TY - GEN
T1 - Modeling PCB assembly lines in EMS provider's environment
T2 - 2011 Winter Simulation Conference, WSC 2011
AU - Li, Jing
AU - Nagarur, Nagen
AU - Srihari, Krishnaswami
PY - 2011
Y1 - 2011
N2 - The manufacturing pattern of most Electronic Manufacturing Services (EMS) suppliers in US has transformed to accommodate a high mix and low volume environment. Printed Circuit Board (PCB) assembly at EMS is characterized as 'product oriented production': based on product designs, assemblies are processed with different routings, while operation times and process yields also vary depending on the complexity of products. Therefore, in order to simulate PCB assembly line, it is necessary to combine design information into simulation models. This research endeavor is focused on integrating design factors into a planning system, which is developed based on Discrete Event Simulation (DES) modeling. By applying this proposed system, EMS suppliers can effectively plan the required manufacturing resources, predict production cycle time and 'optimize' resource deployment for a specific product. This architecture can significantly reduce the uncertainties of predictions that are caused by product mixes and provide customized production profile for individual product.
AB - The manufacturing pattern of most Electronic Manufacturing Services (EMS) suppliers in US has transformed to accommodate a high mix and low volume environment. Printed Circuit Board (PCB) assembly at EMS is characterized as 'product oriented production': based on product designs, assemblies are processed with different routings, while operation times and process yields also vary depending on the complexity of products. Therefore, in order to simulate PCB assembly line, it is necessary to combine design information into simulation models. This research endeavor is focused on integrating design factors into a planning system, which is developed based on Discrete Event Simulation (DES) modeling. By applying this proposed system, EMS suppliers can effectively plan the required manufacturing resources, predict production cycle time and 'optimize' resource deployment for a specific product. This architecture can significantly reduce the uncertainties of predictions that are caused by product mixes and provide customized production profile for individual product.
UR - https://www.scopus.com/pages/publications/84863280517
U2 - 10.1109/WSC.2011.6147944
DO - 10.1109/WSC.2011.6147944
M3 - Conference contribution
SN - 9781457721083
T3 - Proceedings - Winter Simulation Conference
SP - 2336
EP - 2345
BT - Proceedings of the 2011 Winter Simulation Conference, WSC 2011
Y2 - 11 December 2011 through 14 December 2011
ER -