Supply chain risks by Levi 2015

A New Approach to Manage Supply Chain Risk

Featuring MIT professor David Simchi-Levi, a thought leader on supply chain management and business analytics

OCTOBER 21, 2015Delivered by

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OCTOBER 21, 2015

Today’s Speaker

MIT professor David Simchi-Levi, a thought leader on supply chain management and business analytics

A New Approach to Manage Supply Chain Risk

OCTOBER 21, 2015

@HBRExchange | #HBRwebinar

Identifying Risks and Mitigating Disruptions in the Supply Chain

David Simchi-LeviProfessor, MIT

Chairman, OPS Rules

© David Simchi-Levi 2015

• Significantincreaseinsupplychainrisk Outsourcingandoffshoring

Supplychainisgeographicallymorediverse Leanmanufacturing

Just‐in‐time JIT manufacturingandlowinventorylevels

Intel Sales are downGiant blames Thai flood for

$1B drop in sales goals. Toyota, Honda, Goodyear,

Canon, Nikon, Sony… have cut production and lowered financial forecasts because of the flooding in Thailand.

The Wall Street Journal, 2011

General Motors truck plant was shutting down

General Motors truck plant in Louisiana announced that it

was shutting down temporarily for lack of

Japanese-made parts because of the earthquake and tsunami

had struck Japan.New York Times, 2011


• Significantincreaseinsupplychainrisk Outsourcingandoffshoring

Supplychainisgeographicallymorediverse Leanmanufacturing

Just‐in‐time JIT manufacturingandlowinventorylevels

0

50

100

150

200

250

Quake/Tsunami Floods Tornadoes Floods

Japan Thailand USA Australia

Natural Disasters 2011 Cost ($B)


WorldwideNaturalDisasters1980‐2011Source:MunichRe

Hurricane Katrina, 2005

Supply Chain Disruption and Stock Performance

• Mattel, the world’s largest toy maker;• Recalled 18 million toys made in China on August 2007;• The reason: hazards such as lead paint

$‐

$0.50

$1.00

$1.50

$2.00

$2.50 20

03Q3

2003

Q4

2004

Q1

2004

Q2

2004

Q3

2004

Q4

2005

Q1

2005

Q2

2005

Q3

2005

Q4

2006

Q1

2006

Q2

2006

Q3

2006

Q4

2007

Q1

2007

Q2

2007

Q3

2007

Q4

2008

Q1

2008

Q2

2008

Q3

2008

Q4

Hasbro

Mattel

Stock Performance ($1 invested in 2003)

Product Recall

12

• Naturaldisasters• Geopoliticalrisks• Epidemics• Terroristattacks• Environmentalrisks• Volatilefuelprices• RisingLaborcosts• Currencyfluctuations• Counterfeitpartsandproducts• Portdelays• Marketchanges• Suppliers’performance• Forecastingaccuracy• Executionproblems

Unknown-Unknown

Known-Unknown

Uncontrollable

Controllable



Unknown-Unknown

Known-Unknown

Uncontrollable

Controllable



Unknown-Unknown

Known-Unknown

Uncontrollable

Controllable


Epidemics

Fuel Prices

GeopoliticalProblems

Currency Fluctuations

Commodity Prices

Port Delays Product Design

Problems

Forecast Accuracy

Suppliers‘Performance

The Risk Framework

Expected Impact

Ability to ControlLOWUnknown-Unknown

HIGH

HIGHKnown-Unknown

LOW

Counterfeits

Government Regulations

Natural Disasters

Environmental Risks

16

•

•

•

•

Mostlyad‐hoc,intuition,gutfeeling Exposuretoriskmayresideinunlikelyplaces Mayleadtothewrongactionsandwastedresources Noabilitytoprioritizemitigationinvestment


West Coast

East Coast

North American Assembly Plants

Dealers

Truck

Train

North American Engine Plants

Transmission Plants

Stamping Plants

APA Suppliers

EU Suppliers

NA Suppliers

Forging Plants

Casting Plants

APA Suppliers

EU Suppliers

NA Suppliers

NA Sheet Steel Suppliers

APA Suppliers

NA Steel Bar Suppliers

EU Suppliers

NA Suppliers


Complexbillofmaterialsandsupplychainstructure Over50manufacturingplants 10tiersofsuppliers 1400tier1suppliercompanieswith4,400manufacturingsitesinover60countries

55,000differentparts 6millionvehiclesproducedannually


Engine Plants

Contract Manufacturers

Assembly Suppliers

Steel Bar Suppliers

Raw Chemical Suppliers

Sheet Steel Suppliers

• Time‐To‐Recover TTR :Thetimeittakestorecovertofullfunctionalityafteradisruption

Assembly Plants

Stamping Plants


Engine Plants


Assembly Suppliers

Steel Bar Suppliers




Assembly Plants

Stamping Plants

TTR =2 Weeks


Engine Plants


Assembly Suppliers

Steel Bar Suppliers




Assembly Plants

Stamping Plants

2 Weeks1 Week 2 Weeks

2 Weeks

2 Weeks

TTR =2 Weeks

2 Weeks


• Time‐To‐Recover TTR :Thetimeittakestorecovertofullfunctionalityafteradisruption• PerformanceImpact PI :ImpactofadisruptionforthedurationofTTRonagivenperformancemeasure

Engine Plants


Assembly Suppliers

Steel Bar Suppliers



Assembly Plants

Stamping Plants

2 Weeks1 Week 2 Weeks

2 Weeks

2 Weeks

TTR =2 Weeks

2 Weeks


2 Weeks$1.5B

1 Week$100M

• Time‐To‐Recover TTR :Thetimeittakestorecovertofullfunctionalityafteradisruption• PerformanceImpact PI :ImpactofadisruptionforthedurationofTTRonagivenperformancemeasure

Engine Plants


Assembly Suppliers

Steel Bar Suppliers



Assembly Plants

Stamping Plants

2 Weeks

2 Weeks

2 Weeks

TTR =2 Weeks

2 Weeks

2 Weeks$400M

2 Weeks$100M

2 Weeks$2.5B

TTR =2 WeeksPI = $400M

2 Weeks$300M


2 Weeks$1.5B

• Time‐To‐Recover TTR :Thetimeittakestorecovertofullfunctionalityafteradisruption• PerformanceImpact PI :ImpactofadisruptionforthedurationofTTRonagivenperformancemeasure• RiskExposureIndex REI :NormalizesthePIbythemaximumPIoveralldisruptionscenarios

Engine Plants


Assembly Suppliers

Steel Bar Suppliers



Assembly Plants

Stamping Plants

2 Weeks1 Week

2 Weeks

2 Weeks

TTR =2 Weeks

2 Weeks

2 Weeks$400M

1 Week$100M

2 Weeks$100M

2 Weeks$2.5B

TTR =2 WeeksPI = $400M

2 Weeks$300M


2 Weeks0.6

• Time‐To‐Recover TTR :Thetimeittakestorecovertofullfunctionalityafteradisruption• PerformanceImpact PI :ImpactofadisruptionforthedurationofTTRonagivenperformancemeasure• RiskExposureIndex REI :NormalizesthePIbythemaximumPIoveralldisruptionscenarios

Engine Plants


Assembly Suppliers

Steel Bar Suppliers



Assembly Plants

Stamping Plants

2 Weeks1 Week

2 Weeks

2 Weeks

TTR =2 Weeks

2 Weeks

2 Weeks0.16

1 Week0.04

2 Weeks0.04

2 Weeks1.0

TTR =2 WeeksREI = 0.16

2 Weeks0.12


• Fordanditssupplierproductionportfolioandvolumeofproductionbysite

• Billofmaterialsforeachvehicleanditscorrespondingparts• Volumesandprofitmarginsofdifferentvehiclelines• Pipelineinventories• Timedurationofadisruption• Firm’sresponseafteradisruption

Theresponseissimulatedviaoptimization


Number of Sites

Performance Impact

Another 2773 sites with No Impact

2773

805

142 252 154408

1

201

401

601

801

1001

1201

1401

1601

1801

NoImpact VeryLow Low Medium High VeryHigh



• LongTermContracts• TrackInventory

• Partnership• RiskSharingContracts• TrackPerformance• RequireMultipleSites

• Inventory• DualSourcing• NewProductDesign

Time‐to‐Recover TTR :Thetimeforanodeinthesupplychaintoreturntofullfunctionalityafteradisruption.

Time‐to‐Survive TTS :Themaximumdurationthatthesupplychaincanmatchsupplywithdemandafteranodedisruption


0

50

100

150

200

250

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 20 40 >50

Num

ber o

f Sup

plie

rs

TTS (weeks)


• DevelopmentofaDecisionSupportSystemforRiskManagement RiskAnalysis‐‐Strategic

IdentifyExposuretoRiskassociatedwithpartsandsuppliers Prioritizeandallocateresourceseffectively Segmentsuppliersanddevelopmitigationstrategies Identifyopportunitiestoreduceriskmitigationcost

TrackchangesinRiskExposure‐‐Tactical Alertprocurementexecutivestochangesintheirriskposition

RespondtoaDisruption‐‐Operational Identifyaneffectivewaytoallocateresourcesafteradisruption

Central Repository (SQL Server)

Supply Chain Mapping

(Java Graph ETL)

Risk Exposure Model

(Java‐CPLEX)

Data Visualization (Tableau)

Model Interface

Materials Planning & Logistics

Purchasing System

Vehicle Volume Planning System

Vehicle Profit Margins



(Java Graph ETL)

Risk Exposure Model

(Java‐CPLEX)


Model Interface


Purchasing System



Plant Parent Child

Plant‐X Part A Part B

Plant‐X Part B Part C

Plant Part From

Plant‐X Part B Plant‐Y

Plant‐X Part C Plant‐Z

Part Feature Plant

Part A CDHABC AP02A

Part B CDHXYZ AP02A

Plant A

Plant B

DCZSA > RF3S7R ‐ 7144 ‐ CA > 0132A > RF3S7R ‐ 7144 ‐ CA > AG9R ‐ 7144 ‐ EB0001 > AG9R ‐7144 ‐ EB0002 > AG9R ‐ 7144 ‐ EB0003 > AG9R ‐ 7144 ‐ EB0004 > AG9R ‐ 7144 ‐ EB0005 > AG9R ‐7144 ‐ EB0006 > AG9R ‐ 7144 ‐ EB > PCV6R ‐ 7015 ‐ GCA > CV6R ‐ 7015 ‐ GCA > CV6R ‐ 7002 ‐GCC > AP02A >> CDHABCTD >> CDH

T1 SitesFinal

Assembly

Part Structure

Part Supply

Final Assembly

Supply Lineage



(Java Graph ETL)

Risk Exposure Model

(Java‐CPLEX)


Model Interface


Purchasing System



Supplier Vehicle Impacted Total Part Cost Financial Impact Volume Impact Supplier Part Namesx11 cc1 $$$ $$$ vvv x11 y11x12 cc2 $$$ $$$ vvv x11 y12x13 cc3 $$$ $$$ vvv x11 y13x14 cc4 $$$ $$$ vvv x12 y21x15 cc5 $$$ $$$ vvv x12 y22x16 cc6 $$$ $$$ vvv x13 y31x17 cc7 $$$ $$$ vvv x13 y32x18 cc8 $$$ $$$ vvv x13 y33x19 cc9 $$$ $$$ vvv x13 y34x20 cc10 $$$ $$$ vvv x14 y41

Supplier Vehicle Impacted Total Part Cost Financial Impact Volume Impact Supplier Part Namesx11 cc1 $$$ $$$ vvv x11 y11x20 cc10 $$$ $$$ vvv x11 y12x21 cc11 $$$ $$$ vvv x11 y13

x20 y21x20 y22x20 y31x20 y32x20 y33x20 y34x20 y35

• Providedaninternalproactivetoolforriskmanagement

• Generatedcriticalsupplierlist/partlist Previously,Fordmonitored1500suppliersites Themodelidentified2600suppliers’sites,upto$2.5billionrisksonrevenue Amongthe2600sites,1100sitesweremonitoredbyFord

Identified1500newsitesthatarenotcurrentlymonitored About400siteshavebeenassessedaslowrisks

• Examplesofthemodelinpractice Riskmodelidentifiedasensorthathashighvehicleexposureandisbeingsuppliedby

twositesglobally.Thecommodityteamacknowledgedthesourcingconcentrationandhasinvestigatedalternatives

Forthefastenercommodity,themodelenabledFordtoprioritizepartsbasedonexposurelevelandtriggeredfurtherinvestigation.OurinvestigationsegmentindustrystandardpartswithshortTTRintolow‐riskwhilespecialoruniquefastenersintopotentialhigh‐riskcategory

FordSupplyRiskSpecialistsusethemodelroutinelytoprioritizecommoditiesandsuppliersitesthatrepresentthehighestlevelofexposureduringpotentialdisruptionevents i.e.naturaldisasters,laborstrikes,politicalunrest,etc. ,enablingefficientuseofresources


Award Winning Technology

Winnerofthe2014InformsWagnerAward‐ FordMotorCompany:IdentifyingRisksandMitigatingDisruptionsintheAutomotiveSupplyChain

HBRarticle:FromSuperstormstoFactoryFiresdescribesamethoddevelopedby DavidSimchi‐Levi to manageunpredictablesupplychaindisruptions.

ReceivedtheFord2015EngineeringExcellenceAward

• RiskExposuremethodimplementedinindustriessuchasTelecommunication,High‐Tech,Pharmaceutical,AerospaceandAutomotive

• TheUNOfficeofDisasterRiskReductionappliedtheRiskExposuremethodindevelopingcountries Haraguchi,M.andU.Lall,“FloodRisksandImpacts”

•

HBR:FindtheWeakLinkinYourSupplyChain

•

Softwarethatimplementstheconceptsinthistalkhttp://www.opalytics.com/network‐risk/


Questions?Type your question in the chat box in the lower left corner of your screen and click “send”

Thank you for joining us!

This webinar was made possible by the support of UPS.

Delivered byOCTOBER 21, 2015

Business

Supply chain risks by Levi 2015