1. Introduction
PLC technology has become a fundamental component in industrial automation and sustainability, playing a crucial role in optimizing manufacturing processes. As industries transition from Industry 4.0 to Industry 5.0, PLCs are evolving to incorporate IoT (Internet of Things), AI (Artificial Intelligence), and cloud computing, improving efficiency, adaptability, and responsiveness in industrial environments.
The significance of Sustainable Development Goals (SDGs), particularly Goal 8 (Decent Work and Economic Growth) and Goal 9 (Industry, Innovation, and Infrastructure), aligns with advancements in PLC technology. By integrating advanced communication systems, real-time monitoring, and AI-driven automation, PLCs contribute to industrial sustainability while enhancing productivity and competitiveness.
This study analyzes the current state and future trends of PLC technology, emphasizing its role in smart factories, automated production lines, and industrial sustainability.
2. Understanding PLC Technology and Its Industrial Applications
Definition and Significance of Programmable Logic Controllers (PLCs)
Programmable Logic Controllers (PLCs) are essential automation devices that monitor and control industrial processes. Originally designed to replace traditional relay-based systems, PLCs now serve as intelligent control units in robotics, smart factories, and industrial machinery.
Core Components of PLC Technology
PLCs consist of several critical hardware components, each contributing to their operational efficiency:
- Central Processing Unit (CPU): Executes logic operations and manages data processing.
- Memory Unit: Stores program instructions and operational data.
- Communication Modules: Facilitate data exchange between PLCs, sensors, and other industrial systems.
- Power Module: Provides necessary electrical input for stable operation.
How PLC Technology Supports Smart Factories, IoT, AI, and Industrial Automation
Modern PLCs integrate Industry 4.0 and Industry 5.0 technologies, enabling:
- Smart Factory Automation: PLCs facilitate seamless communication between machinery, optimizing efficiency and reducing downtime.
- IoT Connectivity: Real-time monitoring and remote control capabilities enhance operational precision.
- AI Integration: Predictive maintenance and adaptive process management improve system intelligence.
By enhancing automation flexibility and scalability, PLC technology
3. Methodology: Evaluating PLC Technology Trends
This study employed expert interviews, the Fuzzy Delphi Method, and the Fuzzy Analytic Hierarchy Process (FAHP) to analyze the current state and future development trends of PLC technology. The objective was to assess the impact of software advancements (GX Works3) and hardware enhancements (FX5U) to guide industrial innovation.
Expert Interviews
A total of 20 experts from automation control industries and academia were interviewed using a semi-structured format. Experts provided insights into:
- Current PLC capabilities and limitations.
- Emerging industrial requirements for PLC software and hardware.
- Recommendations for future improvements in communication, processing speed, and automation integration.
The interview process followed a structured approach, as shown in Figure 3 of the paper.
Fuzzy Delphi Method
The Fuzzy Delphi Method was used to synthesize expert opinions and reach a consensus on PLC technology improvements. This approach ensured that ambiguous responses were minimized while obtaining accurate industry insights.
Table 1: Expert Consensus on PLC Development Trends (Adapted from the paper)
| Evaluation Item | Expert Consensus Score |
|---|---|
| Visualization in GX Works3 | 6.66 |
| Adding Python as a sixth syntax | 7.38 |
| Enhancing communication modules | 8.21 |
| Dual Ethernet integration | 7.41 |
| Expanding memory capacity | 6.94 |
Consensus scores above 6.0 indicate strong expert agreement on the necessity of these advancements.
Fuzzy Analytic Hierarchy Process (FAHP)
The FAHP method was applied to rank the importance of identified PLC technology trends. Key factors considered included software flexibility, CPU speed, memory expansion, and communication infrastructure. The weighted rankings were determined using expert evaluations, with communication modules rated as the highest priority.
4. Key Findings: Working and Results
4.1 Scientific Validity and Industry Adoption
The analysis revealed several notable trends:
- Visualization and syntax enhancements in PLC programming to improve usability.
- Growing adoption of AI and IoT in industrial PLC systems to facilitate smart automation.
- Industry demand for structured programming languages, supporting Python, LabVIEW, and Java for better integration.
Table 2: PLC Software Advancements (Adapted from the paper)
| Feature | Status in FX5U | Future Recommendation |
|---|---|---|
| Visualization of label settings | Partially visualized | Fully integrated |
| Structured programming support | IEC61131-3 standard | Add Python/LabVIEW |
| Instruction execution speed | 34 ns | Reduce further |
4.2 Accessibility and Scalability in PLC Technology
To meet Industry 5.0 standards, enhancements in communication modules and connectivity expansion were identified as critical. The study highlighted:
- Shift to dual Ethernet channels for improved network integration.
- Need for wireless connectivity to ensure real-time automation in industrial settings.
- Memory expansion capabilities to accommodate increasing computational demands.
Table 3: PLC Communication Module Trends (Adapted from the paper)
| Communication Feature | Current FX5U Capabilities | Future Requirements |
|---|---|---|
| Ethernet connectivity | Single channel | Dual channel |
| External network access | Requires third-party | Direct integration |
| Wireless networking | Not available | Required for Industry 5.0 |
4.3 Performance Metrics and Sustainable Development
Advancements in PLC performance optimization will contribute significantly to sustainable industrial automation. Key findings include:
- Reducing instruction processing time to enhance computational efficiency.
- Expanding memory storage to support large-scale industrial automation.
- Ethical considerations in PLC adoption, ensuring transparency in industrial control applications.
Table 4: PLC Performance Enhancements (Adapted from the paper)
| Performance Factor | Current Status | Future Development |
|---|---|---|
| Instruction processing | 34 ns | Below 34 ns |
| Program storage capacity | 64K steps | Expand beyond 128K |
| Memory expansion | 16 GB | 32 GB or higher |
6. Conclusion: Advancing PLC Technology for Industrial Sustainability
Summary of Findings on PLC Technology and Industrial Sustainability
The integration of Programmable Logic Controllers (PLCs) in modern industrial systems has significantly contributed to automation efficiency, sustainability, and competitiveness. This research highlights the importance of evolving PLC software and hardware to support advancements from Industry 4.0 to Industry 5.0.
Key findings indicate that future PLC development should prioritize improvements in:
- Communication Modules—the shift to dual Ethernet channels and direct external network connectivity to enhance interoperability.
- CPU Processing Efficiency—reducing instruction execution time below 34 ns for optimal industrial operations.
- Software Flexibility—adding structured programming languages such as Python, LabVIEW, and JAVA to expand coding versatility.
- Memory Expansion—allowing SSD-based storage customization for adaptable system scalability.
These technological advancements align with sustainable industrial goals, ensuring efficiency, reduced environmental impact, and integration of IoT, AI, and cloud-based automation.
The Significance of Structured Methodologies for Evaluating PLC Innovation
This study leveraged expert interviews, fuzzy Delphi analysis, and Fuzzy Analytic Hierarchy Process (FAHP) to systematically evaluate PLC trends. By incorporating structured methodologies, researchers identified expert-driven consensus on PLC advancements, ensuring objective, scalable solutions for future implementations.
The Fuzzy Delphi Method enabled precise assessments of industry demands, eliminating ambiguity in expert opinions, while FAHP ranked development priorities, emphasizing that communication improvements and AI integration should be the primary focus.
These methodologies provide a scientific foundation for PLC innovation, ensuring that future PLC models meet industrial efficiency, sustainability, and smart automation requirements.
Practical Recommendations for Future Industrial Applications
To sustain PLC-driven industrial progress, manufacturers and policymakers should:
- Prioritize connectivity enhancements—upgrading dual Ethernet channels and enabling wireless external network access for seamless integration.
- Optimize CPU processing—reducing execution time for real-time automation in complex industrial systems.
- Expand programming languages—adopting Python, LabVIEW, JAVA, and Scratch to increase programming efficiency and compatibility.
- Increase memory and storage options—enabling customized SSD installations to accommodate high-data processing environments.
- Ensure ethical AI-driven automation—integrating secure AI algorithms to promote fair and transparent industry practices.
Future PLC research should focus on developing AI-driven control mechanisms, adapting communication protocols, and enhancing cybersecurity frameworks to ensure long-term industrial sustainability.
By implementing these advancements, PLC technology will continue to drive industrial automation, aligning with Industry 5.0 and sustainable development objectives
Reference: Yao, K.-C.; Lin, C.-L.; Pan, C.-H. Industrial Sustainable Development: The Development Trend of Programmable Logic Controller Technology. Sustainability 2024, 16, 6230. https://doi.org/10.3390/su16146230.
License: This content is based on the original research paper and is shared under the Creative Commons Attribution (CC BY 4.0) License: https://creativecommons.org/licenses/by/4.0/.