Innovative Computing Perspectives

Industry-Specific Applications of Digital Technologies: Frameworks, Sector Adaptations, and Operational Implications Across Contemporary Industrial Domains

Industry-specific applications have transformed general information technology capabilities into specialized digital solutions tailored to the operational, regulatory, and strategic requirements of individual industries. As organizations accelerate digital transformation, technology strategies have evolved from conventional business–IT alignment toward an integrated digital business strategy in which domain-specific adaptation is considered a fundamental design principle. This article examines how industry-oriented digital technologies create organizational value by addressing sector-specific workflow architectures, compliance requirements, and risk profiles. Using a comprehensive literature review, the study analyzes the implementation of cyber-physical systems in manufacturing, clinical decision support systems in healthcare, distributed ledger technology in financial services, smart grid technologies in energy management, and continuous delivery systems in information technology. The findings demonstrate that industry-specific technological configurations significantly improve operational efficiency, decision quality, regulatory compliance, organizational resilience, and competitive advantage when aligned with the unique characteristics of each sector. Furthermore, successful implementation depends not only on technological capabilities but also on organizational factors, including evidence-based decision-making, knowledge management, interdisciplinary collaboration, and continuous organizational learning. These capabilities enable organizations to effectively integrate digital innovation into business processes while adapting to evolving regulatory and market environments. The review also identifies persistent challenges related to interoperability, governance, cybersecurity, workforce readiness, and technology standardization that continue to hinder large-scale implementation across industries. Despite substantial progress, research gaps remain in developing design frameworks and implementation methodologies that systematically integrate technological innovation with industry-specific operational contexts. This study concludes that future research should focus on adaptive, scalable, and sector-oriented design approaches capable of addressing the diverse requirements of contemporary industries. Such frameworks will support sustainable digital transformation while enhancing long-term organizational performance, innovation capacity, and strategic competitiveness across multiple industrial domains

Industry 5.0 and digital transformation for sustainable textile and manufacturing systems

The rapid evolution of digital technologies has significantly transformed modern manufacturing systems, creating new opportunities for improving productivity, sustainability, and industrial resilience. In this context, Industry 5.0 has emerged as a new industrial paradigm that extends the technological achievements of Industry 4.0 by emphasizing human-centric manufacturing, sustainable production, and resilient industrial systems. Simultaneously, digital transformation has become a key enabler for integrating intelligent technologies into both textile and manufacturing industries, supporting resource-efficient production, process optimization, and environmentally responsible manufacturing practices. This paper presents a comprehensive review of the relationship between Industry 5.0, digital transformation, and sustainable textile and manufacturing systems. The study examines the fundamental principles of Industry 5.0 and discusses the role of enabling technologies, including Artificial Intelligence (AI), the Internet of Things (IoT), Digital Twins, Big Data analytics, Cloud Computing, and Cyber-Physical Systems (CPS), in supporting intelligent manufacturing environments. Particular attention is devoted to the application of these technologies in textile manufacturing, where digitalization contributes to automated quality control, smart textile production, resource efficiency, digital product traceability, and circular production strategies. Based on the findings of the literature review, the authors propose a conceptual model of Industry 5.0-driven digital transformation that integrates three complementary dimensions: Digital Technologies, Human-Centred Manufacturing, and Sustainable Manufacturing. The proposed model illustrates how the interaction among these dimensions contributes to improved manufacturing performance through enhanced productivity, flexibility, product quality, environmental responsibility, and industrial resilience. Furthermore, the model demonstrates the importance of integrating technological innovation with human expertise and sustainability principles to support the future development of both textile and manufacturing systems. The proposed conceptual model may serve as a foundation for future research focused on intelligent manufacturing, smart textile production, Artificial Intelligence, Digital Twins, sustainable industrial development, and Industry 5.0 implementation strategies.

Zero-defect manufacturing: state-of-the-art and conceptual framework

SMART MANUFACTURING WITH INDUSTRIAL IOT: ENHANCING PRODUCTIVITY, QUALITY, AND SUSTAINABILITY IN SAUDI FACTORIES

Industrial diversification, localization and sustainability have become central themes of Saudi Vision 2030 and industrial transformation initiatives. Within this setting, smart manufacturing is not just an issue of technological enhancement but a business model for running plants in which machines, people, materials and decision making connect with trustworthy data. This review explores how IIoT architectural design contributes to productivity, quality and sustainability of Saudi plants. Instead of traditional reviews of technology in which sensors, edge computing, cloud platform, artificial intelligence and digital twin are considered separately, this review treats all of these as components of one path from data signals to factory performance. The structured narrative review technique was used for literature and policy analysis between 2020 and 2025. The findings show that productivity comes from real-time data visualization, overall equipment effectiveness tracking, identification of bottlenecks, scheduling adaptation and predictive maintenance. Quality gains come from traceability, machine vision, statistical process control, process capability analysis and fast root-cause analysis. Sustainability is realized by means of operational measurement of energy, water, waste and carbon footprint indicators and their linking to processes of continuous improvement. At the same time, a set of barriers is found such as legacy equipment, cybersecurity risks, weak data governance, fragmented vendors, workforce skill gaps and different levels of digital maturity of small and medium-sized factories. Finally, this paper suggests phased roadmap for implementation of IIoT architecture in Saudi Arabia starting with readiness assessment and pilot projects. The key value of this paper is in the developed review framework for linking IIoT architecture with productivity, quality and sustainability of Saudi Arabia plants.

Zero-defects manufacturing: state-of-the-art and conceptual framework

Zero-defect manufacturing has become essential for delivering defect-free products in increasingly demanding and customised production environments. Within the framework of Industry 4.0, technologies such as the Industrial Internet of Things (IIoT), digital twins, and artificial intelligence offer promising solutions to proactively predict, detect, prevent, and repair manufacturing defects. However, the existing literature reveals critical gaps, including limited technological maturity, insufficient industrial validations, inadequate consideration of human and organisational factors, and a shortage of integrated frameworks to measure economic, quality, and sustainability outcomes across the value chain. Through an umbrella review of 33 literature reviews indexed in Scopus and Web of Science, this article identifies trends and gaps, and proposes a comprehensive conceptual framework structured into six interconnected layers: cyber-physical data infrastructure; digital twins and virtual metrology; prescriptive analytics; ZDM strategies; holistic performance measurement; and organisational governance that integrates human factors.

The influence of digital twins on the rise of Industry 5.0: a scoping review covering future scope and research challenges

Industry 5.0 represents a paradigm shift toward human-centric, intelligent, and sustainable manufacturing systems. At the core of this transformation lies the Digital Twin (DT), a virtual replica of physical assets that enables real-time monitoring, simulation, and decision-making. This article presents a comprehensive meta-analysis of how DT technologies contribute to the realization of Industry 5.0 objectives across domains such as Smart Additive Manufacturing (SAM), Predictive Maintenance (PM), Cyber-Physical Cognitive Systems (CPCS), Intelligent Supply Chain (ISC), and Adaptive Scheduling (AS). By analyzing 125 peer-reviewed studies, we quantify the feature-wise attainment levels of Industry 5.0 and identify critical gaps in current implementations. The findings reveal that, while SAM exhibits the highest Industry 5.0 readiness, other features, such as cognitive systems, remain underdeveloped. The article concludes by outlining key research challenges and presenting a strategic roadmap to advance the real-world integration of DTs within Industry 5.0 frameworks.