Generating Insight from Indoor Positioning Data in Manufacturing Assembly Line

Abstract Indoor positioning systems provide a platform to track multiple objects concurrently in an indoor environment. The primary purpose of these systems is to locate objects. However, the tracks generated by moving objects leave characteristic footprints and can be used to infer valuable information for informed decision making. In this paper, we demonstrate how insightful information can be inferred from indoor tracking data of workers in a manufacturing assembly line setup. We utilise two indoor tracking data sets: (i) a real world (and publicly) available data set which contains the tracking data of multiple workers working together on a tricycle assembly line, and (ii) a synthetic data set of a computer assembly line. We aim to develop a set of data analytics methods to infer several Key Performance Indicators (KPIs) or metrices from the tracking data of the workers that include: resource utilization , worker interaction , deviation from ideal workflow and state of the assembly line . While the methods are developed for analysing data from tricycle and synthetic computer assembly line, they can be generalised to any manufacturing assembly line.