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Inventory Management in Semiconductor Manufacturing Demand Networks: Insights Gained from a Process Control Perspective.

todd-rosario
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  1. Inventory Management in Semiconductor Manufacturing Demand Networks: Insights Gained from a Process Control Perspective Daniel E. RiveraControl Systems Engineering LaboratoryDepartment of Chemical and Materials Engineeringand Institute for Manufacturing Enterprise SystemsArizona State University, Tempe, AZ 85287-6006 daniel.rivera@asu.edu Daniel E. Rivera, Intel GMSNC Speaker Series

  2. About the Presenter • Education: • B.S. ChE degree from the University of Rochester (1982) • M.S. ChE degree from the University of Wisconsin (1984) • Ph.D. in ChE from Caltech (1987) • Positions Held: • Associate Research Engineer, Shell Development Company, Houston, TX (1987-1990) • Associate Professor, Arizona State University, (1990 - present) Daniel E. Rivera, Intel GMSNC Speaker Series

  3. Presentation Outline • The Grand Vision: effective outer and inner loop decision policies for inventory management in large demand networks • Process control fundamentals, illustrated on the inventory management of a single node in a supply chain • Model Predictive Control: a practical implementation strategy • Application to Intel benchmark problems: • Fab/Sort-Assembly/Test networks (Problems 2a and 3a) • Six node, three echelon logistics problem • Future work, references and acknowledgments. Daniel E. Rivera, Intel GMSNC Speaker Series

  4. The ASU-Intel SCM Project Team • Involves multiple faculty, postdoctoral fellows, and graduate students from Engineering and Mathematics • Dept. of Mathematics, CLAS: • Prof. Dieter Armbruster, Tae-Chong Jo (postdoc) and Rama Chidambaram (Ph.D. student) • Prof. Matthias Kawski, Eric Gehrig (Ph.D. student) • Prof. Christian Ringhofer • Chemical Engineering, CEAS: • Prof. Daniel E. Rivera, Junhyung Ryu (postdoc) and Wenlin Wang (Ph.D. student). • Primary Intel collaborators: • Karl G. Kempf, Kirk D. Smith Daniel E. Rivera, Intel GMSNC Speaker Series

  5. Goals of Supply Chain Management in Semiconductor Manufacturing • Maximize customer service factors: • The right products • In the right quantity • In the right place • At the right time • Maximize profits by maximizing revenue while minimizing costs • Production • Transportation • Storage • Material Billions of dollars can be saved by efficient SCM in the semiconductor industry (PriceWaterHouseCoopers, 2000; K. G. Kempf et al., 2001) Daniel E. Rivera, Intel GMSNC Speaker Series

  6. Introduction to Our Approach (from K. Kempf) Mid-Range Tactical FSM + foundries Each Fab/Sort ATM + sub cons Each Assm/Test Factory Floor Execution Long Range Plan (LRP) Each Factory Materials 1 Year Horizon Start Materials into Factory Ship to Customers Daniel E. Rivera, Intel GMSNC Speaker Series

  7. The “Outer Loop” Mid-Range Tactical FSM + foundries Each Fab/Sort Setting The Goals (how much to start, buffer, finish) ATM + sub cons Each Assm/Test Each Supplier Materials 1 Year Horizon Start Materials into Factory Ship to Customers Daniel E. Rivera, Intel GMSNC Speaker Series

  8. The “Inner Loop” Mid-Range Tactical FSM + foundries Each Fab/Sort Setting The Goals (how much to start, buffer, finish) Hitting The Goals (build to X, X= order, stock, …) (Y to order, Y= config, finish, …) ATM + sub cons Each Assm/Test Each Supplier Materials 1 Year Horizon Start Materials into Factory Ship to Customers Daniel E. Rivera, Intel GMSNC Speaker Series

  9. out prod DEMAND OUTER LOOP The Tactical Inventory Control Problem TARGET TARGET TARGET TARGET TARGET fb fb fb adjust adjust adjust Fab Starts Test1 Outs Assm Starts Test2 Outs x ADI Finish SFGI CW in wip wip in wip in ff ff ff INNER LOOP Daniel E. Rivera, Intel GMSNC Speaker Series

  10. Supply Chain Sequence(Fluid Analogy for Single F/S, A/T Node Problem) Fabrication Starts (a control point) Fab/Test1 (a manufacturing system) Test1 Outs and Transport Die/Package Inventory (an inventory storage) Assembly Starts (a control point) Assm/Test2 (a manufacturing system) Test2 Outs and Transport Semi-Finished Inv (an inventory storage) Finish Starts (a control point) Finish (a manufacturing system) Finish Outs and Transport Demand (over time) C1 M1 T1 I12 C2 M2 T2 I23 C3 M3 T3 t D1 D2 D3 Daniel E. Rivera, Intel GMSNC Speaker Series

  11. Backlog One Test2 Fab/Test Shared Capacity Perishable Two F/T xShips Non-shared Capacity Package Splits CrossShips Non-correlated Dmd Backlog Packages DownBinning A/T xShips Correlated Dmd 4a 2 3 5 6 Test1&2 Splits 4b Benchmark Problem Set (developed by Intel) Basic (deterministic) Problem Variable Supply Variable Demand Basic (stochastic) Problem 1 Real Problem Daniel E. Rivera, Intel GMSNC Speaker Series

  12. = Inventory Holding BLUE = Materials In GREEN = SubCons BLACK = Us RED = Products Out 5 Test1 Fuse1 Asm1 28 33 = Manufacturing 5.1 3.1 Box1 7.1 = Materials Mfg = Transport 37 34 3.2 18 7.2 39 43 6.1 2 20 24 44 7.3 pp 7.4 pp 40 vend7 vend8 11 3.3 pp 3.4 pp 41 Fab1 P1 46 Sort1 P1 7 6.2 2.1 3 45 vendor3 vendor4 7.5 42 vendor1 21 12 25 1 38 35 1.1 si One location 1 Box2 3.5 2 19 7.6 13 3 Asm2 8 Fab2 P1,P2 Sort2 P1,P2 Test2 Fuse2 29 14 2.2 3.6 5.2 36 6 4 30 15 5 1.2 si 3.7 3.5 4.2 4.1 6 vendor2 16 9 Fab3 P2 Sort3 P2 vendor5 vendor6 26 22 2.3 3.8 pp 3.9 ram 10 17 27 23 3.11 3.10 4.3 4 31 Asm3 Test3 3.12 6.3 32 A “mini” supply chain Problem represents 10% of Intel’s internal supply chain, excluding logistics Daniel E. Rivera, Intel GMSNC Speaker Series

  13. Some Conclusions • Inventory management in semiconductor manufacturing demand networks represents a challenging nonlinear, stochastic dynamical systems problem which merits a control-oriented approach • Decision and control policies must be robust to supply side variability, while performing well under conditions of uncertain demand. • Effective management of assets (in the form of safety stock levels and capacity) go hand-in-hand with a well designed control policy. • High levels of safety stock and excess capacity simplify the design of the control policy, but are economically inefficient for the enterprise. • The effective, coordinated use of information is key. • Ability to take advantage of all available information will significantly reduce supply chain inefficiencies, despite the uncertainties characterizing the problem. Daniel E. Rivera, Intel GMSNC Speaker Series

  14. Control Engineering • Considers how to manipulate system variables in order to transform dynamic behavior from undesirable to desirable • Open-loop: refers to system behavior without a control policy • Closed-loop: refers to system behavior once a controller or decision policy has been implemented. • Many examples of closed-loop control applications in society: • Cruise control and climate control in automobiles • The “sensor reheat” feature in your microwave oven • The insulin pump for Type-I diabetics • “Fly-by-wire” systems for high-performance jet aircraft • Many, many more… Daniel E. Rivera, Intel GMSNC Speaker Series

  15. LT Demand Supply Chain “Level” Inventory Control Problem ORDER DECISIONS/STARTS CTL production time) d delivery time) Meet demand (with forecast possibly given f days beforehand) for a node with day production (or order fulfillment) time and d delivery time. Daniel E. Rivera, Intel GMSNC Speaker Series

  16. The “Shower” Control Problem The presence of delay or “transportation lag” makes this a difficult control problem Daniel E. Rivera, Intel GMSNC Speaker Series

  17. Definitions • Controlled Variable (y): system variable that we wish to keep at a reference value or setpoint (r). • Manipulated Variable (u): system variable whose adjustment influences the response of the controlled variable; its value is determined by the controller/decision policy. • Disturbance Variable (d): system variable that influences the controlled variable response, but cannot be manipulated by the controller; disturbance changes occur external to the system (hence sometimes referred to as exogeneous variables) Daniel E. Rivera, Intel GMSNC Speaker Series

  18. The “Shower” Control Problem Controlled: Temperature, Total Water Flow Disturbances: Inlet Water Flow and Temperature Variations Manipulated: Hot and Cold Water Valve Positions Think about what may constitute controlled, manipulated and disturbance variables in this system Daniel E. Rivera, Intel GMSNC Speaker Series

  19. From Open-Loop Operation to Closed-Loop Control Temperature Deviation (Measured Controlled Variable) Open-Loop (Before Control) Hot Water Valve Adjustment (Manipulated Variable) Closed-Loop Control The transfer of variance from an expensive resource to a cheaper one is one of the major benefits of engineering process control Daniel E. Rivera, Intel GMSNC Speaker Series

  20. Inventory Management Control Problem • Controlled Variable: Net Stock I(k) (on-hand inventory less backorders); note: could use inventory position as an alternative. • Manipulated Variable: Daily Orders or Starts O(k) • “Disturbance” Variable: Daily Demand VariationsD(k) Daniel E. Rivera, Intel GMSNC Speaker Series

  21. Feedback and Feedforward Control Strategies • In feedback control strategies, a controlled variable (y) is examined and compared to a reference value or setpoint (r). The controller issues actions (decisions on the values of a manipulated variable (u)) on the basis of the discrepancy between y and r. • In feedforward control, changes in a disturbance variable (d) are monitored and the manipulated variable (u) is chosen to counteract anticipated changes in y as a result of d. Daniel E. Rivera, Intel GMSNC Speaker Series

  22. LT Demand Single Node Inventory Control Problem Feedback-Only Control Starts O(k) (Manipulated) production time) CTL D(k) (Disturbance) Net Stock I(k) (Controlled) d delivery time) In the feedback-only control problem, starts decisions are calculated based only on perceived changes to “level” (e.g., net stock, inventory position, or equivalent variable). Daniel E. Rivera, Intel GMSNC Speaker Series

  23. LT Demand Single Node Inventory Problem Combined Feedback/Feedforward Control Starts (Manipulated) production time) Demand Forecast (known f days beforehand) CTL (Disturbance) Net Stock (Controlled) d delivery time) In the combined feedback/feedforward problem, a demand forecast is used for feedforward compensation. Daniel E. Rivera, Intel GMSNC Speaker Series

  24. Combined Feedback/Feedforward Block Diagram (Forecasted Demand Variations) (Desired Net Stock Or Inventory Position) (Unforecasted Demand Variations) (Starts or Orders) (Measured Net Stock Or Inventory Position) C = Feedback Controller CF= Feedforward Controller p = Process “Transfer Function” pd = Disturbance “Transfer Function” Daniel E. Rivera, Intel GMSNC Speaker Series

  25. Internal Model Control (IMC) • The IMC (or Q-parametrization) structure is an alternate yet equivalent means of representing a classical feedback-feedforward structure. • The IMC design procedure is a convenient two-step procedure for designing closed-form Q-parametrized control systems. • IMC design generates insights into the fundamental requirements for inventory management using a control-oriented approach. • Results presented at the 2002 AIChE Annual Meeting (Paper 268c, Rivera et al., “Control-oriented approaches to inventory management in semiconductor manufacturing supply chains.”) Daniel E. Rivera, Intel GMSNC Speaker Series

  26. Three-Degree-of –Freedom (3DOF) FB/FF IMC structure (Demand Forecast) (Forecast Error) (Supply Received by Customer) (Orders /Starts) (Net Stock) (Net Stock Setpoint) Daniel E. Rivera, Intel GMSNC Speaker Series

  27. Three Degree of Freedom IMC Results(random forecast error at t = 90) Feedback-only Combined FB/FF f = 20, 10,d = 2, f = 1, r = 1, d = 1, nr=1, nd=3, nff=2 Daniel E. Rivera, Intel GMSNC Speaker Series

  28. Single Node Supply Chain Inventory Dynamics Simulator (used in ECE 100: Introduction to Engineering Design; developed by M. Pew and D.E. Rivera) • Single node simulator for a single product system with a 3 day order fulfillment time • Graphical policy simulator (spreadsheet)(project description) allows students to graphically visualize animations of standard EOQ and Proportional-Integral Derivative (PID) policies for conditions involving deterministic and stochastic demand, presence or absence of forecast info, and so forth • Interactive policy simulator (spreadsheet) (description) allows students to take a shot at operating the system manually. Daniel E. Rivera, Intel GMSNC Speaker Series

  29. Proportional-Integral Derivative (PID) Feedback Controller Policy • Current Order = Previous Order + Scaled Corrections from Current and Prior Control Errors + Scaled Controller “Move” • K1, K2, K3, and K4 are tuning constants in the controller / decision policy; e(k) is the control error, or difference between setpoint and controlled variable. • The tuning constants are obtained using a model-based rule specified by the IMC design procedure (ECE 100 Pres. 12 ; Pres. 13). Daniel E. Rivera, Intel GMSNC Speaker Series

  30. Forecast-Adjusted Setpoint Strategy (used in the enhanced IMC-PID policy in the graphical simulator) (Demand) (Demand Forecast) (Orders) (Inventory position) (Inventory position Setpoint) C = Controller P = Process “Transfer Function” Pd = Disturbance “Transfer Function” Daniel E. Rivera, Intel GMSNC Speaker Series

  31. Model Predictive Control Daniel E. Rivera, Intel GMSNC Speaker Series

  32. (Inventory Levels, Load) (Actual Demand) (Forecasted Demand) (Starts) Daniel E. Rivera, Intel GMSNC Speaker Series

  33. Model Predictive Control Appeal for Dynamic Inventory Management in Supply Chains • As an optimizer, an MPC-based algorithm can minimize or maximize an objective function that represents a suitable measure for supply chain performance. • As a controller, an MPC algorithm can be tuned to achieve stability, robustness, and performance in the presence of plant/model mismatch, failures and disturbances which affect the system. Daniel E. Rivera, Intel GMSNC Speaker Series

  34. Keep inventory levels at setpoint Penalize change in starts Keep starts at targets Starts constraints { Inventory level, capacity constraints Daniel E. Rivera, Intel GMSNC Speaker Series

  35. Model Predictive Control Advantages • Ability to handle large multivariable systems • Ability to enforce constraints on manipulated and controlled variables • Effective integration of feedback, feedforward controller modes; ability to incorporate anticipation • Novel formulations (such as hybrid MPC) enable the application to systems involving both discrete-event and continuous variables. Daniel E. Rivera, Intel GMSNC Speaker Series

  36. Demand Center F/S SFGI A/T ADI Two Node Example =Transport Link =Mfg Node =Inventory Holding F/S: Fabrication/Sort Facility A/T: Assembly/Test Facility ADI: Assembly-die Inventory SFGI: Semi-finished goods inventory Daniel E. Rivera, Intel GMSNC Speaker Series

  37. LT LT Demand “Fluid” Analogy to the Two Node Network q1 F/S starts (8 weeks) ADI level ADI A/T starts q2 (2 weeks) SFGI level SFGI Shipments q3 (1 week) Daniel E. Rivera, Intel GMSNC Speaker Series

  38. LT LT Demand Centralized MPC Control Structure F/S starts ADI level MPC Forecast ADI A/T starts SFGI level Actual Disturbance: Demand Variations Controlled Variables: ADI and SFGI levels SFGI Shipments Manipulated Variables: F/S and A/T starts Daniel E. Rivera, Intel GMSNC Speaker Series

  39. Simulation Studies • Case studies from our 2003 American Control Conference paper (Wang, Rivera, and Kempf, “Centralized Model Predictive Control Strategies for Inventory Management in Semiconductor Manufacturing Supply Chains” (paper) (slides)) • TPT of F/S = 8 wks, TPT of A/T = 2 wks with no model or demand uncertainty • TPT of F/S = 8 wks, TPT of A/T = 2 wks with TPT mismatchbut no demand uncertainty • TPT of F/S = 8 wks, TPT of A/T = 2 wks with unforecasted autoregressive demand We will present the results of Case Study 3 in this talk, as well as some recent results developed by Ph.D. student Wenlin Wang. Daniel E. Rivera, Intel GMSNC Speaker Series

  40. Un-Forecasted Autoregressive Demand Variations(without TPT variability) Output Weights=[1 1]; Move Suppression=[1 1], p=50, m=20 ADI Level F/S starts SFGI Level A/T starts Demand Delivery Daniel E. Rivera, Intel GMSNC Speaker Series

  41. Un-Forecasted Autoregressive Demand Variations(without TPT variability) Output Weights=[1 100]; Move Suppression =[1 1], p=50, m=20 ADI Level F/S starts SFGI Level A/T starts Demand Delivery Daniel E. Rivera, Intel GMSNC Speaker Series

  42. Un-Forecasted Autoregressive Demand Variations(without TPT variability) Output Weights=[100 1]; Move Suppression =[1 1], p=50, m=20 ADI Level F/S starts SFGI Level A/T starts Demand Delivery Daniel E. Rivera, Intel GMSNC Speaker Series

  43. I30 t D1 D2 D3 Single F/S, A/T problem with uncertainty (Problem 2a) Control Point C1 TPT (30,45) days based on load M10 Yield (0.93,0.97) uniformly distributed I10 Control Point C2 TPT (5,7) days uniformly distributed M20 Yield (0.98,0.99) uniformly distributed I20 Control Point C3 TPT (1,3) days uniformly distributed M30 Yield (0.985,0.995) uniformly distributed Control Point C4 M40 Demand Daniel E. Rivera, Intel GMSNC Speaker Series

  44. Nonlinear Stochastic Relationship between TPT and Load Load Throughput Time Outs Starts 100% 90% 70% Time Load Daniel E. Rivera, Intel GMSNC Speaker Series

  45. LT LT LT Demand t D1 D2 D3 Centralized Control Structure Controller ADI Forecast Real SFGI CW Daniel E. Rivera, Intel GMSNC Speaker Series

  46. Simulation Results: Starts, Inventory levels Output Weights=[1 1 1]; Move Suppression =[10 10 10], P=60, m=50 ADI Inventory F/S Starts SFGI Inventory A/T Starts CW Inventory Shipments Demand Delivery Backorders Daniel E. Rivera, Intel GMSNC Speaker Series

  47. Simulation Results: Load Rates F/S Load A/T Load FinishingLoad Daniel E. Rivera, Intel GMSNC Speaker Series

  48. Simulation Results with Revised Parameters Output Weights=[1 1 1]; Move Suppression =[10 10 10], P=60, m=50 ADI Inventory F/S Starts SFGI Inventory A/T Starts CW Inventory Shipments Demand Delivery Backorders Daniel E. Rivera, Intel GMSNC Speaker Series

  49. Simulation Results with Revised Parameters F/S Load A/T Load Capacity increased from 2500 to 3000 units FinishingLoad Daniel E. Rivera, Intel GMSNC Speaker Series

  50. C13 M50 C15 Single F/S, A/T Problem with Packaging (Prob. 3a) C12 M10 In the Assembly/Test2 process, a “package” must be available for each die I10 I50 C14 M20 M20 I20 C16 To begin Assembly, there must be die in I10 and packages in I50. Otherwise manufacturing in M20 does not start. M30 I30 C17 M40 D1 D2 D3 Daniel E. Rivera, Intel GMSNC Speaker Series

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