risk mitigation (two different documents needed doc 1 atleast 500words and doc 2 atleast 1500 words)

Supply_Chain_Risk_Management_An_Emerging_Disciplin..._----_Pg_206--241.pdf

Home >Business & Finance homework help >risk mitigation (two different documents needed doc 1 atleast 500words and doc 2 atleast 1500 words)

185

10 Using Probabilistic Models to Understand Risk

AMR. research,. now. part. of. Gartner,. has. been. speaking. about. the. com- plexion. of. the. 21st. century. supply. chain. for. some. time,. and. during. that. dialogue. the. topic. of. probabilistic. planning. continuously. arises.. This. planning. process. is. supported. by. stochastic. demand. management. and. dynamic.inventory.planning.

In.this.chapter,.we.will.discuss.models.that.have.been.around.for.some. time,. such. as. stochastic/.probabilistic. models,. deterministic. methods,. discrete-.event. simulation,. and. digital. modeling.. We’ll. also. explore. how. these.methods.are.being.leveraged.to.map.out.complex.supply.chains.and. how. leaders. are. appending. risk. assessments. to. scenarios. supported. by. these.techniques..Next,.we’ll.introduce.risk.response.planning,.the.logical. outcome.of.stress.testing.complex.supply.chains.and.modeling.“what-.if ”. scenarios.in.an.effort.to.develop.a.plan.to.manage.risk.scenarios..We.con- clude.with.several.examples.that.demonstrate.how.leading.companies.are. leveraging.these.powerful.and.dynamic.techniques.to.identify,.assess,.mit- igate,.and.manage.supply.chain.risks.

defining the MOdelS

Stochastic/.probabilistic.models.are.models.where.uncertainty.is.explicitly. considered. in. the. analysis.. Furthermore,. stochastic/.probabilistic. models. are.procedures.that.represent.the.uncertainty.of.demand.by.a.set.of.pos- sible. outcomes. (i.e.,. a. probability. distribution). and. that. suggest. inven- tory. management. strategies. under. probabilistic. demands.1. Stochastic.

Schlegel, Gregory L., and Robert J. Trent. Supply Chain Risk Management : An Emerging Discipline, Taylor & Francis Group, 2014. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/apus/detail.action?docID=1680353. Created from apus on 2021-06-27 06:10:51.

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186 • Supply Chain Risk Management: An Emerging Discipline

optimization.(SO).methods.are.optimization.algorithms.that.incorporate. probabilistic.(random).elements,.either.in.the.problem.data.(the.objective. function,.the.constraints,.etc.).or.in.the.algorithm.itself.(through.random. parameter.values,.random.choices,.etc.),.or.in.both..This.concept.contrasts. dramatically.with.the.deterministic.optimization.methods,.such.as.time. series. analysis,. linear. programming,. integer. programming,. the. simplex. method,.and.regression.models.where.the.values.of.the.objective.function. are.assumed.to.be.exact.and.the.computation.is.completely.determined.by. the. values. developed. in. the. equations.. (Table 10.1. provides. basic. defini- tions.of.some.of.the.key.terms.used.in.this.chapter.)

The. most. critical. difference. between. probabilistic. and. deterministic. models.is.that.there.is.not.an.ounce.of.uncertainty.explained.or.handled. in.deterministic.tools..Therefore,.the.responsibility.of.handling.any.uncer- tainty,. complexity,. and. risk. has. been. the. responsibility. of. supply. chain.

taBle 10.1

Defining.Key.Terms

Technique Description

Time.series. analysis

Deterministic.approaches.that.use.historical.data.to.forecast.future. requirements.

Regression. analysis

Deterministic.models.that.represent.the.relationship.between.a. dependent.variable.[y].and.independent.variables.[x].

Discrete-event. simulation

DES.models.the.operation.of.a.system.as.a.discrete.sequence.of.events. in.time..Each.event.occurs.at.a.particular.instant.in.time.and.marks.a. change.of.state.in.the.system..While.simulations.allow. experimentation.without.exposure.to.risk,.they.are.only.as.good.as. their.underlying.assumptions.

Forecast.error Represents.the.difference.between.an.actual.value.and.a.forecasted. value..The.objective.is.to.minimize.forecast.error.and.maximize. forecast.reliability.

Stochastic/ probabilistic. models

Models.where.uncertainty.is.explicitly.considered.in.the.analysis.. Involves.statistical.procedures.that.represent.the.uncertainty.of. demand.by.a.set.of.possible.outcomes.and.that.suggest.inventory. management.strategies.under.probabilistic.demands.

Design.of. experiments

The.process.of.setting.up.a.series.of.tests.or.experiments.to.determine. what.outputs.result.from.different.combinations.of.inputs.

Sensitivity. analysis

Involves.systematically.changing.quantitative.inputs.or.assumptions.to. assess.their.effect.on.a.final.outcome.

Linear. programming

A.mathematical.technique.used.in.computer.modeling.(simulation).to. find.the.optimal.solution.that.maximizes.profit.or.minimizes.cost. considering.a.set.of.limited.resources,.such.as.personnel,.funds,. materials,.etc.

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Using Probabilistic Models to Understand Risk • 187

professionals..At.this.point.in.the.growth.of.supply.chain.management.as.a. discipline.and.because.of.the.expanded.nature.of.uncertainty,.complexity,. and.risk,.it.is.important.to.use.these.“new”.techniques.to.manage.global. risk..These.methodologies.are.not.new..Academia,.pharmaceuticals,.medi- cal,. finance,. insurance,. and. the. banking. industry. have. been. using. these. methods. to. evaluate. and. mitigate. risk. for. more. than. 50 years.. They. are,. however,.new.to.the.supply.chain.world.

With. the. framework. established. for. stochastic/.probabilistic. methods,. let’s. spend. a. brief. moment. describing. our. supply. chain. comfort. zone. in. terms.of.tools.2.Supply.chain.professionals.have.been.leveraging.determin- istic.methods.to.solve.supply.chain.problems.for.more.than.35 years..We. utilize.time.series.tools.to.forecast.sales,.basically.using.the.least.squares. method.to.fit.a.line.through.a.set.of.sales.or.order.observations.and.project. anywhere.from.1.to.18.or.more.months.of.future.sales..We.regularly.track. forecast.error.by.comparing.actual.demand.versus.projected.demand.and. handle.that.demand.variability.(i.e.,.risk).with.inventory.safety.stock,.buf- fer.stock,.and.simple.brute.force..We.also.utilize.linear.programming.and. the.simplex.method.to.optimize.supply.chain.network.designs.by.model- ing. existing. and. future. network. configurations. and. then. optimizing. an. objective. function. to. either. maximize. sales,. profits. or. service. levels,. or. minimize.costs,.subject.to.a.series.of.constraints.

We.also.utilize,.albeit.sparingly,.regression.analyses.to.build.models.of. our.markets.and.attempt.to.predict.sales.for.new.product.introductions,. which.are.the.dependent.Y.variables.subject.to.independent.X.variables.. And. we’ve. leveraged. these. tools. to. optimize. revenues. or. minimize. the. costs.associated.with.logistics,.truck.and.rail.scheduling,.and.airline.oper- ations. management.. While. these. approaches. have. merit,. none. handles. uncertainty.and.risk..And.in.today’s.volatile.world,.that.in.itself.is.a.com- pelling.reason.to.act.

pROBaBiliStiC VeRSuS deteRMiniStiC MOdeling tOOlS

This.is.a.good.point.in.our.discussion.to.illustrate.the.differences.between. the. two. statistical. methodologies. and. then. follow. up. with. some. actual. cases. showing. how. probabilistic. methods. support. the. effort. to. man- age. risk. within. complex. global. supply. chains.. Think. of. this. in. terms. of.

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188 • Supply Chain Risk Management: An Emerging Discipline

weather.forecasters.on.TV..When.hurricane.forecasters.talk.about.a.new. storm,.they.present.something.they.call.the.“cone.of.uncertainty,”.which. Figure 10..1.depicts.

This. cone. represents. a. set. of. outcomes. from. probabilistic. models. that. attempt. to. predict. where. a. storm. will. travel. based. on. probabilities. of. occurrences.. Compare. this. approach. to. the. traditional. deterministic. methods.where.there.is.no.uncertainty.within.the.model..The.bottom.of. Figure 10.1. depicts. the. extremely. V-.shaped. solution. that. deterministic. methods. attempt. to. achieve,. without. uncertainty,. in. order. to. present. an.

�is is the Face of New Forecasting ... “�e Cone of Uncertainty”

Deterministic Planning vs Probabilistic Planning

Uncertainty & Solution Range

“Best” value “Optimal” value

Supply Chain Cost

Parameter value

Deterministic

Probabilistic Planning

figuRe 10.1 Stochastic/.probabilistic.planning.

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Using Probabilistic Models to Understand Risk • 189

optimal. solution.. The. probabilistic. method,. used. by. weather. forecasters,. provides.an.approach.to.gain.an.optimal.solution.across.a.much.broader.set. of.solution.variables.within.the.model,.explicitly.addressing.uncertainty.

We. are. beginning. to. witness. this. probabilistic. methodology. support- ing. scenario. planning. in. the. context. of. supply. chain. risk. management. (SCRM).. What. does. this. process. look. like?. First,. it. starts. by. digitizing. the. entire. supply. chain. and. building. a. flow. model. of. the. enterprise,. as. illustrated.in.Figure 10.2..Supply.chains.are.nothing.more.than.a.network. with.speeds.and.feeds,.inputs,.outputs,.and.processing.times.that.can.be. digitized. as. dynamic. flow. models.. Next,. companies. populate. the. model. of.the.enterprise.with.base.case.data.from.their.enterprise.resource.plan- ning.(ERP).system.and.by.identifying.the.historical.behavior.and.uncer- tainty. of. all. relevant. factors.. These. factors. include. elements. such. as. lead. times,.capacities,.demand,.production,.inventory,.quality,.yields,.policies,. and.more.

Companies.next.develop.“what-.if ”.scenarios,.or.what.we.call.hypotheses,. that.need.to.be.reinforced.or.refuted,.looking.at.scenarios.such.as.demand. increasing. by. 30%,. demand. decreasing. by. 30%,. lead. times. increasing. or. decreasing,. market. share. to. be. gained,. supplier. disruptions,. plant. dis- ruptions,. complex. competitive. pricing. changes,. geopolitical. changes,. oil. price. fluctuations,. and. more.. Most. probabilistic. tools. maintain. a. library. of. probability. tables. that. indicate. the. probability. distributions. utilized. within.the.scenarios..If.the.tools.can’t.capture.historical.data.for.certain. variables,.users.can.posit.probabilities.of.occurrence.for.certain.changes.

With.these.assumptions.codified.and.historical.data.in.hand,.users.begin. to.run.discrete-.event.simulations.across.the.entire.enterprise.for.every.sce- nario.in.an.effort.to.review.the.cause-.and-.effect.outcomes.and.their.statistical. strengths..The.outcomes.normally.take.the.shape.of.histograms,.sensitivity. curves. with. confidence. intervals. and. probabilities. of. occurrence. along. with. risk. assessments. for. each. scenario,. depicted. on. the. bottom. of. Figure 10.2..This.continuous.running.of.the.model,.requiring.several.hun- dred.iterations,.can.continue.until.the.outcomes,.per.scenario,.are.consid- ered.statistically.significant..This.task.is.accomplished.through.the.use.of. sensitivity.analysis,.optimized.response.curves,.and.design.of.experiments. (i.e.,.a.structured.and.systematic.Six.Sigma–.oriented.testing.methodology. of.the.process.model).

The. outcomes. of. the. scenarios. are. next. prioritized. based. on. their. probabilities. of. occurrence. and. their. associated. risk. index.. This. novel. approach. is. accelerating. SCRM.. By. combining. powerful. tools,. such. as.

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190 • Supply Chain Risk Management: An Emerging Discipline

SCRM Scenario Planning Vision

Probabilistic Simulation

Risk Response

Plan

Supply chain Flow model

Base case data

Decision logic

Design of Experiments

Performance measures

Feasible Tactical plans

No Yes Determine “Most

Appropriate & Critical Scenarios”

Scenario/Risk Response Planning

Scenario Probabilities of Occurrence

High

Low

Risk Associated with Occurrence

Low

High

Take Scenarios & Build A Risk Response Plan (Perform �is Last)

Scenario # 1

Perform �is First

Perform �is Second

Enough Information?

Probability Distributions of uncertain factors

Probability of occurrence & magnitude of

disturbing events

figuRe 10.2 Scenario.and.risk.response.planning.

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Using Probabilistic Models to Understand Risk • 191

probabilistic. methods,. digital. modeling,. and. discrete-.event. simulation,. coupled. with. risk. assessments. for. every. scenario,. these. techniques. are. providing. professionals. the. ability. to. better. manage. risk.. The. final. step. in.this.powerful.new.process.is.to.eventually.develop.a.risk.response.plan. for.the.scenarios.deemed.critical.to.the.enterprise.covering.the.strategic,. tactical,. or. operational. horizons.. This. approach. represents. SCRM. at. its. sophisticated.best.

RiSk ReSpOnSe planS

Industries.such.as.oil.and.gas,.chemicals,.airlines,.and.pharmaceuticals.and. organizations.such.as.the.U.S..Department.of.Defense.have.been.exercising. scenario.planning.and.supporting.those.scenarios.with.risk.response.plans. for.years..Formally,.a.risk.response.plan.is.a.document.defining.the.known. risks. including. the. description,. cause,. likelihood,. costs,. and. proposed. responses. to. risk. events.. It. also. identifies. the. current. status. of. each. risk.3. Risk.response.plans.tend.to.be.the.outcome.of.scenario.planning.exercises.. As.organizations.develop.scenarios.to.either.stress.test.their.supply.chains. in.terms.of.exploring.what-.if.cause-.and-.effect.relationships.or.go.through. exhaustive.scenarios.to.prepare.for.possible.risk.events,.risk.response.plans. are.the.value-.added.outcome.of.most.of.these.risk.management.exercises.. Risk.response.plans,.as.noted.in.Chapter 9,.are.one.of.the.eight.elements.of. the.enterprise.risk.management.(ERM).framework.

Some. might. argue. that. business. continuity. plans. and. risk. response. plans.are.similar..One.key.difference.is.that.risk.response.plans.tend.to.be. outcomes. of. scenario. planning. exercises.. Risk. response. plans. are. docu- ments.that.profile.basically.what.will.be.done.if.a.certain.risk.event.occurs,. be. it. operational,. tactical,. or. strategic.. These. plans. normally. consist. of. four.areas:

•. Identification of Known Risks..This.involves.a.description.about.the. nature.of.risks.(refer.to.the.ERM.framework),.the.risk.causes,.the.like- lihood. of. risk. occurrence. as. defined. by. the. probability. distribution. and.discrete-.event.simulation,.and.the.estimated.cost.of.each.risk.

•. Identification of Risk Owners..This.involves.identifying.what.disci- plines.and.who.from.those.disciplines,.including.existing.roles.and. responsibilities,.will.assume.leader.ship.as.risk.owners.

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192 • Supply Chain Risk Management: An Emerging Discipline

•. Articulation of Risk Responses.. This. part. includes. the. response. plans,. what. everyone. will. do. in. the. event. of. a. risk. event,. and. what. tactics.to.deploy..This.might.also.include.cost/.benefit.relationships.

•. Articulation of the Measure of Successful Risk Mitigation.. This. includes. the. key. performance. indicators. that. reveal. how. well. an. organization.is.succeeding.in.mitigating.a.risk.

As.discussed.earlier,.organizations.that.exercise.scenario.planning.tend. to. force. rank. these. scenarios. using. risk. assessment. techniques,. which. Chapters. 12. and. 13. will. touch. upon.. They. then. determine. the. scenarios. for.which.they.will.develop.risk.response.plans..These.risk.response.plans. are. then. distributed,. either. in. printed. form. or. digitized,. for. distribution. throughout.a.facility,.division,.or.enterprise..This.allows.widespread.access. if.a.risk.event.occurs,.something.that.should.accelerate.a.recovery.

COMpany exaMpleS Of pROBaBiliStiC MOdeling

The.following.examples.highlight.the.efforts.of.three.leading.companies.to. model.risk.probabilities.into.their.risk.management.plans.

Scenario planning at dupont

DuPont,.founded.in.1802,.has.repeatedly.transformed.itself.over.its.200+. year.history.due.to.a.culture.of.innovation.and.renewal..Today,.DuPont.is. at. the. forefront. of. supply. and. demand. chain. management. as. it. develops. a. comprehensive. approach. to. sales. and. operations. planning. (S&OP).. As. mentioned. in. Chapter 3,. S&OP. is. an. essential. process. for. balancing. supply.and.demand..DuPont.has.been.pursuing.S&OP.across.many.divi- sions. starting. after. 2000.. With. that. demand/.supply. balancing. approach. the.company.has.been

•. Relating.improved.demand.management.outcomes.to.business.per- formance.as.the.driver.for.improved.supply.chains

•. Breaking.down.complexity.into.actionable.parts •. Addressing.uncertainty.with.consensus.planning •. Clearly. defining. and. developing. standard. practices,. knowledge,.

and.resources

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Using Probabilistic Models to Understand Risk • 193

•. Developing.a.global.deployment.methodology.including.diagnostics. and.a.current-.to-.future-.state.improvement.path

•. Measuring.for.results.and.enabling.the.process.with.systems.and.tools

Even.with.its.risk.management.and.planning.capabilities,.the.financial. meltdown.of.2008.caught.DuPont.off.guard..One.of.the.reasons.the.com- pany.was.negatively.impacted,.even.though.it.operated.a.comprehensive,. although.somewhat.traditional,.S&OP.process,.was.the.company’s.inward-. rather.than.outward-.looking.focus.when.engaging.in.supply.chain.plan- ning..This.inside-.out.approach.proved.to.be.inadequate.for.responding.to. the.dramatic.market.shifts.that.took.place.during.the.financial.crisis..This. experience.has.made.DuPont.one.of.the.biggest.proponents.of.something. called.scenario.planning.within.the.S&OP.process.

Scenario. planning. uses. probabilistic. methods. to. evaluate. and. plan. for. demand. events. over. various. time. horizons.. DuPont. is. actively. develop- ing. demand. management. scenarios. with. best,. conservative,. and. most. likely. outcomes,. all. supported. with. probabilities. of. occurrence.. To. say. that. DuPont. has. elevated. the. sophistication. of. its. risk. planning. game. would. be. a. tremendous. understatement.. Figure 10.3. summarizes. the. DuPont.approach.

Within. its. S&OP. process,. DuPont. models. demand. projections. with. probabilities.and.develops.risk.response.plans.for.these.demand.scenarios.. Compared. with. an. earlier. era,. many. of. these. scenarios. are. “outside-.in,”. meaning. they. actively. incorporate. external. events. and. factors. that. may. impact. DuPont’s. supply. chains.. To. the. risk. purists,. this. is. called. stress testing.the.supply.chain..This.approach.is.similar.to.the.apparel.industry,. which.uses.probabilistic.demand.planning.because.of.the.nature.of.its.sea- sonal.demand.requirements.

DuPont.routinely.uses.the.scenario.planning.approach.and.an.outside-.in. view.of.its.supply.chains.to.mitigate.risk.within.its.mature.S&OP.process.. A.scenario.planning.approach.to.demand.management.is.now.embedded. across.many.of.the.company’s.supply.chains.and.has.fostered.a.concerted. effort. to. elevate. the. importance. of. SCRM.. Developing. a. functioning. S&OP.process.is.a.good.thing..Developing.an.S&OP.process.that.includes. demand.projections.with.probabilities.and.risk.response.plans.is.even.bet- ter..According.to.a.DuPont.executive,.“The.value.of.this.type.of.approach. to.demand.and.supply. balancing.and.risk. management,.especially. when. linked.to.automation.and.facilitation,.is.that.it.helps.create.planning.sce- narios.that.are.actionable.and.executable,.not.just.academic.exercises.”4

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194 • Supply Chain Risk Management: An Emerging Discipline

Stress testing the Supply Chain at Bayer Material Sciences

Another. application. we. would. like. to. discuss. is. stress. testing. complex. supply.chains.utilizing.the.tools.and.techniques.described.in.this.chapter.. The.story.we’d.like.to.share.is.one.revolving.around.a.large.and.complex. supply.chain.at.Bayer.Material.Science..BMS.is.among.the.world’s.largest. polymer. companies.. Its. business. is. focused. on. manufacturing. high-.tech.

Application – Clarity on how to apply and the level of planning capability needed to support. Define as future “what if” scenarios

Control Plan – How to maintain scenario integrity, ensure they are executable, and ensure a continuous improvement process is in place.

50% 90%10%

V ol

um e,

P ri

ce , D

ri vi

ng R

ev en

Your Base assumptions

Base Assumptions plus additional opportunities

Base Assumptions minus several risks

Cumulative Probability Probability of Plans and Assumptions

• Order History • DMD Variability History • Mix History

• Demand Plans 0–24 • Input to Calculate Stock - Dynamic • Mix Changes

Demand Plan 1

Demand Plan 2

Demand Plan 3

Inventory Effect on Cash

Customer Service

Scenarios for uncertain future conditions

Plan Variation

What If

Optimistic Cases

Most Likely Cases

Conservative Cases

figuRe 10.3 DuPont’s.approach.to.defining.how.to.bring.scenario.planning.to.an.actionable.level.

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Using Probabilistic Models to Understand Risk • 195

polymer. materials. and. the. development. of. innovative. product. solutions. for.its.customers..BMS.maintains.more.than.30.production.sites.around. the.globe.

BMS. was. encountering. unacceptable. levels. of. aged. and. slow-.moving. inventory. across. many. of. its. business. lines,. and. its. compounding. busi- ness. wanted. to. significantly. reduce. its. response. times. to. customers. in. order.to.gain.market.share.in.regions.they.had.never.sold.to.in.the.past.. Several. hypotheses. were. established. to. better. understand. how. the. com- pany’s. supply. chains. would. react. if. these. strategies. were. implemented.. BMS.established.a.Six.Sigma.team.of.black.belts.and.operational.analysts. to. quantify. these. hypotheses.. The. team. developed. a. project. to. test. these. hypotheses. under. the. DMAIC. (define,. measure,. analyze,. improve. and. control).umbrella..The.DMAIC.methodology.is.the.most.frequently.used. framework.for.evaluating.and.improving.existing.processes.

The. BMS. team. realized. early. on. that. the. company’s. complex. manu- facturing.facilities.would.make.it.highly.unlikely.that.the.team.would.be. able.to.test.its.hypotheses.in.live.operations..Thus,.attempting.to.execute. trial. production. runs. in. live. operations. would. be. prohibitively. expen- sive,. time-.consuming,. and. pose. unacceptable. risks. to. the. organization.. With.that.reality,.the.BMS.team.reached.out.to.a.third.party.to.leverage. its. probabilistic/.predictive. modeling. capabilities. to. test. more. than. 40. hypotheses..Probabilistic/.predictive.digital.models.supported.by.discrete-. event. simulation. and. Six. Sigma. frameworks. such. as. DMAIC,. design. of. experiments,. and. failure. modes. effect. analysis. (FMEA). create. a. robust. set.of.approaches.for.qualifying.and.quantifying.the.impacts.of.changing. production.points,.policies,.and.procedures.inside.complex.manufactur- ing.environments.

The. BMS. team. and. the. third-.party. consultants. developed. a. digital. model.of.the.company’s.supply.chain..The.bulk.of.the.data.came.from.the. company’s.ERP.and.supply.chain.systems..The.company.did.a.thorough. job. of. developing. its. hypotheses. and. defining. the. existing. environment.. This. was. accomplished. through. the. use. of. the. Six. Sigma. methodology. FMEA.. The. Bayer. team. also. exercised. lean/.Six. Sigma. methods. such. as. kaizen.brainstorming.events.and.value-.stream.mapping.

When. utilizing. FMEA. the. team. developed. a. comprehensive. tableau. of. rows. and. columns. depicting. more. than. 100. independent. variables. that. the. team. felt. were. impacting. customer. service. and. aging. inven- tory. levels.. These. independent. variables. included. elements. such. as. the.

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196 • Supply Chain Risk Management: An Emerging Discipline

planning.and.scheduling.process,.sales.forecasting.process,.partial.orders,. customer. order. errors,. customer. cancellations,. overproduction,. and. missed.commitments.

Each.row.of.the.FMEA.tableau.depicted.a.cause-.and-.effect.relationship. between. an. independent. variable. and. the. primary. dependent. variable,. aged. inventory.. The. team. then. established. a. prioritized. activity. impact. profile.of.all.variables.based.on.three.years.of.experience..The.initial.data. were.then.entered.into.a.probabilistic.model.in.conjunction.with.histori- cal.orders,.production,.inventory,.shipments,.quality.yields,.and.more..A. digital.model.of.the.supply.chain.was.tested.for.completeness.through.the. use. of. postanalysis.. This. statistical. validation. method. involves. running. hundreds.of.daily.production.schedules.from.actual.data.and.comparing. what. the. model. predicted. would. happen. with. what. actually. happened.. This.resulted.in.a.baseline.that.confirmed.that.the.digital.model.was.indic- ative.of.how.Bayer.operated.its.plants.over.a.fixed.period.of.time.

With. the. define. and. measure. stages. of. the. DMAIC. process. complete,. the.teams.entered.the.analyze.phase..In.this.phase.the.team.captured.the. company’s. physical. process,. operating. policies,. and. decision. rules. and. incorporated. them. into. the. model.. The. team. correlated. operating. per- formance. with. historical. records,. evaluated. and. compared. alternative. process. improvement. scenarios,. and. leveraged. the. probabilistic. model. to. evaluate. more. than. 40. scenarios.. The. team. also. incorporated. the. Six. Sigma.technique.design.of.experiments,.which.they.wrapped.around.the. digital.model.to.ensure.statistically.significant.cause-.and-.effect.relation- ships.between.the.variables.driving.the.outcomes.of.each.scenario.

Many.of.the.outcomes.of.the.modeling.runs.were.revealing,.compelling,. and.at.times.even.counterintuitive..The.outcomes.of.the.scenarios.revealed. that.BMS.could.reduce.lead.times.by.a.substantial.amount.to.capture.new. market.share;.capture.a.significant.amount.of.new.market.share.without. requiring.new.production.capacity;.increase.their.service.levels;.improve. capacity. throughput. without. new. capital. expenditures;. and. reduce. aged. inventory.substantially.while.reducing.working.inventory.as.well.

Stress. testing. a. complex. supply. chain. before. a. company. commits. tre- mendous. resources. is. a. testimonial. to. the. new. tools. coming. online. to. support.increased.uncertainty,.complexity,.and.risk.in.global.supply.chains.. Leveraging. digital. modeling,. probabilistic. tools,. discrete-.event. simulation,. and. risk. assessment. is. a. powerful. environment. to. evaluate. operational. alternatives.without.experimenting.on.customers.5

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next- generation S&Op at huntsman

A.final.example.involves.Huntsman.and.its.inability.to.react.to.sources.of. uncertainty.that.were.impacting.its.S&OP.process..So,.what.did.the.com- pany. do?. Huntsman. recruited. two. external. consultants. to. help. identify,. assess,. mitigate,. and. manage. the. sources. of. uncertainty. that. were. nega- tively.impacting.the.company’s.supply.chain.6

Huntsman. is. a. global. manufacturer. of. differentiated. chemicals.. The. company.maintains.an.extensive.array.of.production.sites.in.Europe.and. the. United. States.. With. a. complex. supply. chain. and. a. product. portfolio. that. runs. the. gamut. of. commodity. chemicals. with. almost. no. margins. to. specialty. chemicals. with. strong. margins,. its. S&OP. process. to. bal- ance.demand.and.supply.was.problematic.and.always.involved.managing. difficult. trade-.offs.. With. a. challenging. operating. environment. to. con- tend. with,. Huntsman. came. to. the. conclusion. that. something. had. to. be. done. to. manage. the. overwhelming. number. of. sources. of. uncertainty. in. its. supply. chain. if. the. company. planned. to. compete. effectively. from. a. global.perspective.

Let’s.get.grounded.a.bit.and.talk.about.the.company’s.supply.chain.and. S&OP.process..At.one.point.Huntsman.had.just.about.every.deterministic. software.enabler.a.company.could.imagine.to.plan,.source,.make,.deliver,. and.return.goods.within.a.complex.global.supply.chain.7.Huntsman.also. maintained.a.sophisticated.ERP.system.to.take.orders,.make.orders,.ship. orders,. and. bill. orders.. Within. this. IT. environment,. the. company. also. managed. a. state-.of-.the-.art. demand. management/.forecasting. tool. with. all.the.bells.and.whistles,.including.single,.double,.and.triple.exponential. smoothing. forecasting. tools.. Even. today. who. wouldn’t. want. these. sys- tems?.These.tools.and.systems.acted.as.an.enabler.for.a.large-.scale.S&OP. process.covering.a.good.portion.of.Huntsman’s.vast.product.portfolio.

Huntsman.was.also.an.avid.user.of.lean/.Six.Sigma.tools.such.as.value-. stream.mapping,.DMAIC,.FMEA,.and.statistical.process.control.charting.. And. the. company. maintained. several. new. advanced. planning. systems. (APSs).that.took.demand.signals.from.the.S&OP.process.and.input.them. directly. into. the. APS. tools. to. create. work. plans. across. their. worldwide. manufacturing. and. distribution. sites.. They. also. maintained. a. huge. data. warehouse. that. enabled. the. company. to. slice. and. dice. supply. chain. data.into.an.almost.infinite.number.of.ways.to.support.improved.decision-. making.capabilities.

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198 • Supply Chain Risk Management: An Emerging Discipline

Most. observers. would. agree. the. company. was. operating. at. a. sophisti- cated. supply. chain. level.. Unfortunately,. these. deterministic. approaches. did.not.represent.the.next.generation.of.risk.management.methodologies.. Most. of. us. are. comfortable. with. what. we. know,. and. that. has. included. the. continued. use. of. deterministic. tools. and. techniques,. none. of. which. addresses.risk..And.therein.was.the.dilemma.for.this.large.and.sophisti- cated.manufacturer..The.deterministic.models.can.only.take.us.so.far.

Huntsman. was. clearly. looking. to. think. outside. the. supply. chain. box. as. it. recruited. outside. experts. to. help. the. company. take. a. risk-.oriented. view.of.the.supply.chain.and.its.processes.in.an.effort.to.manage.risk.and. improve.bottom-.line.results..Let’s.look.at.how.this.change.project.began.

First,.the.two.consultants.developed.several.hypotheses,.gathered.data,. and.tested.hypotheses..They.attempted.to.identify.the.possible.sources.of. uncertainty.that.were.impacting.Huntsman’s.S&OP.process..Huntsman,. as.a.lean/.Six.Sigma.house,.was.already.codifying.many.of.the.sources.of. uncertainties.through.the.use.of.FMEA..The.company.was.capturing.all. the. known. sources. and. then. preparing. frequency. charts. to. profile. these. risks. and. provide. insight. into. their. cause-.and-.effect. relationships.. The. consultants. identified. 13. sources. of. uncertainty. impacting. Huntsman’s. supply.chain.and.its.S&OP.process.

A. value-.added. activity. from. this. project. was. taking. the. sources. of. uncertainty. and. classifying. them. into. categories. of. low,. medium,. and. high.risk.and.then.applying.these.sources.of.uncertainty.across.the.three. supply.chain.planning.horizons..These.time.horizons.include.operational. (0–45 days),.tactical.(1–18 months),.and.strategic.(1–5 years)..Figure 10.4. reveals. what. risks. line. up. where. and. which. time. horizon. maintains. the. highest.number.of.risks..Figure 10.4.reveals.that.a.large.number.of.risks. reside. with. the. S&OP. horizon. (1–18 months).. One. external. assessment. has.revealed.that.more.than.70%.of.companies.that.practice.S&OP.remain. in. Stage. I. (reacting). and. Stage. II. (anticipating). within. an. S&OP. matu- rity.model..Only.17%.achieve.Stage.III.(collaboration).status.and.a.lesser. amount.achieve.Stage.IV.(orchestrating)..Clearly,.there.are.opportunities. for.advancement.here.

So,.what.did.Huntsman.do.with.this.new-.found.insight.on.supply.chain. risks?. Essentially,. Huntsman. and. the. project. team. relied. upon. many. of. the. tools. and. techniques. discussed. earlier. in. the. chapter,. particularly. probabilistic. planning,. digital. modeling,. discrete-.event. simulation,. and. risk. assessment.. The. company. tested. hypotheses. to. determine. the.

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Using Probabilistic Models to Understand Risk • 199

cause-.and-.effect. relationships. of. many. of. the. codified. sources. of. uncer- tainty.and.applied.risk.assessments.to.each.scenario..With.the.outcomes. of. the. modeling. exercises. resulting. in. graphical. images,. the. Huntsman. team.was.able.to.visualize.the.outcome.of.each.scenario.and.discuss.those. outcomes.in.terms.of.operational.improvements.and.risk.mitigation..This. was.a.powerful.set.of.analyses.

What.benefits.were.derived.from.this.approach?.First,.Huntsman.finally. had. hard. data. points. established. in. terms. of. impacts. to. its. supply. chain. and.S&OP.process.through.the.identification.of.the.13.sources.of.uncer- tainty.. Second,. the. company. identified. several. operational. and. tactical. approaches. they. could. take. with. minimal. risk. to. the. supply. chain.. This. included.moving.inventory.upstream.for.more.flexibility,.postponement,. and.then.developing.a.form.of.risk.pooling.for.raw.materials.with.suppliers.

By. leveraging. these. powerful. new. tools. and. techniques,. the. company. was. also. able. to. reduce. its. planning. cycle. times. by. almost. half. and. elimi- nate.the.“panic-.reactive”.efforts.they.relied.on..The.company.also.began.to. institutionalize.a.contingency.planning.approach,.using.the.tools.and.tech- niques.discussed.above,.into.the.monthly.S&OP.process..This.allowed.more.

Sources of Uncertainty

Operational

0–45 Days

Tactical

1–18 Months

Strategic

1–5 Years Exchange rates XXX XX

Supplier lead-times X XXX X

Supplier quality XX X

Manufacturing yield XX XX

Transportation times XX XX X

Stochastic costs X XXX XX

Political environment

Customs regulations X XX XXX

Available capacity XX XX X

Subcontractor availability

XXX XX

Information delays XXX XX

Stochastic demand X XXX XX

Price �uctuations X XXX X

X – Low; XX – Medium; XXX – High

figuRe 10.4 Sources.of.uncertainty.in.the.supply.chain.

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200 • Supply Chain Risk Management: An Emerging Discipline

scenario.planning.and.what-.if.discussions.to.take.place.inside.the.S&OP. process..The.end.result.was.better.mitigation.of.supply.chain.risks..Over. time,. inventory. was. reduced. by. almost. half. and. service. levels. improved. dramatically. through. scenario. planning. and. subsequent. risk. response. plans.to.mitigate.risks,.if.and.when.they.appeared.

Combining. probabilistic. tools. and. techniques. with. formal. risk. assess- ment.methodologies.can,.as.demonstrated.by.Huntsman,.effectively.iden- tify,.assess,.mitigate,.and.manage.risk.while.improving.the.bottom.line.

COnCluding thOughtS

Most. supply. chain. professionals. reside. in. a. comfort. zone. that. is. popu- lated.with.deterministic.models.and.approaches..As.mentioned.through- out. this. chapter,. these. models. have. served. a. clear. purpose. over. the. last. 50 years.or.so,.and.they.will.continue.to.enjoy.widespread.use.and.refine- ment.. However,. these. tools. were. developed. in. an. era. where. SCRM. was. not. even. an. afterthought.. Consequently. they. fail. to. consider. the. supply. chain. uncertainty. that. has. become. a. way. of. life.. If. we. can. sum. up. the. basic.premise.of.this.chapter.in.one.phrase,.it.is.that.deterministic.think- ing.must.give.way.to.probabilistic.thinking..This.will.lead.to.the.develop- ment.of.new.approaches.that.emphasize.uncertainty..It.will.also.lead.to.the. extension.of.existing.tools.and.techniques.where.uncertainty.is.explicitly. considered.in.the.analysis.

Summary of key points

•. Stochastic/.probabilistic. methods. and. models. have. been. around. for. more.than.50 years..Yet,.supply.chain.management.professionals.are. now.just.understanding.that.these.tools.and.techniques.can.be.lev- eraged. to. mitigate. and. manage. risk. because. they. overtly. take. into. account.uncertainty.

•. Many. of. the. stochastic/.probabilistic. tools. and. techniques. support. what-.if.scenario.planning.approaches.to.evaluate.uncertainty,.com- plexity,.and.risk.within.global.supply.chains.

•. Emerging. stochastic/.probabilistic. modeling. tools. are. being. com- bined. with. lean/.Six. Sigma. techniques. such. as. DMAIC. as. a. data-. driven,. fact-.based. framework. and. also. design. of. experiments. to.

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Using Probabilistic Models to Understand Risk • 201

ensure.that.model.outcomes.are.statistically.significant.and.provide. sensitivity. curves. that. explain. the. cause-.and-.effect. relationships. within.model.scenarios.

•. To.manage.risks.in.complex.supply.chains,.leading.companies.will. combine.risk.response.plans.with.digital.modeling.to.provide.pow- erful.risk.management.frameworks.

•. Many. best-.in-.class. companies. that. practice. elements. of. SCRM. are. also.leveraging.additional.lean/.Six.Sigma.methods.such.as.FMEA.to. identify,.codify,.classify,.and.force-.rank.risks.throughout.their.sup- ply.chains.and.processes.

•. Several. leading. companies. in. SCRM. are. also. injecting. stochastic/. probabilistic.tools.and.techniques.along.with.lean/.Six.Sigma.methods. into.their.normal.S&OP.processes.to.not.only.assist.in.supply/.demand. balancing,. but. also. to. develop. a. risk. assessment. process. to. make. informed.decisions.about.their.supply.chains.that.take.into.account. uncertainty,.complexity,.and.risk.

•. The.methodology.of.leveraging.digital.modeling,.probabilistic.tools,. discrete-.event. simulation,. and. risk. assessment. is. a. powerful. envi- ronment.to.evaluate.operational.alternatives.without.experimenting. on.customers.

endnOteS

. 1.. Accessed. from. http://www.apics.org/.industry-.content-.research/.publications/.apics-. dictionary.

. 2.. Arntzen,.Bruce,.PhD.Director.Global.Scale.Risk.Initative,.Massachusetts.Institute.of. Technology..“MIT Scale Survey Presentation.”.APICS.International.Conference.2009.

. 3.. Accessed. from. http://www.apics.org/.industry-.content-.research/.publications/.apics-. dictionary.

. 4.. Murray,.Peter..CIRM,.“Next.Generation.of.S&OP:.Scenario.Planning.with.Predictive. Analytics. and. Digital. Modeling.”. Journal of Business Forecasting,. 29,. 3. (Fall. 2010):. 20–31.

. 5.. Baxendell,. Richard.. “Coupling Lean/ Six Sigma DMAIC Methodology with Digital Modeling/ Discrete- Event Simulation and DOE to Drive Profitable Manufacturing Response.”.IQPC.International.Conference,.April.2008.

. 6.. Van. Landeghem,. Hendrik,. and. Hendrik. Vanmaele.. University. of. Ghent,. Belgium,. “Robust. Planning:. A. New. Paradigm. for. Demand. Chain. Planning.”. Journal of Operations Management,.20.(2002):.769–783.

. 7.. Supply.Chain.Council.SCOR.Model,.Accessed.from.www.Supply-.Chain.org.

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203

11 Using Big Data and Analytics to Manage Risk

By. now. you’ve. probably. heard. about. or. have. had. some. experience. with. something.called.“big.data.”.While.we.may.have.heard.of.the.concept,.tak- ing.advantage.of.the.treasure.trove.of.data.that.resides.at.most.companies. remains.an.evolving.challenge..With.an.estimate.of.more.than.15.million. gigabytes.of.new.information.collected.every.day.(15.petabytes),.which.is. eight.times.the.information.in.all.U.S..libraries,.it’s.no.wonder.most.com- panies.are.wondering.how.to.use.big.data.to.their.advantage.1

But. is. using. big. data. going. to. be. that. straightforward?. A. report. titled. Big Data Insights and Innovations Report revealed. some. findings. that. relate.directly.to.big.data.and.its.uses.2.First,.many.organizations.are.chal- lenged. by. data. overload. and. an. abundance. of. trivial. information.. And. important. data. are. not. reaching. practitioners. in. efficient. time. frames.. Current. technology. is. also. not. yet. at. the. level. of. providing. measurable,. reportable,. and. quantifiable. data. in. areas. including. production. sched- uling,. inventory,. and. customer. demand. across. the. entire. supply. chain.. Furthermore,. despite. the. sophistication. of. current. systems,. data. are. not. always. easily. accessible. to. internal. users.. Finally,. noticeable. gaps. are. present.in.many.end-.to-.end.supply.chain.flow.models..Other.than.these. “minor”.issues,.everything.is.working.just.fine.in.the.world.of.big.data.and. risk.management.

In. this. chapter. we’ll. advance. some. definitions. and. an. overview. of. big. data.and.predictive.analytics;.talk.about.the.process.for.successfully.lever- aging.big.data;.present.barriers.and.challenges.with.big.data;.and.present. tools,.techniques,.and.methodologies.that.support.big.data.and.analytics.. The.chapter.concludes.with.examples.of.companies.using.big.data.and.how. these.companies.are.leveraging.their.data.to.help.manage.supply.chain.risk.

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204 • Supply Chain Risk Management: An Emerging Discipline

What iS Big data and pRediCtiVe analytiCS, Really?

To.some.observers.big.data.got.its.start.around.2003.with.the.advent.of.the. Data. Center. Program. at. Massachusetts. Institute. of. Technology. (MIT).3. Before.this,.most.of.the.early.research.in.the.late.1990s.used.the.term.data analytics. as. a. key. descriptor.. It. becomes. critical. to. define. the. terms. big data.and.predictive analytics.

According. to. the. Leadership. Council. of. Information. Advantage,. big data. is. not. a. precise. term. This. group. sees. it. as. data. sets. that. are. grow- ing.exponentially.and.that.are.too.large,.too.raw,.or.too.unstructured.for. analysis.using.relational.database.techniques..So,.where.is.this.unbeliev- able.amount.of.unstructured.data.coming.from?.According.to.one.source,. the.amount.of.data.available.is.doubling.every.two.years.and.is.emanat- ing.from.not.only.traditional.sources.but.also.industrial.equipment,.auto- mobiles,. electrical. meters,. and. shipping. crates,. just. to. name. a. few. The. information. gathered. includes. parameters. such. as. location,. movement,. vibration,.temperature,.humidity,.and.chemical.changes.in.the.air.4

Predictive.analytics.(PA).encompasses.a.variety.of.techniques.from.sta- tistics,.data.mining,.and.game.theory.that.analyze.current.and.historical. facts.to.make.predictions.about.the.future..In.business,.predictive.models. exploit.patterns.found.in.historical.and.transactional.data.to.identify.risks. and. opportunities.. Models. capture. relationships. among. factors. to. allow. assessment. of. risk. or. potential. associated. with. a. particular. set. of. condi- tions,.guiding.decision.making.for.specific.transactions.5

Predictive. analytics. has. been. traditionally. used. in. actuarial. science,. financial. services,. insurance,. telecommunications,. retail,. travel,. health. care,.and.pharmaceuticals..Yet.it.is.barely.mentioned.in.the.manufacturing. and.supply.chain.arenas..One.of.the.best.known.and.early.applications.of. PA.is.credit.scoring,.which.is.used.throughout.financial.services..Scoring. models.process.customers’.credit.history,.loan.applications,.customer.data,. and.so.forth,.in.an.effort.to.rank-.order.individuals.by.their.likelihood.of. making. future. credit. payments. on. time.. A. well-.known. example. is. the. FICO.score.

IBM,.a.leading.provider.of.big.data.systems,.maintains.that.more.than. 90%.of.the.data.that.exists.in.the.world.today.was.created.within.the.last. two.years..We.are.in.an.age.where.more.than.2.5.quintillion.bytes.of.data.

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Using Big Data and Analytics to Manage Risk • 205

are.created.every.day!.We.are.increasingly.becoming.familiar.with.terms. such.as.follows6:

•. gigabytes.(a.unit.of.information.equal.to.one.billion.(109).or,.strictly,. 230.bytes)

•. petabytes.(250.bytes;.1,024.terabytes,.or.a.million.gigabytes) •. exabytes.(a.unit.of.information.equal.to.one.quintillion.(1018).bytes,.

or.one.billion.gigabytes) •. zettabytes. (a. unit. of. information. equal. to. one. sextillion. (1021). or,.

strictly,.270.bytes) •. yottabytes. (a. unit. of. information. equal. to. one. septillion. (1024). or,.

strictly,.280.bytes)

Don’t. be. concerned. if. these. definitions. are. confusing.. They. confuse. us.also.

IBM.has.been.at.the.forefront.of.articulating.the.concept.of.big.data.7.In. one.of.its.analyses,.the.company.concludes.that.big.data,.which.admittedly. means. many. things. to. many. people,. is. no. longer. confined. to. the. realm. of.technology..It.has.become.a.business.priority.given.its.ability.to.affect. commerce.in.a.globally.integrated.economy..Organizations.are.using.big. data.to.target.customer-.centric.outcomes,.tap.into.internal.data,.and.build. a. better. information. ecosystem.. IBM. has. created. a. topology. that. looks. at. big. data. in. terms. of. four. dimensions. that. conveniently. start. with. the. letter.V.

The. first. dimension. of. big. data. is. volume,. which. represents. the. sheer. amount.of.data..Perhaps.the.characteristic.most.associated.with.big.data,. volume. refers. to. the. mass. quantities. of. data. that. organizations. are. try- ing.to.harness.to.improve.decision.making..As.mentioned,.data.volumes. continue. to. increase. at. an. unprecedented. rate.. However,. what. consti- tutes. truly. high. volume. varies. by. industry. and. even. geography. and. can. be. smaller. than. the. petabytes. and. zettabytes. often. referenced. in. articles. and.statistics.

Next, variety.refers.to.the.different.types.of.data.and.data.sources..This. dimension.is.about.managing.the.complexity.of.multiple.data.types,.includ- ing. structured,. semistructured,. and. unstructured. data.. Organizations. need.to.integrate.and.analyze.data.from.a.complex.array.of.both.traditional. and.nontraditional.information.sources.within.and.outside.the.enterprise.. With.the.proliferation.of.sensors,.smart.devices,.and.social.collaboration. technologies,.data.are.being.generated.in.countless.forms.such.as.text,.web.

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206 • Supply Chain Risk Management: An Emerging Discipline

data,.tweets,.sensor.data,.audio,.video,.click.streams,.log.files,.and.much. more..The.bottom.line.is.that.data.come.in.many.forms.

The.third.dimension,.velocity,.refers.to.data.in.motion..The.speed.with. which. data. is. created,. processed,. and. analyzed. continues. to. acceler- ate.. Contributing. to. this. higher. velocity. is. the. real-.time. nature. of. data. creation,. especially. within. global. supply. chains,. as. well. as. the. need. to. incorporate.streaming.data.into.business.processes.and.decision.making.. Velocity.impacts.latency—the.lag.time.between.when.data.are.created.or. captured.and.when.they.are.accessible.and.able.to.be.acted.upon..Data.are. continually.being.generated.at.a.pace.that.is.impossible.for.traditional.sys- tems.to.capture,.store,.and.analyze,.resulting.in.the.development.of.new. technologies.with.new.capabilities.

Finally,. veracity. refers. to. the. level. of. reliability. associated. with. certain. types. of. data.. Striving. for. high-.quality. data. is. an. important. big. data. requirement.and.challenge,.but.even.the.best.data.cleansing.methods.can- not. remove. the. inherent. unpredictability. of. some. data,. like. the. weather,. the.global.economy,.or.a.customer’s.future.buying.decisions..The.need.to. acknowledge.and.plan.for.uncertainty.is.a.dimension.of.big.data.that.has. been.introduced.to.executives.to.better.understand.the.uncertain.world.of. risk.around.big.data..Veracity.requires.the.ability.to.manage.the.reliability. and.predictability.of.imprecise.data.types.

A. good. portion. of. the. data. within. global. supply. chains. is. inherently. uncertain..The.need.to.acknowledge.and.embrace.this.level.of.uncertainty. is.the.hallmark.of.big.data.and.supply.chain.risk.management..An.exam- ple. is. in. energy. production. where. the. weather. is. uncertain. but. a. utility. company. must. still. forecast. production.. In. many. countries,. regulators. require. a. percentage. of. production. to. emanate. from. renewable. sources,. yet. neither. wind. nor. clouds. can. be. forecast. with. precision.. So,. what. to. do?.To.manage.this.uncertainty,.analysts,.either.in.energy.or.supply.chain. management,.need.to.create.context.around.the.data.

One.way.to.manage.data.uncertainty.is.through.something.called.data fusion,. where. combining. multiple,. less-.reliable. sources. creates. a. more. accurate. and. useful. set. of. data. points,. such. as. social. media. comments. appended. to. geospatial. location. maps.. Another. way. to. manage. uncer- tainty. is. through. advanced. mathematics. that. embrace. uncertainty,. such. as. probabilistic. modeling,. discrete-.event. simulation. and. multivariate,. nonlinear.analyses.coupled.with.failure.mode.effects.analysis.(FMEA).

Most.observers.predict.a.major.impact.of.big.data.and.predictive.ana- lytics.on.the.global.economy..In.a.recent.Fortune.article,.an.expert.from.

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Gartner.suggested.that.over.a.relatively.short.time.period,.more.than.four. million.positions.worldwide.will.emerge.for.analytic.talent,.of.which.only. about.one.third.will.be.filled.8 Dice.com.has.identified.the.Top.10.techni- cal.skills.big.data.will.need.over.the.next.several.years..By.a.large.margin. the. first. is. Hadoop. plus. Java,. which. is. not. surprising. since. Java. powers. Yahoo,. Amazon,. eBay,. Google,. and. LinkedIn.. After. that. it. is. Developer,. NoSQL,.Map.Reduce,.BigData,.Pig,.Linux,.Python,.Hive,.and.Scala.

The.shortage.of.professional.skills.in.Hadoop.and.NoSQL.has.given.rise. to.higher.pay.for.qualified.hires,.topping.$100K.on.average..The.real.win- ner.here.could.be.the.U.S..economy..Anticipating.a.multiplier.effect,.one. observer.predicts.that.for.every.big.data–.related.role.in.the.United.States,. employment.for.three.people.outside.IT.will.be.created.9.While.the.rise.of. big. data. presents. opportunities,. a. shortage. of. qualified. IT. professionals. also.exposes.an.organization.to.risk.

As. we. conclude. this. overview. of. big. data. and. predictive. analytics,. it. would. be. appropriate. to. close. this. section. with. some. key. findings. from. IBM’s.research.into.big.data..First,.across.multiple.industries,.the.business. case.for.big.data.is.strongly.focused.on.addressing.customer-.centric.objec- tives..Second,.a.scalable.and.extensible.information.management.founda- tion. is. a. prerequisite. for. big. data. advancement.. Third,. organizations. are. beginning.their.pilot.and.implementation.programs.by.using.existing.and. newly. accessible. internal. sources. of. data.. Next,. advanced. analytics. capa- bilities. are. required,. yet. often. lacking,. for. organizations. to. get. the. most. value.from.big.data..And.finally,.as.organizations’.awareness.and.involve- ment. in. big. data. grows,. four. stages. of. adoption. emerge,. which. the. next. section.presents.

the pROCeSS Of SuCCeSSfully leVeRaging Big data fOR MaxiMuM Benefit

Many.of.the.cases.we.describe.later.in.the.chapter.maintain.the.hallmarks. of.supply.chain.analytic.implementations..These.hallmarks.include.a.clear. business. problem. with. supporting. metrics;. a. focus. on. fact-.based. decision. making.and.on.improving.business.key.performance.indicators.(KPIs);.and. the.establishment.of.an.end-.to-.end,.enterprise-.wide.process.that.is.champi- oned.by.C-.level.management..Other.characteristics.include.forward-.looking. scenarios. and. causal. analysis. to. understand. variability. and. performance.

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208 • Supply Chain Risk Management: An Emerging Discipline

drivers.without.getting.lost.in.the.data..Scenarios.are.performed.iteratively. to.demonstrate.value.and.self-.fund.subsequent.improvement.opportunities.

Many. organizations. start. with. spreadsheets. as. a. proof-.of-.concept. (POC). and. then. migrate. to. some. form. of. business. intelligence. tool. to. perform.more.rigorous.analysis..Why?.According.to.the.Hackett.Group,. world-.class. procurement. organizations. on. average. spend. less. than. 30%. of.their.time.compiling.data,.compared.with.60%.for.the.bottom-.quartile. companies.. In. other. words,. while. typical. companies. still. compile. data,. world-.class.organizations.spend.more.of.their.time.analyzing.the.data.and. making. informed. decisions.. The. CIO. of. a. leading. company. argues. that. 75%. of. the. effort. and. cost. when. managing. data. is. process. reengineer- ing.and.data.cleansing.and.creation,.and.the.other.25%.is.the.IT.portion.. He. further. states. that. when. people. say. their. systems. didn’t. deliver,. the. chances.are.they.missed.the.75%.part.they.should.have.been.working.on.

An.adoption.process.or.continuum.has.emerged.through.observation.of. big.data.and.predictive.analytic.projects,.or.what.IBM.calls.the.Four.E’s:. Educate,.Explore,.Engage,.and.Execute.”.Figure 11.1.depicts.this.emerging. adoption.continuum..We’ll.briefly.touch.on.the.key.elements.of.each.stage.

Education. is. the. first. stage. in. the. continuum.. Its. primary. focus. is. on. awareness.and.knowledge.development..In.this.stage,.most.organizations. are.studying.the.potential.benefits.of.big.data.technologies.and.analytics. and. trying. to. better. understand. how. big. data. can. help. address. impor- tant. business. opportunities.. Also. within. the. first. stage,. the. potential. for. big. data. has. often. not. yet. been. fully. recognized. and. embraced. by. busi- ness. executives.. Exploration,. the. second. stage,. defines. the. business. case. and.roadmap..Almost.50%.of.respondents.in.an.IBM.study.report.formal,. ongoing.discussions.within.their.organizations.about.how.to.use.big.data.

Focused on knowledge

gathering and market

observations

Developing strategy and

roadmaps based on business

needs and challenges

Piloting big data initiatives

to validate value and

requirements

Deploying two or more big

data initiatives and

continuing to apply

advanced analytics

Educate Explore Engage Execute

figuRe 11.1 The.Four.E’s.of.big.data.adoption.

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Using Big Data and Analytics to Manage Risk • 209

to.solve.important.business.challenges..Key.objectives.in.this.stage.include. developing.quantifiable.business.cases.and.creating.a.big.data.blueprint.or. roadmap..Most.organizations.in.this.stage.are.considering.existing.data,. technology,. and. skills. and. are. contemplating. where. to. start. and. how. to. develop.a.plan.aligned.with.their.organization’s.business.strategy.

Engagement,. the. third. stage,. is. about. embracing. the. value. of. the. data.. In. this. stage,. organizations. begin. to. prove. the. business. value. of. big. data. as. well. as. performing. assessments. of. their. technologies. and. skills.. Companies.in.this.stage.usually.have.one.or.more.proof-.of-.concept.proj- ects. under. way.. These. companies. are. working. within. a. defined. scope. to. understand.and.test.the.technologies.and.skills.required.to.capitalize.on. the.new.sources.of.big.data.

Execute. is. the. final. stage. of. the. continuum.. In. this. stage,. big. data. and. analytics. capabilities. are. more. widely. operational. and. implemented. within.the.organization..Many.organizations.here.manage.at.least.two.or. more.big.data.solutions.at.scale,.which.seems.to.be.a.threshold.for.advanc- ing. in. this. stage.. The. companies. in. the. execute. stage. are. leveraging. big. data.to.transform.their.businesses.and.thus.are.deriving.the.greatest.value. from.their.information.assets.

Most. organizations. are. in. the. early. stages. of. big. data. development. IBM’s. research. concludes. that. 24%. of. companies. are. focused. on. under- standing. the. concept. and. have. not. begun. the. journey,. while. 47%. are. planning. big. data. projects. and. developing. roadmaps.. Another. 28%. of. companies.are.developing.proofs.of.concept.or.have.already.implemented. full-.scale.solutions.

BaRRieRS and ChallengeS MOVing fORWaRd

The.challenges.to.utilizing.big.data.differ.as.organizations.move.through. each.of.the.four.stages.as.featured.in.Figure 11.2..A.consistent.challenge,. regardless.of.stage,.is.the.ability.to.articulate.a.compelling.business.case.. At. each. stage. big. data. efforts. rightfully. come. under. fiscal. scrutiny.. The. current.global.economic.climate.and.supply.chain.risk.landscape.has.left. businesses. with. little. appetite. for. new. technology. investments. without. measurable. benefits.. After. companies. begin. their. proof. of. concept,. the. next. biggest. challenge. is. finding. the. right. skill. sets. to. operationalize. big. data,.including.technical,.analytical,.and.governance.skills.

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210 • Supply Chain Risk Management: An Emerging Discipline

As.shown.in.Figure 11.2,.different.obstacles.surface.as.companies.move. through.the.continuum10:

•. Acquisition of data.. Data. are. available. from. so. many. sources. and. end.users.must.constantly.decide.which.will.be.useful.

•. Choosing the right architecture. This. involves. balancing. cost. and. performance.to.obtain.a.platform.based.around.programming.tech- niques.far.different.from.those.of.the.normal.desktop.environment.

•. Shaping the data to the architecture.. This. involves. spending. time. capturing,.compiling,.and.uploading.the.data.to.be.aligned.with.the. architecture..With.all.the.new.technology,.transforming.the.data.can. be.a.time-.consuming.process.

•. Coding. This.includes.selecting.a.programming.language,.designing. the.system,.deciding.on.an.interface,.and.being.prepared.for.a.rap- idly.changing.environment.

•. Debugging and iteration. This.is.the.process.of.looking.for.errors.in. code,.architecture,.and.making.modifications.quickly.

tOOlS, teChniQueS, and MethOdOlOgieS SuppORting Big data

Let’s.profile.the.techniques.that.are.being.utilized.by.organizations.running. big. data. projects.. Figure 11.3. gives. us. a. sense. of. the. tools. and. techniques. that. are. being. leveraged.. More. than. 75%. of. companies. report. using. core.

Educate Explore Engage Execute

Articulating a compelling business case

Understanding how to use big data

Management focus and support

Data quality

Analytic skills

Technical skills

figuRe 11.2 Big.data.primary.obstacles..(Source:.Adapted.from.IBM.2013.Big.Data.Executive.Report.)

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Using Big Data and Analytics to Manage Risk • 211

analytics. capabilities,. such. as. querying. and. reporting. and. data. mining. to. analyze.big.data,.while.more.than.67%.report.using.predictive.modeling.

These. foundational. methods. are. a. pragmatic. way. to. start. interpreting. and. analyzing. big. data.. The. need. for. more. advanced. visualization. tech- niques. increases. with. the. introduction. of. big. data. because. datasets. are. often.too.large.for.business.or.data.analysts.to.view..The.next.highest.usage. of.techniques.and.tool.sets.are.optimization.models.and.advanced.analyt- ics.to.better.understand.how.to.transform.key.business.processes..Many. organizations. are. embracing. simulation. and. pattern. recognition. to. ana- lyze.the.many.multivariate,.nonlinear.relationships.within.big.data.

As. you. can. see. from. Figure 11.3,. more. and. more. organizations. are. focusing. on. unstructured. data. to. analyze. text. in. its. natural. state,. such. as.transcripts.from.call.center.conversations..These.tools.and.techniques. include. the. ability. to.interpret.and. understand.the. nuances.of. language,. such. as. sentiment,. slang,. and. intention.. And. with. the. tools. emerging. to. analyze.these.new.and.unstructured.forms.of.data,.it’s.no.surprise.that.the. skills. to. manage. these. techniques. are. in. short. supply.. It. seems. apparent.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Voice analytics

Video analytics

Streaming analytics

Geospatial analytics

Natural language text

Simulation

Optimization

Predictive modeling

Data visualization

Data mining

Query & reporting

IBM Big Data Executive Report 2013

Percentages %

figuRe 11.3 Big.data.analytics.capabilities.and.tools.

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212 • Supply Chain Risk Management: An Emerging Discipline

that.there.are.a.host.of.tools.and.techniques.emerging.to.support.the.big. data. effort.. Techniques. such. as. standard. reporting,. ad. hoc. reporting,. query.drill-.down,.cloud-.based.analysis,.classical.deterministic.forecasting. techniques,.predictive.modeling,.simulation,.optimization,.pattern.recog- nition,.and.artificial.intelligence.are.all.coming.on.board.at.an.accelerat- ing.rate.

Changing. gears. a. bit,. but. still. remaining. in. the. tools,. techniques,. and. methodologies. arena. of. big. data,. it. appears. that. organizations. that. are. embracing. Software-.as-.a-Service. (SaaS),. or. cloud-.based. technology,. are. utilizing. those. tools. much. more. pervasively. throughout. their. organiza- tions,.partly.because.they.are.able.to.make.better.use.of.scarce.IT.skills.11.We. mentioned.earlier.the.lack.of.technical.skills,.in.house,.as.an.obstacle..Also,. it.appears.that.organizations.that.leverage.outsourced.IT.tools.and.consult- ing.skills.are.experiencing.a.much.richer.and.more.complete.solution.when. compared.with.organizations.that.are.not.using.the.SaaS.approach.

Here.is.a.quick-.hit.definition.of.the.SaaS.concept:.With.SaaS.or.cloud-. based.business.intelligence.(BI),.the.software.itself.is.not.licensed,.owned,. or.installed.by.the.organization..Instead,.the.software.resides.in.a.remote. third-.party.data.center.and.the.functionality.provided.by.the.software.is. accessed.over.the.Internet.and.rented..This.service.is.typically.paid.for.as. a.monthly.subscription.

One.research.group.has.concluded.that.the.use.of.SaaS.to.drive.big.data. analytics. offers. advantages. across. many. dimensions.12. More. than. 60%. of. organizations.using.a.SaaS.solution.were.satisfied.or.very.satisfied.with.ease-. of-.use.of.this.approach.as.opposed.to.only.41%.of.companies.not.using.SaaS.. Just.over.80%.of.SaaS.BI.users.have.access.to.drill-.down.to.detail.capability. as.opposed.to.58%.for.non-.SaaS.users..And.just.over.60%.of.SaaS.BI.users. are.able.to.tailor.their.solution.quickly.as.opposed.to.only.41%.of.non-.SaaS. users..Companies.that.utilize.SaaS.BI.tools.say.that.they.can.find.informa- tion.they.need.in.time.to.support.their.decisions.within.one.hour.of.raw.data. being.captured,.or.what.is.called.time- to- decision.and.time- to- value,.84%.of. the.time.as.opposed.to.only.70%.of.the.time.with.organizations.not.using. SaaS. BI.. Finally,. organizations. that. use. the. SaaS. approach. are. 40%. more. likely.than.others.to.exchange.data.openly.and.easily.across.business.units.. Other.findings.not.mentioned.here.also.reveal.the.value.of.Saas.

The.idea.of.augmenting.what.you.already.have.in.house.with.third-.party. companies.is.gaining.traction,.especially.with.analytics..Many.companies.

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have.packaged.and.outsourced.supply.market.intelligence.while.others.have. developed. hosted. analytics. tools. and. bundled. professional. services. and. analysis.across.the.entire.supply.chain.spectrum.

We’d. like. to. close. this. section. with. some. comments. on. analytics. in. manufacturing. from. the. vice-.president. of. Invensys. Solutions.. He. talks. about. big. data. for. manufacturing. by. comparing. big. data. for. consumers. and.the.use.of.Google..He.says.we.start.Google.maps.on.our.phone.and.it. immediately.knows.where.we.are..We.click.a.box.and.it.shows.the.traffic. from.here.to.the.airport..If.we’re.hungry,.it.can.pull.down.restaurants.and. menus..Now,.take.this.into.the.supply.chain.arena..All.that.information.is. available,.but.instead.of.restaurants,.we’re.looking.for.the.best.batch,.opti- mal. production. run,. and. more.. A. good. analytics. manufacturing. system. has. to. show. what. is. out. there,. display. the. information. that. is. available,. interpret.what.it.means,.how.to.react.to.it,.and.then.help.predict.what.is. coming.next.

hOW eaRly adOpteR COMpanieS leVeRage Big data

With.a.broad. definition.of.big.data.now.established,.we.want.to.provide. examples.of.industries.that.are.leveraging.big.data.for.competitive.advan- tage..In.this.section.we’ll.touch.on.several.industries.and.dig.a.bit.deeper. into.a.few.name-.brand.companies.that.are.leveraging.this.approach.for.a. competitive.advantage.

During. our. initial. evaluation. of. the. big. data. landscape,. we. speculated. that.there.might.be.an.industry.that.is.far.and.away.the.leader.in.leveraging. big.data.for.competitive.advantage..With.that.hypothesis,.we.attempted.to. gather.data.and.profile.the.use.of.big.data.by.industry..The.hypothesis.that. one. industry. might. dominate. the. landscape. is. far. from. reality.. The. top. four. industries. within. our. sample. that. use. big. data. are. consumer. pack- aged. goods. (CPG)/grocery. (16%. of. firms),. electronics. (10%),. automotive. assembly. (10%),. and. energy. (10%).. All. other. industries. were. 7%. or. less.. It’s. evident. that. these. industries. are. leading. the. way. toward. leveraging. predictive.analytics.to.solve.operational.problems,.followed.by.additional. industries.beginning.their.use.of.big.data..Overall,.we.have.a.long.way.to. go.before.the.use.of.big.data.becomes.routinized.

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Consumer packaged goods

A. large. multibillion. dollar. CPG. company. with. razor-.thin. margins. was. facing.highly.volatile.commodity.prices.on.the.supply.side.of.its.business. and.unforgiving,.price-.sensitive.retailers.on.the.demand.side..The.compa- ny’s.approach.was.to.develop.an.integrated.Sales.&.Operations.Planning. process. and. link. it. to. the. supply. market. by. integrating. supply. market. intelligence.and.purchase.price.forecasts..The.company.used.this.forward-. looking.supply.intelligence.to.create.robust.scenarios,.perform.additional. analyses,. and. then. optimize. all. options. associated. with. each. scenario.. It. attempted. to. mitigate. risk. by. finding. substitute. materials,. modifying. specifications,. reconfiguring. its. product. mix,. changing. its. supply. chain. network. and. delivery. methods. where. possible,. hedging. on. the. financial. side,. as. well. as. modifying. strategies. throughout. the. planning. horizons.. This.effort.resulted.in.minimizing.millions.of.dollars.of.product/.customer. profit.erosion.and.more.robust,.predictable.strategies.

dell Computers

Most.of.us.know.that.Dell.is.a.company.in.transition..After.dominating. the.enterprise.PC.market.for.decades,.the.Texas-.based.configure-.to-.order. manufacturer.is.making.a.definitive.move.away.from.the.product.side.of. the. business. and. toward. services. and. solutions.. Unfortunately,. over. the. past.decade,.Dell’s.strategy,.options,.and.variants.in.models,.software.con- figurations,.memory,.screens,.and.other.customizable.features.has.resulted. in.over.seven.septillion.possible.configurations.of.Dell’s.products!.A.sep- tillion.is.equivalent.to.1,000,000,000,000,000,000,000..Obviously,.product. portfolio.complexity.had.become.a.major.risk.for.the.company.

To.trim.its.product.portfolio.Dell.began.to.utilize.its.abundance.of.big. data.. A. Dell. team. created. a. new. system. called. optimized configuration.. Dell’s. analytics. team. clustered. high-.selling. configurations. from. histori- cal.data.to.create.technology.roadmaps..The.team.also.created.automated. algorithms.to.identify.what.configurations.Dell.should.build.to.order.and. what.Dell.should.produce.for.inventory..The.analysis.leveraged.historical. data.and.ran.cluster.analysis.to.identify.the.most.common.configurations. sought.by.customers.

Clustering. around. commonality. of. product. ordering. allowed. Dell. to. trim. the. seven. septillion. options. to. several. million. and. provided. the. company’s. marketing. and. supply. chain. teams. with. agreement. on.

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preconfigured. products. built. for. inventory. and. ready. to. ship.. This. new. supply.chain.strategy,.driven.by.data.analysis,.also.supports.the.company’s. make-.to-.order.strategy.

Still.another.use.of.big.data.at.Dell.has.been.inside.the.company’s.online. ordering. system.. Dell’s. business. intelligence. team. ran. analytics. on. click. stream. data,. tracing. every. move. and. path. taken. by. customers.. The. out- come. showed. that. customers. navigate. through. more. than. 40. clicks. to. place. an. order.. The. team. used. that. information. to. optimize. the. site. and. reduce.the.number.of.clicks.to.five.

Western digital

Western. Digital,. a. global. manufacturer. of. disc. drives,. is. obsessed. with. quality..To.serve.that.obsession.the.company.has.transformed.its.manu- facturing. process. to. allow. scanning,. recording,. testing,. and. tracking. of. every.disc.drive.produced.while.still.on.the.production.line..By.running. real-.time.shop-.floor.analytics,.the.company.can.locate.and.remove.non- conforming. discs. before. they. reach. the. customer.. Even. if. a. disc. passes. an.initial.analytical.review,.if.further.analysis.reveals.a.problem,.the.disc. can.be.located.and.pulled.from.inventory.bins..This.capability,.supported. by.big.data,.has.resulted.in.the.lowest.warranty.return. rate.in.the.entire. industry..It.has.also.helped.make.Western.Digital.the.supplier.of.choice.for. many.computer.manufacturers.

harley davidson

Harley. Davidson,. the. king. of. the. hogs,. introduced. software. that. tracks. even.the.minutest.details.on.the.assembly.floor,.such.as.the.speed.of.fans. in. the. painting. booths.. When. the. software. detects. that. the. fan. speed,. temperature,.or.humidity.has.deviated.from.the.optimal.settings,.it.auto- matically. adjusts. the. operations.. This. allows. a. consistency. on. the. shop. floor.by.staying.within.preestablished.parameters..The.software.has.also. been. used. to. identify. bottlenecks. on. the. assembly. floor.. One. of. Harley’s. goals.is.to.complete.a.motorcycle.every.86.seconds..A.recent.study.using. shop. floor. data. revealed. that. the. rear. fender. assembly. time. was. taking. longer. than. planned.. The. company. changed. the. factory. configuration. so.the.fenders.would.flow.directly.to.the.assembly.line.rather.than.being. placed.on.carts.and.moved.across.the.floor..This.is.but.one.example.of.how.

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216 • Supply Chain Risk Management: An Emerging Discipline

Harley.Davidson.is.using.big.data.to.streamline.its.operations.and.avoid. operational.risk.

Raytheon

Raytheon,. a. household. name. in. the. aerospace/.defense. manufacturing. arena,.is.betting.on.big.data.to.reduce.the.risk.of.quality.and.operational. problems.. In. its. Huntsville,. Alabama,. missile. plant,. if. a. screw. is. sup- posed.to.be.turned.13.times.after.it.is.inserted.but.instead.is.turned.only. 12.times,.an.error.message.flashes.and.production.of.the.missile.or.com- ponent.stops..“Manufacture.of.a.missile.is.either.right.or.it’s.not;.there’s.no. in.between,”.says.a.Raytheon.executive..Many.manufacturers.are.install- ing. sophisticated. automated. systems. to. gather. and. analyze. shop. floor. data,.known.as.manufacturing.execution.systems.(MES)..Manufacturers. are.looking.harder.at.data.partly.because.of.increasing.pressure.from.cus- tomers. to. eliminate. defects. and. from. shareholders. to. squeeze. out. addi- tional. cost. and. mitigate. risk. to. the. brand.. These. new. capabilities. mean. Raytheon.is.catching.more.flaws.as.they.occur..Raytheon.also.keeps.data. for. each. missile,. including. the. names. of. all. the. machine. operators. who. worked.on.any.part.of.it,.as.well.as.the.humidity,.temperature,.and.more. at.each.workstation.

The.system.is.designed.to.prevent.any.operator.from.performing.a.task. for. which. he. or. she. is. not. certified.. According. to. Raytheon,. leveraging. big. data. systems. is. a. form. of. risk. mitigation. and. management.. Millions. of. dollars. have. been. spent. in. the. past. to. rework. items. that. did. not. meet. specifications. If.Raytheon’s.experience.is.any.indicator,.cost.containment,. real-.time.event.monitoring,.and.process.optimization.are.but.a.few.of.the. key. drivers. supported. by. big. data.. Tracking. physical. items. and. people. throughout.the.supply.chain,.capturing.and.acting.on.streaming.data,.and. enabling.faster.reaction.to.specific.problems.before.they.escalate.in.major. situations.is.becoming.the.norm.rather.than.the.exception.

european electrical utility

A. major. European. electric. utility. company. sought. to. improve. the. man- agement. of. budgeted. versus. actual. spend. for. nonfeedstock. and. indirect. spending.. It. wanted. a. single. system. that. separated. consumption. varia- tion,. within. a. contract. and. across. contracts,. external. market. pricing. variation,. and. procurement-.led. pricing. impacts.. The. company. used. a.

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third-.party.tool.for.spend.analysis.and.procurement.performance.analysis.. These.data.were.cross-.referenced.against.an.external.database.with.thou- sands.of.price.indexes..This.approach.of.combining.internal.and.external. data.and.benchmark.indexes.allowed.for.fact-.based.discussions.and.deci- sion.making.for.continuous.improvement.in.its.cash.flow.management.

This. approach. confirmed. that. analytics. works. best. when. integrated. with. external. information.. The. utility. company. concluded.that. integrat- ing. internal. and. external. data. through. the. use. of. big. data. analytics. is. an.enabler.for.managing.supply.risk,.supplier.risk,.regulatory.risk,.com- petitive.risk,.and.intellectual.property.risk..Managing.these.risks.should. include. analytical. approaches. such. as. scenario. planning,. Monte. Carlo/. probabilistic. modeling. to. quantify. the. probability. of. occurrence. and. impact,.segmenting.and.visualizing.risk.using.heat.maps,.and.predictive. analytics.to.manage.risk.

Schneider

Schneider.National,.a.$3.billion.transportation.and.logistics.company,.has. developed.a.computer.model.that.mimics.human.decision.making,.help- ing. the. company. to. assign. trucks. and. drivers. in. the. most. cost-.effective. way.possible..At.any.given.time,.Schneider.has.10,000.trucks.on.the.road. with.over.30,000.trailers.waiting.to.be.picked.up.or.delivered..Drivers.work. alone. or. in. pairs,. and. Schneider. must. get. them. back. home. by. a. certain. date.and.time..Drivers.also.need.to.conform.to.the.government’s.hours-. of-.service.regulations.regarding.rest.periods.and.breaks.

With.the.help.from.several.Princeton.University.researchers,.Schneider. developed.a.simulator.utilizing.dynamic.programming,.which.takes.into. account.the.presence.of.uncertainty..The.simulator,.which.took.two.years. to.develop,.runs.forward.in.time.for.three.weeks.to.approximate.the.value. of.having.a.truck.and.driver.at.a.certain.location.at.a.certain.time..The.out- put.from.the.report.is.a.called.a.first-.pass.cost.estimate..The.tool.then.runs. backward. in. time,. something. called. postanalysis,. to. reconcile. the. past. results.with.those.that.were.determined.in.the.future.estimate..The.simu- lator. then. runs. forward. again. for. three. weeks. and. then. backward. as. it. seeks.to.improve.the.total.cost.estimates..This.forward–.backward.process. encompasses.hundreds.of.thousands.of.iterations.

Schneider. estimates. its. big. data. tool. has. saved. the. company. tens. of. millions. of. dollars. as. well. as. increased. revenue. by. justifying. price. hikes. to. customers. with. specific. service-.level. constraints.. The. simulator. also.

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218 • Supply Chain Risk Management: An Emerging Discipline

provides.a.quantifiable.methodology.to.exercise.what-.if.analyses,.such.as. determining.the.marginal.value.of.hiring.new.drivers.in.a.certain.region. to.handle.growing.volume..Moving.forward,.Schneider.expects.to.use.the. simulation. tool. to. help. identify. new. businesses. to. pursue.. As. an. opera- tions.research.analyst.at.Schneider.says, “The.tool.is.so.powerful.that.when. someone. presses. us. on. the. impact. of. different. customer. policy. changes,. we.have.the.facts.and.we.have.the.data..The.value.of.this.big.data.tool.is. to. be. able. to. take. these. complex. business. issues. and. opportunities. and. give.them.a.good,.solid.analysis.”13.Schneider.understands.clearly.the.link. between.data.management.and.strategic.risk.management.

COnCluding thOughtS

Few.should.disagree.with.the.notion.that.big.data.and.predictive.analyt- ics. are. here. to. stay.. Big. data,. predictive. analytics,. and. many. of. the. tools. and. techniques. discussed. in. this. chapter. and. in. Chapter 10. are. provid- ing. approaches. for. codifying,. classifying,. analyzing,. and. acting. on. vast. amounts. of. data,. most. of. which. is. the. size. many. of. us. have. never. dealt. with. in. our. professional. careers.. We’ve. witnessed. an. increasing. number. of. companies. that. have. leveraged. their. data. and. predictive. analytics. to. solve.complex.supply.chain,.manufacturing,.and.customer-.centric.issues. to. enhance. revenue,. reduce. cost,. improve. asset. utilization,. and. reduce. supply.chain.risk.

A. study. by. the. Aberdeen. Group. reinforces. the. finding. that. top. per- formers. are. making. advanced. analytics. activities. a. priority. to. take. con- trol. of. manufacturing. complexity. and. supply. chain. risk.. This. is. a. good. thing. since. uncertainty,. complexity,. and. risk. continue. to. grow. globally.. Research. reveals. that. the. companies. that. are. best. at. leveraging. big. data. average. a. 19%. year-.over-.year. increase. in. operating. profit. as. opposed. to. only.a.9%.increase.for.all.other.companies..And.80%.of.companies.that.are. the. best. at. leveraging. big. data. have. witnessed. improvement. in. the. cycle. times.of.their.key.business.processes.over.a.one-.year.period,.as.opposed.to. 47%.for.average.companies.and.39%.for.laggard.companies.14.Increasingly,. the.ability.to.compete.successfully.as.well.as.manage.supply.chain.risk.will. rest.upon.a.company’s.ability.to.leverage.big.data.and.predictive.analytics.

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Summary of key points

•. The. Leadership. Council. of. Information. Advantage. sees. big. data. as. data.sets.that.are.growing.exponentially.and.that.are.too.large,.too.raw,. or.too.unstructured.for.analysis.using.relational.database.techniques.

•. Predictive.analytics.(PA).encompasses.a.variety.of.techniques.from. statistics,. data. mining,. and. game. theory. that. analyze. current. and. historical.facts.to.make.predictions.about.the.future.

•. IBM. has. created. a. topology. that. looks. at. big. data. in. terms. of. four. dimensions:.volume,.variety,.velocity,.and.veracity.

•. One.way.to.manage.data.uncertainty.is.through.data.fusion,.where. combining.multiple,.less-.reliable.sources.creates.a.more.accurate.and. useful.set.of.data.points..A.second.way.is.through.advanced.math- ematics.that.embrace.uncertainty.

•. World-.class. procurement. organizations. on. average. spend. less. than.30%.of.their.time.compiling.data,.compared.with.60%.for.the. bottom-.quartile. companies.. World-.class. organizations. spend. more. of.their.time.analyzing.the.data.and.making.informed.decisions.

•. An. adoption. process. or. continuum. has. emerged. through. observa- tion. of. big. data. and. predictive. analytic. projects,. or. what. IBM. calls the.4-Es:.Educate,.Explore,.Engage,.and.Execute.

•. Foundational.methods.are.a.pragmatic.way.to.start.interpreting.and. analyzing. big. data,. but. the. need. for. more. advanced. visualization. techniques.increases.with.the.introduction.of.big.data.because.data- sets.are.often.too.large.for.business.or.data.analysts.to.view.

•. Consumer. packaged. goods. (CPG)/grocery,. electronics,. automotive. assembly,.and.energy.are.the.four.industries.leading.the.way.toward. leveraging.predictive.analytics.to.solve.operational.problems.

endnOteS

. 1.. McKendrick,. Joe.. “Big. Data,. Big. Issues:. The. Year. Ahead. in. Information. Management.”.2010.

. 2.. Eshkenazi,.Abe..APICS.Big.Data.Insights.and.Innovation.Report,.2012.

. 3.. Schuster,. Edmund.. “Big. Data. Is. a. Big. Reality.”. Accessed. from. http://ingehygd. blogspot.com/2012/02/big-.data-.is-.big-.reality.html.

. 4.. Lohr,. Steve,. “The. Age. of. Big. Data.”. New York Times,. accessed. from. http://www. nytimes.com/2012/02/12/Sunday-.review/.bid-.data-.impact-.in-.the-.world.html.

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. 5.. Accessed. from. http://www.apics.org/.industry-.content-.research/.publications/.apics-. dictionary.

. 6.. All.definitions.were.retrieved.from.Google.

. 7.. IBM. Global. Business. Services. Business. Analytics. and. Optimization. Executive. Report,. “Analytics:. The. Real-.World. Use. of. Big. Data.”. in. collaboration. with. Said. Business.School.at.the.University.of.Oxford,.2012–2013.

. 8.. Fisher,.Ane..“Big.Data.Could.Generate.Millions.of.Jobs.”.Fortune,.May 21,.2013.

. 9.. Fisher,.Ane..“Big.Data.Could.Generate.Millions.of.Jobs.”.Fortune,.May 21,.2013.

. 10.. Crandall,.Richard.“The.Big.Data.Revolution.”.APICS Magazine,.March/.April,.2013.

. 11.. White,. David.. Aberdeen. Group’s. Analyst. Insight. Report,. “Software-.as-.a-Service. Helps.Deliver.Satisfied.Analytics.Users.”.May.2013.

. 12.. White,. David.. Aberdeen. Group’s. Analyst. Insight. Report,. “Software-.as-.a-Service. Helps.Deliver.Satisfied.Analytics.Users.”.May.2013.

. 13.. Coster,.Helen.“Calculus.for.Truckers.”.Forbes,.2013.

. 14.. Lock,.Michael..Aberdeen.Group’s.Embedded.Analytics.Report,.March.2013.

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