By Janice Abel
Abbott’s nutrition business, a division of the global healthcare company, manufactures a wide variety of science-based nutrition products. These range from Similac brand infant formula to adult nutrition brands such as Ensure. Recently, ARC had the opportunity to speak with James Li, an engineering manager in the Abbott Nutrition division. We discussed how the company uses Big Data and analytics to improve manufacturing productivity.
Digital Data Transformation
In 1999, Abbott Nutrition began working with OSIsoft’s PI System to integrate, collect, and contextualize data at the company’s manufacturing plant in Columbus, Ohio. Based on its success at this plant, in 2012 the company entered into an OSIsoft Enterprise Agreement (EA) to include all Abbott Nutrition manufacturing sites globally. This created a huge volume of data, but also presented the challenge of extracting maximum value from the data.
About two years ago, Abbott began focusing on ways to obtain more value from the data. The company used OSIsoft’s Asset Framework (AF) to contextualize and organize the data using AF’s asset-centric models. The company also started using Seeq’s advanced analytics technologies to reduce the time and effort needed to connect to AF, create models, and find insights quicker.
Seeq’s advanced analytics technology can be used to contextualize time series data and create models to help engineers quickly derive insights and value from industrial process data. Abbott decided to use the technology to help reduce clean-in-place rinse time while maintaining or improving product quality. In 2016, it started a modest pilot project on one PI Server workstation to prove ROI. According to Mr. Li, “By the end of the pilot, we felt confident in the value we could obtain and increased the project scope to include additional PI Servers.” Abbott launched a formal analytics program globally at the beginning of 2017, with near-term plans to connect Seeq to all OSIsoft PI Servers globally.