OEE Analytics Platform for Reactor Performance Monitoring
A leading industrial manufacturing and safety technology company partnered with DataTheta to improve reactor performance visibility and production efficiency. The plant lacked a structured way to measure OEE, and production data required significant manual effort to analyze. DataTheta built an automated analytics framework that enabled accurate OEE tracking, faster bottleneck detection, and real-time reactor performance monitoring through interactive dashboards.
Leading Industrial Manufacturing and Safety Technology Company
This organization is a global provider of explosion-protected electrical equipment and safety systems used in hazardous industrial environments. It delivers advanced automation solutions and electrical components designed to operate safely in industries such as chemicals, pharmaceuticals, oil and gas, and energy.
With operations in multiple countries and a strong presence in India, the company focuses on maintaining high manufacturing standards and operational reliability across its production facilities. Efficient monitoring of equipment performance is critical to maintaining consistent production output and ensuring operational safety.
Manufacturing plants rely heavily on Overall Equipment Effectiveness (OEE) to evaluate the performance of production equipment. OEE combines three critical metrics: equipment availability, production performance, and product quality.
At the facility, multiple reactors were used for production processes. Each reactor operated under different specifications and production capacities. Although production data was available through the Distributed Control System (DCS), the company lacked an automated way to process and analyze this data.
Engineers had to manually compile production data from multiple systems and spreadsheets to estimate equipment performance. This approach was time consuming and did not provide timely insights for operational decision making. The absence of a reliable OEE monitoring system made it difficult to identify the root causes of production delays, equipment downtime, or inefficient operating conditions.
To address these challenges, the company partnered with DataTheta to build a data analytics solution capable of extracting production data, computing OEE metrics, and presenting insights in an accessible dashboard environment.
Building a Reactor Performance Analytics Platform
DataTheta developed a comprehensive data engineering and analytics solution designed to process reactor production data and generate real-time performance insights. The first step involved extracting raw production data from the plant’s Distributed Control System using DataLogger. This raw operational data included reactor activity logs, production parameters, and equipment status records.
The extracted data was securely transferred to a centralized database hosted on the DataTheta server. This allowed the organization to maintain a structured repository of production data for analysis. The raw datasets were then transferred to a virtual machine environment using an openSSH client. This processing environment allowed the analytics pipelines to run efficiently while handling large volumes of operational data.
Python-based scripts were developed to clean and transform the raw datasets. These transformation workflows removed inconsistencies, structured the data into analytical formats, and prepared it for accurate OEE calculations. After processing, the transformed datasets were loaded into the database where they could be used for analytics and reporting.
To make the insights accessible to plant managers and operations teams, DataTheta built interactive Power BI dashboards that visualized OEE metrics for each reactor. These dashboards provided real-time insights into equipment availability, production performance, and operational efficiency. Teams could easily monitor which reactors were performing optimally and which required attention.
Weekly and monthly OEE trend analysis was also implemented to help operations teams identify recurring periods of low production and potential process inefficiencies. To ensure continuous monitoring, automated Python jobs were scheduled to run daily. These automated workflows refreshed the datasets and recalculated OEE metrics without requiring manual intervention.
