Introduction

Optimizing production efficiency in modern manufacturing is increasingly complex due to high throughput demands, strict quality standards, and variability in machine performance. Overall Equipment Effectiveness (OEE) is a widely used metric to quantify productivity, measuring losses in Availability, Performance, and Quality [1].

Traditional OEE monitoring often relies on manual data collection or post-hoc analysis, which can delay interventions and reduce operational efficiency. Agentic AI provides autonomous systems that can continuously access structured production data, perform predefined tasks, and calculate OEE in real time, enabling clear visibility into operational efficiency and helping plants meet production targets.

What is Agentic AI in the Context of OEE? 

Agentic AI refers to autonomous systems capable of performing specific, goal-directed tasks without human intervention. In the context of OEE, agentic AI systems are deployed to calculate and report operational efficiency metrics by operating on a pre-existing knowledge graph [2].

The knowledge graph serves as a structured representation of machines, batches, materials, downtime events, and quality outcomes. Agents query or operate on this graph to perform their tasks. For the current deployment, the AI calculates OEE based on actual production, machine runtime, and quality metrics compared to planned schedules, providing transparent and accurate performance reporting [3].

Fig1. Knowledge Graph

Why Agentic AI for OEE?

OEE calculation can be challenging in modern manufacturing environments due to multiple data sources and large volumes of operational and quality information. Manual computation is error-prone and slow, and real-time monitoring is often impossible [1].

By deploying agentic AI, manufacturers can automate data collection, transformation, calculation, and reporting. Agents perform well-defined subtasks on top of the knowledge graph, ensuring accuracy and consistency. The result is a reliable, automated OEE metric that can be calculated per batch, per shift, or aggregated over longer periods such as a month, enabling better monitoring and production planning.

How Agentic AI Performs OEE: A Workflow Snapshot?

• Data Ingestion Agent

  • Collects raw data from MES/ERP (production schedules), SCADA/PLC/DCS (machine counters, runtime), and QC labs (quality data).

  • Normalizes and captures data consistently for further processing.

• Data Transformation Agent

  • Cleans, standardizes, and formats ingested data according to the knowledge graph structure.

  • Aligns fields like production time, units produced, and quality metrics for accuracy.

• OEE Calculation Agent

  • Computes Availability, Performance, and Quality using structured data.

  • Produces Overall Equipment Effectiveness (OEE) at batch, shift, or monthly levels.

  • Makes OEE available for reporting and monitoring.

• Compliance & Deviation Agent

  • Compares actual runs vs. planned production schedules.

  • Identifies deviations (unplanned stops, early/late completions, throughput changes).

  • Logs and flags deviations for analysis to improve schedule adherence.

• Interactive Reporting & Remedial Agent

  • Presents results, deviations, and performance summaries interactively.

  • Suggests remedial measures and corrective actions to reduce future deviations.

  • Closes the loop by turning deviation detection into actionable insights for better OEE.

Fig 2. Workflow for Multi-Agent System

How can it be incorporated into Current Manufacturing Plants?

In a real manufacturing environment, agentic AI can be integrated without altering existing production schedules. Data from MES, SCADA/PLC/DCS, ERP, and QC systems is centralized in the knowledge graph [3]. Agents then perform their respective tasks — ingestion, transformation, OEE calculation, schedule compliance analysis, and interactive reporting — using the knowledge graph as the reference.

For instance, consider a tablet compression machine; the Agentic AI system could monitor machine counters, turret speed, batch outputs, and quality metrics such as tablet weight, hardness, and thickness. The agents calculate OEE for each batch and aggregate the results over longer periods, such as a month, providing plant management with an accurate view of operational efficiency. Deviations from the planned schedule are identified and logged, and remedial suggestions are provided for troubleshooting. This implementation forms a foundation for future expansions, such as predictive insights or root-cause analysis, while ensuring that current production targets are consistently monitored.

Agentic AI in a Pharma facility: How do Shift supervisors with the Agentic system?How It Can Be Incorporated in Current Manufacturing Plants: Tablet Press Example 

In a pharma facility, the shift supervisor asks the system: “What’s the current OEE status for Line 2?”

“Line 2 OEE is currently at 74.6%, 9.4% below its trailing weekly average. Downtime is attributed to prolonged tablet ejection cycles. Analysis suggests that ejection arm lubrication was last performed 36 hours ago, exceeding the recommended interval by 12 hours.

This dynamic, conversational interface, underpinned by process-specific LLMs and retrieval augmented generation, enables continuous, transparent collaboration between human and machine.

Conclusion

The integration of agentic AI into OEE monitoring marks a pivotal shift in how pharmaceutical manufacturers approach efficiency, compliance, and decision-making. By leveraging knowledge graphs and autonomous agents, plants can move beyond retrospective, manual OEE reporting toward a proactive, real-time system that not only measures losses but also prescribes corrective actions. In the case of tablet compression machines, agentic AI transforms raw production and quality data into actionable insights, enabling supervisors and operators to maintain optimal efficiency with minimal disruption.

Ultimately, agentic AI enhances OEE from being a static metric into a dynamic performance management tool capable of scaling across lines, shifts, and entire facilities. This evolution empowers manufacturers to meet stringent quality requirements, improve throughput, and foster a culture of continuous improvement. As the pharmaceutical industry continues to embrace digital transformation, agentic AI stands out as a critical enabler of smarter, more resilient, and highly efficient operations.

 References 

1. J. Doe, A. Smith, “Towards a generic framework of OEE monitoring for driving effectiveness,” Procedia Computer Science, vol. 204, pp. 123–134, 2024. 
2.  L. Zhang, M. Li, “Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review,” IEEE Access, vol. 9, pp. 12543–12565, 2021. 
3.  H. Wang, R. Kumar, “Knowledge graph-based manufacturing process planning: A state-of-the-art review,” Computers in Industry, vol. 150, 103930, 2023.