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<p>Accurate time estimation is critical for process planning<b>,</b> task optimization, and worker safety in modern manufacturing environments. Predetermined Motion Time Systems (PMTSs), such as the widely used Maynard Operation Sequence Technique (MOST), are key tools for time estimation but often lack precision. This study investigates the accuracy of MOST through controlled laboratory experiments, comparing its estimates to direct measurements and predictions using Fitts' law. Twenty participants performed 300 simple movements varying in Action Distance (10–120&#xa0;cm), Object Weight (0.5–2.26&#xa0;kg), Motion Level (68–100&#xa0;cm), Grasp Difficulty (easy/difficult), and Placement Precision (approximate/precise). Movement times were recorded using an accelerometer and compared to MOST and Fitts' law estimations. Results revealed a 38% underestimation by MOST, with average actual times of 3.83 ± 0.46&#xa0;s (range: 2.97–5.13&#xa0;s). Bland–Altman analysis showed significant discrepancies (95% LoA: -2.37 to -0.063&#xa0;s) with a mean bias of -1.5&#xa0;s (SD = 0.44). Regression analysis identified significant effects of variables like Object Weight and Motion Level, which are not accounted for in the MOST system (p &lt; 0.0001). These findings highlight the importance of refining MOST by incorporating overlooked factors, which can significantly improve time estimation accuracy, optimize task performance, and promote safer working conditions in manufacturing systems.</p>

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Improving time estimation accuracy in manufacturing systems: experimental assessment of MOST predetermined motion time system using laboratory data and Fitts' law

  • 期刊论文

  • 发表日期：

    2025年5月31日

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  • Farhad Mazareinezhad,
  • Firdaous Sekkay,
  • Daniel Imbeau

摘要

Accurate time estimation is critical for process planning, task optimization, and worker safety in modern manufacturing environments. Predetermined Motion Time Systems (PMTSs), such as the widely used Maynard Operation Sequence Technique (MOST), are key tools for time estimation but often lack precision. This study investigates the accuracy of MOST through controlled laboratory experiments, comparing its estimates to direct measurements and predictions using Fitts' law. Twenty participants performed 300 simple movements varying in Action Distance (10–120 cm), Object Weight (0.5–2.26 kg), Motion Level (68–100 cm), Grasp Difficulty (easy/difficult), and Placement Precision (approximate/precise). Movement times were recorded using an accelerometer and compared to MOST and Fitts' law estimations. Results revealed a 38% underestimation by MOST, with average actual times of 3.83 ± 0.46 s (range: 2.97–5.13 s). Bland–Altman analysis showed significant discrepancies (95% LoA: -2.37 to -0.063 s) with a mean bias of -1.5 s (SD = 0.44). Regression analysis identified significant effects of variables like Object Weight and Motion Level, which are not accounted for in the MOST system (p < 0.0001). These findings highlight the importance of refining MOST by incorporating overlooked factors, which can significantly improve time estimation accuracy, optimize task performance, and promote safer working conditions in manufacturing systems.