Abstract
Abstract Standard time is a key indicator to measure the production efficiency of the sewing department, and it plays a vital role in the production forecast for the apparel industry. In this article, the grey correlation analysis was adopted to identify seven sources as the main influencing factors for determination of the standard time in the sewing process, which are sewing length, stitch density, bending stiffness, fabric weight, production quantity, drape coefficient, and length of service. A novel forecasting model based on support-vector machine (SVM) with particle swarm optimization (PSO) is then proposed to predict the standard time of the sewing process. On the ground of real data from a clothing company, the proposed forecasting model is verified by evaluating the performance with the squared correlation coefficient (R2) and mean square error (MSE). Using the PSO-SVM method, the R2 and MSE are found to be 0.917 and 0.0211, respectively. In conclusion, the high accuracy of the PSO-SVM method presented in this experiment states that the proposed model is a reliable forecasting tool for determination of standard time and can achieve good predicted results in the sewing process.
Highlights
In recent years, due to greater competition in the market, the product types have become more diverse and their life cycles shorter
Since Taylor defined standard time as the most fundamental way to represent productivity under the basic concept of “A Fair Day’s Work”, many methods on the determination of standard time have been performed such as time study, activity sampling, synthetic timing, analytical estimating, and predetermined motion time systems (PTSs) [4]
In order to further validate the performance of the model, we presented a BP neural network to make a prediction experiment
Summary
Due to greater competition in the market, the product types have become more diverse and their life cycles shorter. To keep up with the business environment changes, improving the enterprise’s agility and response quickly to the customer’s requirements is becoming more and more important In such a context, an efficient method for determination of standard time should be explored even further. Standard time prediction has direct bearing on economic accounting, production schedule control, resource optimization, production cycle shortening, cost control, and product quotation. It promotes the labor productivity of enterprises and enhances their market competitiveness [2]. Pan et al [6] established standard time in the die manufacturing process using an activity sampling method. Park et al [9] used the PTS method to establish standard time for agricultural work in Korea
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