1. Current and Future Efforts: Examine the interaction and optimal configuration between the inner and outer loops that define the strategic and tactical plans. Develop a software design and implementation enabling distributed simulation and optimization model executions Explore computational efficiency and tuning of the MPC decision algorithms using: Simultaneous Perturbation Stochastic Approximation (SPSA) search methods and Bilevel Nonlinear Programming Establish the conceptual basis for the efficient, scaleable solution of the representative semiconductor mfg. network presented above. strategic planning inventory planning tactical execution simulation The Outer Loop Problem The Inner Loop Problem Validation Prediction goals limits Fab/Test1 Node Response Customer Demand Modeling Challenges: Long lead times with nonlinear dependence on load Stochastic throughput time and yield Stochastic and potentially erroneous customer demand Blue = Intel Red = Material Subcontractor Green = Capacity Subcontractor vendor1 vendor2 Fab 2 P1,P 2 Fab 1 P1 Fab 3 P2 T1-3 P2 T1-2 P1,P 2 T1-1 P1 Asm 3 Asm 2 Asm 1 vendor3vendor4 vendo r5 vendor 6 1.1 si 1.2 si 2.1 2.2 2.3 3.1 3.2 3.4 pp 3.3 pp 3.7 3.8 pp 3.9 ram 3.10 3.11 3.12 3.5 3.6 3.5 A 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 26 27 24 25 28 29 30 31 32 Box 1 7.2 7.1 Box 2 7.6 7.5 F 43 7.4 pp 45 44 vend 8 46 7. 3p T2-3 T2-1 T2-2 Fin2 Fin1 4.3 5.2 5.1 33 34 vend 7 4.1 4.2 6.2 6.1 6.3 C B D 35 36 37 38 39 40 41 42 A Representative Semiconductor Mfg Network GOALI: Process Control Approaches to Supply Chain Management in Semiconductor Manufacturing NSF Grant Number: DMI- 0432439 D. E. Rivera H.S. Sarjoughian Control Systems Engineering Laboratory Arizona Center for Integrative M&S Department of Chemical and Materials Engineering Department of Computer Science and Engineering Ira A. Fulton School of Engineering Arizona State University, Tempe, AZ H.D. Mittelmann Department of Mathematics and Statistics College of Liberal Arts and Science Arizona State University, Tempe, AZ K.G. Kempf Decision Technologies Intel Corporation, Chandler, AZ Research Objectives: Investigate the use of approaches based on process control for mid-level planning in semiconductor manufacturing supply chain networks, under simulated settings. Approach: Develop strategies using Model Predictive Control (MPC) contextualized for the requirements of semiconductor manufacturing supply chains. Develop optimization and process modeling techniques that support combined tactical and strategic decision- making for SCM. Develop a prototype simulation/optimization environment supporting discrete process flow and optimization decision models. Broader Impact: The MPC-based control and simulation framework will offer new ways towards hierarchical decision-making in large-scale enterprise systems for discrete-parts manufacturing. Significant Results: A novel prototype MPC-based algorithm for semiconductor mfg supply chains has been demonstrated on benchmark problems. An approach for synthesizing discrete-event simulation models and MPC models has been prototyped. Problem: To efficiently manage a large-scale supply chain in semiconductor manufacturing using hierarchical decision and simulation techniques. fabrication test1 decision – how many wafers to start into which factory when decision – how many of which die to put into which packages in which factory when assembly test2 - die good not good faster slower - variable tpt - product good not good faster slower - variable tpt 6GHz 5GHz X-Inc decision – configure, pack, ship, where & when finish - variable demand (volume and time) pack package The Semiconductor Mfg Process D1 D2 D3 t Finish Outs and Transport C1 C2 C3 M10 M20 M30 I10 I20 Demand (over time) Fabrication Starts (a control point) Assembly Starts (a control point) Finish Starts (a control point) Fab/Test1 (a manufacturing system) Test1 Outs and Transport Assm/Test2 (a manufacturing system) Test2 Outs and Transport Finish (a manufacturing system) I30 C4 M40 Shipment (a control point) Die/Package Inventory (an inventory storage) Semi-Finished Inv (an inventory storage) Components Warehouse (an inventory storage) A Fluid Approximation of A Simple Semiconductor Mfg Network Stationary demand and forecast: f a =0 Sinusoidal demand and stationary forecast: f a =0.01 Sinusoidal demand and forecast: f a =0.01 Case Study Examined a three-node semiconductor manufacturing network composed of Fab/Sort, Assembly/Test and Finish/Pack nodes. Evaluated the effect of demand anticipation and filter gain selection on problems involving erroneous forecasts and periodic demand. Model Predictive Control offers a flexible framework for achieving operational goals under conditions of nonlinearity, stochasticity, forecast error, and uncertainty. Erroneous demand and forecast: f a =0.04 Model Predictive Control: Originates from the chemical process industry Optimization-based receding horizon algorithm Readily incorporates hard and soft constraints Robustness to model mismatch and uncertainty achieved through proper choice of tuning (Inventory Levels, WIP) (Actual Demand) (Future Starts) (Forecasted Demand) (Previous Starts) Step 1: Prediction of future inventories using a state estimator based on a nominal model of the supply chain, and relying on current and previous information of the system variables (inventories, customer demand, forecasts and starts). An adjustable filter gain (f a ) is selected to tune for system stochasticity and uncertainty. Step 2: Optimization of current and future starts according to the following objective function: computing node Process Flow Decision Tactical (Model Predictive Control) Strategic (Linear Programming) Semiconductor Supply-Chain Network System Monthly Projection (weekly buckets) Weekly Projection (daily buckets) Physical Process Flow (daily) Experimental Configuration Performance consideration –data size (bytes) –complexity of computation (possibly with large iteration) –network communications (bits per second) MPCDEVSJAVALP Data Storage large data sets complex computation large data sets complex computation medium/large data sets light computation large data sets light computation Network Projection Algorithm Decision Algorithm Model Composability System Interoperability Projection Execution Engine Projection Model Decision Model Decision Execution Engine Model composability refers to composition of models – e.g., Linear Programming Decision Model and Discrete Event System Simulation Projection Model System interoperability refers to the interoperation of execution engines – e.g., ILOG Solver and DEVSJAVA Simulator Develop multi-modeling scheme and distributed simulation for integrating DEVSJAVA, Matlab, and OPL Studio environments Enable simulation of complex process flows, decision making, and their interactions for a representative Intel semiconductor supply-chain network system MPC calculation steps: Design and Implementation Approach For more information regarding this project, contact D.E. Rivera at daniel.rivera@asu.edu or visit our website at http://www.fulton.asu.edu/~csel/Publications.htmdaniel.rivera@asu.edu