About the Journal
INDUSTRICS
Aims & Scope
INDUSTRICS — Industry, Intelligent Systems and Applied Transformation is a peer-reviewed, open-access journal dedicated to research at the intersection of industrial systems, intelligent technologies, data-driven decision-making, and applied digital transformation.
The journal provides an international scholarly platform for research that advances the design, analysis, implementation, evaluation, and operation of intelligent industrial systems. INDUSTRICS is particularly interested in work that connects methodological or technological innovation with clearly defined industrial problems, operational environments, and measurable practical value.
The journal welcomes theoretical, methodological, computational, empirical, experimental, and application-oriented contributions. A central objective of INDUSTRICS is to narrow the gap between intelligent methods and real industrial systems by encouraging research that demonstrates not only technical performance, but also operational relevance, reproducibility, scalability, integration feasibility, and deployment considerations.
Core Scope Areas
INDUSTRICS covers, but is not limited to, the following four principal research areas:
1. Industrial Artificial Intelligence
- Machine learning and deep learning for industrial systems
- Predictive analytics and industrial forecasting
- Industrial computer vision and automated quality inspection
- Intelligent fault diagnosis and condition monitoring
- Anomaly detection and process monitoring
- AI-enabled decision support and optimisation
- Generative AI and large language models for industrial applications
- Industrial knowledge graphs and intelligent reasoning
- Explainable, trustworthy, and responsible industrial AI
- Human–AI collaboration in industrial environments
2. Smart Manufacturing
- Smart factories and connected manufacturing systems
- Industry 4.0 and Industry 5.0 technologies
- Predictive and prescriptive maintenance
- Digital twins and digital-thread technologies
- Cyber-physical production systems
- Intelligent production planning and scheduling
- Manufacturing process optimisation
- Quality analytics and intelligent inspection
- Flexible and adaptive manufacturing systems
- Data-driven production management
- Energy-efficient and sustainable manufacturing
3. Digital Supply Chains and Logistics
- Data-driven supply chain management
- Supply chain resilience and risk analytics
- Demand forecasting and demand sensing
- Inventory optimisation and replenishment systems
- Logistics analytics and intelligent transportation
- Warehouse intelligence and automated fulfilment
- Supply network design under uncertainty
- Digital supply chain twins
- AI-supported procurement and sourcing
- Supply chain visibility, traceability, and coordination
- Resilient, sustainable, and circular supply chains
4. Automation, Robotics and Integrated Industrial Systems
- Industrial automation and intelligent control
- Robotics and autonomous industrial systems
- Industrial Internet of Things (IIoT)
- Sensor networks and edge intelligence
- Cyber-physical and embedded industrial systems
- Autonomous production and material-handling systems
- Human–robot collaboration
- Industrial communication and interoperability
- Integrated plant, enterprise, and operational systems
- Real-time monitoring, control, and orchestration
Cross-Cutting Themes
INDUSTRICS also welcomes interdisciplinary research that connects the principal scope areas with broader industrial transformation challenges, including:
- Industrial digital transformation
- Industrial data engineering and analytics
- Industrial cybersecurity and system resilience
- Cloud, edge, and distributed computing for industry
- Industrial platforms and enterprise integration
- Reliability, safety, and risk engineering
- Operations research and intelligent optimisation
- Industrial sustainability and resource efficiency
- Technology adoption and implementation in industrial organisations
- Human factors in intelligent industrial systems
- Standards, interoperability, governance, and responsible technology deployment
Applied Relevance
Applied relevance is a defining criterion of INDUSTRICS. Manuscripts should clearly explain the industrial problem being addressed, the operational context in which the proposed method or system is intended to function, and the practical significance of the reported contribution.
Where appropriate, authors are encouraged to validate their work using real industrial data, operational records, physical systems, industrial case studies, pilot deployments, realistic simulation environments, or representative testbeds. Comparative evaluation should use technically and practically meaningful baselines.
Studies should also discuss relevant deployment considerations where applicable, including computational requirements, scalability, integration effort, robustness, reliability, implementation cost, data requirements, operational constraints, safety, maintainability, or organisational implications.
Methodological and Scientific Expectations
INDUSTRICS welcomes methodological innovation, but technical novelty alone is not sufficient where the contribution is presented as industrial research. Authors should demonstrate how the proposed method contributes to understanding, improving, designing, or operating an industrial system.
Research should provide sufficient methodological detail to support critical evaluation and reproducibility. Quantitative claims should be supported by appropriate experimental design, comparative analysis, statistical evidence, sensitivity analysis, robustness evaluation, or other validation procedures suitable for the research question.
Purely theoretical or methodological work may be considered when the manuscript clearly identifies the class of industrial problems or systems to which the contribution applies and explains the assumptions, operating conditions, and practical implications of the proposed approach.
Research Typically Within Scope
- New intelligent methods validated on industrial or industrially realistic data
- Industrial AI systems evaluated against meaningful operational baselines
- Smart manufacturing architectures demonstrated through realistic implementation or experimentation
- Digital twin methods connected to measurable industrial decision or control tasks
- Supply chain analytics addressing uncertainty, resilience, optimisation, or operational coordination
- Industrial automation, robotics, and IIoT systems with demonstrated system-level contribution
- Empirical studies examining the implementation or performance of intelligent industrial technologies
- Case-based research that generates insights transferable beyond a single organisation or facility
- Rigorous reviews that identify research gaps, methodological limitations, and barriers to industrial adoption
Research Generally Outside Scope
Manuscripts are unlikely to be considered suitable when they:
- apply standard algorithms to public benchmark datasets without a meaningful industrial context or contribution;
- report only incremental parameter tuning without methodological, analytical, or operational significance;
- present simulations without explaining their relationship to a realistic industrial system or decision problem;
- describe a commercial product, platform, or implementation without sufficient scholarly analysis;
- make performance claims without appropriate baselines, validation, or supporting evidence;
- focus primarily on consumer applications with no clear connection to industrial systems or operations;
- provide purely descriptive organisational accounts without a transferable analytical, methodological, or theoretical contribution.
Intended Readership
INDUSTRICS serves researchers, engineers, industrial practitioners, system designers, technology developers, operations specialists, data scientists, decision-makers, and policy professionals working across intelligent manufacturing, industrial AI, digital supply chains, automation, robotics, industrial data systems, and applied digital transformation.