Software Defined Automation
Software : Operational Technology : Manufacturing Execution System
We provide Industrial-Control-as-a-Service for automation engineers by turning Programmable Logic Controllers (PLC) into software functions – freeing industrial automation from the constraints of vendor specific hardware.This enables new degrees of freedom, increased productivity and improved resilience for manufacturers. Our offering is centered around cloud-based PLC management (TechOps), modern PLC code versioning and Git based collaboration (DevOps) as well as virtualization of PLCs on non proprietary servers (Virtual PLC).
Software Defined Automation Fuels Growth Through $10M Seed Round Led by Insight Partners
Software Defined Automation, a leading innovator turning factories into software systems, announced it has raised $10 million in a seed round led by global software investor Insight Partners, with additional investment from Baukunst VC, Fly Ventures, and First Momentum. The funds will be used to scale customer adoption and extend its solution portfolio.
Software Defined Automation revolutionizes factory automation with an Industrial-Control-as-a-Service offering. Industrial-Control-as-a-Service (ICaaS) is centered around cloud-based management of existing PLCs (TechOps), Git-enabled PLC code versioning and collaboration (DevOps), as well as virtualization of PLCs on edge servers (Virtual PLC). In combination, these technologies have the power to break down proprietary silos in control technology stacks and enable API-based modern microservices architecture. This new paradigm transforms the daily lives of automation professionals by bringing remote work, cloud security, resilience, collaboration tools and independence from proprietary automation vendor hardware to the modern factory.
Dear vPLC, how real-time are you?
Modernizing the factory automation stack requires more than an update of the latest PLC models. Instead, a paradigm shift towards software-defined automation is required. The design and implementation of flexible manufacturing systems for individualized products are crucial for competitive production systems of the future. In such systems, reconfiguration or redeployment of industrial automation systems can be done for every piece, the application of machine learning and artificial intelligence (AI) algorithms is essential, and full-loop feedback systems enable self-optimizing production systems.
Uncoupling of hardware and software not only allows scaling but also helps to overcome supply chain challenges with proprietary PLC hardware due to the vast availability of standard x86 server hardware. The term virtual PLC refers to a soft PLC that runs within a virtual machine managed by a real-time hypervisor in a commercial-off-the-shelf (COTS) server. Servers and computers can offer enough resources to fulfill the functions of PLCs, Human-Machine Interfaces (HMIs), and programming terminals together. A server hosting virtual PLCs that communicate with the shop floor and cloud. Coupling the cloud and shop floor further allows the implementation of software-based PLC operations (Ops), as well as data collection and use of advanced machine learning algorithms, while still satisfying deterministic real-time requirements. Virtual PLCs help overcome the limitations of hardware-based PLCs by offering more flexibility, better resource usage, scalability, and lower costs.
Unchain the ShopFloor through Software-Defined Automation
But, what happens as soon as insight is generated and the status of the physical process needs to be changed to a better state? In manufacturing for discrete and process industries, the process is defined by fixed code routines and programmable parameters. It has its own world of control code languages and standards to define the behavior of controllers, robot arms, sensors and actuators of all kinds. This world has remained remarkably stable over the past 40-plus years. Control code resides on a controller and special tools, as well as highly skilled automation engineers, who define the behavior of a specific production system. Changing the state of an existing and running production system changes the programs and parameters required to physically access the automation equipment—OT equipment needs to be re-programmed, often on every single component locally. To give a concrete example, let’s assume we can determine from field data, using applied machine learning (also referenced as Industrial IoT), that a behavior of a robotic handling process needs to be adapted. In the existing world, production needs to stop. A skilled engineer needs to physically re-teach or flash the robot controller. The new movement needs to be tested individually and in context of the adjacent production components. Finally, production can start again. This process can take minutes to hours depending on the complexity of the production system.
Production systems will optimize themselves based on simulated and real experiment. Improvements will rapidly be propagated around the globe. Labor will optimize the learning, not the system. This could also differ over time or by external influence. In times where renewable energy was cheap, output could have been one of the core drivers for optimization, while the minimization of input factors could have been paramount in other circumstances.