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Manufacturing Technology Insights | Friday, September 02, 2022
The shift to a new type of smart factories and end-to-end manufacturing processes is a part of Industry 4.0.
FREMONT, CA: Robotic arms, industrial controllers, or CNC machines may replace or augment specific operator-led tasks, but the smart factory is more of long-term evolution. As manufacturing changes in the current market, many processes will benefit. Based on their unique conditions, manufacturers must choose which processes to evolve, improve, or completely transform. Analytics has put together a simple yet exhaustive view of the processes involved in creating a company to help our customers with this seemingly impossible task. These use cases are illustrated below, along with a few tips to get manufacturers started on their transformation journey.
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Advanced forecasting
Machine Learning-based forecasting, leveraging the power of Big Data and advanced Machine Learning algorithms, is helping companies reduce raw material inventories and improve customer service levels. These models will combine internal and external data sources so that they will be more likely to discover and leverage the factors that influence sales and manufacturing needs in the future.
IoT and Edge
The smart factory needs a variety of other connected sensors and actuators in addition to vision. Edge devices allow greater data control, lower costs, quicker insights and actions, and assist in establishing real closed-loop manufacturing by bringing compute and data storage closer to where data is generated.
Vision and instrumentation
The smart factory relies on smart connected sensors as one of its foundational elements. Visualize and analyze supply chain, plant, and distribution data to uncover trends and improve manufacturing. Manufacturing companies can monitor and control production flow in real-time using vision artificial intelligence (AI) at the edge or other advanced process instrumentation approaches. It results in a reduction in waste and an improvement in product quality consistency.
Simulations and digital twins
A digital twin is a simulator constantly getting all the process data to realign its state. They are a real-life system's digital representation and can use for many applications from DRL training to what-if analysis, operator training, and more. Building a solid cloud, data, and edge instrumentation infrastructure will be the first step for any digital twin or simulation project, regardless of the approach.
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