Development of OEE (Overall Equipment Effectiveness) Web-Platform using IoT sensors data
Company Information
ProCogia embarked on a transformative project with a leading energy company to overhaul its equipment effectiveness and operational efficiency. Leveraging IoT sensor data, the initiative aimed to address the significant challenges of real-time equipment monitoring and data analysis. Through the development of an advanced OEE (Overall Equipment Effectiveness) Web-Platform, the project sought to revolutionize the company’s approach to maintenance, operational decision-making, and performance optimization, setting a new benchmark in energy production analytics.
The Challenge
The energy company was challenged by outdated manual reporting and data analysis processes, leading to increased equipment downtime and operational inefficiencies. The lack of a unified system for real-time analytics hindered timely and informed decision-making, crucial for maintaining optimal performance across its plant units.
Procogia’s Approach
Designed logical and physical databases (3NF) from scratch, considering business requirements for staging data and processed data for the web-app with triggers, stored procedures, and views.
Created ETL scripts that run every day in batch processing to load data from the historian database to the in-house staging database.
Developed a complex R Shiny web application for 8 plant units and its respected trains (Total: 8 Menu-Items and 56 Sub-Menu-Items) through shiny modules with various inputs to analyze time-series data.
Deployed the app on RStudio Connect with load-balancing and user controls, but it could be installed and run locally through a docker container or deployed as an R package (golem philosophy) with minor patchwork.
The Results
Provided a plant-wide web application to analyze various domain-specific tags and trends for chemical gasification processes in plant units using IoT sensors data for multi-level internal stakeholders from senior upper-level management.
Allowed users to make smart decisions by ensuring the plant is running smoothly and minimizing downtimes.
Reduced a significant amount of time of data preparation for manual reporting and sudden breakdown times.
Modernized the Shiny app, reducing user wait time, ensuring reproducibility, and improving the app’s performance.
Enabled the company to make data-driven decisions, thus improving operational efficiency and providing new insights.
Upskilled the team in using advanced technologies for analysis and decision-making.
Services Used
Services Used Data Science
We use open source technology to leverage the full potential of your data. Predictive and prescriptive results are actioned using AI and Machine Learning (ML).
Machine Learning
Drawing on statistical methods to enable improvement with experience, ProCogia’s machine learning algorithms predict outcomes and automate processes.
Data Engineering
ProCogia is partnered with the leading cloud providers, enabling our agnostic approach to focus on delivering tailored game-changing solutions for our clients.
Data Operations (DataOps)
We build robust, scalable, and enterprising data environments that enhance the collaboration between data science teams.
Conclusion
The collaboration between ProCogia and the leading energy company marked a pivotal step towards digital transformation and operational efficiency in the energy sector. By harnessing the power of IoT data through a bespoke OEE Web-Platform, the company not only achieved significant improvements in equipment performance but also set new standards for data-driven operational decision-making. This project underscores ProCogia’s expertise in delivering innovative solutions that drive efficiency, productivity, and strategic growth for its clients.
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