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Many manufacturing companies struggle with complex processes, unexpected breakdowns, and a lack of real-time insight. This can lead to inefficient operations, waste, and lost revenue. We’ve compiled some of the most common challenges and how digital twins can be the key to overcoming them.
A digital twin is a virtual model of a physical process, system, or product. It is created by connecting sensor data from the physical world with a digital representation that is continuously updated to reflect the current status. This allows testing, analysis, and prediction without affecting physical operations.
In production optimization, this could, for example, be a digital version of a production line, where you can simulate changes in production, identify bottlenecks, or predict maintenance needs—all without disrupting daily operations.
By analyzing real-time data, the system can predict when machines are at risk of failing. This allows for preventive maintenance planning, avoiding costly breakdowns and reducing unplanned downtime.
A digital twin can simulate changes in production, such as what happens if a machine is moved, the sequence is changed, or a new product is introduced. This provides insight into how to use resources most efficiently.
By comparing real-time data with the digital model, the system can quickly identify deviations and errors in production. This means fewer defective products and better quality control.
Working with digital twins doesn’t require heavy investments or a complete overhaul from day one. Many companies choose to start small, e.g. with a single production line or machine, and scale up from there. The most important thing is to ensure access to reliable data and to have a strategy for how to use the digital insight in practice.
Digital twins are not just a new technology—they’re a new way of thinking about production. At Kruso, we help companies develop and implement digital solutions that generate real business value.