A factory truly becomes digital when it is built on a solid but flexible foundation. This is achieved by creating a digital foundation for machines that seamlessly connects to the rest of the factory. Key elements include standardization within the mechatronic design, a uniform data model, and integration with higher systems using Industrial Internet of Things (IIoT). This provides insight into the system, process, and product - essential for the step towards predictive operations. Industry standards play a crucial role in the digitalization of the factory. ISA-88 focuses on structures within the machines, while ISA-95 addresses the factory level. Following these standards creates a consistent and efficient workflow that forms the basis for integration and collaboration between different systems and departments. An important aspect of this digital foundation is the use of IIoT with protocols such as MQTT. The Unified Namespace concept, applied with MQTT and based on industry standards (ISA-95/88), ensures uniform and standardized data exchange within the factory. This protocol enables real-time communication between machines and applications creating a single source of truth, which is essential for monitoring and optimizing production (and business) processes.

By harnessing the power of ISA-88 standards on the factory floor, the groundwork is laid for a seamless integration with ISA-95 at the factory level. Standardized data from various factory floor, encompassing every machine and process, combined with factory-level applications (like MES/ERP) feeds into the ISA-95 framework, creating a unified namespace at this higher level. This unified namespace serves as a central repository of information, ensuring that data from ERP and MES applications is consistently structured and readily accessible.

At this juncture, leveraging the capabilities of artificial intelligence (AI) and machine learning (ML) becomes transformative. AI and ML can analyze vast amounts of data collected from the ERP, MES, and factory floor systems to identify patterns, predict outcomes, and optimize processes. These insights enable predictive maintenance, enhance quality control, and streamline operations, ultimately driving efficiency and productivity. The integration of AI and ML into the unified namespace provides a holistic view of the factory operations. For instance, predictive analytics can forecast equipment failures before they occur, allowing for preemptive maintenance that minimizes downtime. Similarly, real-time quality monitoring ensures that any deviation from the desired product specifications is immediately addressed, reducing defects and rework costs. In essence, the combination of standardizing data (using ISA-88/95), real-time data from factory floor systems, and advanced AI/ML analytics creates a robust digital foundation. This foundation not only supports the immediate operational needs of the factory but also provides the agility and flexibility required to adapt to future technological advancements and market demands.

Tool usage can be monitored and managed to ensure optimal performance. By tracking tool usage in real-time and predicting wear and maintenance needs, operators can prevent unexpected failures that lead to costly downtimes. The data collected allows for precise adjustments and timely interventions, optimizing tool lifespan and performance. Predictive operations are not solely about the lifespan and performance of tools; they are equally about maintaining product quality while optimizing the balance between cost and quality. By continuously monitoring and analyzing the production process, adjustments can be made in real-time to ensure optimal performance and minimal defects. This holistic approach encompasses both tools and products, ensuring that every aspect of the manufacturing process is fine-tuned for efficiency and excellence.

Efficient tool management is critical in maintaining the productivity and quality of an autonomous factory. With continuous monitoring and predictive analytics, each tool's condition is assessed, and maintenance schedules are dynamically updated to reflect actual usage and wear patterns. This proactive approach minimizes the risk of tool-related defects and ensures consistent product quality.

Predictive Quality operations leverage real-time data from various sources, such as motion (encoder) data and temperature data, to ensure the surface quality of products aligns with planned specifications. By continuously comparing actual surface quality data with predefined standards, operators can detect deviations early and make necessary adjustments. Motion data, collected from encoders, provides precise information about the movements and positioning of production equipment. This data helps in monitoring the consistency and accuracy of operations, ensuring that each product meets the desired shape and surface quality. For instance, if an encoder detects a slight misalignment, an immediate correction can be made to prevent defects.

Temperature data plays a crucial role in maintaining product quality, especially in processes sensitive to thermal variations. By monitoring the temperature at various stages of production, the system can adjust cooling or heating mechanisms or compensate to maintain optimal conditions. These adjustments are critical in processes like CNC machining, where fluctuations in temperature can significantly impact surface finish. Integrating motion and temperature data into the predictive quality framework allows for a holistic approach to quality management. This continuous feedback loop ensures that every product not only meets but exceeds quality standards, minimizing defects and optimizing overall production efficiency. By adopting predictive quality operations, manufacturers can achieve a seamless balance between high-quality output and cost-effective production.

Production flows can be modelled using Discrete Event Simulation (DES), an approach that is very suitable to capture the statistical variance present in manufacturing. In addition to the nominal duration of a specific process step, a probability distribution can be selected to model random variations or specific events that can cause delays. Using historical operational data that is typically available in ERP or MES, this model can be parametrized to accurately simulate the flow of new orders through operations. In itself, this ‘Production Flow Simulator’ could be useful to provide customers with much more accurate lead times based on actual loading of equipment. Next we add a schedule generator, capable of generating a simple production schedule based on a set of customer order and some basic metrics to prioritize the resulting production orders. Instead of directly sending this generated production schedule towards the work floor, we first do a virtual production run using our ‘Production Flow Simulator’. Based on this simulation, we can detect bottlenecks and suggest improvements the schedule generator. After a number of these simulation loops, which typically only take several seconds to complete, we find an optimized production schedule which is then released to the real factory.