Obsolescence Reduction Through Product Segmentation

MIT SCM Research FEST May 19, 2016

Authors: Ranjani Rajan, Ying Wang Advisor: Dr Andre Carrel

Introduc)on

ConceptualModel

BaseModel

Results

Sensi3vityAnalysis

Conclusion

CurrentStrategy

NoCustomerOrdersDifferen3a3on

Obsolescence

3

ProblemStatement

Objectives

Evaluateitsimpact&CompareagainstFIFO

only

4

2-ClusterModel

Reducedobsolescence

Productshelflife

SalesVolumeatDC

NewPickingStrategy

Introduc3on

ConceptualModel

BaseModel

Results

Sensi3vityAnalysis

Conclusion

ConceptualModel

6

0 1 2 3Month 40 1 2 3Month

0monthold

1monthsold

2monthsold

4

FIFOOnly 2Clusters(FIFO+LIFO)

LegendNunitsofinventory(unpicked)

Nunitsofinventory(picked)

Produc3onRate: Nunits/monthOrderingQuan3ty: N/2units/month(fast-movers)

N/2units/month(slow-movers)

Introduc3on

Methodology

BaseModel

Results

Sensi3vityAnalysis

Conclusion

OBSOLESCENCE

BaseModel

FACTORY HERSHEYDC CUSTOMERDC RETAILER

Picking Ordering

FixedTiming

OBSOLESCENCE

FACTORY HERSHEYDC CUSTOMERDC RETAILER

Picking Ordering

FixedTiming

BaseModel

OBSOLESCENCE

FACTORY HERSHEYDC CUSTOMERDC RETAILER

Picking Ordering

Produc)on•  [BaseModel]Constantmonthly

produc3onrateat15kunits

BaseModel

OBSOLESCENCE

FACTORY HERSHEYDC CUSTOMERDC RETAILER

Picking Ordering

Picking•  Pickonlyitemscanbeacceptedbyretailers•  [BaseModel]Monthlyorderingquan3tyisU(12k,17k)

Jan Feb MarPickingShelf2Life 13 14 15

12 78570 10231 7834111 0 0 010 0 0 09 0 0 08 0 0 07 0 0 06 0 0 05 0 0 04 78570 10231 783413 0 0 02 0 0 01 0 0 00 0 0 0

Year22Jan Feb Mar

PickingShelf2Life 13 14 15

12 0 0 011 0 0 010 0 0 09 0 0 08 0 0 07 0 0 06 0 0 05 37769 0 04 119371 20462 649133 0 0 917692 0 0 01 0 0 00 0 0 0

Year22

BaseModel

91769

OBSOLESCENCE

FACTORY HERSHEYDC CUSTOMERDC RETAILER

Picking Ordering

Inventory•  Recordinventorylevelattheendofeachperiod•  CaptureobsolescenceattheHersheyDC

Jan Feb MarInventoryShelf2Life 13 14 15

12 120057 73921 7623811 77299 120057 7392110 125296 77299 1200579 111021 125296 772998 79926 111021 1252967 128958 79926 1110216 146468 128958 799265 90951 146468 1289584 103081 14871 727063 4345 103081 148712 46151 4345 1030811 0 46151 43450 0 0 46151

Expired 0 0 0

Year22

OBSOLESCENCE!

BaseModel

OBSOLESCENCE

FACTORY HERSHEYDC CUSTOMERDC RETAILER

Picking Ordering

FixedTiming

Jan Feb MarInventoryShelf2Life 13 14 15

12 120057 73921 7623811 77299 120057 7392110 125296 77299 1200579 111021 125296 772998 79926 111021 1252967 128958 79926 1110216 146468 128958 799265 90951 146468 1289584 103081 14871 727063 4345 103081 148712 46151 4345 1030811 0 46151 43450 0 0 46151

Expired 0 0 0

Year22

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 2 3 4 5 6 7 8 9 10 11 12

Prob

ability

Remaining/Shelf/Life/(mth)

Risk/of/Obsolescence

2/Clusters(Slow>Moving)

2/Clusters(Fast>Moving)

FIFO/Only

BaseModel

Introduc3on

ConceptualModel

BaseModel

Results

Sensi3vityAnalysis

Conclusion

Whathappenswhen20%isorderedbyslowmoversand80%byfastmovers?

15

0

200

1 13 25 37 49

Obsolescence@RetailersFIFOOnly

2Clusters

•  Olderproductspickedfaster

•  EnoughRemainingShelflifeatRetailers

16

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

1 13 25 37 49 61

Obsolescence@RetailersFIFOOnly

2Clusters

Whathappenswhen20%isorderedbyfastmoversand80%byslowmovers?

Req$Mth$@$Hershey 4

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecPicking

Shelf<Life 49 50 51 52 53 54 55 56 57 58 59 6012 115314 113658 118806 107831 118268 110232 100830 132302 115190 116203 127648 11482811 0 0 0 0 0 0 0 0 0 0 0 010 0 0 0 0 0 0 0 0 0 0 0 09 0 0 0 0 0 0 0 0 0 0 0 08 0 0 0 0 0 0 0 0 0 0 0 07 0 0 0 0 0 0 0 0 0 0 0 06 0 0 0 0 0 0 0 0 0 0 0 05 0 0 0 0 422 0 0 0 0 0 718 04 28829 28415 29702 26958 29145 27558 25207 33075 28798 29051 31194 287073 0 0 0 0 0 0 0 0 0 0 0 02 0 0 0 0 0 0 0 0 0 0 0 01 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0Inventory

Shelf<Life 49 50 51 52 53 54 55 56 57 58 59 6012 34686 36342 31194 42169 31732 39768 49170 17698 34810 33797 22352 3517211 53214 34686 36342 31194 42169 31732 39768 49170 17698 34810 33797 2235210 25684 53214 34686 36342 31194 42169 31732 39768 49170 17698 34810 337979 36730 25684 53214 34686 36342 31194 42169 31732 39768 49170 17698 348108 29145 36730 25684 53214 34686 36342 31194 42169 31732 39768 49170 176987 46042 29145 36730 25684 53214 34686 36342 31194 42169 31732 39768 491706 39812 46042 29145 36730 25684 53214 34686 36342 31194 42169 31732 397685 29946 39812 46042 29145 36307 25684 53214 34686 36342 31194 41450 317324 8755 1531 10110 19085 0 8749 477 20139 5888 7291 0 127433 0 8755 1531 10110 19085 0 8749 477 20139 5888 7291 02 17411 0 8755 1531 10110 19085 0 8749 477 20139 5888 72911 9206 17411 0 8755 1531 10110 19085 0 8749 477 20139 58880 15271 9206 17411 0 8755 1531 10110 19085 0 8749 477 20139

Expired 209447 224718 233923 251335 251335 260090 261621 271731 290816 290816 299565 300042

Year<5

•  Slow-moversdominate

•  Obsolescencefromfast-movingretailersé

InventoryandPickingTablesfor2ClusterModel

17

HigherOverallObsolescenceLowerOverallObsolescence

0

4000

8000

12000

16000

1 13 25 37 49

CumulatedTotalObsolescence

FIFOOnly 2Clusters

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

1 13 25 37 49 61

CumulatedTotalObsolescence

FIFOOnly 2Clusters

Results

18

2-ClusterModelmorebeneIicialSlowMoversVolume<40%

CustomerDCs

HersheyItems

HersheyItems

+

CustomerDCs

Category1(7%)

Fast(83%)

Slow

Category2(14%)

Fast(68%)

Slow

Category3(21%)

Fast(19%)

Slow

Category4(58%)

Fast(65%)

Slow

ClusteringModel

DC1

DC5

DC6

DC10

DC11

DC2

DC37

DC121

DC56

DC78

H1 H2 H3H4 H5 H6 H7H8 H9 H10

H11

H12

H13

H14

H15

H16

H17

H18

H19

H20

H21

H22

HersheyItems

CustomerDCs

Introduc3on

ConceptualModel

BaseModel

Results

Sensi)vityAnalysis

Conclusion

ü Whathappensonrunningthesimula9onmodeln9mes?

ü Aretheresultsreplicatedevery9mefordifferentcutoffvalues?

ü ForTriangulardistribu9onofOrders?FordifferentObsolescencecutatretailers’end?

21

SensitivityAnalysis

22

•  Clustercutat0:FullyLIFO

•  LowerthevolumeoffastmovingDCs,Lowerthecumula3veobsolescence

•  Clustercut<0.5,2clustermodel:notbeneficial

SensitivityAnalysis

-1500000

-1000000

-500000

0

500000

1000000

1500000

Cum

Obs

Diff

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Cut-off

VaryingClusterCut-offs TriangularDistofOrders

•  Mode=Produc3onCapacity

•  Scaferismoreinbasemodelinallclustercutcombina3ons

•  Lesservariabilityinorders

Introduc3on

ConceptualModel

BaseModel

Results

Sensi3vityAnalysis

Conclusion

Conclusion

•  Recommenda3on

•  FutureScopeØ Promo3on/MarkdownØ Costreduc3on/Profitop3miza3on

24

Fast-Movers

Slow-Movers

Fast-Movers Slow-

Movers

2Clusters FIFOOnly•  Produc3onrate•  Demandpafern•  Lead3meinsupplychain•  Retailer’sriskofobsolescence•  ……

25