Most manufacturers now have automated, Internet-enabled technology on their factory floors. These technologies allow for the collection of data from all types of equipment, and analysis of that data can improve the efficiency and quality of production, as well as quality of the product itself. Eli Lilly, for example, collects a variety of metrics from its pharmaceutical manufacturing facilities, including safety metrics such as serious injury rates; operational metrics such as product orders fulfilled and batches produced; productivity metrics such as Six Sigma projects completed and financial benefits from projects completed; and environmental metrics such as energy efficiency, reduction in water intake, reduction in waste to landfills, and savings from environmental efforts. Then, from the company’s data warehouse, employees run reports and conduct analyses “to help us more quickly approve [new] products” and compare data from different operations at each of it’s 24 manufacturing plants, says Maria Crowe, president of Eli Lilly’s manufacturing operations. “You need to have common definitions at the front end if you want good analytics at the back end.” Such a holistic view enables the company to analyze metrics from its SAP system “on a global basis,” in both an aggregate and a granular fashion. To truly see the trends taking place, however, every division and department must use the same defined common data elements and definitions for every data field. “That then gives us the ability to … compare apples to apples,” Crowe says. “You need to have common definitions at the front end if you want good analytics at the back end.” Data used for process control has been around for many years, but engine-maker Cummins continues to get better at using data to track when dies or tools are at the end of their shelf life and need to be changed, says […]