Steps to developing a smart factory

Smart factory insights

• Enhanced connectivity, a structure digital model, data storage and visualization are the key constituents of a smart factory.

• The ultimate goal of a smart factory is to optimize production processes and enhance overall efficiency.

To ensure connectivity across manufacturing plants, to help stakeholders and its different siloes, allocate costs, time and human resources more effectively digitalization is vital.

Once connected, the goal is to create a smart factory, an efficient manufacturing environment that leverages technologies to optimize processes, increase productivity, improve quality and enhance overall operational performance. But, what are the key steps to achieving this?

Enhance connectivity: A connected factory enables data collection and provides a common language for different stakeholders by integrating machines, equipment and sensors right across the factory floor. This allows a maintenance engineer, for example, to gain data-driven knowledge through machine performance data, allowing them to detect potential issues before they escalate into critical failures. This can also be applied to production operators, energy managers and C-suite executives and their respective datasets.

However, data is often complicated, so it is essential that the communication is digestible for everyone.

Digital model

Once the connectivity is in place, design a structured data model that represents the different components, processes and relationships in your factory. This model will serve as a foundation for organizing and analyzing data generated by various devices and systems.

A good option would be to design a digital twin of your manufacturing processes and assets. A digital twin provides a virtual representation of physical assets or systems and this provides a way for manufacturers to simulate and analyze different scenarios. The creation of an accurate digital twin provides insight into production processes, identifies potential bottlenecks and even optimizes workflows before implementing changes in the physical environment.

Data storage

In a smart factory, it is crucial to have a robust infrastructure to store and manage the data generated by sensors, machines and other systems. Databases, data lakes or cloud storage can be used to securely store and handle large volumes of real-time data for further analysis and future reference.

An example of this is historical data. Data stored over time builds up a historical record of operations, which is valuable for trend analysis and performance evaluation. By comparing current production metrics with historical benchmarks, manufacturers can identify long-term trends, spot patterns and determine the impact of process changes or optimizations. As result, a historical perspective enables informed decision-making and aids continuous improvement efforts.

Visualization

Today’s visualization tools help make data analytics and practices an integral part of business processes, by presenting data in a more accessible way. These tools leverage cloud and databases for sorting, organizing and storing data and create graphic representation to display complex data in easy-to-read formats, such as on dashboards and reports.

These visual representations allow production metrics to be monitored, trends to be identified, anomalies to be detected and data-driven decisions to be made, all while making complex data understandable for operators, managers and other stakeholders.

Optimization

The ultimate goal of a smart factory is to optimize production processes and enhance overall efficiency. After collecting data, designing digital models, storing data and using visualization tools, it is time to identify areas for improvement to help allocate costs, time and human resources more efficiently.

Food manufacturers need to maintain high-quality standards to ensure product safety and customer satisfaction. By incorporating real-time monitoring, data analytics and AI-based quality control systems, the quality control process can automatically identify and rectify quality issues promptly, leading to improved product quality, reduced defects and enhanced customer trust.

– This originally appeared on Control Engineering Europe.

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