Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00170-006-0610-7
Title: Framework of a computer-aided short-run SPC planning system
Authors: Zhu, Y.D.
Wong, Y.S. 
Lee, K.S. 
Keywords: Group technology classification and coding
Small-batch manufacturing
SPC planning
Issue Date: Sep-2007
Source: Zhu, Y.D., Wong, Y.S., Lee, K.S. (2007-09). Framework of a computer-aided short-run SPC planning system. International Journal of Advanced Manufacturing Technology 34 (3-4) : 362-377. ScholarBank@NUS Repository. https://doi.org/10.1007/s00170-006-0610-7
Abstract: Statistical process control (SPC) is recognized as a technique to achieve cost-effective quality control through continuous manufacturing process improvement. But with growing demand for small-batch and high-variety products in the current dynamic market, the involved manufacturing processes are becoming more complex, variable, and flexible, which are not suitable for implementing SPC in the traditional way. Hence, short-run SPC is applied instead. Planning is a critical phase in the implementation of short-run SPC, which includes the formation of part families and the determination of corresponding data collection. To ensure homogeneity of the family members, this paper addresses preliminary analysis on the characteristics and applications of pertinent factors, and statistical analysis for SPC-based part family formation. To improve the efficiency of SPC planning and the adaptation for computer-integrated manufacturing, a framework for a computer-aided short-run SPC planning system is proposed using group technology classification and coding concepts. This invokes a 29-digit hybrid code appended to the Opitz coding scheme. Further, a supportive database is also proposed to facilitate coding information retrieval and system updating. A case study is shown with data collected from injection-mold manufacturing, which typically involves small-batch processes. © 2006 Springer-Verlag London Limited.
Source Title: International Journal of Advanced Manufacturing Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/60363
ISSN: 02683768
DOI: 10.1007/s00170-006-0610-7
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