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A New Method for Forecasting the Learning Time of Sensorimotor Tasks
Published in Stefan Trzcieliński, Waldemar Karwowski, Advances in Ergonomics in Manufacturing, 2012
Tim Jeske, Christopher M. Schlick
When new production systems are launched or existing systems are restructured, employees are often assigned to tasks that are unfamiliar to them. In case of assembly areas, tasks are often performed manually and require sensory as well as motor skills. Thus, those tasks are called sensorimotor tasks and employees have to train their regarding skills. Due to that they cannot work from the very start as productively as after being trained (Rohmert et al., 1974). The period necessary to train a task respectively the requisite skills is called learning time. It starts by getting acknowledged to a new task and ends with reaching the capability to perform the task within a reference time. This reference time is usually defined by means of a target time and can be determined with the help of Predetermined Motion Time Systems (PMTS), such as Methods-Time Measurement (MTM), Work Factor (WF) or Maynard Operation Sequence Technique (MOST). In contrary to the target time, the learning time cannot be forecasted economically and reliably using MTM or any other known methods (Bokranz & Landau, 2006). This leads in new or restructured production systems to large scheduling uncertainties and for customers to a low adherence on delivery dates. Both cause disadvantages in time-to-market, customer satisfaction and market share.
The Impact of Work Study on Management in Relation to Productivity of Manufacturing SMEs
Published in Munyai Thomas, Mbonyane Boysana, Mbohwa Charles, Productivity Improvement in Manufacturing SMEs, 2017
Munyai Thomas, Mbonyane Boysana, Mbohwa Charles
According to Kuo and Wang (2012:6520–27) and Larsson (2013:891–901), predetermined motion time systems (PMTS) are critical to operations managers in addressing the productivity of manufacturing SMEs. Findings from the literature studied show that the utilization of PMTS by WS specialists encourages operations managers to ensure that employees know how accuracy, operational standards, skills gained, and efficiency affect the activities carried out. These motions need to be done by taking into consideration standards in place, such as employee posture, in terms of safety, health, and well-being. When these standards are complied with, the productivity of manufacturing SMEs improves.
Hand Gesture Recognition of Methods-Time Measurement-1 Motions in Manual Assembly Tasks Using Graph Convolutional Networks
Published in Applied Artificial Intelligence, 2022
Alexander Riedel, Nico Brehm, Tobias Pfeifroth
Predetermined motion-time systems (PMTS) are used to provide standard target times for predefined assembly tasks. They are based on the fundamental assumptions, that a predetermined time value can be assigned to every basic motion and that the duration of all basic motions sums up to the complete process time (Genaidy, Mital, and Obeidat 1989). The most widespread PMTS include Methods-Time Measurement (MTM) (Maynard, Stegemerten, and Schwab 1948) and Maynard Operation Sequence Technique (MOST) (Zandin 2020), with MTM as the de-facto standard in western industrial countries (Bures and Pivodova 2015). In the MTM basic system MTM-1, assembly tasks are analyzed, structured, and dissected into sequences of five basic movements: grasp, move, position, release, and reach (see Figure 2). Each basic motion is assigned a specific duration, based on defined influence factors, e.g., distance, complexity, or physical effort (Bokranz and Landau 2012). The five MTM-1 motions make up 80–85% of human movements during an assembly task and can be composed to sequences of moves (e.g., “Grasp and Release” or “Put in Place”) for further analysis (Almeida and Ferreira 2009). Conducting a workplace planning using MTM-1 is a time-consuming task because every motion and its corresponding influence factor need to be determined by a qualified MTM expert (Bures and Pivodova 2015). The outcome of an MTM-1 workplace analysis is a table of the basic motions performed during an explicit task and their duration, as shown in Table 1. The motions are encoded as MTM codes that contain information about motion time influence factors like difficulty or distance. The time unit for measuring movements is referred to as Time Measuring Unit (TMU) with 1 TMU = 0.036 s. Predetermined motion-time systems mainly use TMU instead of seconds to avoid decimal places and keep the high accuracy. The use of TMU in this work is due to this convention. Gathering the MTM-1 motions in real-time during production is only viable through the use of technological solutions such as wired sensors, wireless sensors, or camera-based solutions (Fantoni et al. 2021).