how to manage uncertainty in the supply chain david3436

7/17/2019 How to Manage Uncertainty in the Supply Chain David3436

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Management of Uncertainty in

the Supply Chainmanagement

Submitted by :

VIPIN VPMBA(IB)

ROLL NO :58



©Copyright 2003 D. Simchi-Levi

Outline of the Presentation Introduction

Push-Pull Systems

Case Studies High Tech Automotive

Electrical Components




Today’s Supply Chain Pitfalls• Long Lead Time

• !n"e#tain $emand

• %om&'e P#odu"t Oe#ing

• %om&onent A*ai'abi'ity

• Sytem Va#iation O*e# Time




The Dynamics of the Supply Chain

O r d e r S

i z e

Time

Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998

Cutomer !emand

"etailer Order

"etailer Order

!itri#utor Order

!itri#utor Order

$roduction $lan

$roduction $lan




The Dynamics of the Supply Chain

O r d e r S

i z e

Time

Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998

Cutomer !emand

$roduction $lan

$roduction $lan




What are the Causes….

• P#omotiona' a'e

• Vo'ume and T#an&o#tation$i"ount

• In+ated o#de#

• $emand ,o#e"at• Long "y"'e time

• La"- o. In.o#mation


http://slidepdf.com/reader/full/how-to-manage-uncertainty-in-the-supply-chain-david3436 7/47©Copyright 2003 D. Simchi-Levi

Example: Automotive Supply Chain• %utom o#de# ta-e /0120 day

• Many die#ent &#odu"t

3 4ig 'e*e' o. demand un"e#tainty

• $ea'e#6 in*ento#y doe not"a&tu#e demand a""u#ate'y3 7M etimate: Research shows we lose 10%

to 11% of sales because the car is not available”



Supply Chain Strategies• A"ie*ing 7'oba' O&timi9ation

• Managing !n"e#tainty

3 Ri- Poo'ing

3 Ri- Sa#ing



$rocurement$lanning

Manufacturing$lanning

!itri#ution$lanning

!emand

$lanning

Sequential Optimization

Su%%ly Contract&Colla#oration&'ntegration&!SS

$rocurement$lanning

Manufacturing$lanning

!itri#ution$lanning

!emand$lanning

Global Optimization

rom Se!uential "ptimi#ation to

$lo%al "ptimi#ation

Sou#"e: $un"an M",a#'ane



A ne& Supply Chain Paradigm• A i.t .#om a Pu Sytem

3 P#odu"tion de"iion a#e baed on

.o#e"at

• ;to a Pu1Pu'' Sytem



rom Ma'e(to(Stoc' Model)*ConfigurationAssemblySuppliers



Demand Forecast • The three principles of all forecasting

techniques:

3 ,o#e"at a#e a'<ay <#ong

3 Te 'onge# te .o#e"at o#i9on te <o#t i

te .o#e"at

3 Agg#egate .o#e"at a#e mo#e a""u#ate• Ri- Poo'ing






.o#e"at





Push-Pull Supply ChainsThe Supply Chain Time Line

Low Uncertainty High Uncertainty

CutomerSu%%lier

PUSH STRATEGY PULL STRATEGY

ush-ull !oun"ary






.o#e"at


3 Pa#t in*ento#y i #e&'enied baed

on .o#e"at3 Aemb'y i baed on a""u#ate

"utome# demand




)*to Assem%le(to("rder Model ConfigurationAssemblySuppliers





Push-Pull Systems

Case Studies High Tech Automotive

Electrical Components




Shifting the Push(Pull +oundary:

A Case Study • Manu.a"tu#e# o. "i#"uit boa#d and ote# ig1

te" &#odu"t

• Se'' "utomi9ed &#odu"t <it ig *a'ue ando#t 'i.e "y"'e

• Mu'ti1tage BOM3 eg= "o&&e# > ?be#g'a ⇒ "i#"uit boa#d ⇒

en"'ou#e ⇒ &#o"eo#

• %ae tudy "on"e#n a numbe# o. @2=000 S!

• Te "ae tudy em&'oyedInventoryAnalystT .#om Logi"Too'(<<<'ogi"1too'"om)



#o$ to %ea" the Diagrams

PART 2

DALLAS ($0.50)

PART 1

DALLAS ($260) 30

PART 3

MONTGOMERY ($220)

15

0

88

2

0

15

5

A &ray !o' is a processing stage

(umber un"er the bo'

is the processing time

Cost in the bo' is the

value of the pro"uct

(umber on the lane is

the transit time (umber in the $hite

bo' is the commitment

time to the ne't stage

!ins in"icate safety

stoc) levels- more %e"

means more safety

stoc)* empty means no

safety stoc)



PART 2

DALLAS ($0.50)0

PART 1

DALLAS ($260)30

PART 6

RALEIGH ($3)

PART 4MALAYSIA ($180)

PART 5

CHARLESTON ($12)

PART 3

MONTGOMERY ($220)

8

15

5

15

x2

88

7

37

70PART 7

DENVER ($2.50)58

4

3

3

28 2

0

PART 2

DALLAS ($0.50)5

PART 1

DALLAS ($260) 30

PART 6

RALEIGH ($3)

PART 4

MALAYSIA ($180)

PART 5

CHARLESTON ($12)

PART 3

MONTGOMERY ($220)

8

15

5

15

x2

13

7

37

324

3

3

28 2

0

Safety Stoc' Cost , -./01223yr

Safety Stoc' Cost , -/40/223yr5678 savings9

PART 7

DENVER ($2.50)58



Safety Stoc' Cost , -460.223yr5;8 savings0 428 reduction in <T9

PART 2

DALLAS ($0.50)0

PART 1

DALLAS ($260)15

PART 6

RALEIGH ($3)

PART 4MALAYSIA ($180)

PART 5

CHARLESTON ($12)

PART 3

MONTGOMERY ($220)

8

15

5

15

50

7

37

324

3

3

28 2

0

PART 7

DENVER ($2.50)58




Comparison of Performance Measures

Scenario

Safety Stoc(

)olding Cot

*+&yr

-ead Time

to Cutomer

*day

Cycle

Time

*day

'n.entory

Turn

*turn&yr

1: Baseline $74,100 30 105 12

2: Optimization $45,400 30 105 14

3: S!o"ten #ea %ime $53,700 15 105 13



PART 1

DAL ($535)30

PART 2

DAL ($55)55

PART 3

DAL ($6)50

PART 8

DAL ($65) 56

PART 10

DAL ($35)

38

PART 4

DAL ($285)65

PART 5

DAL ($3)4

PART 6

DAL ($18)46

PART 7

DAL ($9)21

PART 9

DAL ($30)82

PART 23

DAL ($30)50

PART 18

DAL ($35)51

PART 11

DAL ($40)54

PART 38

N ($8)8

PART 22

DAL ($28)

23PART 17

DAL ($30)

26

PART 21

N! ($18)41

PART 16

DAL ($21)81

PART 30

"HI ($6)4

PART 20

#AS ($42) 18PART 15

DAL ($60) 26PART 29#AS ($40) 12

PART 19

DAL ($210)61

PART 12

DAL ($260)62

PART 24

N ($30)16

PART 25

#AS ($75)52

PART 26

DAL ($80)25

PART 27

N ($4)1

PART 28

DAL ($12)17

PART 32

N ($22)10

PART 33

#AS ($30)42

PART 34

#AS ($25)49

PART 35

N ($35)3

PART 36

N ($40)20

PART 37

DAL ($8) 10

PART 39

TAI ($15)5

PART 40

N! ($22)12

PART 41

"HI ($32)6

PART 42"HI ($2) 3

PART 13

ME ($11)24

PART 14

ME ($4)10

PART 31

SEA ($20)40

4

1314

&

50

31&3'

28 3

35

32

&28

2

3

2

2

1

13

1

2

3

7

4

4

14

3

8

8

3

305&12

35

15

1231&

3

Safety Stoc' Cost , -7402223yr



PART 1

DAL ($535)30

PART 2

DAL ($55)26

PART 3

DAL ($6)26

PART 8

DAL ($65) 26

PART 10

DAL ($35)

26

PART 4

DAL ($285)26

PART 5

DAL ($3)4

PART 6

DAL ($18)26

PART 7

DAL ($9)21

PART 9

DAL ($30)26

PART 23

DAL ($30)21

PART 18

DAL ($35)22

PART 11

DAL ($40)25

PART 38

N ($8)6

PART 22

DAL ($28)

11PART 17

DAL ($30)

14

PART 21

N! ($18)41

PART 16

DAL ($21)25

PART 30

"HI ($6)

4

PART 20

#AS ($42) 18PART 15

DAL ($60) 26PART 29

#AS ($40) 12

PART 19

DAL ($210)22

PART 12

DAL ($260)23

PART 24

N ($30)14

PART 25

#AS ($75)13

PART 26

DAL ($80)16

PART 27

N ($4)1

PART 28

DAL ($12)16

PART 32

N ($22)8

PART 33

#AS ($30)10

PART 34

#AS ($25)10

PART 35

N ($35)3

PART 36

N ($40)11

PART 37

DAL ($8) 9

PART 39

TAI ($15)5

PART 40

N! ($22)12

PART 41

"HI ($32)6

PART 42

"HI ($2) 3

PART 13

ME ($11)24

PART 14

ME ($4)10

PART 31

SEA ($20)40

4

1314

&

50

31&3'

283

35

32

&28

2

3

2

2

1

13

1

2

3

7

4

4

14

3

8

8

3

305&12

35

15

1231&

3

Safety Stoc' Cost , -6=0=223yr 5=8 savings9




Comparison of Performance Measures

Scenario

Safety Stoc(

)olding Cot

*+&yr,

-ead Time

to Cutomer

*day,

Cycle

Time

*day,

'n.entory

Turn

*turn&yr,1: Baseline $'5,000 30 8& 15

2: Optimization $3&,&00 30 8& 18




Safety Stoc' vs* >uoted <ead TimeSafety Stoc( Cot ./ 0uoted -ead Time

$0

$10,000

$20,000

$30,000

$40,000

$50,000

$&0,000

$70,000

$80,000

$'0,000

$100,000

0 20 40 &0 80 100-ead Time 0uoted to Cutomer *day

S a f e t y S t o c ( C o t * + & y e a r ,

Baseline (ost

Optimize (ost

+or a given lea"-time* the

optimi,e" supply chain provi"es re"uce" costs

+or a given cost* the

optimi,e" supply chain

provi"es better lea"-times





Push-Pull Systems

Case Studies High Tech Automotive Electrical Components




Case Study: Spare Part ?nventory

"ptimi#ation• INVNTORC STRAT7C

3 O&tima' Sa.ety Sto"- and Bae Sto"- 'e*e' at ea" 'o"ation

3 O&tima' %ommitted Se#*i"e Time

• NTDOR $CNAMI%S3 !nde#tanding In*ento#y $#i*e#

3 Seniti*ity Ana'yi

3 Dat1i. ana'yiEP#io#iti9ing O&&o#tunitie

• SO!R%IN7 > PRI%IN73 %ot im&'i"ation <it die#ent u&&'ie#

3 Su&&'ie# %ont#a"t Negotiation

3 $ie#entia' P#i"ing

Source: Analysis is done using InventoryAnalyst

from Logicools !www"logic#tools"com$



( ) * l e S t o * +

S S

, - # % . / a " 0

S S , 1

p s t " e a m S 2 0

3 n t " a n s i t S t o * + Pa#t 5

Pa#t G

Pa#t H

P a#t @

Pa#t F

$000

$1,00000

$2,00000

$3,00000

$4,00000

$5,00000

?nventory Brivers

oot (ause nal)sisn6ento") b) #o*ation

Item Holding Cost

Pa#t F JFH2

Pa#t @ J00@

Pa#t H J00

Pa#t G J0G2

Pa#t 5 J00@

3 n t " a n s i t t o 7 l a n t s

7 l a n t 4 8

7 l a n t 2 G

3 n t " a n s i t t o 9 ! s e

9 ! s e

P a#t F

P a#t @

P a#t H

P a#t G

P a#t

$000

$50000

$1,00000

$1,50000

$2,00000

$2,50000



?A ?mpact of relaxing PBCCST

• %ST .#om P'anti ?ed

• A te %ST todea'e#in"#eae mo#ein*ento#y i e'dat te P'ant and'e at te R$%

0

10002000

3000

4000

5000

&000

7000

8000

'000

10000

7 l a n t 0 ,

7 : (

0

7 l a n t 0 ,

7 : ( 1

$ a )

7 l a n t 0 ,

7 : ( 2

$ a ) s

7 l a n t 0 ,

7 : (

3

$ a ) s

7 l a n t 0 ,

7 : ( 1

9 e e +

7 l a n t 0 ,

7 : ( 2

9 e e + s

7 l a n t 0 ,

7 : (

4

; e e + s

Total )olding Cot

lant %o 9 a"e!ouse <olin= (os t

lant %o lant <olin= ( ost

9 a"e!ouse <olin= (os t

lant Outboun <olin= ( ost

lant nboun <olin= (ost



?A ?mpact of changes inCST to Bealers



?A ?mpact of Supplier CST



0

2000

4000

&000

8000

10000

12000

14000

(u""ent Bas eline lant

0, ( 0 (S%

Onl) (s

!ol n6ento")

eu*e

Supplie" #%

eu*e

/a"iabilit)

n*"ease (

(S%

18/ 12/334/3 34/5 31/3

16/9

n6ento") %u"ns

$2&5 $172 $345 $3&5 $383 "ee (as! lo;

Prioriti#ing Savings"pportunities

7lant nboun <olin= (ost 7lant Outboun <olin= (ost

9a"e!ouse <olin= (ost 7lant %o 7lant <olin= (ost

7lant %o 9a"e!ouse <olin= (ost



e&er Stoc'(outs ?mproved

?nventory Turns

S!PPLIR PLANT

Ra< Mate#ia'

,inied 7ood

Safety Stoc!Savings: ""# %ANA$A

MI%4I7AN

BOSTON

NVA$A

MINNSOTA

D VIR7INA

$NVR

LOS AN7LS

ILLINOIS

$"%&'(

$)"&*%

$"%&+'

$,+&%

$""&%

$"%&."

$'")&'(

$()&'

$"&"'

$%+&*'

$''.&%(J5H00

JG@

J5HF

JH02/

J/HFG

JHG/8

JG8/@

JGH82

JF50G

J//8

%u##ent 4o'ding %ot

O&tima' 4o'ding %ot

Optimize n6ento") ositionin= leas to bette" Se"6i*e #e6els ;it! lo;e" n6ento") #e6els

All num/ers in 0+++1+++s



?A Supplier Choice

• Su&&'ie# F:3 G <ee- %ST

3 5K Se#*i"e Le*e'

3 Lead Time to P#o"

P'ant: $ay

• Su&&'ie# @:3 @5 <ee- %ST

3 8K Se#*i"e Le*e'

3 Lead Time to P#o"

P'ant: F <ee-

$0

$2,000

$4,000

$&,000

$8,000

$10,000

$12,000

$14,000

Supplie" 1 Supplie" 2

Su%%lier Com%arion

lant %o 9a"e!ouse <olin= (ost

lant %o lant <olin= (ost

9a"e!ouse <olin= (ost

lant Outboun <olin= (ost

lant nboun <olin= (ost





Push-Pull Systems

Case Studies High Tech Automotive Electrical Components

S l Ch i St t



!SPLANTS

Supply Chain Structure

ASIAN

PLANTS

!ROPANPLANTS

LATIN AMRI%ANPLANTS

%A PORT

P4IL PORT

MIAMI PORT

PA $%

%A $%

7A $%

IL $%

T $%

M,7 F%utome#In*ento#y A''o<ed

In*ento#y Not A''o<ed

(G=F)

(H5=G)

(F5=H)

(F0=@)

(G=F)

(F=0)

(H=F)

(G=F)

(G=F)

(H=F)

(@=0)

(H=F)

(H=F)

%R M,7

(H=F)

(G=F)

(G=F)

(G=F)

(G=F)

(G=F)

(G=F)

(T#anit Time= Std $e* o. T#anit Time)

S l Ch i Si



Supply Chain Si#e

•2/ P'ant• F0 Da#eoue

• HF05 %utome#

• 8@2 P#odu"t

• 8@2 P'ant 3 Da#eoue T#anit Lane

• @0@H0 Da#eoue 3 Da#eoue T#anitLane

• /G8GH Da#eoue 3 %utome# T#anitLane

Bi t i% ti f ? t



Bistri%ution of ?nventory

• La#ge &a#t o.te In*ento#yi In T#anit3 P'ant to

Da#eoue

3 Da#eoue to%utome#

3 Da#eoue toDa#eoue

• Mot o. teIn*ento#y atteDa#eoue i

in R$%1PA

23C-PA23C-CA

23C-4A

23C-I523C-T6

74 8'74 8*

Across the Supply Chain

Across 9arehouses

3/27

4/67

9/47

9/27

12/27

00>

50>

100>

150>

200>

250>

300>

350>

400>

450>

500>

9a"e!ous e (ustome" n %"ansit ? "om lant n %"ansit bet; een

9a"e!ouse

n %"ansit to

(ustome"

S f t St ' d C l St '



Safety Stoc' and Cycle Stoc'

• To& @0K o. S!a""ount .o#mo#e tan 2K

o. in*ento#y

• Mo#e In*ento#y

i e'd atDa#eouetan at%utome#Lo"ation

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

4500000

5000000

()*le Sto*+ Sa?et) Sto*+

"E)OSES

%op 20> S@s Bottom 80> S@s

0

2000

4000

&000

8000

10000

12000

()*le Sto*+ Sa?et) Sto*+

CSTOME"S

%op 20> S@s Bottom 80> S@s

? t B i



?nventory Brivers

In*ento#y by Lo"ation In*ento#y by Reaon

7 l a n t A 9

! s e 3 n % " a n s i t

9 a " e ! o u s e

9 ! s e A

9 ! s

e 3 n % " a n s i t

9 ! s e A

( u s t 3 n % " a n s i t

(

u s t o m e "

3'A1701

3'A2700A1

&'A12000

50

100

150

200

250

300

350

400

n %"ansit

n6ento") ()*le Sto*+Sa?et) Sto*+

3'A1701

3'A2700A1

&'A1200

0

20

40

&0

80

100

120

140

1&0

180




+144 +9;

+81 +54

+4

+34

+4

+24

+84

+144

+134

Current 'n. $ro%er -e.el O%t

$oitioning

Changing

$olicie

' n . e n t o r

i n

+ M

M

?nventory Savings$1' ?"ee *as!?lo; b) =loball)

optimizin= in6ento")

50

50 In* Tu#n

5H /@ 2F

(oul mo6e ?"om t!elo;e" qua"tile to t!emeium qua"tiles




<essons <earned • 7'oba''y o&timi9ing in*ento#y "an

a*e a d#amati" im&a"t3 Ta-e ad*antage o. #i- &oo'ing and in*ento#y

&oitioning

• Identi.ying in*ento#y d#i*e# i noteay3 Many &o'i"ie and &#a"ti"e <e#e "auing &oo#

in*ento#y tu#no*e# #atio3 %an be done <it an in*ento#y mode'3 4ig'igt a#ea .o# im&#o*ement




<essons <earned

Manu.a"tu#ing "om&any in*ento#y tu#n

4eu#iti"

%a'"u'ation 7'oba' O&timi9ation

• Se#*i"e Le*e' not a'<ay met

• "e In*ento#y at ome

'o"ation

• Sa.ety Sto"- at ea" node

"a'"u'ated inde&endent'y

• ,e< .a"to# "onide#ed

• Se#*i"e Le*e' not a'<ay

met

• Sa.ety Sto"- at ea" node de&end

on att#ibute o. a'' node

• Mot "om&'ete mode' a*ai'ab'e

• Poition a.ety to"- a"#o te

net<o#-

0

1

2

3

4

5

&

7

8

how to manage uncertainty in the supply chain david3436

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