Configuration, monitoring and control of semiconductor supply chains

(1)

Configuration, monitoring and control of

semiconductor supply chains

Jan. 7, 2005

Factory Operations Research Center

(FORCe II)

Shi-Chung Chang (task 1) Argon Chen (task 2)

Yon Chou (task 3)

National Taiwan University

(2)

Multiple Threads of

Manufacturing Services

Design

Fab C/P Packaging Final Test

Optional Captive Fab

Optional Captive C/P

Packaging Final Test

C/P Packaging Final Test

Fab C/P Packaging Final Test

control owner control point

(3)

Challenges of Manufacturing Services

• Effective collaboration between

engineering and manufacturing

• Reliable delivery

• Supply and service monitoring and

control

supply chain

eng

customer

monitoring & control

business

reliable

(4)

Supply Chain Configuration,

Monitoring and Control

Supply Chain Configuration,

Monitoring and Control

Supply Chains

Control

•

Robust configuration

•

Monitoring

•

Dynamic control Performance variation variety Objectives: to enhance

•

Predictability

•

Scalability

•

Service differentiability

Task 1: behavior modeling

Task 2 Task 3

(5)

Task 1: Empirical Behavior Modeling

PI: Shi-Chung Chang

Co-PIs: Da-Yin Liao, Argon Chen To develop methodology:

1. Definition of quality of service (QoS) metrics

– Scalability – Controllability

– Service Differentiability

2. Modeling and simulation

– Performance

– Variability (engineering and business)

– Capacity allocation & control

Empirical Behavior Modeling configuration &control uncertainties environment settings performance metrics

(6)

Mid-year Progress – Task 1

• Definition of Performance Metrics

– Have identified reduced set of QoS metrics from SCOR

– Have defined the QoS translation problem among nodes of

supply chain

– Is developing a queueing network-based QoS translation

method

• Fab Behavior Modeling

– Have developed a baseline model

• open queueing nework with priority • mean and variability

– Have defined a response surface fitting problem – Is developing response surface modeling method

• Simulation

– Have developed a baseline Fab simulator based on QN

(7)

Performance Metrics Definition

• SCOR-based six categories

– quality, cost, cycle time, delivery, speed, and service FAB Process _ProcessCP ASSY Process FT Process Demand Supply Supply Supply Supply Design Houses IDM Supply SSC Levels Service Differentiation

Chain & nodal Requirements

(8)

Average Days per Schedule Change % of Orders Scheduled to Customer Request

D Delivery Performance to Customer

Request Date

Ratio of Actual to Theoretical Cycle Time

Forecast Accuracy Forecast Cycle Make Cycle Time

Actual-to-theoretical Cycle Time

CT

Machine Wait Time

In-process Failure Rates

Finished Goods Inventory Carrying Costs Capacity Utilization

% of Downtime due to Non-availability of WIP

Co

Yield Variability Yield

Q

Responsiveness Lead Time Re-Plan Cycle Time

Schedule Interval

% Orders/Lines Received On-Time to Demand Requirement

Sv

Schedule Achievement

Delivery Performance to Customer Request Date

%Orders/Lines Received with Correct Shipping Documents

Intra-Manufacturing Re-Plan Cycle

% Schedules Generated within Suppliers’ Lead Time

% Schedules Changed within Suppliers’ Lead Time

Sp

Key Level 1 Metrics for SSC

(9)

Characterization of Variability

• Sources

– Process varieties – Engineering changes – Operation excursions – Demand plan

• Hybrid models

– Response surface – Priority queueing – Simulation Arrival Parameters Nodal Performance Measures Service Parameters Characterization by Mean & Variance

Th, Tl machine (τ, 2 s C ) (λh, C )_h2 queue (λl, C ) _l2 dh, dl

(10)

Behavior Modeling Methodology

• Priority open queueing network (OQN)

– Nodal and system characterization by mean and variance – Response surface matching with empirical data

• Simulation for performance prediction/model adaptation

Response surface PQN model matching FAB Process _ProcessCP ASSY Process FT Process Demand Supply Supply Supply Supply Design Houses IDM Supply Simulator Monitored actual or empirical performance Configuration & control Inputs Model adaptation Simulated performance Uncertainties Environment settings Monitoring

(11)

• Discipline : Priority Ma c h in e G ro u p 1 Ma c h in e Gr o u p 2 Ma c h in e Gr o u p M b1 b2 bM+1 bJ -M+1 bJ -M+2 bM+2 bM b2M bJ E xt e r n a l Ar r iv a ls De p a rt u r e s flo w o f ty p e A p a r ts flo w o f ty p e B p a r ts

• Routing : Deterministic with feedback • Arrival : General independent processes • Job Class : Part type

• Queue : Infinite buffer for each step

• Node : Group of identical failure prone machines

• Service : General time distribution (single/batch, failure)

Priority OQN Model: Fab Example

(12)

Decomposition Approximation

• Two Notions

Arrival Parameters Arrival Parameters Nodal Performance Measures Nodal Performance Measures Service Parameters Service Parameters

– Each Network Node as an Independent GI/G/m Queue

– Two Parameters, Mean & SCV, to Characterize Arrival & Service Processes

(13)

Three Flow Operations

ij j C b a C n i ai aj

∑

= + = 1 2 2 ij i 0j j q n i

∑

= + = 1 λ λ λ j j i i qij • Merging ij i ij λ q λ = ij ijC q q C_ij2 = _di2 +1− j j i i qij • Splitting i i ) ( 2 d d C, ) ( 2 a C , λ ) , ( 2 s C τ Input Output •Departure Rate d = λ •Inter-departure Time SCV

(

)

2 2 2 ₁ s a d C C C ₌ ₋ρ2 ₊ρ2

(14)

Traffic Equations:

F(

.

)

Traffic Equations:

F(

.

)

Performance/

QoS

Measures

: WIP, Cycle Time, ... : WIP, Cycle Time, ...

)

,

(

τ

_n

C

_sn2 n a

λ

∑

= + = M m mn n e n 1 λ δ λ λ_a

∑

= + = M m mn m n e q 1 a λ δ λ • Traffic Rate 2 n

C

_a

∑

=

+

=

M m mn m n n

C

b

C

1 2 2 a a

a

• Traffic Variability

(15)

QoS Translation

• Given

– Higher Level/Coarse QoS spec. – Service Node Parameters

– Flow Routing Information

– Priority OQN model F(

α, θ,

Q)

– FCFS Discipline for Each Priority

Q

(

,Q

)

Y = F α ,θ

• Derive by solving

– External control specs.

– Nodal Level QoS reponsibility y = G

(

ω,θ

)

α

_{( , )}

λ

_e 2 e

C

=

θ

=

(

τ

_n

,

C

_sn2

)

Y

(16)

Response Surface Modeling

• Given

– Empirical I/O Characterization (I, O) – Service Node Capacity m_n – Flow Routing Information

– Priority OQN model F(

α, θ,

Q)

– FCFS Discipline for Each Priority

Q

• Fit F(

α, θ,

Q) to (I, O) and derive

(17)

Modeling Capacity Allocation

Deduct Capacity Allocated to Higher Priority PULL+PCA* PUSH+PCA FLOW_IN Estimation MAX_FLOW_IN CONVERGE ? No Yes

Targets (Capac. Alloc.) & Cycle Time Estimates

for the priority

Next Priority *P.C.A: Proportional Capacity Allocation

(18)

Deliverables – task 1

• July 2005

– Selection and definition of key QoS metrics

– Translation algorithm of QoS from chain to nodes – Fab behavioral model

• Priority, capacity allocation, source of variation

– Fab behavioral simulator

• July 2006

– Methodology generalization to the service thread

from design house, fab to circuit probe

– Methodology and tool integration with control

(19)

Task 2: Robust Allocation and

Monitoring

Task 2: Robust Allocation and

Monitoring

PI: Argon Chen Co-PI’s: David Chiang, Andy Guo Will develop:

1. A baseline supply chain allocation strategy

– Robustness on performance

– Robustness on performance variability – Quadratic approximation

2. Supply chain sensitivity and monitoring

– 2nd _{moment performance of priority queueing network}

– Decomposition of supply chain performance – Ranges of optimality and feasibility

(20)

Mid-year Progress – Task 2

• Supply chain simulation model

– Have defined environment variables and variability sources – Have defined control policies for various supply chain threads – Have built a preliminary simulation model using ARENA

• Supply chain allocation programming

– Have defined allocation decision variables – Have formulated constraints

– Have started development of implementation strategies

• Supply chain allocation optimization

– Have studied quadratic programming methodologies – Have studied Wolfe-dual based algorithm

(21)

Semiconductor Supply Chain

Fab CP Assm FT

SC Route SC Control Point

(22)

Supply Chain Routes and Threads

Route (r) r=1 r=2 r=3 Thread (i) i=1 i=2 i=3

(23)

Supply Chain Allocation

• X

_rikq

(%)

– Proportion of production for product type k at

service-level q allocated to supply chain thread i of route r Product k Service level q Route 1 Route 2 X_11kq% X_12kq% X_21kq% X_22kq%

(24)

Supply Chain Behavior Model

y_jkq : the jth performance index for product k at service level q Empirical Model

E(yjkq)=fjkq(xrikq| vrikq, erikq) SD(yjkq)=gjkq(xrikq| vrikq, erikq)

…..

Uncertain Factors vrikq varieties

Allocation %

xrikq

eng changes exceptions

Performance Metrics yjkq . . . . …..

Environment Settings etikq demands capacity thread scheduling policies business mode

(25)

Supply Chain Constraints (I)

Product Mix Constraints

– The proportion of product type k to total production

Priority Mix Constraints

– The proportion of service-level q production to total

production rikq

X

_k r i q

k

ρ

=

∀

∑∑∑

rikq q

X

r i k

q

φ

≤

∀

∑∑∑

(26)

Supply Chain Constraints (II)

Demands Fulfillment Constraint

– The total production is equal to or less than the demand

95 .

0 =

∑∑∑∑

r i k q

rikq

X

1 ≤

∑∑∑∑

r i k q

rikq

X

Example:

(27)

Supply Chain Constraints (III)

Route Mix Constraints

– The proportion of production allocated to route r can not exceed a predetermined limit

Thread Mix Constraints

– The proportion of production allocated to thread i can not exceed a predetermined limit

rikq r i k q

X

≤

α

∀

r

∑∑∑

rikq i r k q X ≤

β

∀i

∑∑∑

(28)

Supply Chain Constraints (IV)

Resource (Capacity) Constraints

– The proportion of capacity consumed by route r cannot exceed a given proportion

of route r capacity to the total capacity

Where

– m_rk=Uω_{ki :}the percent use of route r by one percent of production for product type k allocated to route r

– C_r: the proportion of route r available capacity to total capacity

– ∑C_r: the proportion of available capacity to total capacity

– U(%): capacity utilization (production to total capacity ratio)

– ω_ki: the capacity of route r consumed by one unit of product type k

* r rikq rk r k i q X m C _ _  ≤ ∀    _ _   

∑ ∑∑

∑ ≤

r r

C

1

(29)

Supply Chain Allocation Optimization –

Goal Programming

Supply Chain Allocation Optimization –

Goal Programming

Goal Constraints Business Scenarios: erikq Stochastic Elements: vrikq xrik1 Service Priority (q = 1) Min fjk1(SD(yjk1)) Max or min fjk1(E(yjk1)) Service Priority (q = 2) Min fjk2(SD(yjk2)) Max or min fjk2(E(yjk2)) Service Priority (q = Q) Min fjkQ(SD(yjkQ)) Max or min fjkQ(E(yjkQ)) … xrik2 xrik(Q-1) Goal Constraints Goal Constraints Objectives: Max or Min

E(yjkq)=fjkq(xrikq| vrikq, erikq)

Min

SD(yjkq)=gjkq(xrikq| riikq, erikq)

Constraints:

Resources Constraints

Demands Fulfillment Constraints Product-mix Constraints

Priority-mix Constraints Route-mix Constraints, etc

(30)

Solution Methodology

• Quadratic Stochastic Goal-Programming

– Transform the model to a piecewise

quadratic programming model

– Construct Wolfe-dual based algorithm for

the piecewise quadratic programming

model

– Develop a preemptive goal programming

approach for differentiable service priorities

– Perform sensitivity analysis through

(31)

Implementation

Case 1: Order fulfilled based on X

_rikq

Implementation

Case 1: Order fulfilled based on X

_rikq

Order

max

r=1 r=2 Route

Thread

i=1 i=2 i=1 i=2

X_11A1 _X 12A1 _X 21A1 X_22A1 X_rikq buckets

(32)

Implementation

Case 2: Order fulfilled by a lower priority

Implementation

Case 2: Order fulfilled by a lower priority

Order

over over over over

max Route Thread X_rikq buckets r=1 r=2

i=1 i=2 i=1 i=2

X_11A1 _X 12A1 _X 21A1 X_22A1 r=3 r=4 i=1 i=1 X_31A2 X_41A2

(33)

Deliverables – Task 2

• Supply chain quadratic goal programming model

and solution (Model, Methodology, Report)

(July-05)

– Supply chain simulation model (March-05) – Supply chain planning goals (April-05)

– Supply chain goal programs (May-05)

– Baseline supply chain allocation model and

solution (July-05)

• Supply chain sensitivity analysis and monitoring

methodology (Model, Methodology, Report)

(July-06)

(34)

Supports Needed – Task II

• Supply chain network data

– Number of supply chain levels – Number of facilities at each level

– Capacity and capability of each facility – Locations of facilities, etc.

• Supply chain operations data

– Facility reliability data – Cycle time

– Dispatching policies – Control policies

– Order fulfillment policies, etc.

• Supply chain allocation practice • Supply chain performance data

(35)

Task 3: Dynamic Control

Will develop:

1. A control model for demand support

2. A control method to enhance delivery, speed

and service

1 2 .. n C/P

Fab

dynamic events in eng. & mfg.

orders

PI: Yon Chou

Co-PI: Shi-Chung Chang

1 2 C/P

Fab Sales

channel Salient scope:

advanced info. of inventory and business plan

Microchip Company

(36)

Mid-year Progress – Task 3

1. A control model for demand support

– Have defined the problem scope

– Have outlined the model

2. A control method to enhance delivery,

speed and service

– Have developed a workload flow model

– Have developed an integer program (with

preliminary implementation)

(37)

3.1 Salient feature

• Demand (technology, product, etc.) has a life

cycle

• Demand forecasts and channel inventory are

signals. The total demand is a more reliable

estimate.

Mean-reverting model

growth _mature obsolete

time true

demand

(38)

3.1 A control model for demand support

• Objectives

– To monitor demand-capacity mismatch in medium-long term – To support the demand-capacity synchronization by capacity

decisions (expansion, reservation, prioritization)

• Model scope

– Relationship between capacity, cycle time, WIP and throughput – Integrating channel inventory and demand dynamics with supply

capability 1 2 C/P Fab Channel inventory capacity capacity Demand dynamics

(39)

3.1 Elements of the model

• Channel inventory:

an input, based on market intelligence data

• Demand dynamics

– Demand lifecycle

– Demand learning effect

• Supply capability of the nodes

– Cycle time, WIP, throughput

• Objective functional

– Capacity allocation (to control shortage points)

t by time demand cumulative : (t) X )) ( ( ) (t k X t X• = ) )( (TH CT WIP = ) (t I

(40)

3.2 Delivery control

Delivery Control Schedule, Events differentiated services

•

Service quality

•

Feasible revision

•

Delay information

• Objectives:

– To assess the impact of dynamic events on the

performance under high-mix environment

– To identify feasible revision, shortfall points

and delay information in order to enhance

delivery, speed and service

(41)

3.2 Workload variation propagation

• Elements

– Events: uncertain job arrivals, urgent orders, disrupting events, and material availability

– Modeling of capacity loss due to variety, variation and dynamic events – Cumulative workload time Cumulative workload Shop 1