Smart Factories are about more than automation. While automation has improved productivity exponentially over the decades, unexpected equipment, supply, product, and operator variables can disrupt production, reducing output and driving up costs. Enabling the highly-automated factory to collect data in real-time, trend it with historical and business system, data and refine it into usable information can provide the inputs needed to make proactive decisions and reduce the variables that disrupt your operations.
What role does data analytics play in “smart” factories?
In the evolution from the third industrial revolution to Industry 4.0, factories are transforming from computer-controlled, automated production lines to highly-networked systems. These networks, enabled by the Industrial Internet of Things (IIoT), are comprised of sensor nodes that capture manufacturing data on processes, inventory, equipment, tools, and products and transmit volumes of this data through a network of IIoT gateways that connect to the cloud. This ‘big data’ is at the heart of the smart factory, enabling data scientists to analyze and develop advanced algorithms using artificial intelligence, machine learning, and deep learning to get insights from the data in real-time and drive actions back down to the machines or the operators. Unlike ruled-based automation of the late 20th century, which drove consistency and speed but lacked the flexibility to adapt to people, equipment, and the environment, “smart factories” can continuously learn from historical and new data. Good data science can recognize what data is useful, what is not, and even what is missing. Smart factories aim to not only move fast but to use data to provide direction on where to anticipating issues, proactively adapting actions, and adjusting workflow priorities in the blink of an eye.
How can data analytics help manufacturers’ productivity and streamline assembly operations?
The automation of past decades drove significant advances in productivity and more consistent quality in manufacturing. When operations were running smoothly, it provided higher output, improved quality, and economies of scale. The challenge is there are always variables that impact the assembly line. Operators have varying levels of experience and tribal knowledge that may be hard to capture in data. One piece of equipment can be older with more maintenance issues than the new one next to it. A quality defect, broken machine, operator error, or delayed inventory could hold up the entire production line. In a “smart factory,” the data itself is enabling the systems to work smarter for the business. Data scientists and system engineers can work with operators to learn from their tribal knowledge to collect meaningful data using the right sensor technology. Advanced analytics are developed to identify patterns in the data that enable us to predict when a piece of equipment could fail and make proactive repairs to prevent a line down situation. The factories of today use the data to better plan, track and manage inventory and WIP and adjust manufacturing plans and resources to streamline output and align supply to demand more closely than ever before.
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