Our Quality Analytics solution helps customers identify abnormal events and causes which may lead to poor quality products, utilizing a pattern recognition technology and the following steps:
Maintaining Quality of Products
Ensuring the quality of products is one of the most important issues in quality management, and many plants struggle to achieve this goal. One typical challenge is the difficulty in managing quality variation in raw materials as they tend to produce final products which do not satisfy their customers' needs, despite following their identical operating procedures. Also, some plants are forced to use aging assets, which adds more difficulty to achieving targets. Plants must analyze various data to identify problems, but often depend on operators' experience and knowledge, which is not reliable.
Comprehensive methodologies combining our advanced analytics software with our skilled data analysts
Comprehensive Methodologies to:
Advanced Analytics Software – “Process Data Analytics” (refer to Enabling Technology)
Skilled Data Analysts
Experienced projects in over 120 Japanese process companies with combined knowledge in:
Addressing Problems Promptly
Our quality analytics solution detects subtle differences, imbalances, and changes that operators can not easily discover. This enables plants to find these changes in asset and product quality, and more importantly, to predict abnormalities faster and more precisely. This also leads to greater efficiencies in the data screening process.
Process Data Analytics
This software enables users to analyze production operations using temperature, pressure, flow rate, liquid level, and other process data as well as data on facility operations and equipment maintenance collected by a plant information management system (PIMS), DCS, or PLC.
Process Data Analytics use the MT method for the analysis of multiple statistical variables. The MT method is a pattern recognition technology born by the concept of the variation in quality engineering society. It is used as a method of analyzing multivariate data and developing multidimensional measurement scales for diagnosis and forecasting.
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