The client faced recurring asset failures and operational inefficiencies in their coke oven gas exhaust system due to impeller imbalance, blower clogging, and energy imbalances across the plant. These problems led to unplanned downtimes, resource wastage, and reduced overall equipment effectiveness (OEE).
Bosch IAPM framework
ML-based diagnostic models
Embedded sensors
Condition monitoring tools
Manufacturing is a complex, interdependent process where even minor inefficiencies in a single unit can disrupt the performance of an entire plant—impacting throughput, quality, and profitability. In steel manufacturing, operational precision is especially critical, from optimizing thermal efficiency to minimizing unplanned process interruptions. However, many plants continue to rely on legacy monitoring systems, manual inspection routines, and reactive maintenance workflows. These outdated approaches create significant gaps in reliability, asset uptime, and resource utilization.
For our client, a leading steel manufacturer, the challenges were centered around coke oven operations, particularly the gas exhauster systems. Tar deposition led to impeller imbalance, frequent blower clogging caused unplanned downtimes, and unchecked energy imbalance led to loss of high-energy gas. These compounding inefficiencies hindered productivity and impeded their sustainability goals. To maintain asset integrity, optimize energy usage, and prevent critical failures, the client needed a scalable, predictive solution capable of monitoring asset health in real time, identifying root causes, and reducing manual intervention.
Bosch SDS deployed its Intelligent Asset Performance Management (IAPM) framework, leveraging advanced machine learning models and real-time sensor data integration. This approach enabled proactive monitoring and early detection of anomalies in the gas exhauster system.
This allowed the client to shift from time-based to condition-based servicing, reducing unnecessary interventions and focusing efforts where they truly needed. Key components of the solution included:
Bosch SDS created a strong foundation for consistent uptime and process resilience by seamlessly integrating intelligent analytics with on-ground manufacturing expertise. By enabling intelligent monitoring and predictive insights, Bosch SDS helped transform the client's coke oven operations into a high-performing, failure-resilient system.
Significant reduction in unplanned downtimes
Improved energy utilization through gas recovery
Scalable solution models for replication in other plants
Accelerated root-cause analysis through predictive diagnostics
Lowered maintenance costs through targeted intervention strategies
Enhanced overall equipment effectiveness (OEE)
Real-time visibility into process-performance links and asset health across coke oven units.
Scalable solution models for replication in other plants Strengthened maintenance planning and workforce efficiency
Reduced manual inspection overhead through automated monitoring
Bosch SDS played a key role in the client’s path to manufacturing excellence, bringing together hands-on industry knowledge and practical, scalable solutions designed for the real-world challenges of steel production. By embedding intelligence into industrial asset performance, Bosch SDS enabled the client to convert equipment volatility into a strategic control point. The use of machine learning algorithms and modular IAPM architecture delivered both precision diagnostics and scalable adaptability, without overhauling existing systems. This intervention stabilized current production lines and also laid a blueprint for future-ready operations across the client’s manufacturing ecosystem. The solution is now integral to the client’s roadmap for energy-efficient, digitally intelligent manufacturing.
