GID 96 Code Structure
11.2 RFID PROJECT SELECTION
133
11 RFID Project Management
11.2.1 pRoject seLectIon modeLsAnd fActoRs
In.order.to.rationally.and.consistently.select.an.RFID.project,.the.organization.must.select.what.is.
known.as.a.project.selection.model..The.project.selection.model.is.a.means.by.which.the.organiza- tion.can.rank.competing.processes.for.the.application.of.RFID.technology..Project.selection.models.
generally.contain.a.set.of.factors..It.is.through.evaluating.these.individual.factors.via.the.model.
that.the.organization.selects.the.projects..The.choice.of.factors.is.unique.to.the.organization,.but.in.
general,.many.organizations.utilize.factors.associated.with
•. Production.issues
•. Financial.issues
•. Personnel.issues
•. Marketing.issues
The.project.selection.models.in.which.these.factors.are.examined.can.be.broadly.classified.as.either.
nonnumeric.or.numeric.models..Nonnumeric.models.as.the.name.suggests,.do.not.specifically.uti- lize.values.to.determine.the.ranking.of.projects..Numeric.models,.on.the.other.hand,.rely.exclusively.
on.values.for.the.ranking.of.projects.
11.2.2 nonnumeRIc pRoject seLectIon modeLs
Nonnumeric.project.selection.models.are.generally.older.and.simpler.than.numeric.project.selection.
models..However,.this.is.not.meant.to.imply.that.nonnumeric.models.are.not.necessarily.useful..
They.should,.however,.be.closely.scrutinized.to.determine.whether.or.not.their.use.is.appropriate.or.
desired..Common.nonnumeric.project.selection.models.include
•. The.sacred.cow
•. Operating.necessity
•. Competive.necessity
•. Comparative.models 11.2.2.1 Sacred Cow
The. basis. for. the. sacred. cow.is.that.some. high-level.management.individual.has.decided.that.it.
is.appropriate.for.the.organization.to.apply.RFID.technology.to.a.particular.process..Sacred.cow.
projects.are.difficult.to.deal.with.since.challenging.ill-thought-out,.unsuccessful.project.may.be.
difficult..Often,.the.only.way.to.terminate.a.sacred.cow.implementation.is.when.the.champion.either.
leaves.the.organization.or.the.champion’s.interest.turns.to.other.application.areas.
11.2.2.2 Operating Necessity
The.operating.necessity.model.is.based.on.the.fact.that.the.organization.might.have.to.adopt.an.
RFID.project.in.order.to.keep.the.organization.functioning.on.a.daily.basis..This.might.occur.in.
the.case.of.internal.tracking.of.manufactured.assemblies..In.order.to.prevent.products.from.being.
assembled.incorrectly,.the.organization.may.select.one.RFID.project.over.another.
11.2.2.3 Competitive Necessity
The.competitive.necessity.model.is.the.nonnumeric.project.selection.model.that.is.most.likely.to.be.
encountered.in.RFID.applications..In.fact,.this.type.of.model.is.not.all.that.dissimilar.to.that.associ- ated.with.mandates..In.other.words,.in.order.to.continue.to.be.competitive.in.a.certain.marketplace,.
the.organization.selects.RFID.projects.according.to.what.will.allow.it.to.survive..This.means.that.
an.end-user,.retail-based.RFID.application.might.take.precedence.over.an.internal.manufacturing.
RFID.project.
RFID Project Management 135
11.2.2.4 Comparative Models
Comparative.project.selection.models.are.typically.used.to.compare.RFID.projects.that.do.not.have.
directly.comparable.project.selection.factors..For.example,.one.RFID.project.may.have.great.signifi- cance.to.the.production.process..Another.RFID.project.may.be.needed.to.properly.fulfill.outgoing.
orders..In.order.to.select.among.these.types.of.projects,.an.organization.may.appoint.an.evaluating.
committee..The.responsibility.of.the.committee.is.to.progressively.break.down.the.various.projects.
with.respect.to.importance.of.the.organization..The.top-level.screen.may.lump.projects.into.not.
important,. somewhat. important,. and. very. important.. Each. category. is. then. looked. into. greater.
detail.and.is.subsequently.rescreened.into.additional.categories..Eventually,.only.a.very.few.projects.
are.accepted.as.sufficiently.important.to.be.implemented.
11.2.3 numeRIc pRoject seLectIon modeLs
In.comparison.to.nonnumeric.models,.numeric.project.selection.models.are.new.and.more.compli- cated..Numeric.models.can.be.broadly.classified.as.those.that.rely.on.profit-based.data.and.those.
that.require.some.sort.of.scoring.mechanism..Simple.profit-based.models.include
•. Payback.time
•. Average.rate.of.return
Note.that.as.RFID.projects.do.not.necessarily.make.money,.but.save.money,.the.following.descrip- tions.have.been.modified.to.make.the.general.models.more.applicable.to.RFID.projects.
11.2.3.1 Payback Time
Payback.time.selection.models.are.based.on.the.amount.of.time.the.project.takes.to.recover.the.
amount.of.capital.invested.in.the.project..The.measure.of.performance.is.in.years..This.value.
is.obtained.simply.by.dividing.the.capital.invested.by.the.amount.of.money.that.the.project.is.
expected.to.save.on.an.annual.basis..The.determination.of.the.amount.of.investment.is.rela- tively.straightforward..Similarly,.the.organization.can.determine.the.amount.of.money.saved.
by. examining. savings. in. labor. costs. and. error. resolution.. The. equation. for. determining. the.
payback.time.is
.
Payback Initial investment Savings
=
.
This.means.for.an.RFID.project,.which.initially.costs.$100,000,.the.payback.period.will.be.4.years.
if,.on.an.average,.a.savings.of.$25,000.will.be.realized.each.year:
.
4 $100,000 Initial investment
$25,000 Annual savings year payback=
.
With.this.model,.the.payback.period.or.time.is.in.the.same.units.as.the.savings..For.example,.if.
the.savings.are.projected.on.an.annual.basis,.the.payback.time.will.be.in.years..Organizations.will.
generally.gravitate.toward.the.selection.of.projects.that.have.relatively.short.payback.periods..Thus,.
projects.that.can.be.completed.and.that.have.relatively.low.initial.investments.or.high.savings.will.
normally.be.selected.
11.2.3.2 Average Rate of Return
The.average.rate.of.return.(ARR).model.is.used.to.determine.which.models.yield.the.best.invest- ment..Sometimes,.the.ARR.is.compared.to.how.much.the.organization.may.yield.in.comparison.to.
investing.the.project.funds.outside.of.the.organization..Generally.speaking,.those.projects.with.the.
greatest.ARR.will.be.selected.for.implementation..The.equation.of.ARR.is
.
ARR Average savings Initial investment
= .
In.this.case,.our.$100,000.RFID.project.would.yield.a.25%.ARR:
.
25% ARR $25,000 average savings
$100,000 initial investment
=
.
A.distinct.disadvantage.of.both.the.payback.and.ARR.models,.as.well.as.many.other.financial.mod- els.is.their.inability.to.incorporate.nonnumeric.project.selection.considerations..Thus,.these.models.
could.potentially.only.favor.projects.that.look.good.on.paper..No.consideration.is.included.to.take.
into.account.issues.such.as.competitive.necessity.
To.help.address.this.lack.of.flexibility,.numeric.project.selection.models.based.on.scoring.meth- ods.were.developed..As.previously.discussed,.these.models.require.the.identification.of.scoring.
factors..These.can.include.both.nonnumeric.and.numeric.scoring.factors..In.the.RFID,.context.scor- ing.factors.could.include,.but.are.not.limited.to,.issues.such.as.the.difficulty.of.the.implementation,.
labor.savings,.and.error.reduction.as.a.result.of.the.implementation,.and.the.probability.of.success- ful.implementation..Common.scoring.models.include
•. Unweighted.0–1
•. Unweighted.scoring
•. Weighted.scoring
•. Constrained.weighted.scoring 11.2.3.3 Unweighted 0–1
In.the.unweighted.0–1.scoring.model,.each.candidate.RFID.application.is.scored.as.a.0.or.1.for.each.
factor.that.is.to.be.considered..The.total.score.for.all.of.the.factors.is.then.totaled.and.compared.
against.all.other.RFID.application.candidates..Mathematically,.the.model.appears.as
.
Project score Factor value
=1
=
∑
in i. where
i.=.1–n.factors.used.to.evaluate.the.project Factor.value.=.0.or.1.for.each.factor.i
Note.that.this.model.has.the.distinct.disadvantage.of.being.only.able.to.assign.a.0.or.a.1.to.each.fac- tor..This.model.also.suffers.from.the.limitation.that.every.factor.is.considered.as.equal.importance..
In.reality,.some.factors.may.be.more.important.for.the.organization.than.others..Due.to.these.limita- tions,.this.model.is.not.normally.recommended.for.use.
11.2.3.4 Unweighted Scoring
To.overcome.the.0–1.limitation.of.the.unweighted.0–1.model,.the.scoring.model.may.be.utilized..
This.model.replaces.the.0–1.value.with.a.value.on.some.scale.between.0.and.a.top.value..Typically,.
the.scale.will.be.between.0.and.5,.0.and.7,.or.0.and.10..The.summed.value.of.the.score.will.indicate.
RFID Project Management 137 how.well.the.project.candidate.fulfills.the.project.selection.factor..This.is.mathematically.repre- sented.in.the.following.equation:
.
Project score Factor score
=1
=
∑
in i. where
i.=.1–n.factors.used.to.evaluate.the.project Factor.score.=.0–x.for.each.factor.i
This. scoring. model. overcomes. the. 0–1. limitation,. but. it. still. considers. each. factor. as. of. equal.
importance..To.overcome.this.limitation,.we.have.the.weighted.scoring.model.
11.2.3.5 Weighted Scoring
The.weighted.scoring.model.includes.both.the.ability.to.numerically.score.project.selection.factors,.
as.well.as.consider.that.the.project.selection.factors.may.be.of.different.levels.of.importance..To.
incorporate.this.consideration,.the.weighted.scoring.model.includes.a.factor.weighting.component..
Normally,.this.component.will.be.assigned.a.value.between.0.and.1..It.is.multiplied.by.the.project.
selection.factor.score.and.summed..This.is.illustrated.in.the.following.equation:
.
Project score Factor weight Factor score
=1
=
∑
in i× i. where
i.=.1–n.factors.used.to.evaluate.the.project Factor.weight.=.0–1.for.each.factor.i Factor.score.=.0–x.for.each.factor.i
While.the.weighted.scoring.model.is.a.great.improvement.over.the.previous.numeric.scoring.mod- els,.it.does.still.suffer.from.one.limitation..In.some.cases,.it.must.be.necessary.for.a.given.project.
to.receive.a.minimum.score.on.a.particular.project.selection.factor..If.the.project.scores.below.the.
minimum.value,.some.mechanism.must.be.incorporated.to.ensure.that.the.final.project.score.is.less.
competitive.than.projects.that.are.otherwise.completely.qualified.
11.2.3.6 Constrained Weighted Scoring
The.constrained.weighted.scoring.model.can.be.incorporated.in.two.different.manners..In.the.first.
implementation,.only.the.specific.factor.for.the.project.is.given.a.0.value.if.the.factor.score.is.below.
the.limit.and.a.1.if.it.is.above.the.limit..This.is.represented.in.the.following.manner:
.
Project score Factor weight Factor score Factor constra
=1
=
∑
i× i×i n
iinti
. where
i.=.1–n.factors.used.to.evaluate.the.project Factor.weight.=.0–1.for.each.factor.i Factor.score.=.0–x.for.each.factor.i Factor.constraint.=.0.or.1.for.each.factor.i
This.implementation.will.normally.yield.a.nonzero.score.even.if.the.project.is.unacceptable.in.one.
or.more.factors..However,.it.is.likely.that.the.final.score.is.significantly.lower.than.other.projects.that.
do.not.have.any.unacceptable.factor.scores.
Another.method.of.incorporating.constraint.into.the.above.project.selection.model.is.to.score.any.
model.that.has.any.unsatisfactory.factor.scores.as.a.complete.0..This.would.effectively.eliminate.
any.projects.that.are.unsatisfactory.in.any.manner..Mathematically,.this.could.be.represented.as
.
Project score Factor constraint Factor weight Factor score
=
= × i× i
i11
∑
n. where
i.=.1–n.factors.used.to.evaluate.the.project Factor.weight.=.0–1.for.each.factor.i Factor.score.=.0–x.for.each.factor.i
Factor.constraint.=.0.if.any.factor.i.score.is.unacceptable,.otherwise.1
The.constrained.weighted.scoring.model.represents.the.most.sophisticated.of.the.reasonably.easily.
implemented.numeric.project.selection.models..It.has.the.advantage.of.being.able.to.incorporate.
both.subjective.factors.such.as.competitive.necessity,.as.well.as.financial.performance.such.as.pay- back.time.and.rate.of.return.