An integrated platform system controlled with hardware and software for just-in-time, local manufacturing is disclosed. System hardware may include a fermentation system, a cross-flow system, a disposable formulation system, a robotic fill-finish system, and a quality control test and release system.

The systems and methods described provide improved process control operating range and capabilities and integrate process control monitoring and management with broader process automation (PA) systems process management, extending the real-time operation and control of a process control system to process handling of a PA system, and extending PA-style process management by adding real-time process controls and monitoring, and adding new functionality by permitting management of these processes to externally defined completion goals. This combination provides new functionality in dynamically determined process flexibility, extended operating range and extended process recipe definition capabilities for process control systems using this technology, and provides improved error recovery and exception handling of traditional PA systems.

The systems and methods described provide improved process control operating range and capabilities and integrate process control monitoring and management with broader process automation (PA) systems process management, extending the real-time operation and control of a process control system to process handling of a PA system, and extending PA-style process management by adding real-time process controls and monitoring, and adding new functionality by permitting management of these processes to externally defined completion goals. This combination provides new functionality in dynamically determined process flexibility, extended operating range and extended process recipe definition capabilities for process control systems using this technology, and provides improved error recovery and exception handling of traditional PA systems.

A scalable industrial data ingestion and analysis architecture integrates and collects data from multiple diverse sources at one or more industrial facilities. Data sources can include plant-level industrial devices and higher-level business systems. The data can be integrated and collected from multiple sources at an on-premise edge or gateway device, which sends the data to event queues on the cloud platform. The data queues orchestrate and store the data on cloud storage, and an analytics layer performs business analytics or other types of analysis on the stored data to produce various outcomes. Similar analytic platforms can also be implemented at the device level, and analytic functions can be scaled between the device level and higher levels in accordance with the scope of a given analytic function.

A scalable industrial data ingestion and analysis architecture integrates and collects data from multiple diverse sources at one or more industrial facilities. Data sources can include plant-level industrial devices and higher-level business systems. The data can be integrated and collected from multiple sources at an on-premise edge or gateway device, which sends the data to event queues on the cloud platform. The data queues orchestrate and store the data on cloud storage, and an analytics layer performs business analytics or other types of analysis on the stored data to produce various outcomes. Similar analytic platforms can also be implemented at the device level, and analytic functions can be scaled between the device level and higher levels in accordance with the scope of a given analytic function.

An integrated platform system controlled with hardware and software for just-in-time, local manufacturing is disclosed. System hardware may include a fermentation system, a cross-flow system, a disposable formulation system, a robotic fill-finish system, and a quality control test and release system.

A scalable industrial data ingestion and analysis architecture integrates and collects data from multiple diverse sources at one or more industrial facilities. Data sources can include plant-level industrial devices and higher-level business systems. The data can be integrated and collected from multiple sources at an on-premise edge or gateway device, which sends the data to event queues on the cloud platform. The data queues orchestrate and store the data on cloud storage, and an analytics layer performs business analytics or other types of analysis on the stored data to produce various outcomes. Similar analytic platforms can also be implemented at the device level, and analytic functions can be scaled between the device level and higher levels in accordance with the scope of a given analytic function.

A manufacturing process visualization apparatus is provided. The apparatus includes: at least one processor configured to implement: a data receiving unit which receives apparatus data from at least one production apparatus of a manufacturing process line processing a plurality of products; and a matching unit which extracts product record information about each of the products from the received apparatus data, classifies the extracted product record information as information corresponding to specific product flow properties of the manufacturing process line, generates product flow information indicating the specific product flow properties by using the classified product record information, and generates graphic process information about the manufacturing process line in which the product flow information about each product is represented in graphics; and a screen display which displays the generated graphic process pattern information on a screen.

A method includes installing new communication interfaces in first process controllers of an industrial process control and automation system. The first process controllers are configured to communicate over a first supervisory network, and the communication interfaces are configured to communicate over a second supervisory network of a different type. The method also includes migrating the first process controllers to second process controllers of a different type while maintaining control over an industrial process being controlled by the first process controllers. The second process controllers are configured to communicate over the second supervisory network.

A scalable industrial data ingestion and analysis architecture integrates and collects data from multiple diverse sources at one or more industrial facilities. Data sources can include plant-level industrial devices and higher-level business systems. The data can be integrated and collected from multiple sources at an on-premise edge or gateway device, which sends the data to event queues on the cloud platform. The data queues orchestrate and store the data on cloud storage, and an analytics layer performs business analytics or other types of analysis on the stored data to produce various outcomes. Similar analytic platforms can also be implemented at the device level, and analytic functions can be scaled between the device level and higher levels in accordance with the scope of a given analytic function.