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<p>Predetermined Motion Time Systems (PMTS) are widely applied in industrial environments to estimate task durations, yet concerns about their accuracy persist. Despite their widespread use, few empirical studies have rigorously validated their reliability, leading to uncertainties regarding their dependability in modern production systems. Accurate time estimation is crucial for strategic production planning, resource optimization, and performance evaluation, particularly in high-demand manufacturing industries. This study assesses the accuracy and applicability of time estimates generated by the Maynard Operation Sequence Technique (MOST) and a Digital Human Modeling (DHM)-based method grounded in MOST principles. Data were gathered from a field study conducted at 26 workstations in an automotive manufacturing company. Tasks were observed, recorded, and analyzed using both methods, with video recordings providing actual task durations for comparison. Agreement between estimated and actual times was evaluated using the Bland–Altman test, hypothesizing deviations within 10%. Results showed that MOST had a mean deviation of − 7.04%, while the DHM-based method exhibited − 5.45%, both within acceptable limits. The 95% limits of agreement ranged from − 14.71 to 7.90&#xa0;s for MOST and − 13.81 to 8.45&#xa0;s for the DHM method. Regression analysis identified task duration and action distance as significant factors influencing discrepancies between MOST estimates and actual times (<i>p</i> &lt; 0.05). These findings demonstrate the reliability and applicability of MOST and DHM-based methods for industrial time estimation and highlight their potential for integration into advanced manufacturing workflows to enhance productivity and operational planning.</p>

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Field validation of MOST and a DHM-based time estimation method using real-world manufacturing data

  • 期刊论文

  • 发表日期：

    2025年7月7日

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  • Farhad Mazareinezhad,
  • Firdaous Sekkay,
  • Daniel Imbeau

摘要

Predetermined Motion Time Systems (PMTS) are widely applied in industrial environments to estimate task durations, yet concerns about their accuracy persist. Despite their widespread use, few empirical studies have rigorously validated their reliability, leading to uncertainties regarding their dependability in modern production systems. Accurate time estimation is crucial for strategic production planning, resource optimization, and performance evaluation, particularly in high-demand manufacturing industries. This study assesses the accuracy and applicability of time estimates generated by the Maynard Operation Sequence Technique (MOST) and a Digital Human Modeling (DHM)-based method grounded in MOST principles. Data were gathered from a field study conducted at 26 workstations in an automotive manufacturing company. Tasks were observed, recorded, and analyzed using both methods, with video recordings providing actual task durations for comparison. Agreement between estimated and actual times was evaluated using the Bland–Altman test, hypothesizing deviations within 10%. Results showed that MOST had a mean deviation of − 7.04%, while the DHM-based method exhibited − 5.45%, both within acceptable limits. The 95% limits of agreement ranged from − 14.71 to 7.90 s for MOST and − 13.81 to 8.45 s for the DHM method. Regression analysis identified task duration and action distance as significant factors influencing discrepancies between MOST estimates and actual times (p < 0.05). These findings demonstrate the reliability and applicability of MOST and DHM-based methods for industrial time estimation and highlight their potential for integration into advanced manufacturing workflows to enhance productivity and operational planning.