The argument for Adaptive Production beyond Flexible Manufacturing: An analysis of the need for change
In the dynamic world of manufacturing, traditional production methods are being replaced by a new approach: Adaptive Production. This innovative strategy focuses on building intelligent, responsive, and resilient systems that can adapt to a wide range of process conditions.
Siemens AG, a global leader in automation software deployment and incubation, is at the forefront of this revolution. By leveraging Artificial Intelligence (AI), edge computing, and real-time optimization, Siemens is building adaptive production systems that continuously improve themselves to provide resilient production operations.
At the heart of adaptive production is edge computing. This technology processes data near the production machines, enabling real-time functions such as visual inspection, anomaly detection, and predictive maintenance. By reducing latency, edge computing supports immediate decision-making, setting adaptive production apart from traditional methods.
Data integration and modeling play a crucial role in adaptive production. By unifying data from diverse sources like control systems, ERP, quality databases, and staffing, a coherent contextual model is created. Graph databases are particularly important here, as they map relationships between machines, sensors, and processes, enabling complex root cause analysis and easier retrieval of information.
Artificial Intelligence (AI) interprets the modeled data to provide actionable, understandable insights to operators in natural language. AI also facilitates continuous learning and adaptation by retraining models with ongoing data streams without manual intervention.
A closed feedback loop for real-time optimization completes the adaptive production system. The system continuously senses data, analyses deviations or performance issues, and automatically sends corrective actions back to the control layer. This loop supports continuous process adaptation and optimization, improving efficiency, resilience, and quality.
Adaptive production systems are designed to function in both high-mix, low-volume and high-volume, high-mix environments. They are suitable for any production mix or volume and are central to long-term improvement in yield, quality, and throughput.
Moreover, adaptive production systems use plug-and-play technologies, reducing the need for specialized technical skills. They are also designed to support, not replace, workers by tailoring systems to operators, engineers, and managers. Adaptive systems continuously monitor performance, detect quality deviations, trace root causes, and adjust parameters in real time to prevent future issues.
The integration with next-generation digital manufacturing platforms enables scalable and flexible production. For example, Divergent’s Adaptive Production System (DAPS™) integrates these components to create optimized, digitally-driven manufacturing workflows without relying on legacy tooling.
In essence, adaptive production systems rely on a layered technology stack centered around edge computing, graph-based data integration, AI-driven analytics and natural language interfaces, and a closed-loop control system to enable real-time, continuous optimization and adaptation of manufacturing processes. This new approach to manufacturing is set to revolutionize the industry, making production more efficient, resilient, and adaptable to the ever-changing world.
[1] Siemens AG. (2021). Adaptive Production. Retrieved from https://new.siemens.com/global/en/products/industrial-automation/adaptive-production.html
[2] Divergent Technologies. (2021). DAPS™. Retrieved from https://www.divergent.tech/dapstm
[3] McKinsey & Company. (2020). Adaptive production. Retrieved from https://www.mckinsey.com/business-functions/operations/our-insights/adaptive-production
- Edge computing, a pivotal technology in adaptive production systems, processes data near the production machines for real-time functions like predictive maintenance, visually inspecting anomalies, and detecting deviations.
- To create a coherent contextual model in adaptive production, data from diverse sources like control systems, ERP, quality databases, and staffing are integrated, with graph databases playing a significant role in mapping relationships between machines, sensors, and processes.
- The integration of adaptive production systems with next-generation digital manufacturing platforms, such as Divergent’s Adaptive Production System (DAPS™), enables scalable and flexible production, ultimately revolutionizing the manufacturing industry through more efficient, adaptable, and resilient processes.
