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

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slide1

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”
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 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.
slide14

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.
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

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

slide31

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

slide37

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.
slide40

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.
slide41

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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