TY - JOUR
T1 - A systematic study of the extended X-factor in relation to effective system capacity
AU - Delp, D.
AU - Si, Jennie
AU - Hwang, Y.
AU - Pei, Ker-Wei
N1 - Funding Information:
*The first author is supported by an Intel Foundation Graduate Fellowship Award and in part by NSF under grants ECS-9553202 and ECS-0002098. The second author's work is supported by NSF under grants ECS-9553202 and ECS-0002098.
Funding Information:
Deana Delp received her BS and MS degrees in electrical engineering from the University of Kansas and her PhD in electrical engineering from Arizona State University in 2003. Currently, she is a research and development product engineer for Test Advantage in Tempe, AZ. Her research interests include semiconductor manufacturing processes, capacity planning, performance optimization, and queuing models. She was awarded the 1997 Paul E Huebner Award for Outstanding Teaching Assistants at the University of Kansas, the Achievement Rewards for College Scientists (ARCS) scholarship at Arizona State University, and an Intel Foundation Graduate Fellowship Award.
PY - 2005
Y1 - 2005
N2 - More and more productivity is demanded from semiconductor manufacturing lines, thus applying more pressure on the "throughput vs. cycle time" constraint. Cycle time and throughput are traditionally used as independent fine-performance measures. A new school of extended X-factor theory has recently emerged. Proponents of this X-factor contribution measure contend that such a metric, which explicitly takes into account utilization and raw processing time of each machine group, is effective for identifying system capacity constraints. This paper systematically studies the dynamics of a complex semiconductor line to reveal the relationship between the extended X-factor metric and the effective capacity of the line. This paper also studies the sensitivity of the extended X-factor metric to raw processing time and throughput rate to determine quantitatively its robustness and effectiveness when used to evaluate line performance. The analysis shows that the X-factor contribution measure correctly identifies capacity constraining machine groups and is more effective than utilization for identifying these machine groups when the difference in X-factor contribution is significant among the constraining machine groups. Additional capacity at the high X-factor contribution machine group lowered cycle time beyond adding capacity to the highly utilized machine group in a full-scale model. This study provides important insight on when the extended X-factor measure is more indicative of a system capacity constraint, as compared to a utilization measure.
AB - More and more productivity is demanded from semiconductor manufacturing lines, thus applying more pressure on the "throughput vs. cycle time" constraint. Cycle time and throughput are traditionally used as independent fine-performance measures. A new school of extended X-factor theory has recently emerged. Proponents of this X-factor contribution measure contend that such a metric, which explicitly takes into account utilization and raw processing time of each machine group, is effective for identifying system capacity constraints. This paper systematically studies the dynamics of a complex semiconductor line to reveal the relationship between the extended X-factor metric and the effective capacity of the line. This paper also studies the sensitivity of the extended X-factor metric to raw processing time and throughput rate to determine quantitatively its robustness and effectiveness when used to evaluate line performance. The analysis shows that the X-factor contribution measure correctly identifies capacity constraining machine groups and is more effective than utilization for identifying these machine groups when the difference in X-factor contribution is significant among the constraining machine groups. Additional capacity at the high X-factor contribution machine group lowered cycle time beyond adding capacity to the highly utilized machine group in a full-scale model. This study provides important insight on when the extended X-factor measure is more indicative of a system capacity constraint, as compared to a utilization measure.
KW - Capacity Planning
KW - Sensitivity Analysis
KW - Utilization Measures
KW - X-Factory Theory
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U2 - 10.1016/S0278-6125(06)00014-8
DO - 10.1016/S0278-6125(06)00014-8
M3 - Article
AN - SCOPUS:33947163036
SN - 0278-6125
VL - 24
SP - 289
EP - 301
JO - Journal of Manufacturing Systems
JF - Journal of Manufacturing Systems
IS - 4
ER -