Grading Sewing Operator Skill Using Principal Component Analysis and Ordinal Logistic Regression

Grading Sewing Operator Skill Using Principal Component Analysis and Ordinal Logistic Regression

Thanh Quynh Le (Japan Advanced Institute of Science and Technology, Nomi, Japan) and Nam Van Huynh (Japan Advanced Institute of Science and Technology, Nomi, Japan)
Copyright: © 2018 |Pages: 17
DOI: 10.4018/IJKSS.2018040102
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In the apparel manufacturing process, productivity and quality are somewhat determined by operator skill level. Predicting worker skill level is very important for effective production operation management. However, the current methods for ranking skill level in the manufacturing industry have been based on the subjective evaluation of managers and have failed both in predicting the operator skill level needed for planning and in encouraging operators to develop new skills for quality and productivity. This article develops a new method for grading sewing worker skill levels that employs updated knowledge from experts involved in training, coaching and managing operations in factories. This approach uses the Delphi method combined with principal component analysis to define and classify six qualitative variables that effect on three aspects of operator skill, including coordination skill, sustaining skill, and tool operating skill. Based on these three variables, ordinal logistic regression is applied to grade skill levels, with a statistically significance result.
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In the apparel manufacturing process, the worker is the most important factor that determines the quality and productivity of an assembly line. In an assembly line, employees are usually chosen to undertake tasks based on a variety of criteria, such as target productivity, cross-training programs, task characteristics, and skill level (Niebel & Freivalds, 2003; Nembhard, 2001). Of these, skill level is the factor that receives the most attention when assigning or reassigning workers to a task. The level of “mastery” of a skill is used to grade operator skills in performing these jobs. A higher level of skill mastery results in a shorter time or higher efficiency and performance.

Measuring the performance of workers has been of interest for years. Several studies examined appropriate ratings for different work performance and analyzed the relationship between worker performance and these impacted factors. Deadrick & Madigan (1990) revealed that the worker performance stability coefficients have a steady decline when the interval between measures increases. This decay was evident regardless of previous job experience, cognitive ability, or psychomotor ability. The validity of cognitive ability, psychomotor ability, and prior job experience increased, remained stable, and decreased, respectively over time. Adkins & Naumann (2001) designed a study to examine the relationship between work values and job performance in the service field. A case study applied to telephone sales agents suggested that a direct relationship between achievement and performance in a field setting. Asmus, Karl, Mohnen & Reinhart (2015) analyzed the effect of goal setting on worker performance in an industrial energy process. Worker performance was measured by the quantity and quality of products. They concluded that setting goals is one promising way to improve worker performance in industrial workplaces. Furthermore, as the focus here is on the influence of the role of supervisor-worker relationships in predicting workers emotions, job embeddedness, and in-role and extra-role performance, the research from Chih, Kiazad, Cheng, Lajom & Restubog (2017) suggested that a high-quality working relationship between workers and their supervisors can facilitate workers’ positive emotions, leading to enhanced job embeddedness and superior performance.

Most of previous studies concentrated on studying the relationship between worker performance with external factors or organization factors. However, sewing operator performance results from a combination of operator skill level and the task characteristics. A performance evaluation method is a systematic way to determine how well workers perform their job. Recently, several methods have been used to define worker performance ratings in manufacturing process, such as the Speed rating method, Synthetic Rating, and the Westinghouse system. In the Speed rating method, the sewing worker’s speed operation is the only factor estimated. The manager observes the speed of the worker’s manipulations and compares them with the standard expected speed to estimate the relationship between the two as the rating speed factor. This rating factor can be applied to different elements. However, the standard expected speed does not have a benchmark; managers must base this on their subjective evaluation. On the other hand, in the Synthetic Rating method, employee performance is rated according to values already estimated by a Pre-determined Motion time system. In this procedure, a time study is conducted in the usual manner and then the actual time obtained for certain elements from this study is compared with that of known standards. A ratio is established between these two values and the average ratio is calculated (Landy and Farr, 1980).

The most popular operator performance rating system is the Westinghouse system, which enables production managers to estimate operator performance and therefore production capacity through four factors, including skill, effort, consistency of worker, and work conditions, as shown in Table 1 (Chase and Aquilano, 1998).

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