2014 apics production and inventory management journal

7/21/2019 2014 Apics Production and Inventory Management Journal

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PRODUCTION AND INVENTORY

MANAGEMENT JOURNAL

VOLUME 49, NO. 1 2014

A Concepual Framework for Invenory Managemen:

Focusing on Low-Consumpion Iems

Peter Wanke

Inegraing FMEA wih he Supply Chain Risk Managemen

Processes o Faciliae Supply Chain Design Decisions

V.M. Rao Tummala, Tobias Schoenherr, CSCP, Thomas Harrison

Operaions Managemen Salary Repor

L. Drew Rosen, Thomas Janicki, Judith Gebauer

A Tuorial on Managerial Cos Accouning: Year-End Reporing

Timothy D. Fry, Kirk D. Fiedler


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PRODUCTION AND INVENTORY MANAGEMENT JOURNAL2

ABOUT THE PRODUCTION AND INVENTORY

MANAGEMENT JOURNAL

Through he suppor of APICS Foundaion, he P&IM Journal is commited

o being he premier oule for managerial-focused research in operaionsand supply chain managemen. The APICS Foundaion 2014 board officersand members are:

President:Karl Klaesius, CPIM, KS&E Enerprises

Vice President: Shari Ruelas, CPIM, CSCP, Chevron Producs

Treasurer: Rober Vokurka, PhD, CPIM, CIRM, CSCP

Barbara Flynn, PhD, Richard M. and Myra Louise Buskirk Professor ofManufacuring Managemen, Kelley School of Business, Indiana Universiy

Alan Dunn, CPIM, GDI Consuling and Training Company

Kaie Fowler, Schlumberger

Anonio Galvao, CSCP, Sealed Air Inc.

Michael Wasson, CSCP, Coca-Cola Norh America

Paul Pitman, PhD, Indiana Universiy Souheas

Marco Ugare, PhD, CPIM, CSCP, MillerCoors


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TABLE OF CONTENTS

PRODUCTION AND INVENTORY

MANAGEMENT JOURNAL

Articles

A Conceptual Framework for Inventory Management: Focusing on

Low-Consumption Items

by Peer Wanke

Integrating FMEA with the Supply Chain Risk Management Processes

to Facilitate Supply Chain Design Decisionsby V.M. Rao Tummala, Tobias Schoenherr, CSCP and Thomas Harrison

Operations Management Salary Report

by L. Drew Rosen, Thomas Janicki, Judih Gebauer

A Tutorial on Managerial Cost Accounting: Year-End Reporting

by Timohy D. Fry, Kirk D. Fiedler

Editorial Staff Information

Robert L. BregmanEdior in Chief

Associae Professor

Decision and Informaion Sciences Deparmen

Universiy of Houson

[email protected]

6

24

71

83


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ARTICLE SUMMARIES

A CONCEPTUAL FRAMEWORK FOR INVENTORY MANAGEMENT:

FOCUSING ON LOW CONSUMPTION

This aricle evaluaes he premise o demand adherence o normal disribuion

in invenory managemen models, showing ha his can lead o significan

disorions, mainly o sock conrol o very low and low consumpion iems.

The aricle hus proposes a ramework o help managers deermine he bes

sock policy o be adoped given produc demand characerisics. The aricle

also presens he use o such a ramework in a case sudy, in an atemp o

illusrae he benefis o adoping probabiliy densiy uncions ha are more

adequae o produc demand characerisics, in erms o oal coss o socks.

INTEGRATING FMEA WITH THE SUPPLY CHAIN RISK

MANAGEMENT PROCESS TO FACILITATE SUPPLY CHAIN

DESIGN DECISIONS

We presen a novel approach o inegraing ailure mode and effec analysis

(FMEA) wih a supply chain risk managemen process (SCRMP). Focusing on

he challenging ask o assess and manage supply-side risks in global supply

chains, he approach developed offers an effecive and affordable way or

firms o provide decision suppor or he selecion o heir mos appropriae

supply chain design. The aim o he inegraed approach combining he

srenghs o FMEA and SCRMP is o gaher as much perinen inormaion

as possible, o srucure i, and o comprehensively delineae all poenialsupply chain risk acors, offering valuable decision suppor. We illusrae

he applicaion o he approach a Michigan Ladder Company, where i was

applied o wo specific supply chains or he procuremen o fiberglass ladders.

Specifically, one supply chain spanned rom China o he U.S. via Mexico

(aking advanage o a Mexican maquiladora), and one spanned rom China

direcly o he U.S. The combinaion o FMEA and he SCRMP enhanced he

manuacurers confidence in is supply chain design decision, and enabled he

firm o proacively manage is supply-side risks. Overall, he aricle is mean o

moivae praciioners o embark on he journey o acive risk managemen.

While some may perceive risk managemen as a dauning ask or being

primarily employed by larger firms, we provide guidance or firms o any sizeo apply he approach i can be done, and does no have o consume an

inordinae amoun o resources.

OPERATIONS MANAGEMENT SALARY REPORT

APICS, in conjuncion wih he Cameron School o Business a he Universiy

o Norh Carolina Wilmingon, is pleased o provide he resuls o he 2013

Operaions Managemen Salary Repor. The daa are colleced rom a random

sample o more han 30,000 operaions managemen proessionals worldwide.


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VOLUME 49, NO. 1 5

Twice annually, approximaely fify percen o he APICS membership and

cusomer base receives a reques o complee an online survey collecing daa

concerning curren salary and compensaion by job uncion and ile. The

survey can be accessed a: htp://csbapp.uncw.edu/apics/.

A TUTORIAL ON MANAGERIAL COST ACCOUNTING: YEAREND

REPORTING

Building on he companion aricle A Tuorial on Managerial Cos Accouning:

Living wih Variances by Fry and Fiedler (2011), his curren paper picks

up where he previous paper lef off and illusraes how he managemen

accouning sysem (MCA) is linked o financial accouning (FA) o generae

he year-end financial repors required by shareholders, banks, and he IRS.

The prior paper ocused on he deailed use o inormaion provided by he

MCA hroughou he year and walked hrough he developmen o he yearly

budge, calculaion o produc coss, deerminaion o budge variances,

derivaion o he periodic income and saemen o cash flows repors, and

provides possible examples o dysuncional behavior a a ficiious company

called Mandrake Manuacuring. This uorial concenraes on he ineracion

o he MCA and FA sysems and he producion o year end FA saemens. In

addiion o providing inormaion such as cos o goods sold, invenory values,

and operaing sandards o he FA, he year-end inormaion provided by he

MCA is also used o develop nex years budges. In his presen paper, he

conversion o he MCA repors ino he FA repors will be presened. Also, he

impac o he MCA repors on uure budges will be discussed. As poinedou in F&F, i is vial ha operaions managers undersand how he accouning

sysems used by heir company uncion. Wihou such undersanding, many

o he problems associaed wih he improper use o he accouning sysems

will never be correced.


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A CONCEPTUAL FRAMEWORK FOR

INVENTORY MANAGEMENT: FOCUSING ON

LOW CONSUMPTION ITEMS

Peer Wanke (Corresponding Auhor)

Center for Logistics Studies, Infrastructure, and Management, The COPPEAD

Graduate School of Business, Federal University of Rio de Janeiro, Rio de

Janeiro Brazil 21949-900

ABSTRACT

This aricle evaluaes he premise o demand adherence o normal disribuion

in invenory managemen models, showing ha his can lead o significandisorions, mainly o sock conrol o very low and low consumpion iems.

The aricle hus proposes a ramework o help managers deermine he bes

sock policy o be adoped given produc demand characerisics. The aricle

also presens he use o such a ramework in a case sudy, in an atemp o

illusrae he benefis o adoping probabiliy densiy uncions ha are more

adequae o produc demand characerisics, in erms o oal coss o socks.

Keywords: sock, lead-ime demand, coefficien o variaion, ramework, coss

. Introduction

Invenory managemen permeaes decision-making in counless firmsand has been exensively sudied in he academic and corporae spheres

(Rosa e al. 2010). The key quesions usually influenced by a variey o

circumsances which invenory managemen seeks o answer are: when

o order, how much o order and how much sock o keep as saey sock

(Nami and Chen 1999; Silva 2009). According o Wanke (2011a), invenory

managemen involves a se o decisions ha aim a maching exising

demand wih he supply o producs and maerials over space and ime

in order o achieve specified cos and service level objecives, observing

produc, operaion, and demand characerisics.

These diverse circumsances ha should be aken ino accoun or anappropriae selecion o invenory managemen models have conribued

o he developmen o research and producion o aricles on possible

qualiaive concepual schemes also known as classificaion approaches

aimed a supporing decision-making (Huiskonen 2001). There are several

examples o his kind hroughou he years.

Williams (1984), or example, developed an analyical mehod o classiy

demand as regular (high consumpion), low consumpion, or inermiten, by


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VOLUME 49, NO. 1 7

decomposing he variabiliy o lead-ime demand ino hree pars: variabiliy

o he number o occurrences per uni o ime, variabiliy o demand size,

and lead-ime variabiliy. Boter and Foruin (2000) based heir classificaion

o iems on hree crieria: lead ime, price, and consumpion level, which

underpin he developmen o eigh differen invenory managemen models.

Eaves and Kingsman (2004) revisied Williams (1984) model, reclassiying

spare pars ino five caegories: smooh, erraic, low urnover, slighly

sporadic, and srongly sporadic. Syneos, Boylan and Croson (2005) classiy

iems ino our quadrans, divided by wo axes: he average demand inerval

and he squared coefficien o demand variaion. Years laer Boylan, Syneos,

and Karakosas (2008) presened an applicaion o his mehod in a sofware

firm. The iems consumpion patern is classified as srongly sporadic,

slighly sporadic, and non-sporadic.

The aim o his aricle is o analyze he patern o demand as he main

inervening acor in invenory managemen. I firs o all discusses, in secion

2, how he requenly adoped premises regarding he adherence o demand

o Normal disribuion may no be realisic and cause disorions, especially

in he case o very low when he annual demand is less han one and low

consumpion iems when he annual demand ranges beween one and

a value sufficienly high, say hree hundred or five hundred unis per year,

in order o characerize a daily demand close o one. Secion 3 proposes a

concepual ramework designed o suppor invenory managemen, which

synhesizes hose models ha are mos adequae or specific paterns odemand (mean and variabiliy). Finally, secions 4 and 5 presen a case sudy

underaken in a Brazilian company, which no only showed he pracical

applicaion o he concepual ramework bu also revealed he laters impac

in erms o shorage and excess coss.

. Literature Review

Choosing he mos adequae invenory managemen model is essenially an

empirically-based decision ha may involve he use o simulaion, scenario

analysis, incremenal cos analyses (Silva 2009; Rosa e al. 2010; Rego and

Mesquia, 2011; Wanke 2011b) or qualiaive concepual schemes also known

as classificaion approaches (Huiskonen 2001). The later usually considersha he impac o produc, operaion and demand characerisics consiue

inervening variables in his choice (see, or example, Williams 1984; Hax and

Candea 1984; Dekker, Klen, and De Roo 1998; Boter and Foruin 2000;

Braglia, Grassi, and Monanari 2004; Eaves and Kingsman 2004; Wanke 2011b).

An analysis o he lieraure dealing wih invenory managemen model

selecion shows ha i originally ocused on producion and disribuion

environmens in which demand and lead ime end o be more predicable

or, in oher words, in which i is easier o answer he quesions o wha and


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how much o order (Wanke and Saliby, 2009; Wanke 2011b; Rosa e al. 2010).

However, here is a growing lieraure relaed o he specific problems raised by

low and very low consumpion iems such as spare pars (Boter and Foruin

2000; Silva, 2009; Rego and Mesquia 2011; Syneos e al. 2012).

The inrinsic characerisics o spare pars, which are ypically low and very

low consumpion iems, make he choice o invenory managemen models

paricularly criical under he ollowing circumsances (Cohen and Lee 1990;

Cohen, Zheng, and Agrawal 1997; Mucksad 2004; Kumar 2005; Rego 2006):

low sock urnover, difficul predicabiliy, longer replenishmen imes, greaer

service level demands and higher acquisiion coss.

Thereore, hese special eaures o spare pars deermine he selecion o

appropriae invenory managemen models. According o Boter and Foruin

(2000), here is a consensus ha spare pars canno be managed using radiional

models (see, or example, hose presened in Rosa e al. 2010). Basically, spare

pars do no fi hese models main premises such as, or example, he adherence

o demand o symmeric and coninuous probabiliy densiy uncions (Silva 2009).

The ollowing subsecions explore his issue a greaer deph, linking demand

characerisics (mean and variabiliy) o invenory managemen models

developed in he lieraure. In he case o average demand, he lieraure

provides he basis or he segmenaion o annual consumpion according

o hree differen levels very low consumpion, low consumpion and massconsumpion (Ward, 1978; Silva, 2009; Wanke, 2011a) while he coefficien o

variaion (see or insance, Silver e al., 1998; Hopp and Spearman, 2008) and

he probabiliy disribuion uncions (see or insance, Yeh, 1997; Silver e al.,

1998) orm he basis or segmenaion in he case o variabiliy.

. Very low consumption

According o Tavares and Almeida (1983), very low consumpion pars are

hose whose average consumpion is less han one uni per year. According o

hese auhors, he sock conrol o hese iems should no be perormed using

he usual models because, due o heir paricular consumpion characerisic,

here are no enough previous occurrences o make a precise esimae oprobabiliy disribuion (Croson 1972; Syneos and Boylan 2001; Ghobbar and

Friend 2003; Eaves and Kingsman 2004; Willemain, Smar, and Schwarz 2004;

Regatieri e al. 2005; Hua e al. 2007; Guierrez, Solis, and Mukhopadhyay

2008; Gomez 2008; Teuner and Duncan 2009).

In addiion, ollowing Tavares and Almeida (1983), i is he analysis o oal shorage,

excess and order placemen coss, given a cerain service level, ha makes i

possible o deermine wheher a par should, or should no, be kep in sock, and a


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VOLUME 49, NO. 1 9

replenishmen reques made solely agains an order. Thus, a binary oal cos model

was developed o suppor decision-making regarding wheher o keep one (1) or

no (0) uni in sock based on wo supposiions: adherence o demand o Poisson

disribuion and he possibiliy o placing an emergency order wih a lower han

usual lead ime, whenever a shorage occurs. This model is presened in appendix 1.

. Low consumption

For he purpose o his aricle, low cos iems are hose wih a hisorical

consumpion o beween 1 and 300~500 unis per year, which leads o an

average daily demand close o one, as suggesed by Wanke (2005). As demand

is no oo small o use he model proposed by Tavares and Almeida (1983),

service level-relaed decisions such as he order poin and replenishmen

level assume greaer imporance. Thus, he model, which is widely used in he

lieraure, was considered o be he mos adequae (Rosa e al. 2010).

More precisely, he model involves a coninuous review o sock levels and

replenishmen orders are always placed whenever he sock posiion reaches

he order poin s (Silver and Peerson 1985; Silver e al. 1998). In his case, a

quaniy o replenishmen is used ha is sufficien o raise he sock posiion

o poin S. Tha is, in pracice, he lo size is . According o Hadley and Whiin

(1961), he models resul in uniary lo size orders when order placemen

coss are low. Thus, as Feeney and Sherbrooke (1966) conclude, he policy

consiues a paricular case o models.

Various auhors (Feeney and Sherbrooke 1966; Walker 1997; Porras and Decker2008; Gomes and Wanke 2008) have used he model in spare par invenory

managemen. Using his model, a replenishmen order is requesed as soon as

a uni o sock is consumed in order o recompose he maximum level o sock.

This model is appropriae or very cosly componens ha are essenial or

business operaions (Walker 1997).

In relaion o he probabiliy disribuions o demand and lead ime in he

conex o models, Rosenshine e al. (1976 and Dhakar e al. 1993) iniially

assume ha lead ime is deerminisic. However, various disribuions, have

been considered in he conex o in order o represen he behavior o demand

or lead ime separaely: Normal (Krupp 1997), Gama (Burgin 1975; Das 1976;Yeh 1997), Poisson (Hill, Omar, and Smih 1999) and empirical disribuion wih

sochasic demands and lead imes (Eppen and Marin 1988).

Anoher imporan poin involves he deerminaion o he probabiliy

disribuion ha resuls rom he combinaion o lead-ime demand resuls

(Lau 1989; Silva 2009). According o Tyworh (1992), heNormal disribuion

consiues a reasonable approximaion or high consumpion iems, bu no or

low consumpion ones. In he case o he later iems, disribuion is ypically

asymmeric and possesses a high probabiliy o demand equal o zero. Porras


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and Decker (2008) adoped he Poisson disribuion o esimae he lead-ime

demand o iems wih only one occurrence a each specific ime inerval.

I should be highlighed ha when demand involves more han one

occurrence per ime inerval, various auhors have proposed compound

models, such as he Sutering Poisson (Ward 1978) , he compound

Poisson model (Williams 1984; Silver, Ho, and Deemer 1971) or he

compound Bernoulli model (Janssen, Heus, and Kok 1998; Srbosch,

Heus, and Schoo 2000). More recenly, Syneos e al. (2012) conduced

a comprehensive lieraure review on he disribuional assumpions

or spare-pars managemen, assessing he goodness-o-fi o various

disribuions and heir sock-conrol implicaions in erms o invenories

held and service levels achieved.

However, since mos o he disribuional assumpions are difficul o

apply in pracice as he parameers o more han one disribuion mus

be deermined firs so as o analyze he lead-ime demand behavior and

in order o make he concepual ramework developed in his research

operaional o managers, readily o be implemened in Excel spreadshees,

we decided o narrow he decision regarding he mos adequae lead-

ime demand disribuion beween Poisson and Gamma disribuions. For

examples on he pracically o he implemenaion o hese disribuions in

Excel, readers should reer, or insance, o Hopp and Spearman (2008) and

Wanke (2011a) or Poisson disribuion and Silver e al. (1998) and Tyworhand Ganeshan (2000) or Gamma disribuion. Besides, as deailed nex,

hese disribuions hold sraighorward relaions beween heir defining

parameers and he mean and variance o he variable o ineres.

... Poisson Distribution

In he case o low consumpion iems, Silver e al. (1998) sugges adoping

he Poisson disribuion premise (c. Appendix 2). According o Yeh (1997 ),

however, i is firs o all necessary o veri y he pracical applicabiliy o he

Poisson disribuion. This is because, in he Poisson disribuion, he mean

and variance are numerically equal. Thus, i his disribuion is o be used

in pracice, he variance o demand mus be siuaed wihin an inervaldelimied by a variaion o en percen around is mean: 0.9ED


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VOLUME 49, NO. 1 1

Gamma disribuion adheres easily o real daa and can be mahemaically

manipulaed in invenory managemen.

Following Yeh e al. (1997), he Gamma disribuion is adequae in cases where

periods wih null demand occur more requenly. Under hese circumsances,

i makes sense o consider he ime inerval ha has elapsed beween wo

consecuive demands differen rom zero as a variable o ineres or modeling

purposes, besides demand isel and lead ime. Segersed (1994 and Yeh

1997), or example, developed an invenory managemen model assuming ha

he ime inerval beween wo consecuive non-zero demands (Ti), demand ()

and lead ime (TR) are adheren o he Gamma disribuion.

I is worh noing ha inermiten demand paterns meaning ha demand

arrives inrequenly and is inerspersed by ime periods wih no demand a all

is a criical or choosing he mos adequae disribuional assumpion (Eaves,

2002; Syneos e al., 2012). Boylan and Syneos (2007) used he average

demand inerval, ha is he mean ime beween wo consecuive demands

greaer han zero, o classiy spare pars in conjuncion wih demand and lead-

ime uncerainy. Eaves and Kingsman (2004) developed similar conceps.

The model used by Yeh (1997) uses he probabiliy o no having a sock

shorage during an order cycle, ha is, during he inerval o ime beween

wo consecuive replenishmens, as a measure o he service level. The lowes

and highes desired service levels or each iem are defined according o he

model. The service level or he remaining sock is calculaed by 1-Ps(S), inwhich Ps(S), given in Appendix 4, is he probabiliy o sock shorage during he

order cycle, given he level o sock S.

. Mass consumption

Mass consumpion iems are requenly considered o be hose wih a

hisorical consumpion o over 300~500 unis per year, roughly one uni/day

(Wanke2005). According o Rosa e al. (2010), he classic lo size/reorder poin

model sands ou among mass consumpion iem invenory managemen

models. According o his model, unis are requesed whenever he sock

posiion reaches reorder poin (OP) (Love 1979; Silver e al. 1998; Mucksad

2004; Sherbrooke 2004; Hopp and Spearman 2008). In pracice, he size o loQis deermined by he radiional Economic Order Quaniy ormula (Harris

1913) and he reorder poin is defined so as o assure a specific service level

measure (Eppen and Marin 1988; Rego e al. 2011).

I is necessary o know he orma o he disribuion o lead-ime demand

o deermine he saey sock embedded wihin he reorder poin (Keaon

1995). According o Porras and Decker (2008), his calculaion requires

speciying he disribuion o lead-ime demand so ha he saey acor, K,


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can be deermined. Tradiionally, lead-ime demand is modeled using a Normal

disribuion (Silver and Peerson 1985). Due o he properies o his disribuion,

he saey acor Kor a specific service level is he same as o he sandard

normal disribuion curve, Z, which can be ound in several saisics and

logisics exbooks such as, or example, Levine e al. (2005) and Ballou (2006).

Various sudies, however, criicize his approximaion. According o Menzer

and Krishnan (1988), his approximaion is only valid i he Normal disribuion is

defined beween he inerval o -and +. Moreover, his creaes he possibiliy

o negaive demand in many pracical applicaions. For Moors and Srbosch

(2002), one o he main drawbacks o he Normal disribuion is he symmery

assumpion. Furhermore, according o Eppen and Marin (1988), iems ha

presen a Normal disribuion o lead-ime demand are ound in only a ew cases.

As an atemp o balance he advanages and disadvanages o choosing an

specific premise, Silver e al. (1998) propose a general rule or approximaing

lead-ime demand using he probabiliy disribuion o he coefficien o

variaion (CV), in he specific case o mass consumpion iems. I CVis greaer

han 0.5, he Gamma disribuion should be used and, i i is no, a Normal

disribuion provides a good approximaion or lead-ime demand.

. CONCEPTUAL FRAMEWORK FOR INVENTORY MANAGEMENT

Taking he heoreical ramework presened in previous secions as a poin o

deparure, he presen aricle proposes a concepual ramework or invenory

managemen based on he segmenaion o annual demand ino hreecaegories very low consumpion, low consumpion and mass consumpion

and he coefficien o he variaion o demand ino wo caegories high

uncerainy and low uncerainy. Using hese wo demand patern-associaed

variables, he concepual ramework indicaes he mos appropriae invenory

managemen model or low, very low, and mass consumpion iems, hus supporing

decision-making based on he mos adheren premises o answer he quesions o

how much o order, when o order and how much sock o keep in saey socks.

In he concepual ramework synhesized in figure 1, exremely low

consumpion iems are considered o be hose wih an average hisorical

demand o less han one uni per year. Low consumpion iems correspond oiems whose average hisorical demand may vary beween one and 300~500

unis per year, or a maximum o one uni per day, while mass consumpion

iems are hose wih a demand o over approximaely 300~500 unis per year,

in accordance wih Wanke (2005). The cu-off poin or he coefficien o he

variaion o demand is 0.5, like in Silver e al. (1998).

More specifically, or each quadran o annual demand and he iems

coefficien o variaion o demand, he concepual ramework conains he


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VOLUME 49, NO. 1 1

mos adheren probabiliy disribuion uncions and he mos appropriae

invenory managemen model, incorporaing all he heoreical consideraions

described in he previous secions.

FIGURE : CONCEPTUAL FRAMEWORK FOR INVENTORYMANAGEMENT

In addiion o he developmen o concepual rameworks, Rego e al.

(2011) sress he need o conduc case sudies ocusing on heir pracical

applicabiliy in firms in order o overcome gaps beween heory and pracice.

These case sudies enable researchers o increase heir pracical knowledge

given ha aspecs involving undersanding abou environmens complexiy and

he managerial effors made by firms become eviden. Examples can be ound in

Cohen e al. (1990), Boter and Foruin (2000), Srbosch, Heus, and Schoo (2000),

Trimp e al.(2004), Levn and Segersed (2004), Wanke (2005), Porras and Dekker

(2008), Wagner and Lindemann (2008), Syneos, Keyes, and Babai (2009), and

Silva (2009). The ollowing secions presen and discuss he resuls o he pracicalapplicaion o he proposed concepual ramework in a large Brazilian company.

. PRACTICAL APPLICATION: DESCRIPTION OF RESULTS

The concepual ramework presened in secion 3 ormed he basis or he

developmen o a VBA ool or Excel o help segmen invenory iems using a

daabase srucured in an elecronic spreadshee. In addiion, his ool makes

i possible o obain a quick answer o he quesions o how much o order,

when o order and how much sock o keep in a saey sock or a large number

BinaryModels(Zero

orOne)

Coefficientofvariation

ofdemand

Lessthan

0.5

Great

erthan

0.5

Annual Demand0 1 300~500 Infiniy

Policy (Q, OP)

Order Poin

SS Gamma Disribuion

or Daa Hisogram

Order Poin

SS Normal Disribuion

or Daa Hisogram

Policy (S, S-1)

Shor Lead Time Lo = 1

Lead Time Long

Lo=Consunpion

Forecase during Lead Time

.Premises/Assumpions o

Deermine S:

Poisson

If 0,9 E(D) < Var (D) < 1.1 E(D)

Gamma

If he percenage of periods

during which D=0 exceeds

30% of he oal

Demand LT

Replenishmen LT

Average Demand


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o iems, considering a given level o service ha he firm wishes o offer

cusomers and he previously idenified segmenaion. The ool was applied o

real daa rom a Brazilian company in order o measure possible gains resuling

rom he proposed concepual ramework or invenory managemen.

The firm ha was he objec o his pracical applicaion is one o he worlds

leading manuacurers o agriculural and consrucion equipmen. I has

approximaely one hundred sixy acories worldwide, o which hree are

locaed in Brazil. The Brazilian subsidiary has more han wo housand

employees. In addiion, he firm mainains a oal o more han 20,833 differen

iems in is Brazilian subsidiarys warehouses. The firm considers ha his

diversiy o iems is undamenal or providing suppor or echnical assisance

aciviies and pos-sales services or he equipmen i commercializes.

In order o perorm a comparison beween he invenory managemen acually

verified in he firm and he policy suggesed by he ool (concepual ramework),

he sudy used consumpion daa o he previous ory-eigh monhs or all

20,833 iems. Besides consumpion daa, oher daa inormed by he firm,

which was imporan or modeling purposes, was also used: iems acquisiion

cos; replenishmen/order placemen cos; unavailabiliy and penaly coss;

average supplier lead ime; variance o supplier lead ime; opporuniy cos o

mainaining socks or a year, or each o he differen iems.

The firs sep in he applicaion o he concepual ramework was o use he

Excel ool o segmen he iems according o he classificaion caegorieso very low consumpion, low consumpion and mass consumpion, which

resuled in he ollowing respecive percenages: 22 percen; 74.5 percen

and 3.5 percen. In addiion, based on he hree demand caegories and he

wo coefficiens o variaion, an analysis was underaken o demands degree

o adherence o he Poisson and Gamma disribuions, according o he

discussion presened in secions 2.2.1 and 2.2.2.

Figure 2 shows one o he ool s oupu screens conaining a sample o he

firs en iems analyzed, heir respecive classificaion (very low consumpion,

low consumpion and massconsumpion) and heir adherence o he mos

adequae probabiliy disribuion (Poisson or Gamma). I should be highlighedha, as he adherence o demand o he Poisson or Gamma disribuion was

no verified, i was assumed ha demand adhered o a Normal disribuion in

cases in which he coefficien o variaion was lower han 0.5.


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VOLUME 49, NO. 1 1

All he analyses presened below relae o very low and low consumpion iems

which represen 96.5 percen o oal iems.

As regards very low consumpion iems, i was observed ha, in he case o 99.9

percen o he iems classified in his group, he firm should keep a par in sock,

oaling 4,586 socked iems (one or each iem). The invesmen needed o orm

his sock exceeds BRL$2,540,000, i all pars have o be purchased iniially a he

same ime, hus generaing an annual opporuniy cos o over BRL$563,000.

In he case o low consumpion iems ha adhere o he Poisson or Gamma

disribuions, he sudy calculaed he levels o sock needed o caer o hreedifferen service levels: a niny percen, niny-five percen, and niny-eigh

percen probabiliy o no having a shorage o he iem in sock. For each level

o service he sudy calculaed he opporuniy coss o keeping he pars in

sock, as well he invesmen needed o purchase hese iems i hey iniially

had o be purchased simulaneously.

Specifically or he niny percen level o service, an invesmen o over

BRL$84,000,000 would be needed o orm his sock i all pars iniially had o be

FIGURE : RESULTS OUTPUT SCREENSEGMENTATION ANDADHERENCE TO THE DISTRIBUTION

ID Item Code Classification Poisson Gama

1 A1304031 Low Turnover no yes

2 A162896 Very Low Turnover no no

3 BNHCMP0001 Low Turnover no yes

4 BNHCMP0002 Mass Consumpion no no



7 BNHC0001 Low Turnover no yes



10 BNHC0004 Mass Consumpion no no


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purchased simulaneously. This amoun involves an annual opporuniy cos o

BRL$18,670,000.00. This opporuniy cos o roughly weny-wo percen per year

is basically financial: i relaes o he high ineres raes o he Brazilian economy.

By he ime he case sudy was conduced (2010), he Brazilian base ineres

rae, named SELIC, was around welve percen per year. These addiional en

percen poins consiue he average spreads incurred by large companies when

borrowing money o suppor working capial requiremens, such as invenories.

On average, en pars are kep in sock or each low consumpion iem.

. PRACTICAL APPLICATION: DISCUSSION OF RESULTS

As he company did no divulge is real invenory policy nor wheher radiional

invenory classificaion schemes, such as ABC, subsidized decision-making

wih respec o invenory model segmenaion, he sudy made some working

assumpions in order o be able o evaluae wheher gains would occur in

boh financial and service level erms i he invenory models suggesed by

he concepual ramework were adoped. Some o hese working assumpions

considered he Normal disribuion alogeher wih (S, s)invenory model and

coninuous review or low and very low consumpion iems.

For very low consumpion iems, he sudy analyzed he impac, in erms o

oal coss, o keeping no (CT0) or one (CT1) par in sock, as shown by eqs. (A1)

and (A3), respecively. The resuls showed ha he mos appropriae policy was

o keep a par corresponding o each iem in sock as i led o a gain given

by he sum o he differences (CT0-CT1) or all iems o BRL$14,429,517.56.The gains ha can be achieved by keeping a par in sock or all iems are due

mainly o he high unavailabiliy and penaly coss, which hus consiue an

exremely imporan par o he cos difference beween he wo policies.

In he case o low consumpion iems, which represen 74.5 percen o he oal,

he sudy considered ha he arge-sock S or each iem was equal o he

mean o he hree bigges demand spikes ha had occurred during he previous

ory-eigh monhs. This is a common pracice wihin Brazilian companies

(c. case sudy in Wanke 2011). Then, or each iem, he sudy calculaed he

opporuniy cos or he level o sock suggesed by he ool and he one

effecively praciced by he firm. When he resuling difference was posiive,

i was considered ha he adopion o he concepual ramework would leado a reducion in opporuniy coss. In his case, his amouned o nearly

BRL$8,870,000, due o lower levels o invenory. Similarly, when he difference

was negaive, he sudy assumed ha here would be an increase in opporuniy

coss due o he rameworks adopion which, in his case, oaled nearly

BRL$2,230,000, on accoun o higher levels o invenory. I should be poined ou

ha, on mos occasions, he increase in invenory levels can be atribued o he

need o adjus cusomer service levels, sipulaed, in his case, a niny percen.

Wih he aim o ideni ying he oal ime aken o achieve financial gains


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VOLUME 49, NO. 1 1

resuling rom invenory demobilizaion, he sudy esimaed he amoun

o ime needed o consume excess invenory, based on average monhly

consumpion figures. The BRL$8,870,000 reducion in opporuniy coss is

achieved wihin a maximum imescale o 20.57 monhs, bu in a maximum o

six monhs i is possible o atain BRL$8,446,990.04, or 97.01 percen o he

oal, and in a litle under a year, more precisely in 10.29 monhs, i is possible

o achieve 97.89 percen o gains, amouning o BRL$8,676,001.41 (figure 3).

The ool, which idenified he adherence o he disribuions o low urnover iems o

he Poisson and Gamma disribuions, showed grea efficiency, producing savings o

BRL$2,321,674.55, o be achieved over a maximum period o 20.57 monhs.

FIGURE : Reduction Of The Opportunity Cost Of Maintaining

Stocks Over Time.

. CONCLUSIONS

A vas lieraure relaed o invenory heory has been developed over he pas

fify years. However, many o he heoreical resuls achieved are no easily

applicable in business pracice, given ha mos are based on premises ha

are no verified in he corporae environmen. Wih he aim o helping o fill

his gap, his sudy proposes a concepual ramework designed o suppor he

choice o he mos adequae/appropriae invenory managemen model.

Based on differen demand characerisics, he proposed concepualramework reveals ha he premise ha demand adheres o Normal

disribuion is no always valid and ha oher probabiliy disribuions, such as

he Poisson and Gamma disribuions, should be considered by managers. This

sudy also explored issues relaed o he managemen o socks o low and

very low consumpion iems hrough he rameworks pracical applicaion in a

Brazilian agriculural and consrucion equipmen company.

A

ccumulatedGain(BRLthousand)

Achievement Time (months)

0 5 10 15 20

2500

5000

7500


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APPENDIX DERIVATION FOR VERY LOWCONSUMPTION

ITEMS (TAVARES AND ALMEIDA )

The auhors demonsraed ha he oal cos associaed wih he policy o no

keeping a spare par in sock (CT0 ) can be calculaed as ollows:

(A1)

in which is he average hisorical consumpion (pars/year), CTR is he oal

cos o iem replenishmen/order placemen (BRL), and Cipis he cos o

unavailabiliy and penaly coss relaed o shorage (BRL).

In order o adop he alernaive policy, ha is, he firm keeps a uni in sockunil consumpion occurs, an evaluaion should be perormed o he expeced

racion o ime in sock (FTECE), given by:

(A2)

in which TRis he replenishmen lead-ime.

The expeced value o occurrences during he expeced racion o ime ou o

sock is given by *(1-FTECE). Thus, one can obain he oal cos associaed

wih he decision o always keep a par in sock (CT1 ), aking ino accoun he

possible occurrence o anoher reques during he lead ime, as well as is

implicaions in erms o replenishmen coss and unavailabiliy and penaly

coss, as given below:

(A3)

The pars o eq. (A3) represen, respecively, he opporuniy cos o keeping a

spare par in sock, he oal replenishmen cos and unavailabiliy and penaly

coss. In order o define he mos advanageous policy, i is necessary o

compare he magniudes o CT0 and CT1, oping or he leas-cos decision.

CT1= *Caq*i Cip**1+TR1 +[CTR*]+ 1+*TR

1

CT0=*(CTR+Cip),

FTECE= ,1+*TR

1


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24/100


INTEGRATING FMEA WITH THE SUPPLY CHAIN

RISK MANAGEMENT PROCESS TO FACILITATE

SUPPLY CHAIN DESIGN DECISIONS

V. M. Rao Tummala

Computer Information Systems Department, College of Business, Eastern

Michigan University, Ypsilanti MI 48197

Tobias Schoenherr, CSCP (Corresponding Auhor)

Department of Supply Chain Management, Broad College of Business,

Michigan State University, East Lansing MI 48824

Thomas Harrison

Chief Executive Officer, Michigan Ladder Company, Ypsilanti MI 48198

ABSTRACT

We presen a novel approach o inegraing ailure mode and effec analysis

(FMEA) wih a supply chain risk managemen process (SCRMP). Focusing on

he challenging ask o assess and manage supply-side risks in global supply

chains, he approach developed offers an effecive and affordable way or

firms o provide decision suppor or he selecion o heir mos appropriae

supply chain design. The aim o he inegraed approach combining he

srenghs o FMEA and SCRMP is o gaher as much perinen inormaionas possible, o srucure i, and o comprehensively delineae all poenial

supply chain risk acors, offering valuable decision suppor. We illusrae

he applicaion o he approach a Michigan Ladder Company, where i was

applied o wo specific supply chains or he procuremen o fiberglass ladders.

Specifically, one supply chain spanned rom China o he U.S. via Mexico

(aking advanage o a Mexican maquiladora), and one spanned rom China

direcly o he U.S. The combinaion o FMEA and he SCRMP enhanced he

manuacurers confidence in is supply chain design decision, and enabled he

firm o proacively manage is supply-side risks. Overall, he aricle is mean o

moivae praciioners o embark on he journey o acive risk managemen.

While some may perceive risk managemen as a dauning ask or beingprimarily employed by larger firms, we provide guidance or firms o any size

o apply he approach i can be done, and does no have o consume an

inordinae amoun o resources.

Keywords: supply chain risk managemen, supply-side risks, ailure mode and

effec analysis (FMEA), supply chain managemen decisions, case sudy


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VOLUME 49, NO. 1 2

Introduction

Globalizaion has enabled companies o ake advanage o worldwide supply

and demand markes, creaing new opporuniies and increasing heir profi

poenial. However, wih hese enicing prospecs have also come he dangers

o associaed longer and more complex supply chains (Tang 2006), which ofen

span muliple counries or even coninens. Especially in a ime o increasing

cusomer demands and requiremens on qualiy, delivery lead ime and

responsiveness, managing hese global supply chains has become a challenge.

Ensuing risks are hus muliarious, and can manies hemselves in he orm

o supply disrupions or breakdowns (e.g., Hendricks and Singhal 2005; Sockes

2008; Tang and Musa 2011). This is especially heighened in imes o companies

ocusing on heir core compeencies, and ousourcing he remaining asks,

ineviabiliy increasing heir vulnerabiliy (Narasimhan and Talluri 2009).

Evidence is provided or example by he caasrophes associaed wih super-

sorm Sandy on he Eas Coas o he U.S. in 2012, he floods in Thailand in

2012, and he Japanese sunami in 2011. Besides he incomprehensible human

ragedies, he later even or example also resuled in companies such as

Apple, Sony Ericsson, and many auomobile manuacurers being unable o

quickly adjus heir supply chains; he firms were unable o compensae or he

missed supplies rom Japan, resuling in significan losses (BBC 2011). Furher

examples o imminen risks abound, such as he financial crisis affecing mos

companies and counries (Blome and Schoenherr 2011), as well as recen

poliical insabiliy and regime changes (Doukas e al. 2011).

Due o hese realiies, i has become an imperaive or odays supply chain

managers o ideniy possible risks affecing heir supply chains, evaluae

hem, and develop appropriae risk miigaion sraegies. However, his

underaking can be a dauning ask (Kwak and Soddard 2004), as was

revealed in a survey by Snell (2010): while 90 percen o he responding firms

el hreaened by supply chain risks, ew el confiden and knowledgeable

in managing hese risks. This finding is consisen wih our own anecdoal

observaions and recen ineracions wih supply chain proessionals. As such,

no many firms are employing a srucured approach o assess and manage

risks inheren in heir global supply chains, which is especially he case or

small- and medium-sized enerprises. This negligence can however havesevere repercussions, since even smaller firms and heir supply chains are now

ofen inerconneced globally and hus exposed o a muliude o risks.

I is hereore our objecive in he presen paper o illusrae a novel approach

combining ailure mode and effec analysis (FMEA) wih a supply chain risk

managemen process (SCRMP). This inegraed mehodology provides

guidance or managers on how o beter ge a handle on risks associaed

wih heir exising or poenial supply chain designs. The aim o he approach


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combining he srenghs o FMEA and SCRMP is o gaher as much perinen

inormaion as possible, o srucure i, and o comprehensively delineae all

poenial supply chain risk acors. We illusrae his applicaion a Michigan

Ladder Company (MLC), which uilized he inegraive approach o provide

decision suppor or is supply chain design. The mehodology srucured

he decision problem and offered he firm more confidence in is final choice.

The approach was applied o assess he supply-side relaed risks inheren in

wo alernae supply chain designs or he procuremen o fiberglass ladders.

While one supply chain design involved he procuremen o he finished

produc rom China, he oher involved he procuremen o pars rom China,

assembling hem in a Mexican maquiladora, and hen shipping hem o he

U.S. locaion o MLC. The risks associaed wih each were assessed relaive

o he firms objecives o cos reducion and responsiveness. Wihin his

conex, supply chain risk was defined as he hrea or probabiliy o supply

chain disrupions ha adversely affec he smooh flow o producs, impacing

operaional perormance measures such as responsiveness and cos.

The developmen and illusraion o he approach is imporan boh rom

a pracical and a heoreical perspecive. From a pracical angle, he value

consiss o he presenaion o an effecive, comprehensive, and inegraed

ramework or risk managemen, consising o boh FMEA and he SCRMP,

as well as an illusraion o how i can be applied. The imporance inheren

in he later is he demonsraion ha risk managemen does no have o be

rocke science or hugely expensive, and ha sraighorward approachescan yield significan insigh. I is hus our hope ha he aricle provides an

impeus, moivaion and guidance or pracicing managers o ollow his

ramework, especially also or small- and medium-sized firms, which may have

been hesian o adop risk managemen approaches in he pas due o heir

poenial associaed expenses and effor. We are also direcly addressing

a shorcoming menioned in Snell (2010), who ound in heir survey ha

respondens did no eel confiden and knowledgeable in managing hese

risks. The approach presened herein is a means o increase confidence and

knowledge in risk managemen, and represens a ready-o-use ool o beter

manage global supply chain risks.

The sudy also conribues o lieraure and heory in supply chain risk

managemen in ha i proacively idenifies and evaluaes risk and miigaion

sraegies, insead o examining ex pos scenarios (c. Trkman and McCormack

2009); his leads o he urher improvemen o he firms confidence in is

supply chain design ex ane, anicipaing poenial ailures and developing

prevenive and response acion plans. The successul experiences made by

Michigan Ladder Company demonsrae he useulness o he approach and

is significan poenial in aciliaing supply chain decisions ocusing on risk


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VOLUME 49, NO. 1 2

assessmen and managemen. We hus also answer he call or urher insigh

ino risk managemen approaches o aciliae and srucure supply managers

sraegic decision making on he opimal configuraion o heir supply chains

(Zsidisin e al. 2004). In addiion, we ollow he example o Chan e al. (2012),

who encouraged he inegraion o FMEA wih risk analysis approaches. As

such, overall, we make imporan conribuions o boh pracice and heory/

lieraure in producion and operaions managemen.

The nex secion provides a brie inroducion ino he general area o supply chain

risk managemen. This is ollowed by a descripion o he inegraive approach

combining FMEA and he SCRMP. Michigan Ladder Company and he conex in

which he approach was implemened are presened nex. This is ollowed by he

applicaion o our suggesed approach a MLC. The ensuing secion discusses he

resuls, illusraes he derived value or MLC, and describes he acions resuling

rom he approach. A las secion concludes he aricle.

Supply Chain Risk Management

We define supply chain risk as he hrea or probabiliy o supply chain

disrupions ha adversely affec he smooh flow o producs, impacing

operaional perormance measures such as responsiveness and cos.

This definiion was derived rom exan lieraure, bu was also specifically

influenced by he conex o MLC and how i viewed supply chain risk.

Mos definiions ound in lieraure go back o he concepualizaion offered

by he Briish Sandards Insiue, which described risk as a combinaion oprobabiliy or requency o occurrence o a defined hazard and magniude o

occurrence (BS 4778, 1991). O paricular relevance in he presen sudy is he

risk o inerruped supply. Wihin his conex, supply risk has been defined

as ailures associaed wih inbound goods and services ha affec he firm

o mee cusomer demand (Zsidisin e al. 2004, 2005). Similarly, Harland e

al. (2003), reerring o Meulbrook (2000), described supply risk as adversely

affecing inward flow o any resource ha hinders scheduled operaions. Our

concepualizaion is in line wih hese prior definiions.

The criicaliy o ensuring he smooh flow o producs hrough he supply

chain and he impac o supply chain disrupions has been highlighed byrecen evens. For example, supply chains have been impaced by naural

disasers, such as he super-sorm Sandy on he U.S. Eas Coas in 2012, floods

in Thailand in 2012, he Japanese sunami in 2011 (Dawson 2011), he 2006

earhquake in Java, he hurricanes Karina and Ria in 2005 (Devlin 2005), and

he ongoing hackings o vessels by piraes off he coas o Somalia (Bowman

2010). The immediae impac o announcemens concerning such supply

chain disrupions on shareholder value has been shown by Hendricks and

Singhal (2005). I is hereore crucial or managers o ideniy and undersand


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he associaed risks wih heir supply chains, as well as o hen develop risk-

reducion sraegies (Chopra and Sodhi 2004).

Responding o hese realiies, academic research ino supply chain risk

managemen has flourished. For example, Waers (2011) ocused on supply

chain risk managemen rom he perspecive o logisics, and Sodhi and

Tang (2012) called or companies o more proacively manage he associaed

risks. Frsl e al. (2011) repored on wha represens excellence in supply

risk managemen across differen indusry secors, and Olson (2012) offered

analysis ools or supply chain risk managemen. Allen and Schuser (2000)

examined risk managemen a Welchs, Inc., or heir harvesing o grapes,

and Maneti (2001) emphasized he imporance o r isk managemen or

he choice and implemenaion o echnologies in manuacuring. Risk

managemen was described as an essenial ingredien or projec planning

by De Reyck (2010). Pohl e al. (2010) summarized exan lieraure in supply

chain risk managemen, complemening earlier work by Tang (2006) who

ocused on he review o quaniaive models or he managemen o supply

chain risk. A review o recen sudies in supply risk is also provided in Blome

and Schoenherr (2011).

Various approaches have been suggesed or he managemen and miigaion

o supply chain risks. As such, Kilgore (2004) inroduced an analyical

risk miigaion ramework consising o five seps, Giannakis and Louis

(2011) proposed a muli-agen decision suppor sysem or supply chainrisk managemen, and Wagner and Nesha (2010) assessed supply chain

vulnerabiliy using graph heory. De Waar (2006) developed an inormal

assessmen ool o implemen risk miigaion sraegies, Sheffi and Rice

(2005) used he dimensions o disrupion probabiliy and consequences

as caegorizaion scheme, and Kleindorer and Saad (2005) proposed a

ramework reflecing he aciviies o risk assessmen and risk miigaion.

Sinha e al. (2004) suggesed a prescripive risk mehodology applied o

he aerospace supply chain, Cucchiella and Gasaldi (2006) proposed a real

opions-based supply chain risk managemen approach, and Huhn and Kahn

(2012) uilized robus opimizaion o manage supply chain risks. We build

on and exend his sream o research and propose a novel and innovaivesupply chain risk managemen approach, which will be described nex.

FMEA and SCRMP

In our inegraed approach we combine he srenghs o radiional FMEA

(1995), and uilize i ogeher wih specific echniques such as he U.S. Miliary

Sandard 882C and he Hazard Toem Pole (Grose 1987), as par o he SCRMP.

Wihin his conex, FMEA enables he assessmen o poenial ailures and

heir effecs, ogeher wih he developmen o prevenive acion plans and


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VOLUME 49, NO. 1 2

associaed implemenaion coss, effecively complemening he SCRMP.

A recen sudy illusraed he power o inegraing FMEA wih general risk

managemen approaches, noed he dearh o research in his area, and

hus called or he urher invesigaion o his area (Chan e al. 2011). Chan

e al. (2011) specifically noed ha here is a lack o pracical guidance in he

inegraion o risk and ailure analysis, and sressed he significan poenial o

doing so. We answer his call in he presen sudy.

The value o inegraing FMEA wih oher approaches can be considerable,

given exan research ha repored on such inegraion effors. For example,

Shahin (2004) inegraed FMEA wih he Kano model, Chang and Paul (2009)

combined FMEA wih daa envelopmen analysis, and Chin e al. (2009)

inegraed i wih he evidenial reasoning approach. In he presen sudy, we

inegrae FMEA wih he supply chain risk managemen process and illusrae

is combined applicaion.

Failure Mode and Effect Analysis (FMEA)

Failure mode and effec analysis (FMEA) is a srucured approach o ideniy

and preven produc and process ailures beore hey occur (McDermot e

al. 2009). Having evolved rom firs applicaions in he aerospace indusry in

he 1960s, i has since hen been widely employed or produc and process

improvemen effors and or he purpose o reducing he risk o ailures

(Samais 2003). In FMEA, every possible maluncion or breakdown is

assessed in erms o he poenial causes o he ailure, he poenial effecsand consequences, prevenive acions possible, and coss o hese prevenive

acions (Taikonda and Taikonda 1994). FMEA has been applied or example

o projec risk managemen (Tummala and Mak 2001; Ng e al. 2003; Carbone

and Tippe 2004) and o he implemenaion o enerprise resource planning

sysems (Shirouyehzad e al. 2011). I was also noed ha benefis rom FMEA

are mos ully realized i i is par o a qualiy managemen sysem (McDermot

e al. 2009). Exending his idea, we orward he noion ha he applicaion o

FMEA ogeher wih he SCRMP is mos effecive.

While mos FMEA sudies have ocused on produc and manuacuring

process improvemens, and no on supply chain managemen, he inherenineres o hese prior sudies was in reducing he risk o produc or process

ailures (Samais 2003). Wha is hereore imminen is he applicabiliy o

supply chain risk managemen. Wih a ew excepions, however, exan

research has no deal wih he applicaion o FMEA o supply chain risk

managemen, an observaion which suggess o have lef many opporuniies

on he able. Wihin he conex o supply chain managemen, Elkins e al.

(2005) proposed using FMEA o race back he roo cause o a ailure and learn

rom he even, and Teng e al. (2006) provided guidance or implemening


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FMEA in a collaboraive supply chain environmen. We exend hese works by

illusraing he combined applicaion o FMEA wih he SCRMP.

Supply Chain Risk Management Process (SCRMP)

The Supply Chain Risk Managemen Process (SCRMP) is a ramework or he

assessmen o he risk profile associaed wih a specific supply chain, and

was concepually developed by Tummala and Schoenherr (2011). I offered

a pracical emplae o manage supply chain risks more effecively in a

srucured ashion. However, he auhors ailed o illusrae he approach

in he real world, quesioning is applicabiliy and relevance or managerial

pracice. In he curren research, we work owards alleviaing his omission,

and inegrae i wih FMEA.

The core o he SCRMP consiss o hree phases: (I) risk idenificaion, risk

measuremen and risk assessmen, (II) risk evaluaion, risk miigaion and

he developmen o coningency plans, and (III) risk conrol and monioring

(Tummala and Schoenherr 2011). The purpose o phase I is o enumerae all

possible poenial supply chain risks, and assess heir severiy and likelihood o

occurrence. Phase II evaluaes he idenified risks o develop appropriae risk

response sraegies or risk reducion and managemen. Phase III develops a

risk-based daa managemen and analysis sysem o monior he effeciveness

o he implemened risk reducion plans.

In he curren sudy we ocus on phases I and II o he SCRMP, heir inherenprocess seps, associaed echniques and evaluaion approaches. Specifically,

he seven process seps ha we consider are he ollowing: (1) ideniy

poenial supply chain risk acors; (2) assess he severiy o consequences

o he idenified risk acors; (3) assess he likelihood o occurrence o he

idenified risk acors; (4) classiy he idenified risk acors; (5) deermine he

cos o implemening risk response acion plans; (6) deermine risk prioriy

scores; and (7) consruc he Hazard Toem Pole char. The las phase and

remaining process seps no considered reer o acions aken afer he risk

miigaion and coningency plans have been implemened, and deal wih

ongoing risk conrol and monioring. These seps are no considered since

hey are ouside o he scope peraining o he inegraion o FMEA; raher,hey uilize resuls o he prior seps and FMEA o effecively manage risk.

CONTEXT AND BACKGROUND

We illusrae he applicaion o our approach a Michigan Ladder Company

(MLC), a small manuacurer and disribuor o ladders, locaed in Ypsilani,

Michigan. The produc specrum o MLC is narrow bu deep, including

aluminum, fiberglass and wood ladders. The firm has been enjoying good

financial healh despie he economic downurn, providing evidence o is


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VOLUME 49, NO. 1 3

sound risk managemen approaches. Esablished in 1901, MLC is he oldes

U.S. ladder manuacuring company, supplying ladders primarily or he

commercial secor, including he miliary. The coninued success o he

company is atribued o is innovaive and loyal workorce, as well as MLCs

emphasis on and dedicaion o qualiy producs. The firm was chosen o serve

as an exemplar or he applicaion o our approach, since i had been very

proacive in is houghs oward risk managemen.

An earlier approach o risk managemen ha he company was aking,

involving he analyic hierarchy process (AHP), was chronicled in Schoenherr

e al. (2008). This evaluaion was riggered by changes in he compeiive

landscape and he wish o move he sourcing o wo o he firms major

produc lines rom a Mexican supplier o an alernae source. The five

alernaives considered were (1) he sourcing o finished goods rom Mexico, (2)

he sourcing o finished goods rom China, (3) he sourcing o pars rom China

and assembly in he U.S., (4) he sourcing o pars rom China, assembling

hem in a maquiladora in Mexico wih invesmen, and (5) he sourcing o

pars rom China, assembling hem in a maquiladora in Mexico wih no

invesmen. These supply chain designs and associaed sourcing locaions

were considered due o heir appeal in erms o boh qualiy and cos, which all

alernaives demonsraed. A oal o seveneen risk acors were idenified as

being relevan or he firm. Using AHP modeling, he risk acors were assessed

across he five alernaives, yielding a preerence score minimizing he risks

or each alernaive. As a resul, he hree alernaives wih he leas risk wereimplemened (alernaives (2), (5) and (1)). Three supply chain designs were

pursued, so as o urher diversiy he risk inheren in each. This approach

has been receiving grea ineres by praciioners, and was also eaured

a a dinner presenaion held or he Greaer Deroi Chaper o APICS. We

hereore conaced MLC o solici is paricipaion or he presen sudy.

Due o he dynamic environmen, MLC was consanly re-evaluaing is

sraegic choices regarding is ousourcing aciviies. A he ime he sudy

was conduced, he company had abandoned alernaive (1), which was merely

reained unil he new supply chain designs were ully operaional. As such,

he company was operaing wih wo supply chain designs: (A) he sourcingo finished goods rom China, and (B) he sourcing o pars rom China,

assembling hem in a Mexican maquiladora (wih no invesmen aken in he

venure), and hen shipping hem o he Midwes locaion o he firm. We will

reer o opion (A) as he China-Midwes supply chain, whereas opion (B) is

reerred o as he China-Mexico-Midwes supply chain.

Alhough hese wo supply chains represened he leas risk, based on he

prior AHP analysis, qualiy problems began o emerge abou a year afer he


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commimen o he China-Midwes supply chain had been made. A more

deailed invesigaion o he risk acors involved in his supply chain was

hereore needed, offering a ormidable conex o apply FMEA inegraed

wih he SCRMP. In order o provide a comparison benchmark, i was

decided o also include he second leas-risky alernaive in he evaluaion,

he China-Mexico-Midwes supply chain, which had also been in operaion.

The design, srucure and configuraion o hese wo supply chains is quie

diverse, involving a differen se o supply chain risks and differing inensiy

levels or each. As such, a more rigorous assessmen o hese wo scenarios

was now warraned. In addiion, in conras o he earlier evaluaions via AHP,

he wo supply chains were now ully operaional, and acual experiences

had been made o beter gauge he risks. Thereore, in collaboraion wih he

company, he approach presened herein was developed, combining FMEA

and he SCRMP. The goal was o aciliae he decision on he opimal supply

chain design, minimizing supply-side relaed risks. The overriding objecives

o he assessmen were o reduce coss along he supply chain, wih

however a he same ime improving (or a leas no deerioraing) service

levels and responsiveness.

Differentiation to Prior Work

The curren work builds on and exends prior sudies, and we would like o

highligh how he curren paper differeniaes isel. Specifically, above we

noed ha we are ollowing up on a decision Michigan Ladder Company had

made peraining o five offshoring alernaives, which was aciliaed wihhe Analyic Hierarchy Process and presened in Schoenherr e al. (2008).

Our work differeniaes isel in ha we are no relying on AHP o offer

decision suppor. While we conduced our research a he same company

wih he same risk acors (since hese risk acors were sill deemed valid by

he company), he ramework presened in he presen paper is differen in

ha we apply a novel approach inegraing FMEA and he SCRMP.

In addiion, he presen paper describes an approach or decision suppor

ha was aken laer on in he imeline o he company, once he decision

chronicled in Schoenherr e al. (2008) had been made. As such, we are

ocusing on he wo leas risky alernaives idenified in Schoenherr e al.(2008) , and provide more enhanced and deailed decision suppor, building

on and exending his prior work. Furhermore, we base he assessmen

on he acual operaion o he wo supply chains, or boh o which

experiences had now been made. Moreover, even hough we consider

he same seveneen risk acors as hose idenified by Schoenherr e al.

(2008) , he use o FMEA and he applicaion o he principles inheren in he

SCRMP, such as he U.S . Miliary Sandard 882C and he Hazard Toem Pole,

enabled us o provide a much more deailed analysis o assess he poenial


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INTEGRATING FMEA WITH THE SCRMP: AN APPLICATION AT MLC

This secion describes he inegraion o FMEA wih he SCRMP, as well as how

i was applied a MLC. Table 1 provides a summary o his inegraion. Overall,

his expos provides a valuable applicaion and emplae or oher firms acing

similar problem scenarios and decisions.

TABLE :INTEGRATION OF SCRMP AND FMEA

Step Step in the SCRMP Integration with FMEA

1 Idenify risk facors

Define he poenial failure mode (i.e. how he specific risk manifess

iself in erms of failures or breakdowns for MLC), poenial causes of

he failure or breakdown, poenial effecs or consequences of he

failure or breakdown, prevenive acions ha could be done, and he

cos of such acions

2 Assess he severiies of consequencesUpdae (add or modify) he FMEA framework by more deailed

informaion having emerged hrough his SCRMP sep

3 Assess he likelihood of occurrenceRefer o informaion colleced in he FMEA o make more informed

decisions on he assignmen of individual likelihood values

4 Classify he idenified risk facorsRefer o informaion colleced in he FMEA o subsaniae

classificaion; if necessary, make adjusmens o beter reflec realiy

5Deermine he cos of implemening risk

response acion plans

Provide more deail in he FMEA framework peraining o specific

coss, as well as subsaniaion for specific cos esimaes

6 Deermine risk prioriy scores

Subsaniae he classificaion ino risk prioriy scores wih

qualiaive informaion recorded in he FMEA, combining he

srenghs of boh approaches

7 Consruc he Hazard Toem Pole char

Uilize informaion from he FMEA framework o subsaniae

classificaions and overall srucure of he Hazard Toem Pole, refuing

he criicism ha SCRMP scores can be subjecive; his can be

especially useful in he presenaion o ohers affeced by he decision


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VOLUME 49, NO. 1 3

Step 1: Identify Risk Factors

We commenced wih he firs sep o he SCRMP, i.e. he idenificaion o

supply chain managemen risk acors. Useul approaches a his sage

include brainsorming or he nominal group echnique. All easible risks should

be idenified ha could poenially influence he desired oucomes. The

overriding objecives o he risk assessmen in he case o MLC were o reduce

coss along he supply chain, wih however a he same ime improving (or a

leas no deerioraing) service levels and responsiveness. These objecives

were kep in mind when considering poenial supply-side risk acors or MLC.

Afer a review o poenial risks, he research eam deemed he seveneen

risk acors as idenified earlier (Schoenherr e al. 2008) as sill represening

he curren siuaion. Furher discussions and eedback obained rom

colleagues no involved in he research and in oher uncions did no lead o

any addiional applicable acors. These seveneen risk acors, as well as heir

definiions and corresponding abbreviaions used in he ensuing discussion,

are summarized in Table 2.


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TABLE :IDENTIFIED RISK FACTORS

Risk

Factor

Code

Risk Factors Label Risk Factor Definitions

ACR ANSI ComplianceMinimum requiremens ha he companys producs mus a leas fulfill, and he risk ha he

supplier fails o mee hese requiremens.

PQR Produc QualiyLikelihood of he supplier no providing an excellen produc in erms of qualiy (measured in

erms of he number of defecive producs).

PCR Produc Cos Price ha he company pays for he produc, and he risk associaed wih a price increase.

CCR Compeior Cos

A measure of how he price ha he company receives from is suppliers compares o he price

compeiors are likely o pay for comparable inpu. I represens he risk of he compeior having a

relaive cos advanage.

DMR Demand RiskMeasures he likelihood of severe swings in demand, and he responsiveness he respecive

supply chain would exhibi in accommodaing hese swings.

SFR SupplierFulfillmen Risk

Esimaes how accurae suppliers are fulfilling he orders, boh in erms of qualiy, quaniyand puncualiy.

LGR Logisics RiskRisks due o organizaional aspecs of logisics, such as paperwork involved, scheduling roues,

deermining wha o ship wih wha mode and a wha ime, selecion of pors and carriers, ec.

TBROn-Time and

On-Budge Risk

Deals direcly wih he abiliy of he supplier o deliver he produc o he company on-ime

and on-budge, i.e. wihou delays and wihou any higher coss.

OFROrder Fulfillmen

Risk Addresses he risk ha producs ordered are no delivered in he quaniy and qualiy demanded.


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VOLUME 49, NO. 1 3

WPRWrong Parner

Risk

Risk of engaging wih he wrong parner, and hus, due o heir poenial poor performance,

no being able o mee cusomer needs and/or demand requiremens.

OSR Overseas RiskConsiders he possibiliy ha relaionships overseas are more difficul o manage, due o for

example culural and poliical facors, bu also disance and language barriers.

SUR Supplier RiskConcerned wih he chance ha he supplier goes ou of business / goes bankrup as a resul

of poor managemen capabiliy.

SSMSuppliers

Supplier Risk

Deals wih how he immediae (or Tier 1) supplier manages is sources of supply and he associaed risk,

i.e. he buying companys second and possibly also hird and fourh ier suppliers.

EIREngineering and

Innovaion Risk

Concerned wih he suppliers capabiliy o collaborae on design, he poenial for join

innovaions, as well as he poenial for he leakage of confidenial informaion shared.

TPR TransporaionRisk

Measures he exen o which carriers can have problems in he physical movemen of goods.

SVR Sovereign RiskAssesses he risk associaed wih giving up conrol when going overseas, including poenial

poliical insabiliy, srikes, and sringen governmen regulaions

NTRNaural Disasers/

TerrorismLikelihood ha he supply chain can fall vicim o naural disasers and errorism atacks.

Risk

Factor

Code

Risk Factors Label Risk Factor Definitions

TABLE :IDENTIFIED RISK FACTORS CONTINUED

Insrumenal a his sage, peraining o he inegraion o FMEA wih heSCRMP, was ha as much deail as possible or each risk acor was colleced.

The opporuniy o gaher his inormaion a his sage is given, since here

is usually an underlying raionale or a eam member o sugges a paricular

risk as being imporan. Specifically, or each idenified risk acor, we aimed o

define he poenial ailure mode (i.e. how he specific risk maniess isel in

erms o supply chain ailures or breakdowns or MLC), poenial causes o he

ailure or breakdown, poenial effecs or consequences, possible prevenive

acions, and he cos o such acion. These caegories were revised and/or


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complemened wih addiional inormaion ha was riggered in he ensuing

sages o he SCRMP. For example, or he on-ime and on-budge risk

(risk acor code TBR; his risk acor will be used as an illusraive example

hroughou), which addresses he abiliy o he supplier o deliver he produc

wihou delays and wihou any higher coss, he poenial ailure mode or MLC

was idenified as no having wha is needed and when i is needed. Poenial

causes were atribued o he lack o manuacuring capabiliy and ransporaion

flexibiliy on he par o he supplier. Effecs o his ailure were lower saey sock

levels, los sales, and higher replacemen coss. Prevenive acions ha were

idenified included a closer working relaionship wih logisics personnel and

suppliers, he provision o help o he supplier or improving heir processes and

capabiliies, and more effecive communicaion. The implemenaion o hese

iniiaives was esimaed o cos $60,000. The FMEA analysis or all risk acors

peraining o he China-Midwes supply chain is presened in Appendix A.

Step 2: Assess the Severities of Consequences

In he second sep o he SCRMP we uilized Miliary Sandard 882C (1993)

o define he caegories o consequence severiies ino caasrophic, criical,

marginal or negligible (yielding he Risk Consequence Index). The our-

level sandard was adaped o MLC in a cross-uncional ashion involving

he academics and he CEO o he company. As such, a caasrophic

consequence (risk severiy index = 4) was described as he plan being shu

down, equivalen also o no delivery occurring or more han one monh due

o lack o componens and zero saey sock levels. The consequence wasdescribed as criical (risk severiy index = 3) i he process slowed down or i

no delivery was received or more han one week due o lack o componens

and zero saey sock levels. A siuaion wih decreasing service levels and

depleing saey sock was described as marginal (risk severiy index = 2),

while an insance wih service levels no being impaced due o sufficien

saey sock levels was considered negligible (risk severiy index = 1). These

degrees o magniude can be adaped based on he individual companys

assessmen o wha would represen a caasrophic, criical, marginal or

negligible even; according o he miliary sandard, he wors possible even

is considered in each caegory. Wih hese definiions in place, we evaluaed

each o he seveneen risk acors along heir respecive consequence severiyon he objecives o (a) cos reducion and (b) responsiveness (service levels).

Since i was our inen o assess he curren offshoring sraegy

2014 apics production and inventory management journal

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