Abstract
Data Science for Motion and Time Analysis Motion and time analysis has been a popular tool in operations research for analyzing work performance in manufacturing and service operations. The current practice in motion and time analysis involves many labor-intensive steps such as stop-watching, videotaping, and manual data analysis. It is too inefficient to be practiced regularly for continuous improvement. Whereas modern sensing devices have automated and eased motion measurements, the motion analytics transforming the new data into knowledge are largely underdeveloped. Unsolved technical questions include: How can the motion and time information be extracted from the motion sensor data? How are work motions and work rates statistically modeled and compared? How are the motions correlated to the rates? This paper develops solutions to the technical questions into a novel data science framework for motion and time analysis. The new framework is demonstrated with industrial use cases for a smart factory.
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