TY - JOUR
T1 - Model predictive control for tactical decision-making in semiconductor manufacturing supply chain management
AU - Wang, Wenlin
AU - Rivera, Daniel
N1 - Funding Information:
Manuscript received May 17, 2006; revised February 5, 2007. Manuscript received in final form September 12, 2007. First published July 9, 2008; last published July 30, 2008 (projected). Recommended by Associate Editor J. Lee. This work was supported by a grant from the Intel Research Council and by the National Science Foundation under Grant CMMI-0432439.
PY - 2008
Y1 - 2008
N2 - Supply chain management (SCM) in semiconductor manufacturing poses significant challenges that arise from the presence of long throughput times, unique constraints, and stochasticity in throughput time, yield, and customer demand. To address these concerns, a model predictive control (MPC) algorithm is developed which relies on a control-oriented formulation to generate daily decisions on starts of factories. A multiple-degree-of-freedom observer formulated for ease of tuning is implemented to achieve robustness and performance in the presence of nonlinearity and stochasticity in both supply and demand. The control algorithm is configured to meet the requirements of meeting customer demand (both forecasted and unforecasted), and track inventory and starts targets provided by higher level decision policies. Unique features of semiconductor manufacturing, such as capacity limits, packaging, and product reconfiguration, are formally addressed by imposing different constraints related to starts and inventories. This functionality contrasts that of standard approaches to MPC and makes this controller suitable as a tactical decision tool for semiconductor manufacturing and similar forms of high-volume discrete-parts manufacturing problems. Two representative case studies are examined under diverse realistic conditions with this flexible formulation of MPC. It is demonstrated that system robustness, performance, and high levels of customer service are achieved with proper tuning of the filter gains and weights, as well as the presence of adequate capacity in the supply chain.
AB - Supply chain management (SCM) in semiconductor manufacturing poses significant challenges that arise from the presence of long throughput times, unique constraints, and stochasticity in throughput time, yield, and customer demand. To address these concerns, a model predictive control (MPC) algorithm is developed which relies on a control-oriented formulation to generate daily decisions on starts of factories. A multiple-degree-of-freedom observer formulated for ease of tuning is implemented to achieve robustness and performance in the presence of nonlinearity and stochasticity in both supply and demand. The control algorithm is configured to meet the requirements of meeting customer demand (both forecasted and unforecasted), and track inventory and starts targets provided by higher level decision policies. Unique features of semiconductor manufacturing, such as capacity limits, packaging, and product reconfiguration, are formally addressed by imposing different constraints related to starts and inventories. This functionality contrasts that of standard approaches to MPC and makes this controller suitable as a tactical decision tool for semiconductor manufacturing and similar forms of high-volume discrete-parts manufacturing problems. Two representative case studies are examined under diverse realistic conditions with this flexible formulation of MPC. It is demonstrated that system robustness, performance, and high levels of customer service are achieved with proper tuning of the filter gains and weights, as well as the presence of adequate capacity in the supply chain.
KW - Inventory control
KW - Predictive control
KW - Production control
KW - Production management
KW - Semiconductor device fabrication
KW - Supply chain management
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U2 - 10.1109/TCST.2007.916327
DO - 10.1109/TCST.2007.916327
M3 - Article
AN - SCOPUS:49249108284
SN - 1063-6536
VL - 16
SP - 841
EP - 855
JO - IEEE Transactions on Control Systems Technology
JF - IEEE Transactions on Control Systems Technology
IS - 5
ER -