Industrial gas manufacturing companies gather huge volumes of unstructured data through their advanced process control (APC) and manufacturing optimization systems that can help examine and predict equipment performance in real-time. Today industrial gas manufacturing companies can no longer rely on simulation models to acquire and analyze data, as such models generally assume the steady-state of operations and do not consider real-time conditions that impact machine performance. As a result, industrial gas manufacturing companies are on the lookout for advanced predictive models and analytics solutions that can help them visualize the value and devise systems for improved outcomes. Predictive analytics can help the oil and gas industry predict downtime by analyzing factors impacting exploration, drilling, and refining processes.
Downstream oil and gas companies can also leverage predictive analytics models to gain deeper insights into transport, supply chain, and distribution processes. This not just helps them to better manage end-to-end processes but fosters an analytics culture driven by data-driven decision making and advanced analytics.
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Benefits of Using Predictive Analytics in the Industrial Gas Manufacturing Sector
The use of dynamic predictive analytics models with self-tuning abilities offers several advantages for players in the oil and gas industry, including:
- Ensuring optimal machine performance without compromising the safety of workers and production quality
- Identifying and analyzing areas of concern when machine performance falls below the threshold value
- Predictive maintenance to prevent unplanned shutdowns and machine downtime
About the Client
The client is a leading privately-held industrial gas manufacturing company based out of North America. Being one of the leading players in the industrial gas segment in NA, the industrial gas manufacturing company has a complex, fully integrated supply network with 100+ manufacturing hubs and over 900 distribution vehicles. The company also has three major pipelines located in various parts of North America. As the total production volume and sales declined drastically over a period of five years, advanced predictive analytics was perceived as the only way for the client to analyze factors and improve their production curve.
Industrial gas manufacturing companies face several challenges that can curtail growth and profitability. Facing similar challenges? We’ve got the right solutions for you. Speak to a predictive analytics expert now!
The client’s business processes mainly revolved around industrial gas manufacturing, refining, and distribution, which resulted in the generation of complex machine & sensor data. With both real-time and historic data volumes increasing with every passing day, the industrial gas manufacturing client sought smarter ways to use predictive analytics models to drive greater results. To do so, the industrial gas manufacturing company wanted to develop a robust predictive analytics program and build the foundation for an analytically astute organization that focuses on the use of predictive data analytics models to improve decisions and enhance outcomes.
The industrial gas manufacturing company’s challenges revolved around four core areas, including:
- Machine downtime prediction
- Production capacity optimization
- Evaluation of pipeline risks
- Creation of remediation strategies
Our predictive analytics solutions empowered the industrial gas manufacturing client to analyze both past and real-time data being generated by planning, drilling, production, refining, and distribution processes to better analyze factors curtail productivity. By leveraging predictive analytics, the client was also able to interpret, visualize, cross-filter, and interact with the complex data sets to drive efficiency across processes.
To help the client tackle the above-mentioned challenges, our predictive analytics experts adopted a comprehensive three-phased approach.
Phase 1: Data Aggregation and Analysis
Comprehensive data analytics capabilities coupled with effective exploratory data analysis are important prerequisites for leveraging predictive analytics in the oil and gas industry. As such, the first phase of this predictive analytics engagement revolved around data munging to ensure the data is suitable for exploration.
Phase 2: Exploratory Analysis & Predictive Data Modeling
In the second phase, our predictive data analytics experts conducted an exploratory data analysis on the structured data sets by leveraging statistical and graphical techniques to uncover meaningful patterns and make crucial data-driven decisions. The use of an exploratory data analysis approach also helped them validate assumptions using advanced predictive models.
Phase 3: Model Evaluation and Deployment
The final phase of this engagement focused on evaluating the accuracy of the devised predictive analytics model to verify its accuracy and ability to solve business complexities.
The deployment of predictive analytics models involved the effective combination of a decision management approach with a robust, modern analytics platform. Using such an approach, the industrial gas manufacturing company was well-positioned to address the issues and effectively integrate the results into a centralized operational system for faster, profitable decision-making, reducing the barriers to using predictive analytics in driving improvements in the oil and gas industry.
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- Increased industrial gas manufacturing and recovery rates by 23%
- Fewer incidents were reported of all types, including product spillage and emissions
- Enhanced operation excellence by adhering to industry standards
- Better risk management through accelerated decision-making
In addition to the results obtained through the use of advanced predictive analytics models, this engagement helped the industrial gas manufacturing company to devise and implement an enterprise-wide analytics operating model that aligns with their objectives. This not only paved the way for the industrial gas manufacturing company to analyze business process implications but enabled them to adopt a comprehensive data-driven approach to resolve complex issues, enhance process efficiency, and improve safety.