Inventory Management in Semiconductor Manufacturing Supply Chains (and Beyond): Insights Gained from...
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Inventory Management in Semiconductor Manufacturing Supply Chains (and Beyond): Insights Gained from a Process Control Perspective.

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About the presenter

Inventory Management in Semiconductor Manufacturing Supply Chains (and Beyond): Insights Gained from a Process Control Perspective

Daniel E. RiveraControl Systems Engineering LaboratoryDepartment of Chemical and Materials EngineeringIra A. Fulton School of EngineeringArizona State [email protected]


About the presenter

About the Presenter

  • Born and raised in San Juan, Puerto Rico

  • Education

    • B.S. ChE degree from the University of Rochester (1982)

    • M.S. ChE degree from the University of Wisconsin (1984)

    • Ph.D. from Caltech (1987)

  • Positions:

    • Associate Research Engineer, Shell Development Company, Houston, TX (1987-1990)

    • Associate Professor, Arizona State University, (1990 - present)


Control systems engineering laboratory projects

Control Systems Engineering Laboratory Projects

  • Chemical Process Control.

    • American Chemical Society-Petroleum Research Fund: “Constrained Multisine Inputs for Plant-Friendly Identification of Chemical Processes”

    • Honeywell Intl. Foundation: “Control Systems Engineering Laboratory”

  • Supply Chain Management.

    • National Science Foundation: “GOALI: Process Control Approaches to Supply Chain Management in Semiconductor Manufacturing”

    • Intel Research Council:

      “Supply Chain Management Research Using Process Control Approaches”

      “Improving Short-term Demand Forecasting in Supply Chain Management”

  • Behavioral Health.

    • NIH-NIDA (subcon via Penn State): “Control Engineering Approaches to Adaptive, Time-Varying Interventions in Drug Abuse Prevention”


  • Http www fulton asu edu csel

    http://www.fulton.asu.edu/~csel


    Presentation outline

    Presentation Outline

    • Control engineering basics review

    • Supply Chain Management (SCM) as a process control problem

    • Application to SCM in semiconductor manufacturing

    • Adaptive interventions in drug abuse prevention

    • Summary and conclusions


    What to take with you from this talk

    What to take with you from this talk

    • The transfer of variance from a valuable system resource to a less expensive one is an important outcome of well-designed control systems, in any application setting.

    • Both feedback and feedforward strategies are needed in the design of effective control systems for delayed, nonlinear, stochastic plants.

    • Process control ideas have significant application in diverse problem settings, for example:

      • supply chain management for semiconductor manufacturing, and

      • adaptive interventions in behavioral health

    • Prepare yourself for life-long learning, since you may very well work on problems you have never imagined (in a not-too-distant future).


    Control engineering

    Control Engineering

    • Control engineering is a broadly-applicable field that spans all areas of engineering:

      • Chemical

      • Electrical

      • Mechanical and Aerospace

      • Civil / Construction

      • Industrial

      • Biomedical

      • Computer Science and Engineering

    • Control engineering principles play a role in everyday life activities.


    Control engineering continued

    Control Engineering (Continued)

    Considers how to manipulate or adjust system variables so that its behavior over time is transformed from undesirable to desirable,

    • Open-loop: refers to system behavior without a controller or decision rules (i.e., MANUAL operation).

    • Closed-loop: refers to system behavior once a controller or decision rule is implemented (i.e., AUTOmatic operation).


    Open loop manual vs closed loop automatic control

    Open-Loop (Manual) vs. Closed-Loop (Automatic) Control

    Open-Loop “Manual”

    Closed-Loop “Automatic”


    An improved closed loop system dual climate control

    An Improved Closed-Loop System(Dual Climate Control)


    An industrial process control problem

    An Industrial Process Control Problem

    are needed to see this picture.

    QuickTime™ and a

    BMP decompressor

    Objective: Use fuel gas flow to keep outlet temperature under control, in spite of occasional yet significant changes in the feed flowrate.


    The shower control problem

    The “Shower” Control Problem

    Disturbances:

    Inlet Water Flows,

    Temperatures

    Controlled: Temperature, Total Water Flow

    The presence of delay or “transportation lag”

    makes this a difficult control

    problem

    Manipulated: Hot and Cold

    Water Valve Positions


    Feedback and feedforward control strategies

    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 (e = r - y, the control error).

    • 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.


    About the presenter

    Flow setpoint

    Controller

    Temp. setpoint

    T

    Sensors

    F

    Actuators

    Shower Problem: Automatic Feedback Control

    Controlled: Temperature, Total Water Flow

    Disturbances:

    Inlet Water Flows,

    Temperatures

    Manipulated:Hot and Cold

    Water ValvePositions

    Cold

    Hot


    Closed loop feedback control block diagram

    d

    Pd

    ec = r - ym

    u

    r

    y

    +

    +

    C

    P

    -

    n

    +

    ym

    Closed-Loop Feedback Control “Block Diagram”

    Disturbances:

    Inlet Water Flows,

    Temperatures

    Manipulated: Hot and Cold

    Water Valve Positions

    Reference:

    Desired Temperature, Total Water Flow

    Controlled:

    Temperature, Total Water Flow

    sensor

    noise

    C = Controller

    P = Plant Model/“Transfer Function”

    Pd = Disturbance Model/“Transfer Function”


    From open loop operation to closed loop control

    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


    Supply chain management

    Factory

    Warehouse

    Retailer

    R

    F

    W

    Supply Chain Management

    • A supply chains consist of interconnected entities (e.g., factories, warehouses, and retailers) which transform ideas and raw materials into delivered products and services


    Motivation

    Motivation

    • In the modern economy, products do not simply compete against other products; supply chains compete against other supply chains.

    • Billions of dollars in potential savings by eliminating supply chain inefficiencies (PriceWaterHouseCoopers, 2000; Kempf, 2004)

    • An effective SCM system can

      • Improve an enterprise’s agility to respond to market upturns (and downturns)

      • Increase revenue while reducing manufacturing and transportation costs.

      • Eliminate excess inventories and reduce safety stocks

      • Lower lead times and improve customer satisfaction


    The business literature can inspire a control engineering approach

    The Business Literature Can Inspire a Control Engineering Approach

    • The “bullwhip” effect (Lee et al., "Information Distortion in a Supply Chain: The Bullwhip Effect", Management Science 43(4) 546, 1997); demand distortion caused by variance amplification of orders upstream in the supply chain

    • This and similar terminology highlight issues relating to stability and performance of a dynamical system, which merit a control-oriented approach.

    • Not strictly an engineering/scientific problem: financial, organizational, and social issues come into play in this problem.


    Bullwhip effect

    “Bullwhip” Effect


    Supply chain inventory management as a level control problem

    LT

    Demand

    Supply Chain Inventory Management as a “Level” Control Problem

    ORDER DECISIONS/STARTS

    CTL

    production time; also

    known as throughput time)

    Starts (Manipulated)

    Net Stock (Controlled)

    (Disturbance)

    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.


    Feedback only inventory control problem

    LT

    Demand

    Feedback-Only Inventory Control Problem

    Starts (Manipulated)

    production time)

    CTL

    Net Stock (Controlled)

    (Disturbance)

    d delivery time)

    In the feedback-only control problem, ordering decisions are calculated based only on perceived changes to “level” (e.g., net stock or equivalent variable).


    Single node inventory problem combined feedback feedforward control

    LT

    Demand

    Single Node Inventory Problem Combined Feedback/Feedforward Control

    Starts (Manipulated)

    production time)

    Demand Forecast

    (known f days beforehand)

    LIC

    Net Stock (Controlled)

    (Disturbance)

    d delivery time)

    In the combined feedback/feedforward problem, a demand forecast is used for feedforward compensation.


    3dof internal model control results random unforecasted demand at t 90

    3DoF Internal Model Control Results(random unforecasted demand at t = 90)

    Feedback-only

    Combined FB/FF

    f = 20, d = 2, f = 1, r = 1, d = 1, nr=1, nd=3, nff=2


    The asu intel scm project team

    The ASU-Intel SCM Project Team

    Involves multiple faculty and graduate students from various departments in Engineering and Mathematics

    • Dept. of Mathematics, CLAS:

      • Professors Dieter Armbruster, Matthias Kawski, Christian Ringhofer and Hans Mittelmann; Eric Gehrig (Ph.D. student), Dominique Perdaen, Ton Geubbels (Visiting Researchers from TU-Eindhoven, The Netherlands).

    • Chemical Engineering, Fulton School:

      • Prof. Daniel E. Rivera; Wenlin Wang and Jay D. Schwartz (Ph.D. students), Michael D. Pew (UG student), and Asun Zafra Cabeza (Visiting Researcher from the University of Seville, Spain)

    • Computer Science and Engineering, Fulton School

      • Prof. Hessam Sarjoughian; Donna Huang and Weilong.Hu (Ph.D. students)

    • Intel collaborators:

      • Karl G. Kempf, Kirk D. Smith, Gary Godding, John Bean, Mike O’Brien


    Proposed architecture

    Proposed Architecture

    The Outer Loop

    Problem

    strategic

    planning

    Validation

    inventory

    planning

    goals

    goals

    tactical

    execution

    limits

    Prediction

    simulation

    The Inner

    Loop Problem


    Semiconductor manufacturing process

    Semiconductor Manufacturing Process


    Fluid analogy for single fab test1 assembly test2 and finish nodes

    Fluid Analogy for Single Fab/Test1, Assembly/Test2 and Finish Nodes


    Modeling issues and challenges

    Modeling Issues and Challenges

    • The manufacturing process displays long throughput times (TPT) which are stochastic and nonlinearly dependent on load

    • Yields are also stochastic

    • There is an error between the forecasted and actual demand, which is also stochastic

    • Additional problem features include package dynamics, stochastic splits in die properties, and multi-factory issues involving cross-shipments, shared capacity, and correlated demands.


    Fab test manufacturing node dynamics

    Fab/Test Manufacturing Node Dynamics

    Load

    Outs

    Throughput Time

    Starts

    Load

    Time


    About the presenter

    Model Predictive Control

    (Inventory Levels,

    WIP)

    (Actual Demand)

    (Forecasted Demand)

    (Previous Starts)

    (Future Starts)


    Model predictive control advantages

    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.


    Case study assembly test2 stochastic split problem

    X

    Number of Die

    Slow devices

    Fast devices

    I31

    I30

    C40

    C41

    M40

    M40

    E1

    E2

    E3

    D1

    D2

    D3

    Case Study: Assembly/Test2 Stochastic Split Problem

    Fab/Test1

    C35

    M10

    The outcome of the Assembly/Test2 process is stochastic in terms of the number of fast and slow devices that result.

    Fast devices can be used to make high speed products (C37). Slow devices can be used to make low speed products (C39).

    I10

    Assmbly/Test2

    C36

    Speed

    M20

    I20

    I21

    C90

    C38

    C37

    C39

    M30

    M30

    M30

    Fin/Pack


    Case 2 no move suppression

    A/T2 Load

    Finishing Load

    Case 2: No Move Suppression

    F/T1 Starts

    CW (Fast)

    Reconfiguration Starts

    CW (Slow)


    Case 2 with move suppression 10 10 10 0 10

    A/T2 Load

    Finishing Load

    Case 2: With Move Suppression [10 10 10 0 10]

    F/T1 Starts

    CW (Fast)

    43.2% variance reduction

    98.9% variance reduction

    69.3% variance reduction

    CW (Slow)

    Reconfiguration Starts

    4.7% variance reduction

    51.5% variance reduction


    Customer service comparison

    Customer Service Comparison

    No Move Suppression

    With Move Suppression

    Unfilled Orders: 4.62%

    Unfilled Orders: 0.34%

    Fast Device Backlog

    Fast Device Backlog

    Unfilled Orders: 7.41%

    Unfilled Orders: 2.38%

    Slow Device Backlog

    Slow Device Backlog


    About the presenter

    D1

    E1

    E2

    D2

    D3

    E3

    E1

    D1

    E2

    D2

    E3

    D3

    “Combination” Problem

    M11

    C37

    C35

    C36

    C38

    M10

    M51

    M50

    I50

    I51

    I10

    I11

    C40

    C42

    C41

    C39

    M20

    M21

    C90

    C91

    I20

    I23

    I21

    I22

    C46

    C43

    C45

    C44

    C47

    C48

    M30

    M30

    M31

    M31

    M31

    M30

    I33

    I30

    I31

    I31

    C49

    C52

    C51

    C50

    M41

    M40

    M40

    M41


    A small semiconductor mfg problem

    = Mats Mfg

    T2-1

    Fin1

    Asm1

    33

    28

    = Inv Hold

    4.1

    5.1

    3.1

    Box1

    7.1

    = Prod Mfg

    37

    34

    3.2

    18

    = Transport

    7.2

    39

    43

    B

    6.1

    20

    24

    40

    44

    7.3

    pp

    7.4

    pp

    vend7

    vend8

    11

    3.3 pp

    3.4 pp

    Fab1

    P1

    46

    T1-1

    P1

    7

    41

    6.2

    2.1

    45

    C

    vendor3

    vendor4

    42

    7.5

    vendor1

    21

    12

    25

    1

    38

    35

    1.1 si

    3.5

    2

    19

    A

    7.6

    Box2

    13

    3

    T2-2

    Fin2

    Asm2

    8

    29

    Fab2

    P1,P2

    T1-2

    P1,P2

    14

    4.2

    5.2

    2.2

    3.6

    36

    F

    4

    30

    15

    5

    1.2 si

    3.7

    3.5

    6

    vendor2

    16

    9

    Fab3

    P2

    T1-3

    P2

    vendor5

    vendor6

    26

    22

    2.3

    3.8 pp

    3.9 ram

    10

    17

    27

    23

    3.11

    3.10

    31

    D

    T2-3

    6.3

    Asm3

    4.3

    3.12

    32

    A “Small” Semiconductor Mfg Problem

    E

    Blue = Intel

    Red = Mat. Sub.

    Green = Cap. Sub.


    Adaptive interventions

    Adaptive Interventions

    • Adaptive interventions individualize therapy by the use of decision rules for how the therapy level and type should vary according to measures of adherence, treatment burden and response collected during past treatment.

    • Adaptive interventions represent an important emerging paradigm for prevention and treatment of chronic, relapsing disorders, such as drug and alcohol abuse, depression, hypertension, obesity, and many other maladies.

    • Also known as stepped care models, dynamic treatment regimes, structured treatment interruptions, and treatment algorithms.


    About the presenter

    Home Counseling-Parental Function Intervention

    • Based on the Fast Track Program (a multi-year intervention designed to prevent conduct disorders in at-risk children).

    • Parental function (the tailoring variable) is used to determine the frequency of home visits (intervention dosage) according to the following decision rules:

      • - If parental function is “low” the intervention dosage should correspond to weekly home visits,

      • - If parental function is “average” then intervention dosage should correspond to bi-weekly home visits,

      • - If parental function is “high” then intervention dosage should correspond to monthly home visits.


    About the presenter

    Parental Function Feedback Loop Block Diagram*

    (to decide on home visits for families with at risk children)

    Disturbances

    Clinical Judgment

    +

    Goal

    Intervention

    I(t)

    Outcomes

    +

    Decision Rules

    Process

    ReviewInterval

    If PF(t) is “Low” then Weekly Home Visits If PF(t) is “Medium” then Bi-Weekly VisitsIf PF(t) is “High” then Monthly Home VisitsIf PF(t) is “Acceptable” then No Visits

    Tailoring Variable

    Estimation

    +

    Reliability/

    Measurement

    Error

    +

    Estimated Parental Function PF(t)

    *Based on material from Collins, Murphy, and Bierman, “A Conceptual Framework

    for Adaptive Preventive Interventions,” Prevention Science, 2004.


    Parental function feedback only control problem

    LT

    Depletion

    Parental Function Feedback-Only Control Problem

    I(t) (Manipulated)

    CTL

    PF(t) (Controlled)

    D(t) (Disturbance)

    In the feedback-only control problem, intervention dosages are calculated based only on perceived changes to “inventory” (parental function PF(t)).


    Summary and conclusions

    Summary and Conclusions

    • The transfer of variance from a valuable system resource to a less expensive one is an important outcome of a well-designed control system, in any application setting.

    • Both feedback and feedforward strategies are needed in the design of effective control systems for delayed, nonlinear, stochastic plants.

    • Process control ideas have significant application in diverse problem settings, for example:

      • supply chain management for semiconductor manufacturing, and

      • adaptive interventions in behavioral health

    • Prepare yourself for life-long learning, since you may very well work on problems you never imagined (in a not-too-distant future).


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