Development of a framework to implement time analysis in digital human modeling systems using predetermined motion time systems

Abstract Digital human modeling (DHM) systems are increasingly utilized to design and optimize human work processes. A key challenge in employing DHM systems is precisely estimating task times for activities depicted through 3D human models. This study introduces an automated approach for time analysis derived from DHM data, focusing on integrating the Maynard Operation Sequence Technique (MOST) time system. Traditional time analysis using MOST often requires extensive manual effort from trained analysts to evaluate task execution, which can be both time-consuming and costly, particularly when applied to 3D designs. However, the automated methodology presented in this study significantly streamlines this process by minimizing manual intervention, thereby facilitating the creation of efficient and ergonomic human work processes during the design phase. The proposed method offers a systematic approach to integrating the data required for a Predetermined Motion Time System (PMTS) analysis, such as MOST, within a 3D environment. The study specifies the data that simulation tools can automatically generate for time analysis and highlights where manual input is needed during DHM simulation. By combining automated data with manual input, the method ensures a complete PMTS analysis. The method was validated through a field study, showing acceptable performance compared to both MOST estimates and actual observed task times. To illustrate its application, the method is implemented using Dassault Systèmes Delmia Ergonomic Workplace Design (EWD) software in a case example. EWD enables automatic time estimations for 3D-designed tasks while allowing for comprehensive ergonomic assessments. This multifaceted analysis equips design engineers with a powerful tool to evaluate design effectiveness, significantly saving time and resources before creating a physical prototype.