Monitoring of Human-Intensive Assembly Processes Based on Incomplete and Indirect Shopfloor Observations

Abstract

As manufacturing companies move towards producing highly customizable products in small lot sizes, assembly workers remain an integral part of production systems. However, with workers in the loop, it is necessary to monitor the production process for timely detection of deviations and timely provisioning of worker assistance. Grounded in an industrial case study describing the assembly of construction vehicles, we outline a generic heuristic-based approach for monitoring progress in human-intensive assembly systems. Specifically, we highlight the challenges in dealing with uncertainty stemming from the limitations in accurately, timely, and completely observing human physical assembly steps. We discuss a motivating example to showcase these challenges and present a set of heuristics that manages to accurately infer assembly progress from indirect and incomplete observations of deviating worker behavior. Validated against ground truth obtained from a real industrial assembly line, on average our approach correctly estimates completion times for steps that are associated with shopfloor observations within 14 seconds or less of their true value.