ASQ

Exclusive Content & Downloads from ASQ

The Q.LIFE® Engine: A Work of Statistical Engineering

Summary: The Q.LIFE® Engine is a complex predictive system that leverages corporate databases, information technology, sophisticated modeling algorithms, well-defined business protocols, and expertise in chemistry, engineering, information technology, and statistics. The system allows Lubrizol to create value for customers by tailoring products to meet and exceed technical needs and specifications at the lowest testing and formulating cost in the shortest amount of time. Therefore, it serves to solve complex formulation optimization problems. The Q.LIFE® Engine is the culmination of years of collaborative work between many divisions at Lubrizol, including research, testing, information solutions, marketing, and sales. The system is accessible to employees worldwide, with selected parts available to Lubrizol’s customers. It is regarded as a core competency of Lubrizol engine oil formulation practices. Whereas the heart of the Q.LIFE® Engine is Lubrizol’s set of corporate statistical models, it is the integration of the models into a worldwide-accessible system that enables cost-effective formulation development and problem solving. It is an excellent example of statistical engineering as defined by Hoerl and Snee (2010). In particular, in statistical engineering, statistical concepts, methods, and tools are integrated with information technology and/or other relevant sciences to generate improved results. Through the leveraging and integration of corporate databases, business protocol knowledge, and expertise in chemistry and engineering, statistical concepts and complex statistical methods are used to build the Q.LIFE® Engine, a sustainable system that is greater than the sum of its parts.

Please sign-in or register to download this information. Registration is FREE and gives you access to ASQ's articles, case studies and general information.


Other Ways to Access content:

Join ASQ

Join ASQ as a Full member. Enjoy all the ASQ member benefits including access to many online articles.

  • Topics: Statistics
  • Keywords: Information technology, Prediction, Statistical engineering, Statistics, Tools
  • Author: Pocinki, Sara B.; Scinto, Philip R.; Wilkinson, Robert G.
  • Journal: Quality Engineering