Manufacturing analytics

Siemens manufacturing analytics solutions are meticulously designed to address critical challenges that manufacturers face daily. This includes mitigating unexpected equipment downtime through proactive insights, resolving persistent product quality issues by identifying root causes, optimizing inefficient production processes to boost throughput and empowering stakeholders with better data-driven decision-making capabilities across the entire manufacturing lifecycle, from design to delivery.

Siemens manufacturing analytics solutions significantly enhance OEE by providing deep, granular insights into its three core components.

• For availability, predictive maintenance capabilities reduce unplanned downtime by anticipating and addressing equipment issues proactively.
• For performance, the solutions identify and optimize bottlenecks, analyze cycle times and streamline workflows to maximize production speed.
• For quality, they minimize defects and rework through real-time monitoring, anomaly detection and root cause analysis.

This integrated, data-driven approach empowers manufacturers to comprehensively maximize their production efficiency and asset utilization.

Altair's solutions harness the power of advanced AI and machine learning algorithms to transform raw manufacturing data into actionable intelligence. This translates into capabilities like predictive maintenance, where potential equipment failures are anticipated before they occur; precise forecasting of quality defects to prevent scrap and rework; optimization of operational parameters for maximum efficiency and providing AI-powered engineering insights that accelerate design iterations and refine manufacturing processes.

Furthermore, these solutions often utilize knowledge graphs to provide context and structure to the AI/ML models. By mapping out the complex relationships between various manufacturing entities—such as machine components, process parameters, material properties and historical failure modes—knowledge graphs enhance the AI's ability to understand, reason and deliver more accurate and explainable predictions and recommendations. This approach moves beyond traditional descriptive analytics, offering proactive, prescriptive intelligence that drives continuous improvement.

Manufacturers can build and validate Rapidminer models centrally, then deploy them to edge environments, including on-prem infrastructure and Siemens Industrial Edge, so inference runs close to machines where latency is low, and operational decisions happen in real time. This approach is especially valuable for use cases like predictive maintenance, anomaly detection, quality prediction and process control, where seconds matter and network interruptions can occur. By running models at the edge, plants can continue operating even with intermittent cloud connectivity, reduce bandwidth requirements by scoring locally and keep sensitive production data inside OT/IT boundaries.

At the same time, Rapidminer AI Hub and Rapidminer Graph Studio can be deployed on-premises or in a private cloud to support enterprise governance, security and scalability requirements. In these deployment models, organizations can enforce data residency policies, integrate with internal identity and access controls and maintain tighter compliance with industry and regional regulations. Rapidminer AI Hub provides centralized lifecycle management for models, including versioning, deployment orchestration, monitoring and retraining workflows across multiple sites. Rapidminer Graph Studio adds contextual intelligence by linking machines, materials, process parameters, quality events and maintenance records into a knowledge graph, which improves explainability and accelerates root-cause analysis.

Together, this architecture enables a hybrid operating model: train and govern at the enterprise layer, infer at the edge where operations occur and continuously improve models using feedback from plant data. The result is faster time-to-value, stronger cybersecurity posture and more reliable AI outcomes for smart manufacturing initiatives.