Title :
A neural network and CBR-based model for Sewing Minute Value
Author :
Lai, Lucas K C ; Liu, James N K
Author_Institution :
Dept. of Comput., HongKong Polytech. Univ., Kowloon, China
Abstract :
Sewing minute value (SMV) is a benchmark to measure the production efficiency. It is an important business data for costing, production planning as well as key reference for product development. The traditional method to calculate SMV is complicated and not robust. There are so many rules to calculate SMV such as the speed of the machine or the motion of the machinist. It lacks randomness and flexibility. We proposed a new model which is based on neural network forecasting theory and case based reasoning technique. We will demonstrate the advantage of using the new method in this paper with better result representing an increase of 11% in accuracy compared to that of the old method.
Keywords :
case-based reasoning; costing; forecasting theory; neural nets; product development; production planning; sewing machines; CBR-based model; business data; case based reasoning technique; costing; forecasting theory; machine speed; neural network; product development; production efficiency; production planning; sewing minute value; Costs; Databases; Machinery production industries; Manuals; Motion analysis; Neural networks; Power engineering and energy; Predictive models; Robustness; Time measurement;
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2009.5178803