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Application of Big Data Technology to Solve Production Quality Challenges

Cleveland, OH (June 18, 2015) Vertex, a leading provider of Advanced Analytics services and solutions, has embarked on a new data-driven Quality Management and tracking System (QMS) for a major steel manufacturer in North America, to design and implement a Complex Event Processing (CEP) system and related analytic applications for manufacturing data.

The Vertex solution combines time-series based quality and sensor information from multiple plant systems with Predictive Modeling and Forecasting. The system will allow the steel manufacturer to increase quality metrics and evaluation, reduce customer returns and re-manufactured stock, conduct root cause analysis of defects, and deliver intervention recommendations and preventive guidance.

Erica Pettigrew, Business Analytics Practice Director of Vertex Computer Systems, stated, "This represents a major shift in the analysis of manufacturing and sensor data, from unthreaded time and sensor location-based analysis, to batch level, start to finish, product-centric analysis and quality monitoring. As is the case for many manufacturers, the visibility of quality and how it is impacted by a multitude of factors across mills and across a stream of activities is limited."

"While there are machine sensors and isolated analytics tools which help to manage machine health and individual tolerance activity, there is currently no system in place which monitors product quality and activity throughout the lines, and across the mills throughout the plant, providing a complete view and tracking of manufactured materials / finished goods from start to finish. The Vertex solution is first of a kind in that sense," Erica concluded.

Each mill has tens of thousands of sensor and quality metrics being captured and stored on second and sub-second time intervals, generated in different formats and totaling over 20TB. In particular, since all data is stored based on timestamp, there was no product view from a production "length" perspective, supporting the ability to track quality of each individual product or batch, and visibility into what is happening as the product is going through each line. This would result in "QA Approved" finished goods being released to customer – and then rejected, indicating a challenge with manufacturing tolerances which cannot be pinpointed today.

This use case, along with several others, is being addressed directly in the new data-driven QMS system, providing a holistic solution from multiple perspectives: Data Enablement for multiple audiences and use cases, Advanced Analytics for correlations and insights, and Predictive Analytics for actionable real-time information.

Phase 1 of the Data-Driven Quality Management System is currently underway, delivering historical evaluation, multi-variant correlations and analysis, and root cause analysis.

In Phase 2, the solution will be expanded to additional mills and moved to real-time with analysis and alerts, enabling a fine-tuned, optimized QA process which exceeds measurements and metrics in place today. The QMS system, analytics, and applications will be completed in 2015.

For more information on how the Quality Management System can benefit your manufacturing process, contact us at

About the Vertex Business Analytics Practice.

Vertex supports innovation and business enablement through the incorporation of advanced analytics and technology frameworks to drive competitive edge for organizations in multiple industries. From strategic engagements to technical advisory and implementation activities, Vertex provides the full-spectrum of services for an enterprise to scale Analytics incorporated solutions.

About Vertex Computer Systems.

Incorporated in Ohio in 1989, Vertex is a global IT company delivering innovative business solutions across Business Analytics, Salesforce and Custom technologies. With more than 300 consultants, Vertex provides its solutions to several Fortune 500 companies.

Media Contact:
Dibin Raj
+1 330 963 0044 (Ext 329)



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