| Summary: | 碩士 === 義守大學 === 工業工程與管理學系碩士班 === 95 === For the continuous mass production models constructed in the past, even though time researchers spent a lot of time on setting the standard time, these data were still useful to businesses and employees. Nevertheless, due to the change in both the production and marketing models of products, the production ways of the manufacturing industry have been changed to be in a small-quantity and multiple-variety form. Thus, using the traditional way to set the standard time is not only time- and effort-consuming, but may also be a futile effort.
In view of this, the study investigates the traditional considerations and setting ways of the standard time. Taking electronic assembling industry for example, the study collects and collates such information as the materials being used, related production equipments, standard motions and standard time data. Based on the properties, attributes and parameters of the information, the study makes systematic analysis and induction. Having made the analysis and induction of the information, the study firstly finds out the correlation among the BOM, standard time data and standard time, and then practically constructs a standard time setting system for products according to the correlation.
After practical operation and testing of the system, it is proved that the system constructed by the study not only possesses simple concepts and operation, but also simplifies the setting process of standard time of products, and “rapidly” and “accurately” sets the standard time of products. The proved result shows that when comparing the use of this system with the original method for setting the standard time of a new product, above 70% of time can be saved in average, achieving a mean deviation of below 3%. Under the small-quantity and multiple-variety production form as well as the requirement of labor downsizing, only a small amount of time researchers are needed to set the standard time for many new products. In this way, businesses are able to precisely grasp the operation cost within the shortest period of time, and further enhance the accuracy of making management decisions.
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