Big Data Has Come to the Factory Floor

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 Stan Woszczynski, chief manufacturing officer of the Columbus, Ind. company.

Cummins also analyzes data to see how plants’ key performance indicators are doing, to determine whether there are good correlations between how the company manages certain processes and how that, in turn, impacts safety or quality. Like Eli Lilly, Cummins analyzes data to compare the performance of similar plants, “so we can learn from each other and make sure we are utilizing the best practices.”

Another aspect that impacts manufacturing, Woszczynski says, resolves around customer order preferences and trends. “That type of data analysis might help us with forecasting accurately, which helps our plants with capacity and workforce management.”

Stanley Black & Decker’s tracking systems within its facilities give the New Britain, Conn. tool and fastening systems manufacturer “real-time quality data, production data, even safety data,” says John Lundgren, chairman and CEO of the firm.

“These are the same systems we use to track infants in maternity wards and Alzheimer’s patients in care facilities, but we’re applying the technology to a manufacturing line, or an entire facility,” Lundgren says.

The company obtains real-time granular data on metrics such as overall equipment effectiveness or labor productivity, which drives successful root-cause analysis and informs corrective actions when needed, he says. “Accurate real-time metrics drive better decisions,” Lundgren says. “It’s as simple as that.”

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