(41ac) Digitization, digitalization and other disruptions in process safety

Data has become the primary driver of ROI in industries that previously saw capital investment in plant equipment as the main means to debottleneck processes. A popular misconception is that in order to effectively analyze data or leverage machine learning techniques, a data science background is needed. While a knowledge of data science is useful, a knowledge of the plant – how it’s designed, how it’s operated, how the process has adapted over time – is imperative. Advanced analytics software Seeq is putting the power of data science and machine learning in the hands of those who know the process best. Wrapping advanced algorithms in point-and-click experiences lowers the activation energy for solving previously unsolvable problems enabling organizations’ existing workforce to capture business value without capital investment. This approach has led chemicals and other process industry companies to millions in reliability, productivity, quality, and sustainability improvements. In this talk, we’ll share best practices on software rollouts that transform the technical workforce and a use case identification methodology that scales with the organizations’ increasing analytics capabilities.