Enterprise AI Analysis
A Bibliometric Analysis on Artificial Intelligence in the Production Process of Small and Medium Enterprises
Industry 4.0 is the current trend of automation and data exchange in manufacturing technologies, allowing businesses to monitor physical processes, create virtual copies of the real world and make decentralized and automated decisions. This industrial revolution leads to the creation of intelligent networks of machines and systems, autonomously controlled value chains and highly flexible production. The fundamental principles of Industry 4.0 include the following: Interoperability, Virtualization, Decentralization, Production flexibility, Technical assistance. The push towards Industry 4.0 is due to three main structural changes in the manufacturing sector: the growing demand for customized products, greater market volatility and the need for environmental sustainability and resource efficiency. However, the transition to Industry 4.0 may encounter cultural resistance and requires a change in mindset. Common mistakes include separating new systems from existing operational realities, lacking adaptability, and waiting for the perfect architecture before implementation. Among the different technologies of Industry 4.0, the one with a comprehensive impact in many different field is artificial intelligence (AI), which refers to the simulation of human intelligence through algorithms and techniques such as machine learning and deep learning, allowing machines to perform complex tasks, such as learning, reasoning, problem solving, natural language understanding, and environmental perception. From a supply chain perspective, AI has a significant impact on improving the forecasting of product demand and the analysis of socioeconomic dynamics. Internally, AI addresses issues such as skills shortages and managing large amounts of data, enhancing business processes and enabling the production of personalized content at scale. However, implementing AI brings risks, such as lack of transparency, inaccurate responses and bias, requiring rigorous control systems and the protection of confidential company data. These challenges become particularly relevant in Small and Medium Enterprises (SMEs).
Executive Impact & Key Metrics
Explore the foundational data driving AI adoption in SMEs, highlighting significant research trends and geographic distribution.
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Papers Published since 2020
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Computer Science Articles
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Published by Elsevier
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Articles from China
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AI Integration in Industry 4.0
This cluster, encompassing keywords like artificial intelligence, machine learning, Industry 4.0, and Internet of Things (IoT), highlights the foundational role of AI within the broader Industry 4.0 landscape. It explores connections to other 4.0 technologies such as Cloud Computing and Digital Twin. The focus extends to smart manufacturing, industrial research, digital transformation, and technology adoption, emphasizing the critical link between AI and sustainability, engineering education, and e-learning for skill development.
ANN for Process Optimization
This cluster delves into the application of Artificial Neural Networks (ANN) for process diagnosis and optimization. Key themes include automation, integration, and the interaction between neural networks and human expertise. It highlights how ANNs are leveraged to streamline production processes, integrating seamlessly with robotics and simulation tools while maintaining human involvement for optimal efficiency.
Deep Learning & Cost-Benefit
Focused on deep learning, this cluster examines various techniques used to create intelligent systems that learn from data, often collected via edge computing devices. The analysis emphasizes the strong correlation between deep learning implementations, cost-benefit analysis, and energy efficiency. It explores how deep learning algorithms contribute to process optimization and automation, making it an attractive option for resource-constrained SMEs seeking performance improvements and return on investment.
Big Data for Predictive Insights
This cluster centers on the strategic utilization of Big Data for informed decision-making. It highlights the importance of data integration and information management for effectively processing large volumes of data. The primary applications include forecasting and predicting system behaviors, enabling SMEs to optimize operations, improve resource allocation, and gain a competitive edge through data-driven insights.
74.93%
of articles published from 2020 onwards, showing rapid AI adoption in SMEs.
Enterprise Process Flow: PRISMA Protocol
Records identified through Scopus (n = 10939)
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Records after Keywords filter (n = 1122)
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Records screened (n = 383) & Excluded (n = 639)
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Full-text articles assessed for eligibility (n = 383 + 8 snowballing)
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Articles included (n = 391)
| Technology | Key Benefit |
|---|---|
| IIoT |
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| Digital Twin |
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| CPS |
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| Robot 4.0 |
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| Big Data Analysis |
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| AI |
|
Real-World Impact: Predictive Maintenance in Manufacturing SMEs
AI-powered predictive maintenance, a key application in Industry 4.0, allows SMEs to monitor equipment health, anticipate failures, and schedule maintenance proactively. This significantly reduces costly downtime and extends asset lifecycles. By integrating AI with IoT sensors and cloud-based platforms, even resource-constrained SMEs can gain real-time insights, optimize operations, and achieve substantial cost savings and improved efficiency. This approach exemplifies how AI, when combined with other I4.0 technologies, delivers tangible benefits to small and medium enterprises, fostering resilience and competitiveness.
Key Learnings:
- Reduced unplanned downtime by anticipating equipment failures.
- Optimized maintenance schedules, extending asset lifespan.
- Significant cost savings from fewer emergency repairs and increased operational efficiency.
- Improved product quality by maintaining equipment in optimal condition.
- Empowered data-driven decision making for operational managers.
- Enhanced overall production flexibility and responsiveness to market demands.
Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings AI can bring to your enterprise based on industry benchmarks and your operational data.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A strategic approach to integrating AI into your operations, from initial assessment to ongoing optimization.
Phase 01: Strategic Assessment & Planning
Identify critical business areas where AI can deliver the most significant impact, define clear objectives, and establish a foundational AI strategy aligned with your enterprise goals.
Phase 02: Data Preparation & Infrastructure Setup
Prepare and standardize your data, ensuring quality and accessibility. Establish the necessary IT infrastructure, including cloud resources and IoT integration, to support AI model development and deployment.
Phase 03: Pilot Project & Model Development
Begin with a focused pilot project to develop and train initial AI models. Iterate on model performance, validate results, and refine the solution based on real-world data and feedback.
Phase 04: Integration & Deployment
Integrate the validated AI solution into your existing production processes and systems. Ensure seamless deployment, user adoption, and provide necessary training for your teams.
Phase 05: Monitoring, Optimization & Scaling
Continuously monitor AI model performance, collect feedback, and identify opportunities for further optimization. Strategically scale successful AI applications across your enterprise for broader impact.
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