Modeling Laying Time of Gerber Machines in Cutting Department: A Study on Sri Lankan Apparel Industry

Providing the required cut panels to the sawing department on time, is the major task and responsibility of the cutting department of an apparel manufacturer. Therefore, the cutting process is one of the main value adding processes. Several functions are included in the cutting process; namely fabric laying, cutting and bundling of cut panels. Since other processes are depended on the fabric laying, it plays a crucial role. This study attempts to develop a model to determine the fabric laying time of Gerber machines used in the cutting department. First the factors that affect to the lay time are identified. The actual times taken for each of these factors are collected for non-woven trouser patterns for a period of month totaling 89 data points. Descriptive statistics, correlation analysis and multiple regression analysis are used to analyze the data. Overall, the results show that six factors out of identified seven are significantly useful in predicting total lay time. Particularly, the results of the regression analysis indicate that at a α=0.01 level of significance, loading time, damage check time, joint time, preparation time, reverse time and cutting time are significantly contributing to total lay time. The regression model has an overall accuracy rate of 79.2 percent. This study attempts to develop a model to determine the fabric laying time of Gerber machines used in the cutting department. First the factors that affect to the lay time are identified. The actual times taken for each of these factors are collected for non-woven trouser patterns for a period of month totaling 89 data points. Descriptive statistics, correlation analysis and multiple regression analysis are used to analyze the data. Overall, the results show that six factors out of identified seven are significantly useful in predicting total lay time. Particularly, the results of the regression analysis indicate that at a α=0.01 level of significance, loading time, damage check time, joint time, preparation time, reverse time and cutting time are significantly contributing to total lay time. The regression model has an overall accuracy rate of 79.2 percent.