Generative AI: Reshaping the Industrial Value Chain with the Industrial Copilot

Companies worldwide are faced with a shortage of skilled labor as experienced workers retire and not enough new talent enters the workforce to compensate. At the same time, design and manufacturing continues to grow increasingly complex and competitive. To address this, companies must find a way to capture knowledge from experienced workers and make it both accessible and enticing to new hires. Generative AI can help address this by transforming the way humans and machines interact and collaborate using solutions such as Siemens Industrial Copilot. Industrial copilots are poised to revolutionize the way companies design, plan, develop, engineer, and work by combining the strengths of humans and AI to meet challenges across the industrial value chain.

Generative AI allows users and information to interact in ways that were never possible before and can serve as a layer between complex systems, such as those found in design tools or within state-of-the-art factories, and the people using them. Generative AI excels at natural language processing or NLP, granting it the ability to converse with humans in a way that is natural and intuitive, bridging the gap between the way humans and computers see the world.

A New Paradigm of Design

It is no secret that the tools used to design products, production lines, and even entire factories are incredibly complex, requiring extensive training and years of experience to master. Industrial copilots can help flatten the learning curve by providing conversational access to knowledgebase data as well as linking into various functionalities within a tool itself to provide pertinent suggestions based on training from expert users and past designs. Beyond providing easy to access training and assistance, an industrial copilot can also help maximize the impact of a user’s time through intelligent automation and process improvements, allowing them to spend less time completing mundane tasks and more focusing on the elements only a human can do.

Industrial copilots can help streamline the design process by assisting users in leveraging the full capabilities of engineering tools for creating mechanical design concepts by tapping into a vast database of expert knowledge and complex tool functionality. This not only broadens possibilities for engineers and designers, but also accelerates the design phase and ensures adherence to all standards, criteria and design language. Furthermore, it expedites the development of initial prototypes and mock-ups, connecting with other AI elements found in design tools to help generate new design variations for faster iteration and experimentation.

A copilot can also help to optimize simulation and planning by improving resource allocation and ensuring the most efficient use of materials, time and people. By generating different planning scenarios, potential risks and optimal strategies can be worked out efficiently not only before any changes are made in the real world, but before any valuable expert time is spent on the problem. For example, an industrial copilot could analyze the throughput of a production line, test a potential improvement such as adding a new machine to a particular step in the process, then generate a report detailing the benefits, changes and even specific details such as required throughput across different conveyer belts all without the need to spend any expert time and resources.

Automating Automation with Generative AI

Generative AI has many potential applications when it comes to engineering and development. This includes challenging and specialized tasks like assisting software developers and automation engineers in rapidly generating, optimizing, debugging, and documenting PLC code. Engineering teams will be able to significantly reduce time, effort, and the likelihood of errors by generating PLC code through natural language input as opposed to the current process using a highly specialized language and syntax. These capabilities also enable maintenance teams to identify errors and generate step-by-step solutions faster, with conversational access to systems and technology rather than having to sift through or write hundreds or thousands of lines of code.

A generative AI-powered industrial copilot could also take on many of the repetitive and mundane tasks that are replete throughout the engineering and automation fields. With an industrial copilot integrated directly across many common tools, it would be a simple matter to ask it to process data, create a simple automation workflow, categorize parts, or automate the creation of simple machine code from pre-defined, natural language requirements. These are just a few of the abilities an industrial copilot would have since, with comprehensive training and complete access to all the tools used by developers and engineers, the copilot will become something akin to a colleague, taking on work and offering support in a very human-like way.

Copilots Ensure Smooth Operations

Keeping a factory operating smoothly with minimal downtime, where every minute down incurs extreme cost, is a key concern and major challenge faced by manufacturing companies the world over. When it comes to efficiently handling issues and proactively locating problems, data is, once again, the key. With production machines and even entire factories growing smarter and more connected through the continuing efforts of IT/OT convergence, data itself is becoming more plentiful while extracting useful insights from it in a timely manner remains challenging in brownfield scenarios. Now, thanks to the data processing abilities of AI, industrial copilots can sift through the massive amounts of available information to extract key insights, helping minimize downtime while maximizing uptime.

The technology will allow operators and maintenance engineers to interact with machines directly, enabling them to ask the machine about problems or for its current status like a doctor might ask a patient. However, unlike a doctor and patient, a machine augmented by an industrial copilot can instruct a maintenance engineer on its own correct course of treatment, which is to say guidance on how to get the machine up and running again. By leveraging AI to index knowledge from existing documentation, like worker instructions or manuals, alongside process and sensor data through IIoT and edge devices, users can easily be presented with the most critical and pertinent information first, leveraging an industrial copilot to not only identify problems but present solutions.

As a result, fast and reliable maintenance assistance will be ensured in the event of a machine failure, minimizing the downtime any unexpected problems may cause. Additionally, getting access to critical events and undigested shift data will help identify and solve production bottlenecks thanks to generative AI’s powerful reasoning and analytical abilities. Looking at vast quantities of data across multiple shifts and critical events an industrial copilot could identify production bottlenecks and inefficient processes, while at the same time providing potential solutions and detailed action plans for improvement.

It can also interface with predictive maintenance tools to facilitate asset intelligence across plants without the need for manual analysis, helping to not just minimize downtime but maximize uptime of monitored equipment. Predictive maintenance, enabled by AI, allows companies to both reduce scheduled maintenance and avoid unplanned downtime.

Connecting an industrial copilot to the vast quantities of sensor data streaming out of a factory then using that to train the AI model on what is and is not normal behavior allows it pick up on minute changes preceding a failure and bring that to the attention of maintenance staff in a timely manner.

Through automated service instructions and personalized recommendations, a generative AI-powered industrial copilot can also deliver tailored maintenance, upgrades, and additional services using extensive data collected from across a factor or even a company as a whole. This enables companies to boost productivity, promote sustainability, and expedite digital transformation by helping break down the barriers between people and information.

With the information age well underway it is now all too easy to get lost in the seas of data flowing in and out of everything from smart appliances to cutting-edge design tools. Generative AI is, in many ways, a natural response to this, helping make sense of the increasingly complex and data-rich world and make it accessible in a human way. Innovations like Siemens Industrial Copilot will bring that change into industry, changing forever the way products are designed, manufactured, and maintained.

This article was written Dr. Michael “Michi” Lebacher, Head of AI and Digital Businesses, Germany, Siemens AG, Digital Industries. Erik Scepanski is an Innovation Manager, Siemens Digital Industries (Munich, Germany). For more information, visit here  .

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