Industry 4.0 concepts in small- and medium-sized enterprises (SMEs) need to be clear as the industrial environment has been changing in recent years. The introduction of concepts and technologies consisted on industry 4.0 should extend to the complete production and supply chain. Production on the basis of Industry 4.0 principles makes the conditions necessary to replace traditional structures.
The traditional production structures are based on centralized decision-making mechanisms and rigid organizational forms. These structures are replaced by;
- Flexible reconfigurable manufacturing and logistics systems
- Decentralized and collaborative decision-making mechanisms
- Digitally supported processes.
The small- and medium-sized enterprises (SMEs) have moved into the focus of multiple economies. These have proved to be more robust than large and multi-national enterprises because of their flexibility, entrepreneurial spirit, and innovation capabilities. SMEs will only get Industry 4.0 by adapting SME-customized implementation strategies and methods.
In this article, we would learn about some theoretical models related to the introduction of Industry 4.0 concepts in small- and medium-sized enterprises.
Smart Technology in Sustainable Agriculture
- Technological development and digitalization contours possible boundaries to enhance resource use performance.
- Smart agriculture decreases the negative environmental impacts of farming.
- It increases resilience and soil health and reduces costs for farmers.
- Different challenges coupled with smart farming expand across several agricultural production systems when it comes to IoT applications.
- One of the solutions is the plant factory to resolve the problems regarding foods, resources, and the environment.
- Methodologies and ways have been developed by which the production and quality of foods are enhanced.
- Production can be improved with less consumption of resources and less environmental degradation.
- The potential advantages of the plant factory are improved economic and environmental sustainability.
AI, ML and Deep Learning in Smart Logistics
- The implementation of AI, ML, and DL technologies is still in an initial stage of development in the context of Smart Logistics.
The following approaches may be observed as promising areas within the Smart Logistics framework.
- The continuous reporting of machine settings
- Machine states
- Quality parameter settings
- Predictive maintenance
- Decision-making support systems
- Advanced scheduling in the research fields of inventory management
- Flow shop problems
- Traditional job shop scheduling problems
- Production process optimization
- The enhancement of operational logistics processes
This is very important to integrate different research areas. For example;
- Information technology
- Mechanical engineering
- Industrial engineering
- Mathematics, and statistics
Smart Manufacturing Assembly System
One important part of the industry 4.0 concept is the full digitalization of production processes. That is significant to enhance the effectiveness of SMEs manufacturing. We have to be used the appropriate technologies for rapid digitalization, data storage, and transfer, and for data mining.
Capturing and transferring Data
We can better understand from the below study that how to capture and transfer data from the production process to the extended digital twin 3D model with contactless technologies in a smart manufacturing assembly process.
- An OPC technology for data synchronization is applied using OPC UA Server.
- The OPC server makes sure three communications:
- First to the digital twin model
- Second to the cloud platforms
- Third to the PLC system
- The customized OPC server is written in Python Programming Language. That is designed and applied for this purpose.
- The IoT gateway MindConnect is used for data transfer to the MindSphere cloud platform.
- Three camera modules vision system is applied into the experimental manufacturing assembly process to analyze the shapes and surfaces of assembly parts and to measure their dimensions.
- The RFID system is utilized to localize parts on the conveyor line by RSSI signal from tags.
- The RFID gate writes sole information to the main assembly RFID tag label of every product.
- Therefore, a smart identification system is implemented in the experimental manufacturing assembly system.
- Additionally, the MEMS sensor data acquisition in joint with IoT communication technologies is tested.
- The product vibrations are measured by an integrated accelerometer.
- The IoT data is processed by Node-Red data conversion technology to the NoSQL database with Grafana visual interface.
- Independent IoT communication technologies Sigfox and LoRaWAN are used for data transfer to a cloud platform.
- There are some limitations that arise from currently available technologies.
- The key limitation is the cloud platform as it does not help the storage of customized digital twins.
- It delivers a minimum delay of about one second in data transfer.
- The realized experimental smart manufacturing assembly system would serve to more research.
- Educated students and workers may help to develop the sustainable production of SMEs by using advanced technologies based on the Industry 4.0 concept.
- Applications of digital twins into manufacturing is an essential condition for product lifecycle management (PLM) to make sure sustainable production.
Maturity Level-Based Assessment Tool
SMEs have a chance to get a new level of competitiveness by adapting the industry 4.0 concept. Several SMEs are already trying to apply Industry 4.0. Though, there is still a lack of particular instruments for introducing them. Look at another study.
- The maturity level-based assessment model for SMEs is demonstrated.
- The model consists of 42 Industry 4.0 concepts identified by literature analysis.
- Those are rated on a Likert scale of 1 to 5.
- The target level and the importance and potential of the Industry 4.0 concepts are evaluated by the user.
- The gathered results show in a developed norm strategy matrix.
- That of the Industry 4.0 concepts should be approached immediately and may need more time or are not immediately applied because of lower potential.
- The suggested assessment model shows many benefits compared to already existing models.
- It delivers a detailed overview of existing Industry 4.0 concepts and is very simple for SMEs to adopt.
- The occurrence of the maturity level is facilitated via a brief description of the five maturity levels for each of the 42 Industry 4.0 concepts.
- The occurrence and combination of a target level and the potential of each of the Industry 4.0 concepts in the norm strategy matrix permit SMEs to plan and schedule the application of Industry 4.0 in a very systematic way.
- The researchers plan to use these data for more analysis and benchmarking functionality.
- The assessment model would similarly show SMEs their position for each Industry 4.0 concept compared to those of the average of companies of a similar size and the same industrial sector.