Transcript 슬라이드 제목 없음 - Welcome to SNU-MAI

공급망 계획
(SCP: Supply Chain Planning)
Contents
1. 공급망 계획(SCP) 이란?
2. Demand Planning
3. Supply Network Planning
4. Production and Distribution Planning/Scheduling
5. Commercial Software Products
6. New Trends
7. 결론
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ERP 핵심개념
Order
Information
Released
Order
ERP
Master Data
Gantt chart
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SCM 개념
원자재
재료
Oil field
재활용
철 판
노트북
자동차
철광석
반도체
모 래
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PDP
제품
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Supply Chain Management 의 시작은?
( J. Forrester, “Industrial Dynamics”, 1961, MIT press)
T.C. Jones and D.W. Riley, “Using Inventory for Competitive Advantage through Supply Chain
Management,”Int’l Journal of Physical Distribution and Materials Management. v.15 n.1, 1985.
“Supply Chain Management” 라는 용어가 처음 사용된 것은 1982년, Oliver and Weber공저,
"Supply Chain Management: logistics catches up with strategy" 라는 책자에서.
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Bull Whip Effect: 10% 수요가 급증하였을 때 Retail, Distribution,Factory수준에서
의 재고, 주문, 판매량의 변동 추이(J. Forrester저, “Industrial Dynamics”, 1961,
MIT press)
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Supply Chain(Network) Management
Customers
Product and material flows
Information and financial flows
Retailers
1st Tier
suppliers
Strategic business units
Distribution Centers
Assembly/Mfg
1st Tier Suppliers
2nd Tier Suppliers
1st Tier Suppliers
2nd Tier Suppliers
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2nd Tier Suppliers
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SCP(Supply Chain Plan)와 SCE(Supply Chain Execution)
• SCM란 ‘공급사슬 상에서 발생하는 모든 활동들을 효과적으로
운영하기 위한 의사결정과정’
• SCC(Supply Chain Council)에서는 Supply Chain의 process를
• 전략적 단계인 Configuration,
• 전술적 단계인 Planning,
• 작전의 단계인 Execution 으로 분류.
• SCP란 결국 Plan on Supply Chain(SC에 대한 계획):
– 소비자/소매/도매/물류 센터/공장/공장창고/공급자 에 관한 계획
– 장기적/중기적/단기적 계획
• SCE(Execution)이란 “SCP에 근거한 시행”으로써 Plan에 대한 결과
를 보고하고 새로운 Plan을 위한 정보 데이터를 제공.
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SCOR(Supply Chain Operation Reference-model) version 6.1
Process Category는 Process와 Process Type으로부터 결정됨
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MAI Lab. Seminar
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14/43
(Customer) Customer Requirements
(D1.3, D1.10) Order Backlog, Shipments
(EP.3) Planning Data
(EP.9) Revised Aggregate Forecast and
Projections, Revised Business Assumptions
P1.1
P1. Plan Supply Chain(SCOR6.1)
(EP.1) Planning Decision Policies
(EP.2) Supply Chain Performance
Improvement Plan
(EP.4) Inventory Strategy
Identify, Prioritize, and
Aggregate Supply-Chain
Requirements
P1.3
P1.2
Balance Supply-Chain Resources
with Supply-Chain Requirements
P1.4
Establish and Communicate
Supply-Chain Plans
Identify, Assess, and Aggregate
Supply-Chain Resources
Supply Chain Plans
(P2.1, P3.1, P4.1) (Customer)
(P2.4) Sourcing Plans
(P3.4) Product MAKE Plans
(P4.4) Delivery Plans
(EP.3) Planning Data
(EP.5, EP.6) Projected Internal and External Production Capacity
(EP.5, EP.6) Revised Capital Plan
(EP.5, EP.6) Outsource Plan
(EP.8) Regulatory Requirements
(Customer) Inventory
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P2. Plan Source(SCOR6.1)
P2.1
Identify, Prioritize, and
Aggregate Product
Requirements
P2.3
P2.4
Balance Product
Resources with Product
Requirements
Establish Sourcing
Plans
P2.2
Identify, Assess, and
Aggregate Product
Resources
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Sourcing Plans (P1.2,
P4.2, P5.1, S1.1, S2.1,
S3.1, S3.3, D1.3, D2.3)
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P3. Plan Make(SCOR6.1)
P3.1
Identify, Prioritize, and
Aggregate Production
Requirements
P1.3
P1.2
Balance Supply-Chain
Resources with Production
Requirements
P1.4
Establish
Production Plans
Identify, Assess, and
Aggregate Production
Resources
Production Plans
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P4. Plan Deliver(SCOR6.1)
P4.1
Identify, Prioritize, and
Aggregate Delivery
Requirements
P4.3
P4.2
Balance Delivery
Resources with Delivery
Requirements
P4.4
Establish
Delivery Plans
Identify, Assess, and
Aggregate Delivery
Resources
Delivery Plans
Stocking Requirements
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P5. Plan Return(SCOR6.1)
P5.1
Identify, Prioritize, and
Aggregate Return
Requirements
P5.3
P5.2
Balance Return
Resources with Return
Requirements
P5.4
Establish and
Communicate
Return Plans
Identify, Assess, and
Aggregate Return
Resources
Source Return Requirement
Return Production Requirements
Return Plans
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SCP의 구성요소
•
크게 4부문 정도로 나눌 수 있음:
– Demand Planning
– Supply Network Configuration/Planning
– Production/Operation Planning and Scheduling*
– Distribution/Return Planning
(*)APS(Advanced Planning Scheduling)라고도 불림. 기본적으로ATP(Available To
Promise), CTP(Capable To Promise)를 포함.
이들은 Order Fulfillment라고도 칭함.
•
일반적으로 SCM 소프트웨어들은 SCP에 관한 것:
–
–
–
–
–
“SAP AG”의 “APO:Advanced Planner and Optimizer)”
“i2 Technologies” 의 “Business Optimization Service”
“Oracle”의 “Supply Chain Planning 11i.10”
“SSA Global”의 “APS;WM;TM;Event&Performance Mgt”
…
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2. Demand Planning
• 주요기능:
–
–
–
–
–
–
–
–
Top-down/Bottom-up/Consensus-based Forecasting
Multi-tier/Multi-facet Forecasting
Promotional Planning
Causal Analysis
Life-cycle Management
What-if Simulation
Data Management(Multi-facet, Multi-level, Multi-source)
Function Libraries
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3. Supply Network Planning
• Supply Chain의 목표:
– 완제품과 원자재 조달비의 절감
– 고객 서비스 증진
– 재고수준의 절감
– 모든 사내 자원의 활용
• SNP:
– SC의 목표에 가장 큰 영향을 미치는 중장기 계획 수립
– 조달기간이 길고 병목자원과 관련된 핵심제품의 BOM(Bill of
Material)를 포함
– 무리한 자원동원 없는 적재, 적량, 적시 공급을 목표로 함
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Customer
Distribution center
Plant
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Customer
Distribution center
Plant
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Customer
Distribution center
Plant
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Customer
Distribution center
Plant
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SNP 의 기능(SAP사의 APO의 예)
– Supply Network Planning Strategies
• Heuristic : infinite planning
• Capable-to-Match (CTM) : Rules-based finite planning
• Optimization : integrated finite planning
– Deployment (short-term Replenishment Planning)
• Deployment adjusts the stock transfers for short-term changes on the supply
and demand side.
– Transport Load Builder
• Plan for optimal use of transportation method.
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SNP Planning Sequence (SAP사의 APO의 예)
Set up Master Data and
Supply Chain Model
Group together loads for
Non-assigned
Transport orders
Release Demand
Plan to SNP
Perform SNP Heuristic,
Optimization, or CTM run
Check plan/
Solve problems
TLB run
Deployment run
Release ConstraintBased SN plan to DP
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Finalize SNP plan
(available to PP/DS)
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4. Production and Distribution Planning(연구사례)
•
채찍 효과 (Bullwhip Effect)
– 수요에 대한 분산이 상위단계로 갈수로 커지는 현상
•
•
•
•
Main factors of bullwhip effect
Errors in demand forecasting
Batch ordering
Price fluctuation
Inflated orders
Batch ordering을 하지 않으면?  LFL?
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연구의 동기
•
Traditional manufacturing control framework
Master Production Scheduling
Material Requirement Planning
Scheduling
Shop Floor Control
Successive and segregated planning
•
Drawback of separate planning [Dudek*, 2005]
–
–
–
No sufficient support for transport and distribution of goods
Plant order are generated with an isolated view of the item in question
without taking account of the interdependencies with other items
Independently operated at various facilities based on locally available data,
leading to segregated planning processes along the SC
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*Lecture notes in economics and mathematical
systems 2005
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연구의 동기
• Manufacturing control framework in SC (Supply Chain Planning matrix)
– x-axis: the business functions across a supply chain
– y-axis: the levels of planning intervals
Software modules covering the SC planning matrix (Meyr et al., 2002)
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연구의 동기
•
생산 계획과 연결되지 않은 분배 계획이나 e-Procurement는 큰 의미가 없다
생산용량 제약초과
비용 관점에서만 국부적으로
Site 5
Site 3
Site 1
(Supplier1)
(Factory1)
(DC1)
Site 6
Site 4
Site 2
(Supplier2)
(Factory2)
(DC2)
Supplier
Factory
Distribution
Center
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(Client1)
분배계획을 수립하면?
(Client2)
Client
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전체 공급 사슬에서의 발생 비용을 최소화 시키는 생산 및 분배 계획 작성
–
–
–
–
–
4계층 모델 (고객, 물류센터, 공장, 1차 납품업체)
고객 사이트에 대한 수요는 확정적
복수 제품, 복수공급업체(특정 제품에 대해 복수공급업체 존재)
생산 또는 수송 관련 준비 비용 존재, 재고 유지 비용 존재
최종 고객 수요는 반드시 만족
고
객
1차 납품업체 a
공장a
물류창고 a
고
객
고
객
1차 납품업체 b
공장b
물류창고 b
고
객
1차 납품업체 c
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Production Planning
•
Purpose of Production Planning
Product A - Resource A
Time
1
2
Demand
0
60
3
100
4
80
5
50
6
30
7
80
8
90
9
100
10
30
Product B - Resource A
Time
1
2
Demand
80
50
3
30
4
80
5
30
6
90
7
80
8
100
9
50
10
100
Planning Sheet A - Resource A(180)
Time
1
2
3
4
5
6
7
8
9
10
Item
P-A P-A P-A P-A P-A P-A P-A P-A P-A P-A
Production
0
60 100
80
50
30
80
90 100
30
Item
P-B P-B P-B P-B P-B P-B P-B P-B P-B P-B
Production
80
50
30
80
30
90
80 100
50 100
Used Capa
80 110 130 160
80 120 160 190 150 130
Planning Sheet B - Resource A(180)
Time
1
2
3
4
Item
P-B P-A P-B P-A
Production 130 160 140 160
Used Capa 130 160 140 160
5
10
P-B P-A P-B P-A P-B
170 170 150 130 100
170 170 150 130 100
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7
8
9
LFL Production
• 잦은 생산 준비 비용 발생
• 생산 용량 조건 위반
Production Planning
• 재고 보유를 통한 생산 횟수 감소
• 생산 용량 조건 고려
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Distribution Planning
DC A
Product A - Resource A
Time
1
2
Demand
80
60
3
100
4
80
5
50
6
30
7
80
8
90
9
100
10
30
3
0
4
80
5
50
6
0
7
80
8
90
9
0
10
30
3
100
4
0
5
0
6
30
7
0
8
0
9
100
10
0
DC A
Factory A
Product A - Resource A
Time
1
2
Demand
0
60
Factory A
Factory B
Factory B
Product A - Resource A
Time
1
2
Demand
80
0
요구되는 수요를 공급 사슬내의 관련된 모든 사이트에 분배하여 생산 및 운송 기능을 맡게 한다.
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Product Master(MPS Level)
•
생산 분배 계획에서는 생산계획을 수립하는 데 필요한 각종 비용요소를 구하기 쉬
운 MPS 수준의 제품 (주요 외주 부품, 최종 제품)만을 고려한다. 이를 위해서는 기
존의 BOM을 MPS 수준의 BOM으로 향상시키고 계획을 세워주어야 한다.
주요 외주 제품
최종 제품
a
A
Resource3
b
B
Resource3
c
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C
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Formulation
•
목적식
Min (siwYiwt  hiw I iwt )    sipYipt  hip I ipt  pip X ipt    sisYist  his I ist  pis X ist 
i
w
t
i
p
t
i
s
t
 tsiwcm Ziwctm  tpiwcmQiwctm    tsipwm Zipwtm  tpipwmQipwtm    tsispm Z isptm  tpispmQisptm 
i
•
Q
w
c
t
m
제약식
i
p
w
t
m
i
p
t
m
Minimize
{재고유지비용+생산비용+운송비용+생산(입고)준비비용+운송준비비용}
i  I , c  C , t  T
(1)
I iw ( t 1)   Qipwtm  I iwt   Qiwc ( t liwcm ) m
i  I , w  W , t  T
(2)
I ip ( t 1)  X ipt  I ipt   Qipw ( t lipwm )
i  I , p  P, t  T
(3)
I is ( t 1)  X ist  I ist   Qisp ( t lispm )
i  J , p  S , t  T
(4)
iwct
s
 Dict
w
p
m
c
w
p
m
m
m
 Q jsptm  aij X ipt


i  I , j  j aij  0 , p  P, t  T
(5)
ciw I iwt  Awtr
w  W , t  T , r  R
(6)

cip X ipt  Aptr
p  P, t  T , r  R
(7)

cis X ist  Astr
p  S , t  T , r  R
(8)
s

고객의 수요 만족
당기 재고=전기 재고+당기 생산량 – 당기 수요
m
i ( r )
i ( r )
i ( r )
Q
Q
Q
iwctm
 Bwctm
w  W , c  C , t  T , m  M
(9)
ipwtm
 B pwtm
p  P, w  W , t  T , m  M
(10)
 Bsptm
s  S , p  P, t  T , m  M
(11)
i  I , p  P, t  T
i  I , s  S , t  T
i  I , w  W , c  C , t  T
i  I , p  P, w  W , t  T
j  J , s  S , p  P, t  T
(12)
(13)
(14)
(15)
(16)
(17)
생산/수송 용량 조건
i
i
isptm
i
X ipt  MYipt
X ist  MYist
Qiwctm  MZ iwctm
Qipwtm  MZ ipwtm
Q jsptm  MZ jsptm
Yipt , Z iwct , Z ipwt , Z jspt
 0,1

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and Integration Lab.
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Notation
I : set of products
C : set of customers
W : set of warehouses
P : set of plants
S : set of suppliers
T : set of time periods
M: set of trasport modes
R: set of resources
(r): set of items which use resource r
i : index of product
c : index of customer
w : index of warehouse
p : index of plant
s : index of supplier
t : index of time period
m: index of trasport mode
r : index of resource
A wtr : Maximum capacity of resouce r of warehouse w in period t
A ptr : Maximum capacity of resouce r of plant p in period t
A str : Maximum capacity of resouce r of supplier s in period t
Bwctm : Maximum capacity of transport mode m between warehouse w and client t in period t
Bpwtm : Maximum capacity of transport mode m between plant p and warehouse w in period t
Bsptm : Maximum capacity of transport mode m between supplier s and plant p in period t
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Notation
siw : production setup cost of end product i in warehouse w
hiw : inventory holding cost of end product i in warehouse w
sip : production setup cost of end product i in plant p
hip : inventory holding cost of end product i in plant p
pip : production cost of end product i in plant p
sis : production setup cost of end product i in supplier s
his : inventory holding cost of end product i in supplier s
pis : production cost of end product i in supplier s
tsiwcm : transportation setup cost of end product i from warehouse w to customer c using transport mode m
tpiwcm : varialbe transportation cost of end product i from warehouse w to customer c using transport mode m
tsipwm : transportation setup cost of end product i from plant p to warehouse w using transport mode m
tpipwm : varialbe transportation cost of end product i from plant p to warehouse w using transport mode m
ts jspm : transportation setup cost of item j from supplier s to plant p using transport mode m
tp jspm : varialbe transportation cost of item j from supplier s to plant p using transport mode m
liwcm : lead time of itme i from warehouse w to customer c using transport mode m
lipwm : lead time of itme i from plant p to warehouse w using transport mode m
lispm : lead time of itme i from suppier s to plant p using transport mode m
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Decision Variable
I iwt : inventory quantity of end product i in warehouse w at the end of period t
X ipt : production quantity of end product i in plant p at period t
I ipt : inventory quantity of end product i in plant p at the end of period t
X ist : production quantity of end subproduct i in supplier s at period t
I ist : inventory quantity of end subproduct i in supplier s at the end of period t
1,
Yipt  
0
1,
Yist  
0
Qiwctm : transportation quantity of end product i from warehouse w to customer c in period t using transport mode m
1,
Z iwctm  
0
Qipwtm : transportation quantity of end product i from plant p to warehouse w in period t using transport mode m
1,
Z ipwtm  
0
Qisptm : transportation quantity of item j from supplier s to plant p in period t using transport mode m
1,
Z isptm  
0
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유전자 알고리즘이란?
• Selection(선별)
– 현재 임의의 해 집단이 있을 때 이중 n개를 새로 뽑는 작업을 선별이라
고 한다.
이때 우수한 해는 열등한 해에 비해 뽑힐 확률이 높도록 선별작업을 계
속한다면 초기 해의 가장 우수한 해만 n개 남을 것이다. 이렇게 새로운
해 집단이 생성되는 과정을 generation(세대)라고 한다.
• Crossover(교배)
– 임의의 두 해를 교배하여 새로운 해를 만드는 것이다. 선별과정을 반복
하면 그 전에 비하여 우수한 해들로 세대가 구성되어진다. 우수한 해들
은 최적해의 특징 스키마를 가지고 있다.세대가 진행됨에 따라 초기 해
집단에서의 교배와 달리 우수한 해들끼리의 교배가 빈번해진다. 우수한
해들의 교배는 최적해의 특징 스키마를 가지는 새로운 해를 생성한다.
• Mutation(변이)
– 단순히 교배 연산만을 하여 세대를 진행시킬 경우 최적해에 근접하지
못하고 해 집단의 모든 해가 특정 해로만 구성되어질 수 있다. 임의로
gene값의 변화를 주어 새로운 해를 만드는 것을 변이라고 한다.
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Genetic Algorithm
•
Genetic algorithm is an algorithm hinted from natural evolution and can be
seen as an optimization method through probability search.
Decoding
New Generation
Encoding
Evaluation & Selection
Creating new population
Initial Population
Genetic operation
Applying the rule to
satisfy capacity constraint
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Meta Heuristic
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Genetic Algorithm
•
Expression of solution
i
Handling Item
s
Chromosome
Site
Representation of solutions
1 If production decision is made
Time
i
t
V t,s,i = 0 or not
Handling Item
N t,n,i =
n
Network
Chromosome
1 If network is linked
0 or not
Time
t
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Genetic Algorithm
Example of crossover
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Genetic Algorithm
•
How to meet capacity constraints at sites
Forward Method
Backward Method
Modified Mutation Method
LFL based creation of initial population
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Experiment
•
The purpose of the experiment
– To see if the proposed algorithm can find a solution to the problem of
the given size and if so how fast?
– To find how effective the LFL-based creation of initial population and
a modified mutation are in meeting production capacity constraints
SCM Solver Ver 1.0
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Experiment I
•
Optimal solution and comparative experiment
– Experiment model (3 models)
Model name
Model 1
Model 2
Model 3
Customer company
Logistics company
Plant
Supplier
Final product
Major part
Period
# of binary variables
2
1
2
2
2
2
12
312
2
2
2
2
2
2
18
613
6
2
3
4
3
3
12
1236
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Experiment I
•
Optimal solution and comparative
experiment
Method
CPLEX
GA
CPLEX/GA
Time
114
111
1.03
CPLEX
Cost
311951
321001
0.97
Time
13210
137
96.42
Cost
452880
478013
0.95
Time
120262
339
354.76
Cost
839880
933404
0.9
GA
140000
120000
100000
80000
60000
40000
20000
0
Model1
Model2
Model3
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1000000
900000
800000
700000
600000
500000
400000
300000
200000
100000
0
CPLEX
GA
Model1
Model2
Model3
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Experiment II
•
Evaluation of techniques used to help meet production capacity
constraints
Methods to help meet production capacity constraints
GA-FB
forward method, backward method.
GA-FBM forward method, backward method, modified mutation operation
GA-FBL
forward method, backward method, creation of LFL-based initial population
GA-FBML forward method, backward method, modified mutation operation creation of LFL-based initial population
•
Experiment model
– 22 models (Altering the problem size : M1 ~ M2)
• The number of entities
• Time period
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Experiment II
•
Evaluation of techniques used to help meet production capacity constraints
Algorithm
Model
GAFB
GAFBM
M01
221080
1
221102
3
M02
310914
2
310929
2
M03
94378
1
94504
2
M04
264108
4
263828
2
M05
254793
3
254453
2
M06
399675
1
399831
2
M07
457947
3
457884
2
M08
448423
4
444869
2
M09
527507
2
525693
1
M10
347866
4
347059
3
M11
463659
3
463913
4
M12
615559
4
614938
3
M13
351686
4
348867
3
M14
960203
4
913671
3
M15
579512
4
576895
3
M16
647206
4
631536
2
M17
899523
4
883321
3
M18
662158
4
656968
3
M19
1147245
3
1095149
2
M20
Inf
4
911605
3
M21
Inf
4
1134977
1
M22
Inf
4
1505003
2
Average
Inf
4 Integration
593499 Lab.
2
Mfg Automation
and
Seoul National University
GAFBL
221080
311107
94564
263742
255021
402132
457981
445063
529981
345108
462150
611638
347938
910960
573196
631854
881325
650617
1191424
903479
1145220
1509138
597487
1
4
3
1
4
3
4
3
3
2
2
1
1
2
1
3
1
1
4
1
3
3
3
GAFBML
221103
311020
94588
263942
253902
402226
457597
444244
531122
344456
460753
611881
348446
906896
574536
630293
881779
651083
1080998
908500
1137436
1501061
591721
4
3
4
3
1
4
1
1
4
1
1
2
2
1
2
1
2
2
1
2
2
1
1
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Another Model – Multi-level Lot Scheduling Problem
• Multi-facility production planning
final item
component
Assumptions
• Demands for the final products of the supply chain were deterministically determined.
• No external demand for components is allowed.
• No backlog is allowed for all component parts and final products.
• Neither positive initial inventories nor scheduled receipts are introduced.
• No more than two facilities produce the same item.
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Model - Formulation
• Formulation
P
T
{s
i 1 t 1
Setup Cost
it
yit  hit I it  pit xit 
 tp
j ( i )
ijt
qijt }
Inventory Holding Cost Production Cost Transportation Cost
subject to
Parameters for the problem
I i ,t  I i ,t 1  xi ,t  di ,t
................. (1)
qi , j ,t  ci , j x j ,t l , j  (i)
................. (2)
ij
d i ,t 
q
j ( i )
i , j ,t
, (i) Φ
................. (3)
xi ,t  Myi ,t  0 , yi,t {0,1}
................. (4)
I it , d it, , xit , qijt  0
................. (5)
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MA - Memetic Algorithm
•
Memetic Algorithm
–
–
–
–
first proposed by Moscato in 1989
hinted from the cultural evolution.
crucial feature of memetic algorithm  inclusion of local refinement procedure
also commonly known as hybrid evolution algorithm and genetic local search
General procedures of memetic algorithm
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MA – Local refinement procedure
•
Local refinement procedure for multi-facility production plan
– Single level local refinement procedure
• Wagner-Whitin (optimal solution)
• Silver-meal (near optimal solution)
– Multi-facility production plan
• The application of a single level optimization technique in higher level products may lead
to unfavorable dependent demand for its immediate lower level products
Cost
Inventory Holding Cost
Setup Cost
A
*
S global
*
S global
*
Slocal
?
?
# of setup
Multi-facility production plan
Production plan for item A in multi-facility environment
How to modify the multi-facility production plan in order to improve the solution quality?
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MA - Local refinement procedure
• The local refinement procedure based on the benchmark
Inventory holding cost
Cost
Setup cost
Agent 1
Agent 2
Local refinement
Agent
3
Local refinement
procedure
procedure
Local refinement
procedure
The memetic agent which shows
the best performance in population
The best
Agent
Mutation
Recombination
Total cost occurred in the
production plan of item A
Agent 3
A
Integrated Production Plan
# of setup
<local refinement procedure for the production plan of the item A>
N
TC   TCi
i 1
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Test against optimality (for small problems)
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• Analysis of experimental result
– Performance evaluation according to the problem size
Relative rate of cost
The problem size
• MA shows the best performance regardless of the problem size
• Differences in performance become apparent among solutions with increasing sizes of problems
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• Analysis of experimental result for mid-sized problems
– Performance evaluation according to the complexity of product structure
(Complexity of the product-value)
Relative rate of cost
C-Value
• MA shows the best performance regardless of the problem size
• The ability of CPLEX to search the near-optimum solutions is reduced with increasing complexity
in comparison to meta-heuristic techniques such as MA and GA.
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• Analysis of experimental result
– Performance evaluation according to the type of TBO( Time Between
Orders)
Relative rate of cost
Type of TBO
• MA shows the best performance regardless of the problem size
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5. Commercial Software Products
• 유명 소프트웨어 공급사들:
–
–
–
–
–
–
–
–
–
SAP AG
i2 Technologies
Oracle(Peoplesoft)
Baan
SSA Global
J.D. Edwards
Aspentech
Manugistics
Adexa
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Overviews of SAP APO
• Supply Chain Cockpit
– Graphic user interface environment for all the links in the Supply Chain
• Demand Planning
– Statistical tools for accurate demand forecasting
• Supply Network Planning and Deployment
– Modeling of systems and constraints for the whole supply chain.
– Decision support system(e.g., inventory management)
• Production Planning and Detailed Scheduling
– Production planning and scheduling for the efficient resource utilization
– Global Available to Promise and Capable to Promise(ATP/CPT)
• Transportation Planning
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SNP – SAP APO Supply Chain Cockpit(Courtesy: SAP AG)
• Graphic user interface to manage total supply chain
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System Integration
Master Data APO
R/3
Demand Planning
Network Design
SEM
BW
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Supply Network
Planning
Courtesy: SAP AG
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System Integration: Reporting with BW
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Courtesy: SAP AG
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i2 Technologies의 BOS(Business Optimization Service)
•
•
•
•
•
Master Data Management
Order Fulfillment
Factory Planner
Demand Planner
Supply Chain Planner
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Michelin사의 Demand Planning 사례
• Significant improvements to the forecasting process and capabilities have been made
since 1998(processes around consensus, collaboration, reviews and accountability)
• Common metrics for measuring progress
– Forecast accuracy has improved each year (from mid 50% in 1998 to above 70% in 2003
at the national level)
– Inventory has dropped dramatically over the last 5 years while fill rates have remained ste
ady (40% drop in total volume inventory)
– An estimated one-half of this improvement is due to forecast accuracy(나머지 절반은 fle
xibility and responsiveness of the manufacturing plants and simplifying DC network).
– Freeing $75-$100 million in working capital and approximately $6 million in annual inve
ntory carrying cost reductions and interest, in addition to reduced obsolescence and divers
ion costs
• It is key to recognize however, that the metrics are indicators of the health and streng
th of the process
– Metrics are used to report on the business, to analyze results and to take corrective actions
– The business is not held hostage to the achievement of the metric targets
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Courtesy: i2 Technologies
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i2 Tech: Closing the loop requires P-D-C-A across multiple
silos and systems
Local
Plan
Local
Plan
Finance
Line of
Business
SCP
PDM
DP
ERP
Local
Plan
Logistics
CRM
Sales Region
Local
Plan
POS
Fulfillment
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Courtesy: i2 Technologies
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Supplier Relationship Mgt
i2Tech: High value closed loop processes in SCM
Supply Chain Design
Demand-Supply Planning
CSM
S&OP
CDM
Demand-Supply Execution
CSM: Continuous Supply Management
CDM: Continuous Demand Management
S&OP: Sales and Operations Planning
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Courtesy: i2 Technologies
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i2 Enablers: Closed-Loop SCM Solutions
Supply Chain Design
Inventory
Optimization
Supply Chain
Strategist
Demand-Supply Planning
Collaborative
Supply
Execution
Supply Chain
Planner
Enterprise
Business
Planner
Factory
Planner
Demand
Manager
Revenue & Profit
Optimization
Customer
Order
Fulfillment
Demand-Supply Execution
Product
Sourcing
Service Parts
Planner
Demand
Fulfillment
Transportation
Mgmt. System
Distributed
Order Mgnt.
Replenishment
Planner
Master Data
Management
Biz. Process
Execution
Performance
Manager
Contents & Supplier
Management
SCM Infrastructure
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Supply Chain
Event Mgt.
Courtesy: i2 Technologies
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Customer Collaboration
SRM / Supplier Collaboration
Strategic
Sourcing
Paradigm shift for RTE : SCM Best Practices
As-is
Best practices
Slow and infrequent (monthly, weekly)
Fast and frequent (daily, hourly)
Forecast Driven
S&OP Driven
$ ↔ Volume disconnect
$ ↔ Volume synchronization
No early warning mechanism
Monitoring, event mgnt., analytics
Manual & calculating processes
Intelligent business levers
Demand—supply match
Demand—supply shaping
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6. New Trends
• RFID Technology
• Ubiquitous Society
• No standards, but they are coming…
– Sales/Return Velocity
– Telematics
•
RFID Maturity Levels:
1. Goal Setting and Assessment, 2. Slap and Ship, 3. Application Integration
4. Business Process Improvement, 5. Collaboration Business Intelligence
•
Another View Points:
1. Read, 2. Read/Write, 3. Sensing, 4. Network, 5. Control
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7. 결론
• SCM은 mrp, MRP, ERP의 자연스런 확장.
• 제조업 경쟁력 향상의 중요 요소(2.5차산업).
• SCP는 SCM의 중요 컴포넌트:
–
–
–
–
Demand Planning
Supply Network Configuration/Planning
Production/Operation Planning and Scheduling*
Distribution/Return Planning
아주 커다란 최적해 문제가 됨.
• SCM, KMS(Knowledge Management System),
CRM/SRM(Customer/Supplier Relationship Mgt),
PLM(Product Life-cycle Management), u-Business는 실현 가능한
기술이며 준비된 회사는 앞서 나갈 수 있을 것임.
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