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Smart manufacturing is a general term often used to describe the use of advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT) and automation to optimize manufacturing processes. . This advanced manufacturing method aims to improve productivity, efficiency and flexibility, resulting in higher quality products and better customer satisfaction.
Big data plays a key role in smart manufacturing by providing valuable insights into manufacturing processes, enabling predictive maintenance, and optimizing product design and performance. In smart manufacturing, big data is generated from various sources such as sensors, machines or complete production lines. This data is collected and processed in real time to better understand the manufacturing process, identify bottlenecks, and optimize production operations.
Benefits of using data for smart manufacturing:
Optimize production processes
One of the key benefits of big data in smart manufacturing is the ability to optimize production processes. With big data analytics, manufacturers can monitor production operations in real time, identify inefficiencies, and adjust processes to increase productivity.
For example, manufacturers can use data collected from sensors in machines to monitor their performance and identify potential problems before they occur. This data can be used to enable preventative maintenance, prevent downtime and ensure production lines run smoothly.
Reduce costs and improve product quality
Another way big data is used in smart manufacturing is to optimize product design. By analyzing data from customer feedback, manufacturers can identify popular product features and those that are less desirable. This information can be used to improve product design and tailor it to meet customer needs.
Furthermore, big data can be used to optimize manufacturing processes by identifying the best materials, suppliers, and production methods. This helps manufacturers reduce costs and improve product quality.
Predictive maintenance
Big data also plays an important role in predictive maintenance, which is using data to predict when a machine or part is likely to fail. By analyzing data from sensors in machinery, manufacturers can identify patterns that indicate when the machine is likely to fail. This information can be used to schedule maintenance at a time when it has the least impact on production. Predictive maintenance reduces downtime and maintenance costs while increasing machine longevity.
In addition to optimizing production processes, big data can also be used to improve supply chain management. With big data analytics, manufacturers can track the movement of raw materials, finished products, and inventory throughout the entire supply chain. This helps manufacturers identify inefficiencies and bottlenecks, optimize the supply chain, and improve overall efficiency. By improving supply chain management, manufacturers can reduce delivery times, improve inventory management, and reduce costs.
Identify defects and monitor quality control
Another important way big data is used in smart manufacturing is to improve quality control. By analyzing data from sensors, manufacturers can identify errors in products and take corrective action to prevent them from occurring in the future.
Furthermore, big data can be used to track quality control data over time, identifying trends and patterns that indicate the need for process improvements. By improving quality control, manufacturers can reduce defects, improve customer satisfaction, and reduce costs associated with recalls and warranty claims.
Big data challenges
Data overload: The sheer volume of data generated in the manufacturing industry is immense and without proper analysis it can become overwhelming.
Individual systems and discrete data: Smart manufacturing requires analyzing data from many different systems and data sources such as control systems, ERP systems, MES and MRP (Manufacturing Resource Planning). export). However, most of these systems are individual systems, so businesses face many difficulties in integrating and consolidating data from these systems.
Data security: Manufacturers must ensure that their data is protected from cyber threats and other security risks. Another challenge is the need for skilled personnel who can manage and analyze data generated in smart manufacturing.
Strategic use of big data to support smart manufacturing
Manufacturing companies need to develop appropriate strategies and be ready for radical organizational and technological changes. These changes focus on the following points:
Develop a data analysis strategy and specific roadmap
Businesses need to build a clear picture of goals, an easy-to-understand roadmap, and sharing the benefits of data will create conditions for managers and employees to unanimously support strategy implementation.
Build digital capacity
To create and improve digital initiatives that use and analyze data, businesses need to build digital capabilities that are a combination of technology skills and specialized industry knowledge.
For example, implementing a raw material demand forecasting feature requires coordinated development by AI engineers, data engineers, and managers and employees who understand operations in production and planning. However, building a team of technology experts for manufacturing enterprises, especially in Vietnam, faces many difficulties due to lack of expertise and funding to maintain the team. Therefore, businesses can look for technology partners with experience in digital transformation to support the implementation of these projects.
Build the right foundation
Data exists in many systems of different departments. Therefore, combining and linking data from different systems is a key factor to help businesses optimize production and business activities, as well as use data to support decision making.
For data security, businesses need to be equipped with solutions and security experts to prevent data leak risks, protect reputation and important information. At the same time, training employees on data security awareness is also important to ensure the safety of the entire organization.
Conclusion
Implementing the above priorities helps businesses achieve breakthrough changes, improve productivity and provide innovative products/services to support customers. To avoid errors during strategy implementation, businesses need to invest resources and cooperate with a partner with experience in the field of digital transformation. This helps assess the current status and maturity level of data application in production, and build strategies and roadmaps to quickly achieve goals.
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