learning, time series predictions, and ensemble algorithms. The practical implications of predictive analytics can be seen in numerous real-life applications across the healthcare, financial, marketing, and industry sectors. Issues like data quality, interpretability, scalability, and ethical issues are addressed. The emerging trends such as explainable AI, AutoML and real-time analytics are introduced with the focus on the increased significance of intelligent and data-driven decision-making systems.
Copyright (c) 2025 A Singh (Author) https://creativecommons.org/licenses/by-nc/4.0
Optimization of Power Systems Using AI: A Review of Modern Approaches
This review paper reviews recent Artificial Intelligence (AI) techniques for power system optimization. It underscores the shortcomings of conventional approaches and stresses the importance of AI approaches such as machine learning, evolutionary algorithms, swarm intelligence and deep learning in addressing complex problems such as economic dispatch, unit commitment and optimal power flow. The research also explores applications for renewable energy, smart grids, and demand response. Challenges such as data quality, computational efficiency and interpretability are discussed. Emerging challenges include explainable AI, real-time operation, and hybrid approaches. In conclusion, AI improves efficiency, reliability, and sustainability of power systems.
Copyright (c) 2025 Fawad Khan (Author) https://creativecommons.org/licenses/by-nc/4.0
Automation and Smart Manufacturing in Medical Device Production
By improving precision, efficiency, and regulatory compliance, automation and smart manufacturing are changing the medical device production process. The review is based on the idea of the transformation of manual operations to Industry 4.0 systems featuring robotics, IoT, AI, and digital technologies. These enhancements enhance quality management, real-time monitoring, and support predictive maintenance and traceability. The advantages are increased productivity, cost reduction, increased safety and accelerated innovation. But there are still obstacles to overcome, including the cost of investments, cybersecurity concerns, and integration concerns. The upcoming tendencies such as AI-based automation, digital twins, and customized production are expected to bring additional innovations. These technologies are reshaping medical device manufacturing into a more intelligent and efficient system.
Copyright (c) 2025 Mannan Mohammed, Salman Kunnipurayil (Author) https://creativecommons.org/licenses/by-nc/4.0
Intelligent Data Analytics: A Review of AI-Based Approaches
Intelligent Data Analytics (IDA) combines artificial intelligence with recent data processing algorithms to discover valuable insights out of big and complex data. The current review of the history of data analytics as the development of traditional statistical tools into modern AI-powered systems reveals the main concepts of machine learning, deep learning, data mining, and big data processing. It talks about major AI-based strategies, applications, tools, across industries, challenges, recent developments, comparative analysis and future trends. IDA enhances decision-making in healthcare, finance, business, cybersecurity, and IoT. With such obstacles as data quality, privacy, and interpretability, new developments keep enhancing efficiency, accuracy, and automation in analytics systems.
Copyright (c) 2025 Vidya Devi Oad (Author) https://creativecommons.org/licenses/by-nc/4.0
Recent Advances in Artificial Intelligence for Data Science: A Review
Artificial Intelligence (AI) has significantly changed the face of Data Science to the extent that analysis, prediction, and automation of various fields can occur at an advanced level. This review examines the fundamental concepts of AI, such as machine learning, deep learning, and natural language processing and its use in combination with data preprocessing, feature engineering, and model validation. The recent developments AutoML, big data analytics, generative AI and transformer-based models have expanded the capabilities of AI and have enhanced its efficiency, scalability, and usability. Healthcare, financial, marketing, and scientific applications create specific domains that can be seen to have tangible benefits, whereas the issues of data quality, interpretability, computational needs, and ethical issues have become critical. The directions of future research include transparency, flexibility, ethically friendly AI and incorporation with the latest technologies. On the whole, AI keeps reinventing Data Science and provides disruptive insights and solutions in any industry.
Copyright (c) 2025 Global Journal of Multidisciplinary Sciences and Arts
From Data to Decisions: AI-Driven Machine Learning in Modern Healthcare
Data-driven health care is being revolutionized by artificial intelligence (AI) and machine learning (ML). To inform clinical practice, AI uses electronic medical records, imaging and wearable’s to improve prediction, diagnosis, treatment and disease monitoring. ML (supervised, unsupervised, reinforcement and deep learning) enhances diagnosis, treatment and workflows. However, AI and ML in health care face issues such as privacy, bias, explain ability, regulatory and clinical practice. Explain ability, multimodal data integration and human-in-the-loop approaches can overcome these challenges to achieve safe, explainable and efficient AI-based medical care. Predictive, diagnostic and personalized medicine AI can transform care, outcomes and efficiency. Further research and ethical deployment will be crucial to unlocking the full potential of AI and ML in health care.
Next-Generation Healthcare Informatics: The Role of Artificial Intelligence, Predictive Analytics, Block chain, and Secure Data Systems
The blistering development of digital technologies has revolutionized healthcare informatics and permitted intelligent, safe, and data-driven systems. This review will examine how Artificial Intelligence (AI), Machine Learning, Predictive Analytics, Block chain, and secure data system have been integrated in the modern healthcare. The health care AI and Machine Learning enhance clinical decision-making, medical imaging analysis, and personalized treatment that determines patterns and predicts outcomes using extensive datasets. Predictive analytics help in proactive care of patients, risk management, and resource utilization. Tamper-proof management of data and interoperability are achieved with the help of block chain, whereas sensitive health information is secured by cybersecurity frameworks. These technologies, albeit with challenges including information quality, standards incompatibility, ethics, and the cost of implementation, all can be used to facilitate next generation, patient-centered, and efficient healthcare systems. The review identifies the existing applications, challenges and future trends and stresses on the opportunities offered by integrated electronic solutions to transform healthcare delivery, optimize patient outcomes, and operational efficiency.
Convergence of Artificial Intelligence, Block chain, and Predictive Analytics in Healthcare Informatics: Opportunities and Challenges
The integration of Artificial Intelligence (AI), block chain, and predictive analytics is revolutionizing healthcare informatics because these systems allow intelligent, secure, and data-driven systems. With the help of the AI, one can detect the correct diagnosis, clinical decision support, and make a specific treatment with the help of specific data analysis. Predictive analytics enable an active intervention, risk evaluation, and the efficient deployment of resources, whereas block chain can guarantee transparent, safe, and data exchange. The adoption of these technologies will result in improved patient care, efficiency, and the trust of the healthcare system. Their joint implementation has enormous possibilities of intelligent, patient-centered, and efficient healthcare systems despite their issues like data privacy, interoperability, and computational requirements as well as regulatory compliance that will define the future of digital health.
AI-Enabled Digital Healthcare Informatics: Cybersecurity, Block chain Technologies, Supply Chain Analytics, and Predictive Healthcare Intelligence
The fast digitalization of healthcare has provided enormous possibilities on how AI-enabled healthcare informatics can transform the care of patients, the efficiency of its operations, and decision-making. This review discusses the use of Artificial Intelligence, machine learning, block chain, cybersecurity, predictive analytics, and supply chain management in the contemporary healthcare systems. The use of AI and machine learning allows processing data intelligently, predicting diseases and treating patients individually, block chain and cybersecurity provide healthcare data management that is secure, transparent, and free from any manipulations. Proactive provision of healthcare and allocation of resources are improved with the help of predictive analytics and supply chain optimization. The review shows the following obstacles as data privacy, interoperability, ethical issues, and regulatory compliance, where responsible implementation is needed to develop secure, data-driven, and patient-centered healthcare ecosystems.
Future of Organic Chemistry in the Age of Artificial Intelligence
Organic chemistry is being reshaped by artificial intelligence (AI), which can be used to implement data-driven methods in the process of reaction prediction, retrosynthesis, molecular design, and process optimization. The patterns found by machine learning and deep learning models are used to accelerate chemical discovery, direct reaction conditions, and explore chemical space, based on large chemical datasets. Closed-loop experimentation can be provided by integrating with automation and self-driving laboratories, which enhances efficiency, reproducibility, and sustainability. Green chemistry is also aided by AI in reduction of waste and maximization of resources. In spite of these obstacles to information quality, meaning, and responsible application, AI is viewed as a complement to human knowledge and potentially transformative within the field of organic chemistry, which will become faster, more innovative, and greener in the next several decades.
Quality and Efficiency in the Age of AI: A Review of Healthcare Informatics, Computer Science Innovations, Nanocarrier Drug Delivery, Cybersecurity, Food Production, and Lean Six Sigma
Computational technologies and artificial intelligence (AI) are revolutionizing the healthcare, food production, and biomedical research spheres through the increase of efficiency, quality, and optimization. This review discusses AI-controlled healthcare systems, health informatics, nanocarrier drug delivery, computational modeling, and cybersecurity and their role in diagnostics, patient care, and optimization of operations. Simultaneously, AI-based solutions in the agricultural sector, food production, and supply chain management enhance resource use and sustainability. The combination of the Lean Six Sigma and AI indicates quantifiable improvements in the efficiency of processes in the domains. Ethical, technical and regulatory issues are presented, focusing on the interdisciplinary cooperation and future research strategies on the way to the sustainable, intelligent, and high-quality systems.
From Algorithms to AI: A Comprehensive Review of Core Concepts in Computer Science
The review delves into the very ideas of Computer Science and constantly-developing fields, thoroughly reviewing the theoretical basis and the most modern technologies. The theory is started with basic concepts like algorithms, data structures and models of computation then moves to programming paradigms, architecture of computation, and operating systems. The explanations are carried out to its practical fields such as databases, networking, software engineering and artificial intelligence. Emerging fields like quantum computing, human-computer interaction, cloud and edge computing and ethical considerations are also outlined. Through the combination of fundamental knowledge and its application in the real world, the review highlights the cross-disciplinary purpose of Computer Science and its central role in innovation of the present times. The goal of this synthesis is to help learners, educators, and professionals learn the depth and relevance, as well as the future development direction of the field.
Privacy, Policy, and Progress: Reviewing the Regulatory Landscape in Health Informatics
Because of health informatics, digital systems can now gather, analyze and trade health information. Nevertheless, using new technologies such as electronic health records, artificial intelligence and telehealth brings up important issues related to data privacy, proper use and being controlled by rules. It reviews basic health informatics principles and studies international rules, like HIPAA and GDPR, pointing out the difficulties in fulfilling these rules and the need for similar regulations. It also deals with ethical issues about consent, who owns data and the bias found in algorithms, as well as rules that ensure both progress and responsibility. AI, block chain and remote monitoring have huge potential, but to avoid problems, strong guidelines should be adopted. The review underlines how adaptive and inclusive governance guarantees progress in health informatics by focusing on patients’ rights and technology at the same time.
Digital Health Revolution: Wearables in Health Informatics
Advancements in wearable technology have enabled health informatics to continuously monitor an individual’s health and behavior.The review looks at various wearables for consumers and medical use and how they support chronic disease management, monitoring patients from a distance, spotting diseases early, and personalized care. Wearable data can be added to health information systems so that healthcare professionals can improve decisions and offer preventive healthcare. Still, issues such as data accuracy, privacy, challenges with sharing information, and following regulations are making it difficult for more people to use Block chain. Innovations in sensors, AI, smart clothes, and handling data are expected to remove these problems and improve the accuracy, confidentiality, and user experience of wearables. Because of their continual advancements, wearables will have a greater impact on health informatics to help patients and support public health. It brings out how wearable technology can have a significant impact on healthcare and points out the main obstacles to using them successfully.
Harnessing AI for Product Design and Processing
This review brings forth the major changes that Artificial Intelligence is bringing to product design and manufacturing. Thanks to generative design, machine learning and computer vision, AI makes creativity, optimization and automation much easier in product development. In designing products, AI speeds up coming up with new ideas, testing ideas and meeting customer needs and in processing products, it raises the quality, helps prevent problems and automates several tasks in smart facilities. The writing covers the key aspects of artificial intelligence, important tools involved and recent advancements. While AI greatly helps, still, some obstacles, for instance, problems with data quality, mixing multiple systems, transparent explanations and ethical questions persist. After that, considerations are given to areas such as sustainable design, working together with AI and explainable AI, revealing how AI could lead to more advanced, efficient and ethical development of products. This overview proves useful for researchers, engineers and industry leaders who want to apply AI to succeed against competition.
Next-Generation Healthcare: How AI is shaping the Future of Healthcare Informatics
The field of healthcare informatics is also being transformed by artificial Intelligence (AI), which allows conducting intelligent data analysis, predictive modeling, and automating clinical and administrative processes. This review discusses the underlying technologies in AI, such as machine learning and deep learning and natural language processing and computer vision, and their current applications in electronic health records, diagnostics, clinical decision support, and population health management. The combination of AI and healthcare infrastructure is associated with the opportunities of the personalized care, workflow optimization, and proactive approaches to the population health, as well as with ethical, legal, and operational issues. New developments in generative AI, digital twins, and autonomous systems point to a more efficient future being easier to achieve, patient-focused.
Copyright (c) 2024 Global Journal of Multidisciplinary Sciences and Arts
Advancing Healthcare through Artificial Intelligence: A Comprehensive Review of Machine Learning Applications in Health Informatics
Deep learning and other types of Artificial Intelligence (AI) are quickly changing the healthcare and health informatics landscape to allow deeper data analysis, predictive modeling, and customized care of patients. Convolutional and recurrent neural networks are examples of deep learning architecture that have demonstrated spectacular performance in medical imaging, disease prediction, drug discovery and telemedicine applications. Health informatics will ensure the required data collection, data storage, and integration structures that will ensure efficient and safe operation of AI models. The emerging techniques are mitigating these drawbacks despite the various problems posed by data quality, interpretability, ethical issues, and infrastructure needs. The review presents the existing innovations, uses, challenges, and prospects, focusing on the synergistic opportunities of AI and deep learning to transform the modern healthcare.
Advancements and Integrative Approaches in Modern Healthcare: AI-Driven Health Informatics, Nanocarrier-Based Drug Delivery, Cybersecurity, and Lean Six Sigma Applications
Healthcare industry has been experiencing revolutionary developments in the fields of artificial intelligence (AI), drug delivery using nanocarriers, cybersecurity, and Lean Six Sigma. The artificial intelligence-based health informatics improves the quality of diagnostics, predictive analytics, and individual therapy, and nanocarriers provide the delivery of drugs to specific tissue sites in a targeted and controlled manner, improving therapy effectiveness and minimizing side effects. At the same time, effective cybersecurity allows protecting sensitive patient data and medical systems, which promotes safe online healthcare activities. Lean Six Sigma streamlines operations, diminishes mistakes, and improves the efficiency of operations. This review identifies the synergistic prospects of these areas, current applications, challenges, and future prospects, and the overall thrust of these areas in delivering precise, efficient, and secure healthcare delivery.
Integrating AI in Healthcare, Health Informatics, and Food Production: Advances in Machine Learning, Nanocarrier Drug Delivery, Computational Methods, Cybersecurity, and Quality Assurance
Artificial Intelligence (AI) is transforming the healthcare system, health informatics, drug delivery, and food production as it allows making predictions, tailored treatment and streamlining processes. This review discusses the combination of AI and machine learning, nanocarrier-based drug delivery, computational solution, cybersecurity, and quality assurance in these fields. In the healthcare sector, AI has improved diagnostics, treatment planning, and patient monitoring, whereas in the food production industry it is applied in precision agriculture, optimization of the supply chain, and safety monitoring. Data privacy, bias in algorithms, and compliance with regulations are the other issues that were discussed in the paper. Future looks indicate interdisciplinary innovations that may offer safer, effective and sustainable solutions in the health and nutrition.
Harnessing C–H Activation and Photoredox Catalysis in Organic Synthesis: A Contemporary Review
C–H activation and photoredox catalysis have become complementary and are leading to the synergistic integration into a new potent approach to modern organic synthesis that is highly selective, efficient, and sustainable in its ability to transform molecules. Site-selective functionalization of inert C-H bonds is achieved through C-H activation, and the formation of reactive radical intermediates in mild conditions under visible light is achieved through photoredox catalysis. Their combination enables two-catalytic processes, which provides the possibility to build new bonds and functionalize functional groups late, as well as bring complex molecules, such as natural products, pharmaceuticals, and heterocycles, into easy reach. Recent developments include the emphasis on environmentally friendly procedures, widening the range of substrates, and reaction design that is guided by calculations. This review has outlined key principles, mechanistic understanding, applications, challenges and emerging trends in this field of synthetic chemistry with particular focus on its transformative nature through this dual approach.
AI in Lean Six Sigma: A Review of Industrial Implementations, Benefits, and Barriers
The present review addresses the concept of Artificial Intelligence (AI) integration in the methods of Lean Six Sigma (LSS), its industrial applications, advantages, and obstacles. The AI technologies like machine learning, predictive analytics, and computer vision can support LSS by facilitating real-time processing of data, forecasting decision-making, and optimization of processes. The paper presents the examples of successful applications in manufacturing, healthcare, services and logistics showing that the efficiency, quality and innovation are strongly improved. Nevertheless, the problems of data quality, cultural resistance, and lack of skills remain. The review concludes that AI-based LSS promotes smart and flexible and sustainable continuous improvement in line with Industry 4.0 and beyond.
Synergizing AI and Lean Six Sigma: A Comprehensive Review of Smart Quality Assurance Systems
This review examines how Artificial Intelligence (AI) can be integrated with Lean Six Sigma (LSS) in making intelligent and data-driven Quality Assurance (QA) systems. The new AI technology can complement the old LSS techniques by adding predictive analytics, automation, and optimization of processes at any moment, allowing to control the quality pro-actively and constantly improve the processes. The article discusses the important uses, advantages, issues and future of AI-driven Lean Six Sigma within the contemporary industries. The results indicate that AI is highly useful in enhancing the accuracy, efficiency, and decision-making in the DMAIC framework; in addition, it corresponds to the Industry 4.0 and 5.0 paradigms. The paper concludes that AI-enhanced LSS can be taken as a radical change to smarter, adaptive, and sustainable quality management systems.
Artificial Intelligence in Product Management: Driving Innovation and Market Success
The use of Artificial Intelligence (AI) in product management is changing very fast, as it allows making decisions based on data, optimizing processes, and introducing novel solutions. This review examines how AI can be used throughout the product lifecycle including market research, product strategy, development and project management. Using AI, predictive analytics, customer insights, allocation of resources, and risk assessment are increased, making AI more efficient, personalized, and time-to-market. Although it has considerable benefits, the issues that may arise when adopting AI include problems with data quality, ethical aspects, and complexities with integrations, as well as adapting the workforce. The review shows industry case studies and trends, with an accent on the possibility of strategic, responsible AI implementation to drive innovation, improve performance of the products and provide market success with the time of growing competition of the business world.
Artificial Intelligence as a Catalyst for the Next Generation of Healthcare Innovation
The application of Artificial Intelligence (AI) in healthcare is changing the industry, making it more precise, efficient, and personalized in the care of patients. This review examines the history, underlying technologies, and various uses of AI in diagnostics, treatment plan, and healthcare management. It emphasizes the significant advantages, which are better diagnostic accuracy, predictive medicine, and the efficiency of its operation, and problems in ethics, law, and social issues, which include data privacy, biases, and responsibility. The paper is also focused on the existing constraints and opportunities with a particular emphasis on the transition to explainable, equitable, and human-centered AI systems. AI is an effective innovation driver that can influence a smarter, more equitable and compassionate future of healthcare.
AI in Manufacturing Quality Management: A Review of Techniques, Tools, and Industrial Adoption
It is true that Artificial Intelligence (AI) is fast transforming the quality management of manufacturing services by providing smarter, data-driven solutions to detect defects in manufacturing processes, optimize processes and provide predictive maintenance. Conventional approaches to quality assurance are easily surpassed by complex data and dynamic production contexts, whereas AI-based technologies, including machine learning, deep learning, computer vision, and reinforcement learning, demonstrate increased accuracy, scale and flexibility. This review discusses the principles of AI in quality management, its major technologies, tools, and implementation in Industry 4.0 ecosystems. It also emphasizes industry adoption in the automotive, electronic, pharmaceutical and aerospace sectors and discusses the issues of data quality, implementation cost, employee preparation and readiness and interpretability. The trends of the future focus on explainable AI, sustainable manufacturing and human-AI cooperation. In contemporary manufacturing AI is offering radical possibilities of attaining better quality management that is efficient, reliable, and sustainable.
Transforming Supply Chains: Innovations, Sustainability, and Resilience in the Modern Era
Supply chain management continues to transform at present because of technological innovations together with environmental sustainability goals and market demands for operational stability in an unstable worldwide system. Supply chain operations experience revolutionary changes through key trends which include automation with artificial intelligence (AI) and machine learning and the Internet of Things (IoT) that improve efficiency and help make better decisions and provide enhanced visibility. Both Block chain technology improves system transparency and security while digital twins achieve real-time monitoring operations which optimize supply chain process functions. Sustainability stands as the new core priority alongside which companies now devote their resources to establishing sustainable sourcing methods and reducing waste and increasing energy operational efficiency. Business operations focus on resilience together with risk management because companies implement adaptable supply chains that can withstand multiple types of disruptions including geopolitical tensions and natural disasters and unexpected threats. Supply chains of the future will evolve under current scientific innovations while maintaining their emphasis on agile structures and circular economy practices. Firms adopting these trends will better satisfy shifting customer needs and operate with improved efficiency through a global market that requires competitiveness. The abstract details crucial supply chain-transforming trends and disruptive innovations alongside their effects on businesses that plan to succeed in developing complicated market conditions.