- Complete Traceability through Digital Thread: The AI-powered factory can preserve digital thread and provide traceability (i.e., capture digital data at every production step and integrate all the data). This capability is paramount to meeting the process traceability requirements and earning customer trust by delivering consistent quality. This requires maintaining a detailed record of every step in the manufacturing process. For example, consider a company making a side panel for a speciality vehicle. It begins by sanding a surface, then paints it, and finally polishes it to remove residue. It must ensure that each step was performed correctly. The surface must be properly prepared, the paint process must produce the desired surface appearance, and the polishing step must not introduce any scratches.
In the AI-powered factory, each step is autonomous or automated and integrated with autonomous inspection, with a complete digital record captured throughout the process. This creates an end-to-end digital thread. If a quality were to be discovered on a product manufactured in this factory, the manufacturer would be able to identify exactly where a quality issue occurred and apply corrective action at the specific step responsible. In contrast, if any step in the specialty vehicle example is performed manually, then the digital thread is broken. The value of data generated by an automated step is dramatically reduced because a subsequent manual operation cannot deliver the required data. Complete traceability can only be achieved when every step is highly automated, fully inspected, and all steps are digitally connected.
In AI-powered factories, data from each step flows seamlessly into the next. Before a new step begins, validation is performed to confirm that the required quality standards from the previous step have been met. This prevents wasting of resources by ensuring that downstream processes are not performed if a quality issue occurs at an upstream process.
Achieving full traceability enables manufacturers to reduce the warranty costs and build trust with the customers. It also enables faster diagnosis and resolution of quality problems. It also dramatically reduces rework and scrap. For example, autonomous surface finishing solutions from GrayMatter Robotics have reduced rework at a fire truck manufacturer by 90%.
- Autonomous Operation: AI-powered autonomous robots are able to cope with labor shortages, enabling the factory to consistently meet its production targets and eliminate unnecessary delays. Traditional factories that mix automated steps with manual operations often experience significant bottlenecks, especially during labor shortages. For example, consider a process in which a part is formed using casting and subsequently goes through sand blasting and coating. Let us assume that sand blasting and coating steps are automated. However, the inspection step after the automated sand blasting is not automated and needs to be performed by a highly skilled human inspector. If the inspector is not available for a few days due to a family emergency, then the subsequent coating operation cannot proceed. In this example, even though the coating step itself is automated, it remains blocked until the manual inspection is completed.
This type of dependency creates serious challenges in meeting production targets and schedules in today’s era of labor shortages. Particularly, if manual steps require specialized skills, then finding a substitute worker is very challenging. Many tasks require months of training. So a generalist cannot perform these tasks. When such labor constraints arise, the entire production flow can be disrupted.
Autonomous robotic cells address this issue by ensuring that all critical steps leverage physical AI to function autonomously and do not depend on expert human intervention. Human involvement is limited to tasks that can be performed by personnel with minimal training, allowing new workers to step in without months of preparation or reliance on specific individuals.
Expertise and intelligence are embedded directly into the manufacturing systems in AI-powered factories. Autonomous operation delivers consistency and decision-making at all critical steps, while humans focus on higher-level problem-solving. This approach prevents human labor from becoming a bottleneck due to the labor shortage and enables achieving more consistent production capacity. For example, autonomous surface finishing solutions offered by GrayMatter Robotics reduced ergonomically challenging work at an aircraft manufacturer site by 90%.
- Rapid Reconfigurability to Meet Changing Market Needs: Another important attribute of the AI-powered factory is the speed at which new manufacturing capabilities can be added or an existing capability can be reconfigured. Consider a factory producing unmanned vehicles that experiences increased demand for a new model and reduced demand for an older one. To respond to this new product demand, the factory must be rapidly reconfigured. The demand for the new vehicle is expected to fade out in one year. So after the one year period the factory may undergo significant changes and might have to decommission some manufacturing equipment and move it to a different site. These new capabilities should be added in a timescale of months and not years. For example GrayMatter Robotics can set up a new autonomous production line with multiple applications within four months after signing the contract with a manufacturer. Most of the wait time is due to supply chain delay in sourcing hardware.
Traditionally, making significant changes to a factory can take years. New tools and methodology enabled by physical AI in the factory of the future concept offer capabilities to automatically calibrate new manufacturing cells, optimize the process, automatically generate digital twins, and seamlessly connect to the existing cells in the factory. These capabilities ensure that news manufacturing equipment can become functional very rapidly after arriving at the factories. Many cells in the factories will be constructed using robots. So a sanding cell can be reconfigured to function as a polishing cell. When the needs for a manufacturing cell cease to exist, then the cell can be decommissioned and quickly moved to another site. These new capabilities enable manufacturers to rapidly react to new market conditions.
- Optimized production: AI-powered robotic cells on their own are islands of automation. These cells deliver value at the individual process level. But their impact on the overall factory will be limited due to bottlenecks created elsewhere. To optimize manufacturing in factories, each process must be automated and digitally connected to other processes. Therefore, each robotic cell has to become a node in the manufacturing network.
AI can be used to optimize the performance of the cell based on the constraints of other cells in the factory. Each individual cell should be optimized for its throughput and quality to ensure that it does not become a bottleneck in the network. AI tools are used to perform this function automatically. This requires each cell to conduct its own experiments to generate data, build a model, and adapt its performance to support system-level optimization. This optimization process needs to be performed continuously to ensure that the cell can adapt to changes over time. This approach can be used to minimize consumption of resources, maximize throughput, and sustainability at the factory level. For example, autonomous surface finishing solutions offered by GrayMatter Robotics have more than doubled throughput at a sporting goods manufacturer.
- Resilient operation: Problems in a manufacturing cell can cause significant quality challenges and limit throughput. AI-powered factories incorporate prognostics and health monitoring (PHM) AI in each cell to ensure that the cell reliability and availability are high during operation, enabling each cell to efficiently deal with adverse events and reduce unplanned downtime in high-mix environments. Robotics cells deployed by GrayMatter Robotics deliver 95-99% system availability.
