a quantitative model for the introduction of rfid in the fast moving consumer goods supply chain
DESCRIPTION
A Quantitative Model for the Introduction of RFID in the Fast Moving Consumer Goods Supply ChainTRANSCRIPT
International Journal of Operations & Production ManagementEmerald Article: A quantitative model for the introduction of RFId in the fast moving consumer goods supply chain: Are there any profits?Giovanni Miragliotta, Alessandro Perego, Angela Tumino
Article information:
To cite this document: Giovanni Miragliotta, Alessandro Perego, Angela Tumino, (2009),"A quantitative model for the introduction of RFId in the fast moving consumer goods supply chain: Are there any profits?", International Journal of Operations & Production Management, Vol. 29 Iss: 10 pp. 1049 - 1082
Permanent link to this document: http://dx.doi.org/10.1108/01443570910993483
Downloaded on: 28-01-2013
References: This document contains references to 64 other documents
Citations: This document has been cited by 7 other documents
To copy this document: [email protected]
This document has been downloaded 2107 times since 2009. *
Users who downloaded this Article also downloaded: *
Giovanni Miragliotta, Alessandro Perego, Angela Tumino, (2009),"A quantitative model for the introduction of RFId in the fast moving consumer goods supply chain: Are there any profits?", International Journal of Operations & Production Management, Vol. 29 Iss: 10 pp. 1049 - 1082http://dx.doi.org/10.1108/01443570910993483
Giovanni Miragliotta, Alessandro Perego, Angela Tumino, (2009),"A quantitative model for the introduction of RFId in the fast moving consumer goods supply chain: Are there any profits?", International Journal of Operations & Production Management, Vol. 29 Iss: 10 pp. 1049 - 1082http://dx.doi.org/10.1108/01443570910993483
Giovanni Miragliotta, Alessandro Perego, Angela Tumino, (2009),"A quantitative model for the introduction of RFId in the fast moving consumer goods supply chain: Are there any profits?", International Journal of Operations & Production Management, Vol. 29 Iss: 10 pp. 1049 - 1082http://dx.doi.org/10.1108/01443570910993483
Access to this document was granted through an Emerald subscription provided by INSTITUTE OF BUSINESS ADMINISTRATION
For Authors: If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service. Information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.comWith over forty years' experience, Emerald Group Publishing is a leading independent publisher of global research with impact in business, society, public policy and education. In total, Emerald publishes over 275 journals and more than 130 book series, as well as an extensive range of online products and services. Emerald is both COUNTER 3 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.
*Related content and download information correct at time of download.
A quantitative modelfor the introduction of RFIdin the fast moving consumer
goods supply chainAre there any profits?
Giovanni Miragliotta, Alessandro Perego and Angela TuminoDepartment of Management, Economics and Industrial Engineering,
Politecnico di Milano, Milano, Italy
Abstract
Purpose – The purpose of this paper is to describe an analytical model to assess the costs andbenefits of radio frequency identification (RFId) applications in the fast moving consumer goods(FMCG) supply chain.
Design/methodology/approach – The paper is based on an in-depth literature review and aclassification of the main contributions regarding the assessment of RFId applications. The impact ofRFId technology on supply chain processes has been modelled using an activity-based approach. Anextensive, six-month discussion and refinement process with the logistics and supply chain managersof 30 FMCG companies is conducted to validate the model and to collect the required inputs.
Findings – Pallet- and case-level taggings have been explored. The former scenario shows limitedbenefits, whereas the actual potential of RFId becomes clear in the latter. The profitability of theseprojects is significantly affected by the costs of RFId tags and by the characteristics of the base-linesupply chain in terms of efficiency, quality requirements and, of course, product features. The modelprovides a clear assessment of how and when a positive return on investment can be achieved, evenwith today’s technology (in terms of costs and performances).
Originality/value – This is one of the first attempts to provide a comprehensive analysis of the costsand benefits of an RFId application, taking into account all the major factors involved. The model canbe a valuable support to manufacturers and retailers in evaluating their investments.
Keywords Radio frequencies, Fast moving consumer goods, Return on investment
Paper type Research paper
1. IntroductionRadio frequency identification (RFId) technology is considered to have great potential toimprove the efficiency and accuracy of many processes and even to enable a substantialredesign of the supply chain in the fast moving consumer goods (FMCG) industry(Angeles, 2005; Dutta et al., 2007; Cannon et al., 2008; Cantor and Macdonald, 2009).
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0144-3577.htm
The authors would like to thank GS1 Italy, which supported this research project, and BoltonAlimentari, Cameo, Campari, Carrefour, Conad, Coop Italia, Crai, Despar-Aligroup,Despar-Sadas, Diageo, Esselunga, Fater, Ferrero, Fhp, GlaxoSmithKline, Heineken, Henkel,Johnson&Wax, Lavazza, Lombardini, Manetti&Roberts, Nestle, Nordiconad, Procter&Gamble,Selex, Sisa, and Unilever for their precious collaboration in the research workgroup. The authorswould also like to thank the reviewers for their valuable comments and suggestions.
The introductionof RFId in FMCG
supply chain
1049
Received 3 June 2008Revised 2 February 2009
Accepted 22 May 2009
International Journal of Operations &Production Management
Vol. 29 No. 10, 2009pp. 1049-1082
q Emerald Group Publishing Limited0144-3577
DOI 10.1108/01443570910993483
Immediately, after Wal-Mart’s mandate in 2003, a substantial interest in RFId aroseworldwide, leading to several pilot projects introduced by supply chain leaders (e.g.Metro Group, Tesco, Sainsbury’s, Marks & Spencer, Albertson, Target, Gillette, Procter& Gamble). These projects mainly aim to provide initial results on the strengths andweaknesses of RFId. Indeed, many companies are still biding time, primarily because ofa lack of confidence in the benefits and, consequently, in the impact on return oninvestment (ROI; Dutta et al., 2007; Reyes and Jaska, 2007; RFId-IPO, 2007), which, inturn, generates a greater perception of risk. For instance, upstream suppliers generallyfeel they are required to invest in this technology only to pass on the benefits to theirpowerful retail customers and they still have a limited sense of how to exploit RFIdwithin their own processes (Agarwal, 2001).
In the Italian FMCG supply chain, there are only a few examples of feasibilityanalyses or tests (RFId-IPO, 2005, 2006, 2007). Despite their declarations of interest inRFId, manufacturers and retailers are reluctant to invest in this technology. Among thevarious barriers they usually cite – along with technological and organisational issues– the difficulty in assessing the value of RFId applications is the most common(Agarwal, 2001; RFId-IPO, 2007, Cannon et al., 2008). This is why in 2005, GS1 Italy,the Italian subsidiary of GS1, the global standard organisation which promotes theelectronic product code (EPC) RFId standard for FMCG, decided to set up a workinggroup to assess and quantify the costs and benefits stemming from the application ofRFId in the FMCG supply chain. The working group included Politecnico di Milano,Accenture and representatives of 30 major manufacturers and retailers operating inItaly (e.g. Henkel, Lavazza, Procter&Gamble, Unilever, Carrefour, Coop Italia,Nordiconad, Esselunga), who provided the knowledge input for the subsequent workpresented in this paper.
Following these introductory remarks, the paper is structured as follows: Section 2provides a classification of the main scientific contributions on the evaluation of RFIdapplications. After the presentation of the objectives and the methodology in Sections 3and 4, respectively, Section 5 describes our original model. Section 6 describes in detailthe results obtained when applying the model to a test-bed supply chain and discussesthe main business implications. Finally, Section 7 draws some conclusions andsuggests future research paths.
2. Literature reviewThere is a growing body of literature both in the academic and generalist press on theevaluation of RFId projects. Even if there are several qualitative analyses of thebenefits of RFId applications, there are few sound, quantitative assessments, as moststudies are incomplete and based only on a limited number of test projects. In general,the available contributions on the evaluation of RFId projects can be classified asfollows:
. qualitative analyses of the value of RFId;
. quantitative analyses based on empirical evidence (e.g. case studies) or expertevaluations; and
. quantitative studies based on structured assessment models.
These contributions will be briefly discussed in the following paragraphs, payingparticular attention to those focusing on the FMCG supply chain. A taxonomy of the
IJOPM29,10
1050
quantitative models is given at the end of the section, in order to position the presentpaper within the existent body of knowledge.
2.1 Qualitative analyses of the value of RFIdThe papers in this cluster provide a general introduction to RFId technology and aqualitative analysis of its implications. This is the largest group in terms of number ofavailable papers, which can be classified into three sub-groups.
A first set of contributions describes the strategic implications of applying RFId tosupply chain management (Keen and Mackinttosh, 2001; Srivastava, 2004;Gunasekaran and Ngai, 2005; Michael and McCathie, 2005; Pramatari et al., 2005;Barratt and Choi, 2007; Boone and Ganeshan, 2007; Bhattacharya et al., 2007). Inparticular, some of these studies focus on the advantages and disadvantages of RFId,both in general (Srivastava, 2004; Michael and McCathie, 2005) and within specificindustries (e.g. the retail industry, Bhattacharya et al., 2007). RFId technology is alsorecognised as a facilitator in collaboration practices such as collaborative planning,forecasting, and replenishment (CPFR) and an enabler of “smarter supply and demandchains” (Pramatari et al., 2005). With respect to supply chain strategy, Gunasekaranand Ngai (2005) maintain that RFId may support the development of emerging supplychain configurations.
Other papers provide a taxonomy and a qualitative evaluation of the benefitsachievable through RFId technology (Helders and Vethman, 2003; Lapide, 2004;Angeles, 2005; Curtin et al., 2007; Sellitto et al., 2007). Curtin et al. (2007), for example,analyse how RFId affects individuals, business processes, organisations and markets.The authors focus only on the benefits of the technology, while others seek to offer awider (qualitative) comparison of the strengths and weaknesses of RFId (Jones et al.,2005; Attaran, 2007).
Finally, various contributions analyse the implementation process and the ensuingissues. The focus in such papers is two-fold: on the one hand, the implementation ofRFId technology is described and analysed (Johansson, 2005; Curtin et al., 2007), whileother studies aim to provide a roadmap for successful adoption (Gale et al., 2004;Chuang and Shaw, 2005; Wu et al., 2006; Reyes and Jaska, 2007).
2.2 Quantitative analyses based on empirical evidence or expert evaluationsThese studies seek to provide both a taxonomy and a quantitative evaluation of thebenefits stemming from the adoption of RFId. Since these analyses are mainly based onempirical evidence (e.g. case studies, field studies), most refer to the FMCG supplychain, where the first pilot projects were launched.
A first important group of contributions is found in the white papers issued by theAuto-ID Labs, a global network of academic research laboratories in the field of RFId.The labs comprise seven of the world’s leading research universities, including theMassachusetts Institute of Technology and the University of Cambridge. Some of thestudies aim to assess the impact of RFId on the supply chain as a whole (Chappell et al.,2002b), while others focus on specific players such as manufacturers (Chappell et al.,2003b), distribution centres (DCs) (Alexander et al., 2003a) or the retail stores (Chappellet al., 2003a). Other papers look at specific issues that are particularly relevant to the FMCGsupply chain, e.g. product shelf-availability and out-of-stock (Alexander et al., 2002),
The introductionof RFId in FMCG
supply chain
1051
shrinkage (Alexander et al., 2003c), product obsolescence (Alexander et al., 2003b) anddemand planning (Chappell et al., 2002a).
In addition to the Auto-ID Labs papers, other work can also be considered in thisgroup (Karkkainen, 2003; Loebbecke, 2005; Hardgrave et al., 2006; Delen et al., 2007;Langer et al., 2007; Fosso Wamba et al., 2008; Kim et al., 2008). Loebbecke (2005)examines RFId applications in the retail supply chain using the example of the earlyMetro Group pilots in Germany and suggests that IT innovations (including RFId) incombination with the marketing paradigm of “future stores” have contributed to asales increase and a reduction in out-of-stock. On the basis of the RFId trial conductedat Sainsbury’s, Karkkainen (2003) discusses the potential of RFId technology toincrease efficiency in the supply chain of short-life products. Hardgrave et al. (2006)present a study commissioned by Wal-Mart in which they examine the influence ofRFId on out-of-stocks.
2.3 Quantitative studies based on structured assessment modelsThese studies aim to develop mathematical and simulation models to assess the impactof RFId on supply chain performance. Some contributions focus on just a few areas ofimpact, e.g. stock out reduction or improved inventory management (Atali et al., 2004;Fleisch and Tellkamp, 2005; Bendavid et al., 2007; Gaukler et al., 2007; Lee and Ozer,2007; de Kok et al., 2008; Liu et al., 2008, Szmerekovsky and Zhang, 2008; Rekik et al.,2008a, b; Wang et al., 2008). For instance, Fleisch and Tellkamp (2005) analyse theimpact of RFId on inventory accuracy, arguing that the technology might reduce thecosts of shortage, holding, and handling, as well as the consequences of not detectingmissing or mismatched items in the incoming delivery. Further contributions in thisdirection are provided by Atali et al. (2004) and Lee and Ozer (2007), who use a simulationmodel to quantify the “indirect” benefits of RFId in inventory accuracy, shelfreplenishment policy and inventory visibility across the entire supply chain. Gaukleret al. (2007) present a model to assess the benefits of “item level RFId” for two supplychain members. The authors also investigate the improvement in inventoryreplenishment decision making as a result of increased “information visibility”.Nevertheless, comprehensive analyses of the costs and benefits of an RFId applicationthat go beyond an evaluation of only a few factors are relatively uncommon in literature.
2.4 Quantitative studies: a detailed review and a taxonomyThe previous paragraphs provide a general picture of the main analyses on theevaluation of RFId projects. Since the present study concerns a quantitativeassessment model to evaluate RFId applications in the FMCG supply chain, a deeperinsight into the quantitative studies presented above is of fundamental importance tohighlight the innovative contribution provided by this paper. In particular, fourdimensions of analysis have been introduced:
(1) Processes. Which processes are considered by the authors (e.g. materialhandling, inventory management, and demand planning)?
(2) Supply chain topology. Which types of supply chain have been studied (e.g.single company, dyadic two-tier supply chain, networked multi-echelon supplychain)?
(3) Methodology. How do the authors assess the benefits (e.g. through case study,survey, mathematical model, simulation, and experts’ evaluation)?
IJOPM29,10
1052
(4) RFId scenarios. Which are the main technological scenarios examined (RFId onpallet loads, RFId on cases, RFId on items, RFId on returnable assets)?
Table I gives the taxonomy developed as a function of the above dimensions. While thefirst attempts were mainly based on empirical evidence, more attention has been paidin recent years to mathematical and simulation models. However, these models mainlyanalyse individual companies and do not provide an evaluation of the overall benefitsalong the supply chain. Moreover, the analyses mainly consider item level tagging(ILT), which both for technological and economic reasons is still somewhat futuristic inthe FMCG industry. Finally, most of the models focus on a limited subset of benefits(e.g. out-of-stock reduction, inventory replenishment) and do not provide acomprehensive evaluation of the profitability of the investment. Indeed, only a fewanalyses aim to evaluate the overall benefits introduced by case-level or pallet-leveltagging. An attempt in this direction was made by Tellkamp (2003) who proposed aROI model for the FMCG supply chain. Unfortunately, the author does not describe theevaluation model but rather presents only the results obtained in the item-level taggingscenario. Veeramani et al. (2008), on the other hand, analyse only the receiving andshipping processes, and do not consider the other benefits that can be obtained in theremaining material handling activities (e.g. putting away and picking).
The model proposed in the present paper seeks to address many of theshortcomings in the existing contributions. First, the study refers to a realistic supplychain, including all its players (manufacturers and retailers) and nodes (plants, plantwarehouses, manufacturers’ DCs, retailers’ DCs and supermarkets). Second, it includesa complete and detailed set of activities, both operational (e.g. receiving, putting away,and picking) and administrative (e.g. addressing the contentious issues between twosupply chain members, such as a dispute about the quality and quantity of the goodsreceived). Consistent with the process model, an overall cost function can be calculatedas the key performance indicator for RFId introduction. Furthermore, the paperpresents an in-depth analysis of the results, including a variance analysis, thusenabling a broad understanding of both the outcomes and the managerial implicationsof RFId investment decisions. Finally, the structure of the model and its main inputsare described in detail, so as to ensure the contribution is scientifically verifiable.
3. Objectives and research questionsThis paper aims to fill the gap highlighted in Table I, i.e. it aims to present a generalmodel which analyses the impact of the RFId introduction on a FMCG supply chainand its processes. In this sense, the model is meant to investigate the mechanisms bywhich RFId impacts on the current supply chain processes and help to answer thefollowing research questions:
RQ1. What are the main benefits afforded by an RFId application? Whichprocesses/activities and which actors in the chain are expected to benefit themost from RFId adoption?
RQ2. What are the main costs (both capital and operational expenditures) of suchan application in various sensible (i.e. non-futuristic) technologicalscenarios? In which processes/activities and stages of the chain are theyconcentrated?
The introductionof RFId in FMCG
supply chain
1053
RF
Idsc
enar
ios
Pap
erP
roce
sses
Su
pp
lych
ain
top
olog
yM
eth
odol
ogy
Pal
let
Cas
eIt
emA
sset
Ale
xan
der
etal.
(200
2)In
ven
tory
man
agem
ent
–ou
t-of
-sto
ckF
our-
tier
sup
ply
chai
n(f
our
pla
nts
,te
nm
anu
fact
ure
rD
Cs,
fou
rre
tail
erD
Cs,
800
poi
nts
ofsa
le)
Ex
per
tsX
Ch
app
elet
al.
(200
2a)
Mat
eria
lh
and
lin
gS
ing
leco
mp
any
–re
tail
erC
ase
stu
dy
XC
hap
pelet
al.
(200
2b)
Dem
and
pla
nn
ing
Sin
gle
com
pan
y–
man
ufa
ctu
rer
Cas
est
ud
yX
Ale
xan
der
etal.
(200
3a)
Mat
eria
lh
and
lin
gF
our-
tier
sup
ply
chai
n(f
our
pla
nts
,te
nm
anu
fact
ure
rD
Cs,
fiv
ere
tail
erD
Cs,
800
poi
nts
ofsa
le)
Ex
per
ts,
surv
eyX
XX
Ale
xan
der
etal.
(200
3b)
Inv
ento
rym
anag
emen
t–
shri
nk
age
Fou
r-ti
ersu
pp
lych
ain
(pla
nts
,m
anu
fact
ure
rD
Cs,
reta
iler
DC
s,p
oin
tsof
sale
)
Ex
per
tsX
X
Ale
xan
der
etal.
(200
3c)
Inv
ento
rym
anag
emen
t–
obso
lesc
ence
Fou
r-ti
ersu
pp
lych
ain
(pla
nts
,m
anu
fact
ure
rD
Cs,
reta
iler
DC
s,p
oin
tsof
sale
)
Ex
per
tsX
X
Ch
app
ellet
al.
(200
3a)
Pro
du
ctio
nm
anag
emen
t,in
ven
tory
man
agem
ent,
mat
eria
lh
and
lin
g
Sin
gle
com
pan
y–
man
ufa
ctu
rer
Cas
est
ud
yX
X
Ch
app
ellet
al.
(200
3b)
Mat
eria
lh
and
lin
gan
din
ven
tory
man
agem
ent
Poi
nt
ofsa
leC
ase
stu
dy
XX
Kar
kk
ain
en(2
003)
Mat
eria
lh
and
lin
gT
hre
e-ti
ersu
pp
lych
ain
(pla
nt,
reta
iler
DC
,p
oin
tof
sale
)C
ase
stu
dy
X
Tel
lkam
p(2
003)
Mat
eria
lh
and
lin
gan
din
ven
tory
man
agem
ent
Fou
r-ti
ersu
pp
lych
ain
(pla
nts
,m
anu
fact
ure
rD
Cs,
reta
iler
DC
s,p
oin
tsof
sale
)
Mat
hem
atic
alm
odel
XX
X
Ata
liet
al.
(200
4)In
ven
tory
man
agem
ent
Sin
gle
com
pan
yS
imu
lati
onX
Fle
isch
and
Tel
lkam
p(2
005)
Inv
ento
rym
anag
emen
tT
hre
e-ti
ersu
pp
lych
ain
(pla
nt,
dis
trib
uto
r,re
tail
erD
C)
Sim
ula
tion
X
Loe
bb
eck
e(2
005)
Mat
eria
lh
and
lin
gan
din
ven
tory
man
agem
ent
Sin
gle
com
pan
y–
reta
iler
Cas
est
ud
yX
X
Har
dg
rav
eet
al.
(200
6)In
ven
tory
man
agem
ent
–ou
t-of
-sto
ckP
oin
tof
sale
Cas
est
ud
yX
(continued
)
Table I.An overview of the mainquantitative studies
IJOPM29,10
1054
RF
Idsc
enar
ios
Pap
erP
roce
sses
Su
pp
lych
ain
top
olog
yM
eth
odol
ogy
Pal
let
Cas
eIt
emA
sset
Ben
dav
idet
al.
(200
7)D
eman
dp
lan
nin
g,
pro
du
ctio
nm
anag
emen
t,m
ater
ial
han
dli
ng
Fiv
e-ti
ersu
pp
lych
ain
(on
ep
lan
t,tw
ore
tail
erD
Cs,
90p
oin
tsof
sale
,on
ed
istr
ibu
tor,
one
recy
cler
)–
hig
hv
alu
ep
rod
uct
s
Mat
hem
atic
alm
odel
,ca
sest
ud
yX
Gau
kle
ret
al.
(200
7)In
ven
tory
man
agem
ent
Sin
gle
com
pan
yS
imu
lati
onX
Lan
ger
etal.
(200
7)M
ater
ial
han
dli
ng
–sh
ipp
ing
Sin
gle
com
pan
yC
ase
stu
dy
XL
eean
dO
zer
(200
7)In
ven
tory
man
agem
ent
Sin
gle
com
pan
yS
imu
lati
onX
de
Kok
etal.
(200
8)In
ven
tory
man
agem
ent
–sh
rin
kag
eS
ing
leco
mp
any
Mat
hem
atic
alm
odel
X
Fos
soW
amb
aet
al.
(200
8)M
ater
ial
han
dli
ng
–sh
ipp
ing
,re
ceiv
ing
,p
ut-
away
Tw
o-ti
ersu
pp
lych
ain
(tw
oman
ufa
ctu
rers
,on
ere
tail
er)
Cas
est
ud
yX
Kim
etal.
(200
8)In
ven
tory
man
agem
ent,
mat
eria
lh
and
lin
g–
stor
e,d
eman
dp
lan
nin
g
Sin
gle
com
pan
y–
reta
iler
Su
rvey
X
Liu
etal.
(200
8)In
ven
tory
man
agem
ent
–d
yn
amic
pri
cin
gP
oin
tof
sale
Sim
ula
tion
X
Rek
iket
al.
(200
8a)
Inv
ento
rym
anag
emen
t–
shri
nk
age
Poi
nt
ofsa
leM
ath
emat
ical
mod
elX
Rek
iket
al.
(200
8b)
Inv
ento
rym
anag
emen
t–
inv
ento
ryin
accu
racy
Poi
nt
ofsa
leM
ath
emat
ical
mod
elX
Szm
erek
ovsk
yan
dZ
han
g(2
008)
Inv
ento
rym
anag
emen
tT
wo-
tier
sup
ply
chai
n(o
ne
man
ufa
ctu
rer,
one
reta
iler
)M
ath
emat
ical
mod
elX
Vee
ram
aniet
al.
(200
8)M
ater
ial
han
dli
ng
,in
ven
tory
man
agem
ent,
adm
inis
trat
ive
issu
es
Th
ree-
tier
sup
ply
chai
n(o
ne
pla
nt,
one
man
ufa
ctu
rer
DC
,on
ere
tail
erD
C)
Mat
hem
atic
alm
odel
X
Wan
get
al.
(200
8)In
ven
tory
man
agem
ent
–re
ple
nis
hm
ent
Sin
gle
com
pan
yS
imu
lati
onX
(Th
isp
aper
)M
ater
ial
han
dli
ng
,in
ven
tory
man
agem
ent,
adm
inis
trat
ive
issu
es
Fiv
e-ti
ersu
pp
lych
ain
(pla
nts
,p
lan
tw
areh
ouse
s,m
anu
fact
ure
rD
Cs,
reta
iler
DC
s,p
oin
tsof
sale
)
Mat
hem
atic
alm
odel
XX
Table I.
The introductionof RFId in FMCG
supply chain
1055
RQ3. Which parameters have the greatest influence on RFId project profitability?
RQ4. What are the likely RFId-adoption paths that could be successful in thisindustry?
To answer these questions, the model is divided into two parts, namely the assessmentof benefits and the assessment of capital and operational expenditures. Thecombination of these two components will allow us to evaluate the profitability of theinvestment and so address our research issues (Figure 1).
4. Research methodologyAs stated in the Introduction, a research project was started in 2005 with the aim ofdeveloping a general model to analyse the impact of the introduction of RFId on theItalian FMCG supply chain and its processes. The developed model intended toreproduce the entire supply chain and include all the relevant activity drivers, so as tobe able to assess in depth the costs and benefits of RFId adoption and possibly supportthe decision-making process for managers in this industry.
Since a model of general validity was sought, an analytical approach rather than adiscrete time simulation was preferred because of the former’s flexibility and thegreater transparency of the assumed relationships (it is enough to scan the equations inthe model to understand the hypothesised relationships in terms of direction andintensity). The research programme was therefore divided into three phases. The firstphase (February 2005-October 2005) was devoted to assessing in detail the FMCGsupply chain processes. The second (November 2005-April 2006) concentrated on thedevelopment of the model, its coding into a usable IT tool and its validation. Finally,the third phase (May 2006-December 2006) defined a proper test-bed and a set ofrelevant RFId implementation scenarios (including sensitivity analysis of the mostuncertain parameters), ran the model and analysed the results. The three phases usedad hoc research methodologies in line with their specific objectives.
In the first phase, direct interviews with companies in the FMCG industry werecarried out. In this regard, the cooperation with GS1 Italy was fundamental in gainingaccess to the most important actors in the Italian market (see acknowledgements for alist of the companies involved). The interviews involved the distribution andwarehousing managers, chief controller officers, and operational employees of
Figure 1.The structure of theassessment model
Benefits(e.g. impact on supply
chain productivity,reduction of out of stocks)
Capital and operationalexpenditures
(e.g. cost of the tags,maintenance cost)
Investment profitability(e.g. net present value,
pay-back time)
IJOPM29,10
1056
27 prominent companies. Semi-formalised questionnaires have been preferred, sincethey gave us the possibility to discuss the proposed questions and topics more freely.A draft of the questionnaire was prepared and validated with a couple of testcompanies, in order to check its completeness and clarity. The final questionnaire wassent to the interviewees before the meetings.
The main objective was to get a broad understanding of the supply chain operatingprocesses in the respective companies, in order to generalise these activities into aflexible, parametric model. Such a model should ideally be flexible enough to reproduceall of the observed processes, together with any other sensible configurations thatmight have been overlooked during empirical observation (Fosso Wamba et al., 2008).
In the second phase the interviews and the collected data were used to develop theanalytical model. In this task, the literature review provided some useful inputs, but theactivities relied mainly on the experience of the research group (Miragliotta et al., 2007,2008). The modelling methodology is based on the well-established activity-basedmodelling approach (Kaplan and Bruns, 1987), as used by other authors (Subirana et al.,2003; Laubacher et al., 2005). The framework of the model is illustrated in detail below.The second phase ended with a validation test, in which we applied the model toevaluate the cost structure of various real supply chains. The observed errors wereconsidered to be unbiased and acceptable.
In the third phase, a focus-group methodology was adopted to define the supplychain to be modelled (how many stages, which relationships, which volumes, etc.), theRFId scenarios to be tested, and the range of parameters for the sensitivity analysis.More specifically, the final configuration was obtained by sharing ideas with seniormanagers from the 27 collaborating companies, and using a modified Delphi technique.More specifically, two sub-groups with representatives of companies operating in thesame stage of the chain (i.e. producers and retailers) were set-up. Six meetings wereorganised on a monthly basis and group interviews, i.e. guided discussions addressingthe above-illustrated topics, were carried out. In this last phase, computation runs werealso performed using the deterministic data set generated by the process above. Forthis reason, no statistical analysis was required, while the sensitivity analysis helped tohighlight non-linear relationships in the overall model and to point out the most crucialparameters that will affect the investment decision.
5. The model5.1 The reference supply chain and the studied processesThe reference supply chain is a generalisation of the most common FMCG supplychain (BCG-ECR, 1996). It is structured in five stages, three regarding themanufacturer’s network (plant, plant warehouse, and DC) and two involving theretailer’s network (DC and point of sale) (Figure 2). It is assumed that products flowalong this supply chain in full pallet loads until they are picked by case in the retailerDC. The point of sale receives mixed pallet loads.
The model includes all the handling activities from the end of the manufacturerproduction line to the receiving docks at the point of sale. At the end of themanufacturing process, an identification label is applied on every case and pallet load toensure their traceability along the supply chain. The full pallet loads are transferred tothe plant warehouse, usually located in proximity to the plant, where they aretemporarily stocked and then shipped in full truck loads to the manufacturer DC.
The introductionof RFId in FMCG
supply chain
1057
Here, the pallet loads are received and checked against possible errors before beingmoved to their storage location. Using the orders received (picking list), the forkliftdriver extracts the full pallet loads to be shipped. The pallet loads are then moved to theshipping dock, where they are checked and loaded onto the truck. The same processesare carried out in the retailer DC, but here the order assembly is more complex, sinceretail stores receive mixed pallet loads. Therefore, the full pallet loads have to beunwrapped and individual cases are picked to prepare the order. When the productsarrive at the point of sale, some are stocked in the store backroom, while others aredirectly moved to the sales area, where they are put on the shelves. In addition to the dailyactivities described above, inventory controls are periodically carried out in all thewarehouses in order to guarantee correspondence between the physical inventory andthe data in the information systems.
Table II summarises the macro-activities considered for each stage of this chain. Allthese activities have been studied in depth and the associated consumption of resourceshas been modelled using an activity-based approach. The results were extensivelyreviewed and validated by the logistics directors of the FMCG companies who joined theworking team in the first stage of the study (Methodology). Transportation activitieshave been excluded on the assumption that they are substantially unaffected by the
Figure 2.The reference supplychain
Manufacturer Retailer
Plant PoSDistributioncentre
Distributioncentre
Plantwarehouse
Supply chainManufacturer Retailer
Macro-activity Plant Plant warehouse DC DC Point of sale
Packaging (production line end) XReceiving X X X XPutting away Xa X X X XStorage X X XInventory controls X X XOrder assembly X X XShipping X X XComplaints management X X XOut-of-stock management XContentious issues management X XShrinkage management X X
Note: aIn the manufacturer plant the putting away activity includes only the movement of the palletloads to the plant warehouse
Table II.The macro-activitiesconsidered in the model
IJOPM29,10
1058
introduction of RFId, with the exception of reduced reverse flows due to reduced deliveryerrors. Since the largest cost of return activity is on the management side and not on thetransportation side, this assumption is quite acceptable.
5.2 The RFId scenariosTwo RFId scenarios using different levels of tagging have been analysed:
(1) RFId tags placed on pallet loads (R1); and
(2) RFId on both pallet loads and cases (R2).
In both scenarios, the RFId tags are initialized at the end of the production lineaccording to the EPC standard, which states that a unique identifier must be written onthe tag applied to pallet loads or cases. In the R1 scenario (tags only on pallet loads), thebenefits at the point of sale are negligible and, therefore, this echelon of the chain is notincluded in the computations. The impacts on the point of sale, at least up to the pointat which the cases are broken into single items, are, however, considered in the R2scenario.
Unlike several models in the literature (Table II), in the present proposal, we did notconsider an ILT scenario. There is much debate about the technological and economicfeasibility of such a scenario. While ILT is deemed to ensure great benefits at the point ofsale, the first two years of activity of the GS1 EPC-lab in Milan (www.indicod-ecr.it)showed that current technology performance means RFId EPC is far from a reliableidentification solution for a significant number of products. Moreover, as will be discussedin the Results section, ILT is still largely infeasible from the economic point of view. Forthese reasons, ILT has been excluded from our study, at least at this stage of thetechnology introduction process and taking account of the overall FMCG sector. Of course,different considerations would emerge for particular categories, e.g. textile-apparel,consumer electronics, or processes, e.g. promotions’ management, customer relationshipmanagement (Section 6).
For both R1 and R2, we assumed the RFId EPC technology to be perfectly reliable(i.e. 100 percent reading rate in both scenarios). This is not an unrealistic assumption.In the experimental activity of the GS1 EPC-lab, 100 percent identification of theRFId-tagged cases and pallet loads proved to be an achievable target, even withchallenging products containing liquids or metals, although some packaging redesignmight be required for certain products (EPC Lab, 2008).
As a benchmark, we chose a base-line scenario (B) which assumes that every DCand store is equipped with a Wi-Fi network and that barcodes – , i.e. standardEuropean Article Number/Uniform Code Council labels on cases and serial shippingcontainer code on pallet loads, plus barcodes on rack locations and loading/unloadingdocks – are used for all the identification needs. This is a very challenging benchmark,if we consider that several companies and nodes in the “average” FMCG supply chain(not only in Italy) do not comply with these requirements. Nevertheless, the base-linescenario was chosen in order to assess the mid-term potential value of RFId innovationnot only in today’s business scenario but also in future ones as well. As will bedescribed later, the reference scenario has been further detailed into three differentlevels of efficiency (see the sensitivity analysis below), so as to appreciate more fullythe improvement potential and the return on investment for RFId projects.
The introductionof RFId in FMCG
supply chain
1059
5.3 The assessment of the benefitsIn line with the project scope, different broad classes of benefit were considered. Indeed,we included both benefits related to savings in resources (i.e. workload, materials, andspace) and increases in productivity within the operational material handlingprocesses, as well as benefits that accrue through better visibility in the supply chain.The latter include out-of-stock reduction, reduced contentious issues and reducedproduct shrinkage. This distinction is very practical, since it differentiates thoseproductivity benefits which are directly achievable and easily measurable from thosewhich are subsequent to the increased visibility and information availability, but whichmay require a substantial re-thinking of the process in order to be fully exploited.
For the material handling productivity-related benefits, each macro-activity shownin Table II (i.e. packaging, receiving, putting away, storage, inventory controls, orderassembly, shipping, and complaints management) was first split into a hierarchy ofelementary activities. Then, the impact of RFId in terms of reduced resourcerequirements based on relevant operational resource consumption drivers wasevaluated by leveraging the experience gathered at the EPC-Lab. Conversely, forvisibility-related benefits, an articulated cause-effect relationship was derived from theliterature and used as the basis for the development of the assessment model.
For the sake of illustration, the model regarding the “Receiving activity in the retailerDC” (material-handling productivity-related benefits) will be detailed here. Six standardactivities were considered: truck acceptance, truck unloading, re-labelling (whenneeded), controls, re-palletisation (when needed), and definition of the pallet loadstocking location (Table III). Upon arrival at the retailer DC, the truck driver hands theshipping documents to the receptionist. Then, the truck is opened and unloaded at thecorrect dock. If a non-standard bar-code serial shipping container code (SSCC) is used,the retailer has to print new labels and apply these to the pallet loads, in order to supportthe internal material handling activities. Then, controls are performed to check that themanufacturer has shipped the right products and that the expiry date is acceptable.Sometimes, pallet loads need to be lowered (re-palletised) to fit within the warehousingracks. Finally, all data are recorded in the information system, the pallet location in thewarehouse is identified and goods are put-away. Non-standard activities – e.g. themanagement of an incorrect shipment – have also been modelled. For each elementaryactivity, the proper resource consumption drivers have been indicated and thecomputation formula to assess the overall “Receiving cost” (cost_receiving in Table III)obtained. Application of this formula to the various RFId supply chain scenarios allowsus to estimate the cost differences and thus the productivity-related benefits.
The structure of the model regarding all the other material handling activities (i.e.packaging, putting away, storage, inventory controls, order assembly, shipping, andcomplaints management) is similar. Overall, more than 300 cost drivers are required asinputs to the model, which includes about 200 elementary computation formulas. Forthe interested reader, the complete model is accessible on request and made availablefor self-consultation on the “White Papers” page at www.rfidsolutioncenter.it
As regards the visibility-related benefits, i.e. stock out reduction, reducedcontentious issues and reduced product shrinkage, all the causal models that have beendeveloped and the related assessment formulas are presented below, as these issues arecovered less in literature, and the models are more specific.
IJOPM29,10
1060
Mac
ro-
acti
vit
yA
ctiv
ity
Ele
men
tary
acti
vit
yR
esou
rce
con
sum
pti
ond
riv
ers
For
mu
la
RetailerDC
Rec
eiv
ing
Tru
ckac
cep
tan
ceT
ruck
acce
pta
nce
Tim
eto
acce
pt
the
ente
rin
gtr
uck
(t_
acc)
Tru
cku
nlo
adin
gT
ruck
un
load
ing
Tim
eto
un
load
the
tru
ck(t
_u
nlo
ad)
(Re-
lab
elli
ng
)M
ove
toth
ep
rin
ter
Tim
eto
mov
eto
the
pri
nte
r(t
_la
bel
_m
ovin
g)
Mea
nn
um
ber
ofp
alle
tlo
ads
per
ing
oin
gtr
uck
(N_
pal
_in
)M
ove
bac
kto
the
pal
let
load
Tim
eto
mov
eb
ack
toth
ep
alle
tlo
ad(t
_p
alle
t_m
ovin
g)
Pri
nt
the
lab
elM
ean
nu
mb
erof
pal
let
load
sp
erin
goi
ng
tru
ck(N
_p
al_
in)
Tim
eto
pri
nt
ala
bel
(t_
lab
el_
pri
nt)
Sti
ckth
ela
bel
onth
ep
alle
tlo
adT
ime
tost
ick
the
lab
elon
the
pal
let
load
(t_
lab
el_
stic
kin
g)
Con
trol
sId
enti
fyth
ep
alle
tlo
adT
ime
toid
enti
fyth
ep
alle
tlo
ad(t
_id
_p
l)co
st_
rece
ivin
g¼
t_re
ceiv
ing
*cos
t_la
bou
r/u
sag
e_fa
ctor
Cou
nt
the
case
son
the
pal
let
load
Tim
eto
cou
nt
the
case
son
the
pal
let
load
(t_
cou
nti
ng
)R
e-co
un
tth
eca
ses
onth
ep
alle
tlo
ad[B
]T
ime
toco
ntr
olof
the
man
ual
cou
nti
ng
(t_
cou
nti
ng
_ct
rl)
Con
trol
the
case
Tim
eto
con
trol
the
case
(t_
case
_ct
rl)
t_re
ceiv
ing
¼t_
accþ
t_u
nlo
adþ
t_la
belþ
Nu
mb
erof
chec
ked
case
sp
erp
alle
tlo
ad(N
_ca
ses_
ceck
)t_
con
trol
þt_
erro
rþ
t_re
pal
þt_
loca
tion
Per
cen
tag
eof
chec
ked
pal
let
load
s(p
erc_
ceck
)Q
ual
ity
con
trol
sT
ime
toq
ual
ity
con
trol
(t_
qu
alit
y_
ctrl
)t_
lab
el¼
t_la
bel
_m
akin
gþ
t_la
bel
_st
ick
ingþ
(Err
ors
man
agem
ent)
Tim
eto
com
pil
eth
esh
ipp
ing
not
e(t
_co
mp
il)
t_m
ovin
g/N
_p
al_
inþ
t_p
alle
t_m
ovin
g
Tim
eto
relo
adth
ere
ject
edp
alle
tlo
ad(t
_re
load
)t_
con
trol
¼t_
id_
pal
let_
load
þt_
cou
nti
ngþ
t_q
ual
ity
_ct
rlþ
t_co
un
tin
g_
ctrlþ
t_ca
se_
ctrl
*(continued
)
Table III.The benefits
evaluation – receivingin the retailer DC
The introductionof RFId in FMCG
supply chain
1061
Mac
ro-
acti
vit
yA
ctiv
ity
Ele
men
tary
acti
vit
yR
esou
rce
con
sum
pti
ond
riv
ers
For
mu
la
Per
cen
tag
eof
erro
rsth
atre
qu
ire
toco
mp
ile
the
ship
pin
gn
ote
(per
c_er
r_co
mp
il)
N_
case
s_ce
ck*p
erc_
ceck
Per
cen
tag
eof
erro
rsth
atre
qu
ire
toca
lla
sup
erv
isor
(per
c_er
r_ca
ll)
Per
cen
tag
eof
erro
rsth
atre
qu
ire
adm
inis
trat
ive
acti
vit
ies
(per
c_er
r_ad
min
)T
ime
toca
llth
esu
per
vis
or(t
_ca
ll)
t_er
ror¼
(per
c_er
r_co
mp
il*t
_co
mp
ilþ
Tim
efo
rad
min
istr
ativ
eac
tiv
itie
s(t
_ad
min
)p
erc_
err_
call
*t_
call
)þ
(per
c_er
r_re
turn
ed*
t_re
load
)þ
(per
c_er
r_ad
min
*t_
adm
in)
(Re-
pal
leti
sati
on)
Tak
ea
new
pal
let
Tim
eto
tak
ea
new
pal
let
(t_
pal
let)
Per
cen
tag
eof
re_
pal
leti
sati
on(p
erc_
rep
)U
nw
rap
pin
gT
ime
tou
nw
rap
the
pal
let
load
(t_
un
wr)
t_re
pal
¼p
erc_
rep
*(t_
pal
letþ
t_u
nw
rþ
t_p
ositþ
t_la
yþ
t_ad
jþ
2 *t_
wra
pþ
t_la
belþ
t_co
un
tin
gþ
t_id
_p
lþ
t_is
)P
osit
ion
ing
Pos
itio
nin
gti
me
(t_
pos
it)
Mov
eon
eor
mor
ela
yer
sto
the
new
pal
let
Tim
eto
mov
eth
ela
yer
s(t
_la
y)
Ad
just
the
new
pal
let
load
Tim
eto
adju
stth
en
ewp
alle
tlo
ad(t
_ad
j)W
rap
pin
gof
the
new
pal
let
load
Wra
pp
ing
tim
e(t
_w
rap
)t_
loca
tion
¼(t
_id
_p
lþ
t_w
msþ
t_is
)
(continued
)
Table III.
IJOPM29,10
1062
Mac
ro-
acti
vit
yA
ctiv
ity
Ele
men
tary
acti
vit
yR
esou
rce
con
sum
pti
ond
riv
ers
For
mu
la
Wra
pp
ing
ofth
eor
igin
alp
alle
tlo
adW
rap
pin
gti
me
(t_
wra
p)
Lab
elli
ng
ofth
en
ewp
alle
tlo
adse
eth
ere
-lab
elli
ng
acti
vit
y
Cou
nt
the
case
son
the
pal
let
load
Tim
eto
cou
nt
the
case
son
the
pal
let
load
(t_
cou
nti
ng
)Id
enti
fyth
en
ewp
alle
tlo
adT
ime
toid
enti
fyth
ep
alle
tlo
ad(t
_id
_p
l)C
omm
un
icat
ion
wit
hth
eIS
Tim
eto
com
mu
nic
ate
the
dat
ato
the
info
rmat
ion
syst
em(t
_is
)D
efin
itio
nof
the
pal
let
load
loca
tion
Iden
tify
the
pal
let
load
Tim
eto
iden
tify
the
pal
let
load
(t_
id_
pl)
WM
Sin
terr
ogat
ion
Tim
efo
rW
MS
inte
rrog
atio
n(t
_w
ms)
Loa
dth
ed
ata
onth
eIS
Tim
eto
load
the
dat
aon
the
info
rmat
ion
syst
em(t
_is
)
Table III.
The introductionof RFId in FMCG
supply chain
1063
Starting with out-of-stock management, various authors argue that RFId has greatpotential to reduce the stock outs at points of sale, generating benefits for both themanufacturer and the retailer (Alexander et al., 2002; Hardgrave et al., 2006; Veeramaniet al., 2008). The estimates for out-of-stock incidence in FMCG range from 4 to 6 percentof total turnover (Gruen et al., 2002; ECR Europe, 2003). Hardgrave et al. (2006) maintainthat this phenomenon is mainly due to errors at the store (e.g. delays in re-ordering,delays, and errors in shelf re-filling) and to supply chain inefficiencies. In agreement withHardgrave et al., we can say that RFId could reduce stock outs through:
. automatic identification of goods, which increases the accuracy of both theshipping and the receiving processes; and
. improved product visibility along the supply chain, which increases the accuracyof the inventory information and, consequently, the service level at both themanufacturer and the retailer facilities. At the retail store, increased visibility cantrigger a stock out on the sales floor, while there may be stock in the backroom.
Figure 3 shows the above cause-effect relationships, as proposed in literature and asvalidated by the experts during the face-to-face meetings in the third phase of the project.
Table IV presents the main inputs and formulae used to evaluate these benefits(referred to as “benefits_oos” in the Table). Specifically, the model attempts to evaluate theincreased sales enabled by RFId due to the greater product availability on the retail shelf.
Moving to the reduced number of contentious issues, there is agreement that theRFId-enabled automatic identification of pallet loads, cases, locations and loadingdocks during picking and shipping activities can considerably reduce the possibility oferrors (Veeramani et al., 2008). Most contentious issues have to do with disputes aboutthe quantity and quality of the received goods with the retailer claiming to have beensent fewer or different or defective goods. The use of RFId can dramatically reduce thenumber of such events, first of all because fewer errors occur in the process and,second, because the content of each unit load can be controlled at the case level beforeleaving the manufacturers’ premises. Moreover, RFId-enabled supply chain visibilitycan shorten the reaction time when errors occur, thereby reducing error managementcosts. The causal model and the formulae to estimate this cost item (referred to as“benefits_cont”) are reported in Figure 4 and Table V, respectively.
Figure 3.The assessmentof out-of-stock reduction
Error reduction(product inversions,
destination inversions,shrinkage)
Improved inventorymanagement
Real time inventoryupdateEnabling factors
Supply chainvisibility
Automaticidentification
Out-of-stockreduction
Out-of-stock
Improved processaccuracy
Increased controls
Inventory visibilityalong the supply
chain
IJOPM29,10
1064
Ben
efit
Roo
tca
use
sim
pac
ted
by
RF
IdIn
pu
tF
orm
ula
(to
asse
ssth
ean
nu
alb
enefi
ts)
Ou
t-of
-sto
ckre
du
ctio
nS
up
ply
chai
ner
rors
An
nu
alsa
les
(an
n_
sale
s)A
ver
age
pro
fit
mar
gin
(per
c_m
arg
)P
erce
nta
ge
oflo
stsa
les
du
eto
out-
of-s
tock
(per
c_lo
st)
Per
cen
tag
eof
out-
of-s
tock
inth
est
ore
wit
hou
tR
FId
(per
c_oo
s)b
enefi
ts_
oos¼
ben
efits
_sc
þb
enefi
ts_
stor
eP
erce
nta
ge
ofou
t-of
-sto
cks
du
eto
erro
rsin
the
sup
ply
chai
n(p
erc_
sc)
Per
cen
tag
eof
out-
of-s
tock
red
uct
ion
wit
hR
FId
(per
c_oo
s_re
d_
sc)
ben
efits
_sc
¼an
n_
sale
s *p
erc_
mar
g*p
erc_
lost
*E
rror
sw
ith
inth
est
ore
An
nu
alsa
les
(an
n_
sale
s)p
erc_
oos *
(per
c_sc
2(1
-A
ver
age
pro
fit
mar
gin
(per
c_m
arg
)p
erc_
oos_
red
_sc
))
Per
cen
tag
eof
lost
sale
sd
ue
toou
t-of
-sto
ck(p
erc_
lost
)b
enefi
ts_
stor
e¼
ann
_sa
les
Per
cen
tag
eof
out-
of-s
tock
inth
est
ore
wit
hou
tR
FId
(per
c_oo
s)*p
erc_
mar
g*p
erc_
lost
*P
erce
nta
ge
ofou
t-of
-sto
cks
du
eto
erro
rsw
ith
inth
est
ore
(per
c_st
ore)
per
c_oo
s *(p
erc_
stor
e2
(1-p
erc_
oos_
red
_st
ore)
)P
erce
nta
ge
ofou
t-of
-sto
ckre
du
ctio
nw
ith
RF
Id(p
erc_
oos_
red
_st
ore)
Table IV.The assessment
of out-of-stock reduction
The introductionof RFId in FMCG
supply chain
1065
Finally, different authors (Alexander et al., 2003c; de Kok et al., 2008) point out the potentialof RFId to reduce inventory shrinkage, which still represents a significant issue in theFMCG supply chain for both manufacturers and retailers. ECR Europe (2003) estimates thatshrinkage leads to a sales reduction of 0.56 percent for manufacturers and 1.75 percent forretailers. RFId-enabled automatic identification offers two advantages. First, it can reduceone of the main causes of shrinkage, i.e. process errors. Second, the increased visibility islikely to reduce the risk of stolen goods in the distribution process, since leakages will beimmediately tracked and attributed to the actor responsible for that stage. For example, ifRFId scanning of wrapped pallet loads at shipping and receiving points became commonpractice, then any missing item could be immediately detected and charged to the actorresponsible for the transportation process. Figure 5 and Table VI illustrate the hypothesisedcausal relationship and the evaluation model for this kind of benefit (labelled “benefits_shr”in the table), focusing on the reduction of process errors and its impact on annual sales.
Given this structure, the model is able to assess costs and benefits in the threetechnological scenarios (B, R1, R2). For each scenario, a report is provided which includes:
Benefit
Root causesimpactedby RFId Input
Formula(to assess the annual benefits)
Contentiousissues reduction
Shippingerrors
Percentage of contentiousissues due to shipping errors(perc_ship)Percentage of reduction ofcontentious issues due toshipping errors(perc_cont_red_ship)
benefits_cont ¼ cost_as_is_ship 2 cost_rfid_ship ¼perc_ship*cost_as_is 2 ((1 2 perc_cont_red_ship)*perc_ship*cost_as_is)cost_as_is ¼ n_fte*c_fte
Number of full timeequivalents to manage thecontentious issues (n_fte)Annual cost of a full timeequivalent (c_fte)
Table V.The assessmentof contentious issuesreduction
Figure 4.The assessmentof contentious issuesreduction
Improved process accuracy
Increased controls
Error reduction(product inversions,
destination inversions)
Increased responsibility ateach stage of the supply
chain
Enabling factors
Supply chainvisibility
Automaticidentification
Contentious issuesreduction
Contentious issuesIJOPM29,10
1066
(1) the total logistics costs by supply chain stage and activity;
(2) the (differential) benefits of the RFId technology by supply chain stage andactivity; and
(3) by comparing the three scenarios, a clear assessment of RFId adoption benefitsis obtained.
5.4 The assessment of capital and operational expendituresThe implementation costs of an RFId project include both the initial investment(capital expenditure, CapEx) and the recurrent annual costs (operational expenditure,OpEx) in function of the RFId scenario (R1 vs R2). In particular, the CapEx includes thecosts of hardware (e.g. readers, antennas, and tags), as shown in Table VII, thesoftware (middleware and software development/integration), and projectmanagement (design, implementation, test and change management, projectmanagement). These costs have been evaluated by starting from the physicalinfrastructure of each stage of the supply chain (e.g. number of loading/unloadingdocks, number of forklifts) and then deriving and costing (using average purchasingprices) the equipment to be purchased. The OpEx includes the cost of tags (on palletloads and/or cases), the maintenance of the RFId infrastructure, and the informationtransmission costs.
Figure 5.The assessment of
shrinkage reduction
Reduced process errors
Enabling factors
Automaticidentification Shrinkage reduction
Shrinkage
Immediate trackingof leakages and thefts
Benefit
Root causesimpacted byRFId Input
Formula(to assess the annualbenefits)
Shrinkagereduction
Inventorydiscrepancies
Annual sales (ann_sales)Percentage of shrinkage on the annualsales (perc_shr)Percentage of inventory discrepancies andthefts among the causes of shrinkage(perc_shr_inv)Percentage of process errors among thecauses related to inventory discrepanciesand thefts (perc_shr_proc)Percentage of reduction of shrinkage dueto the reduction of process errors(perc_shr_proc_red)
benefits_shr ¼ ann_sales*(perc_shr_proc_as_is 2perc_shr_proc_rfid) ¼ann_sales*perc_shr_as_is*perc_shr_proc_red
perc_shr_proc_as_is ¼perc_shr*perc_shr_inv*perc_shr_proc
Table VI.The assessment
of shrinkage reduction
The introductionof RFId in FMCG
supply chain
1067
6. The application of the model6.1 The structure of the analysisThe model has been applied to a simulated supply chain, whose parameters werechosen – with the help of the industry experts who took part in the research project –to reproduce the flows, the product mix and the physical infrastructure of a typicalFMCG supply chain consisting of medium-large manufacturers and medium-largeretailers (Table VIII).
This reference supply chain is only apparently dyadic, since we are merelyconsidering one manufacturer and one retailer managing the same flow of goods, butfrom the manufacturer perspective this represents the outbound flow directed to
Manufacturer Retailer
PlantPlant
warehouse DC DC PosRFId Hardware R1 R2 R1 R2 R1 R2 R1 R2 R1 R2
Labelling station – cases No Yes – – – – – – – –Labelling station – pallet loads Yes Yes – – – – – – – –Unloading docks – – Yes Yes Yes Yes Yes Yes No YesLoading docks Yes Yes Yes Yes Yes Yes Yes Yes – –Storage locations – – Yes Yes Yes Yes Yes Yes No NoForklifts – – Yes Yes Yes Yes Yes Yes – –Order pickers – – – – – – No Yes – –Wrapping station – – – – – – No Yes – –Hand-held – – Yes Yes Yes Yes Yes Yes – –Gate between backshopand sales areas
– – – – – – – – No YesTable VII.The HW infrastructurefor CapEx estimation
General parametersFlow of cases per year (cases/year) 30,000,000Manufacturer plantPackaging lines 8Forklifts 4Warehouses
Manufacturer plantwarehouse
ManufacturerDC
RetailerDC
Area (m2) 7,500 38,000 42,000Pallet locations (storage) 8,400 42,000 36,500Pallet locations (picking) – – 9,200Inventory (days) 5 25 25Forklifts 6 12 15Order pickers – – 35Unloading docks – 15 20Loading docks 6 15 25Point of saleTotal number of stores served by theretailer DC 100Backroom area (m2) 100Pallet locations 80Unloading docks 2
Table VIII.The inputs to the model –the main flow andinfrastructuralparameters
IJOPM29,10
1068
multiple retailers, while from the retailer perspective the inbound flow coming frommultiple manufacturers is modelled. The flow for the two actors (manufacturer andretailer) is the same so that the respective costs and benefits can be compared.
With regard to the parameterisation of the reference supply chain, the followingprocedure was followed. Preliminary data were first collected during the extensiveface-to-face interviews (Section 4); then, industry experts were involved using a focus groupmethodology in order to choose the most suitable parameters to be used in the referencechain, on the basis of both their experience and the data collected from their supply chains.
The profitability analysis compares two RFId scenarios (R1 – tag on pallet loadsand R2 – tag on cases and pallet loads) with a baseline scenario (B), of which threedifferent performance levels were tested (Table IX):
(1) Average, i.e. a supply chain representing an average case for the industry interms of quality requirements, service level and handling efficiency.
(2) Efficient, i.e. the initial handling cost per pallet or case is below average, due toless frequent quality controls, lower service level requirements, higher handlingefficiency, higher process quality; this case provides a reliable lower bound ofthe benefits stemming from RFId.
(3) Inefficient, i.e. the initial handling cost per pallet or case is above average, due tomore frequent quality controls, higher service level requirements and lower
Efficient Average Inefficient
Product featuresAverage number of cases per full pallet load 60 50 50Average number of cases per mixed pallet load 52 40 40Average number of cases per each order line 2 1.5 1Percentage of re-palletisation – retailer DC 50% 75% 75%Quality requirementsPercentage of inbound controls – retailer DC 50% 100% 100%Percentage of inbound controls – PoS 8% 12% 15%Percentage of outbound controls – retailer DC 5% 10% 20%Percentage of non availability of cases at the picking level 2.5% 5% 8%Percentage of errors – outbound controls – retailer DC 0.2% 0.6% 0.6%Outbound controls – retailer DC – time (s) 8 15 20EfficiencyTime for a single bar-code scan (s) 6 7 10Counting of the cases in a pallet load (s) 15 20 25Controls of a pallet load – PoS (s) 15 20 25Out of stocks (OOS)OOS at the retail store 6% 8% 10%Average losses due to OOS – manufacturer 40% 45% 50%Average losses due to OOS – retailer 25% 30% 35%ShrinkagePercentage of losses due to shrinkage – manufacturer 0.28% 0.56% 0.84%Percentage of losses due to shrinkage – retailer 0.875% 1.75% 2.625%Contentious issuesPercentage of losses due to contentious issues – manufacturer 0.01% 0.02% 0.04%Percentage of losses due to contentious issues – retailer 0.02% 0.03% 0.05%
Table IX.The inputs to the model –
the main performanceparameters
The introductionof RFId in FMCG
supply chain
1069
handling efficiency, lower process quality; this case provides an upper bound ofthe benefits expected from RFId applications.
Table X reports the main unit costs for the RFId hardware. In addition to these, a verysignificant parameter is the cost of the tag, and three values have been used in thesensitivity analysis:
(1) current tag costs, i.e. e0.17 for tags on pallets and e0.10 for tags on cases;
(2) mid-term tag costs, i.e. e0.14 for tags on pallets and e0.07 for tags on cases,considering the expected cost of tags in 2-3 years; and
Component Quantity Costs (e)
RFId infrastructure to identify the UHF tags on pallet loads (R1)UHF reader 1 2,250UHF antenna 2 520Movement sensor 1 400Traffic light 1 200Monitor 1 200Installation 8 hours 500Total cost 4,070RFid infrastructure to identify the UHF tags on pallet loads and cases (R2)UHF reader 1 2,250UHF antenna 4 1,040Movement sensor 1 400Traffic light 1 200Monitor 1 200Installation 8 hours 500Total cost 4,590RFId infrastructure – forkliftsUHF reader 1 2,250UHF antenna 4 1,040Wireless terminal 1 1,000Installation 20 hours 1,250Total cost 5,050RFId infrastructure – wrapping stationUHF reader 1 2,250UHF antenna 2 520Monitor 1 200Installation 16 hours 1,000Total cost 3,970RFId infrastructure – production line – labels on the casesUHF printer – Production line 1 10,000RFId infrastructure – production line – labels on the pallet loadsUHF printer 1 5,000RFId infrastructure – UHF kit to identify a pallet locationUHF tag 2 1.6Installation 4 minutes 4.4Total cost 6RFId infrastructure – UHF kit to identify a loading/unloading dockUHF tag 2 20Installation 20 minutes 20Total cost 40
Table X.The inputs to the model –the unit hardware costs
IJOPM29,10
1070
(3) long-term tag costs, i.e. e0.10 for tags on pallets and e0.05 for tags on cases, ourbest estimate of the expected costs in 5-7 years.
It is assumed that the on pallet RFId tags usually cost more than those attached tocases, as best performance (i.e. omni-directional) tags are explicitly requested byretailers to ensure maximum flexibility in logistics activities, Conversely, thecharacteristics of the tags used on cases depend on the specific product, meaning thattheir “average” cost is lower.
6.2 The results in the RFId on pallet loads scenario (R1)The model allows evaluation of the benefits by comparing the cost of the performedactivities in a base-line scenario to the expected cost in the RFId scenario. Forillustrative purposes, Table XI illustrates the results obtained for the receiving activityin the retailer DC when an efficient base-line supply chain is considered. The modelfacilitates understanding of the overall benefits – , e.g. 15 percent cost reduction in thereceiving activity carried out in the retailer DC – as well as in every elementaryactivity –, e.g. a productivity increase of 45 percent in performing the requiredcontrols.
The overall results are reported in Table XII, which shows the benefits achieved inthe three baseline supply chains when the RFId tags are used only on pallet loads (R1).In this scenario, the benefits are comparable with the cost of applying the EPC tag andmay vary from 1.13 ecent/case (about 0.65 e/pallet load) when we consider an efficientbaseline supply chain, to 2.69 ecent/case (about 1.5 e/pallet load) when the inefficient
ActivityCosts in the base-line scenario
(ecent/case)Costs in the R1 scenario
(ecent/case)Benefits
(ecent/case)
Retailer DC – receivingTruck acceptance 0.04 0.04 0Truck unloading 0.44 0.44 0Re-labelling 0.06 0 0.06Controls 0.36 0.20 0.16Re-palletisation 0.81 0.82 20.01Definition of the palletload location 0.08 0 0.08Total 1.75 1.50 0.25
Table XI.The results of the model
– the receiving activity inthe retailer DC – efficient
base-line vs R1 scenario
Efficient Average Inefficient
Traditional base-line scenario (B1)(COST per case (ecent/case)) 49.14 63.76 75.41RFId on pallet loads scenario (R1)(COST per case (ecent/case)) 47.84 61.84 72.72RFId on pallet loads scenario (R1)(DCOST ( ¼ BENEFIT) (ecent/case)) 1.13 1.92 2.69RFId on pallet loads scenario (R1)(%DCOST ( ¼ % BENEFIT)) 2.3 3.0 3.6
Table XII.The results of the
model – the benefitsin the RFId on pallet
loads scenario (R1)
The introductionof RFId in FMCG
supply chain
1071
baseline is used. Other interesting results emerge from the breakdown ofthe operational benefits by macro-activity and by supply chain stage (Figure 6) Thevisibility-related benefits proved to be negligible.
Overall, the manufacturer and the retailer enjoy similar benefits. The automaticidentification of the pallet loads, the loading/unloading docks and of the storagelocations generates improvements for the manufacturer mainly in the receiving andputting away activities. There are also benefits in despatching as a result of automaticidentification and the elimination of controls. Similarly, the retailer benefits from theuse of the technology mainly in the receiving and putting away activities in the DC, asthe automatic identification of the pallet loads permits automatic checking ofcompliance with the transportation documents. Conversely, order preparation is notsignificantly affected by the RFId technology when the tag is applied only to palletloads, and the same is true for out-of-stocks, contentious issues and shrinkage.
CapEx and OpEx in function of the cost of the EPC-RFId tags are reported inTable XIII. They have been assessed by using the unit costs reported in Table X andsizing the required infrastructure on the basis of the features of the warehouses whichwere summarised in Table VIII (e.g. the number of loading/unloading docks is used todefine the number of RFId gates).
Table XIV shows the calculation of both the payback time and the net present value(NPV) taking into account a “transitory period” (one year) during which we haveassumed that only 50 percent of the benefits can be achieved. The NPV has beencomputed using a ten-year time horizon and a discount rate of 8 percent, whereas thepayback time is based on non-discounted cash flows.
If we consider the efficient base-line supply chain with few quality restrictions, apositive NPV cannot be realised, irrespective of the cost of the RFId tags. Furthermore,
Figure 6.The results of the model –the benefits in the RFId onpallet loads scenario (R1)
Benefits: Breakdown per SC player
0.98
0
0.5
1
1.5
2
2.5
3
Efficient EfficientAverage AverageInefficient Inefficient
Supply chain
1.13
1.44
1.92
2.69
Benefits: Breakdown per activity
0.680.43 0.50
0.88
0.440.61
0.94
0.19
0.60
0.18
–0.07–0.06
0.25
0.01
0.01
0.011.13
1.92
–0.07
Packaging
Receiving
Putting away
Order preparing
Shipping
Other activitiesRetailer
Manufacturer
2.69
0.680.94
1.36
0.45
Supply chain
–0.5
0
0.5
1
1.5
2
2.5
3
0.13cent
/cas
e
cent
/cas
e
IJOPM29,10
1072
Man
ufa
ctu
rer
Ret
aile
rS
up
ply
chai
n
Cap
ital
exp
end
itu
res
(e)
950,
000
750,
000
1,70
0,00
0H
ard
war
e(e
)60
0,00
055
0,00
01,
150,
000
Sof
twar
e(e
)10
0,00
050
,000
150,
000
Pro
ject
man
agem
ent
(e)
250,
000
150,
000
400,
000
Current
tagcosts
Mid-term
tagcosts
Long-term
tagcosts
Current
tagcosts
Mid-term
tagcosts
Long-term
tagcosts
Current
tagcosts
Mid-term
tagcosts
Long-term
tagcosts
Op
erat
ion
alex
pen
dit
ure
s(e
)17
5,00
016
0,00
014
0,00
018
0,00
016
0,00
014
0,00
035
5,00
032
0,00
028
0,00
0M
ain
ten
ance
(e/y
ear)
60,0
0060
,000
60,0
0055
,000
55,0
0055
,000
115,
000
115,
000
115,
000
RF
Idta
gs
(e/y
ear)
85,0
0070
,000
50,0
0010
5,00
085
,000
65,0
0019
0,00
015
5,00
011
5,00
0E
DI
(e/y
ear)
30,0
0030
,000
30,0
0020
,000
20,0
0020
,000
50,0
0050
,000
50,0
00
Table XIII.The results of the model
– CapEx and OpEx in theRFId on pallet loads
scenario (R1)
The introductionof RFId in FMCG
supply chain
1073
this case appears to be unattractive both for the manufacturer and the retailer. Betterresults are obtained when the average baseline supply chain is considered, even if thepay-back time remains quite long. With the current costs of the technology, the RFIdapplication is still not profitable, but when the costs of the tags decrease, a positive NPVcan be realised. Even if it is the manufacturer that usually reaps most of the benefits, theretailer has slightly better investment profitability, due to the lower investmentssustained. Finally, moving to the inefficient baseline supply chain, which presents a lowerstarting efficiency and more restrictive quality requirements, a positive NPV can bereached even at the current cost of the technology; both the manufacturer and the retailerhave positive returns and the pay-back time is about five and three years, respectively.
6.3 The results in the RFId on pallet loads and cases scenario (R2)Table XV illustrates the results obtained from RFId adoption on pallet loads and casesfor the receiving activity in the retailer DC when an efficient base-line supply chain isconsidered. As expected, case-level tagging offers higher benefits – e.g. 83 percent costreduction in performing the required controls, which leads to a 22 percent costreduction in the receiving activity.
Table XVI shows the total benefits in the three base-line supply chains.The benefits achieved in this scenario are much higher than in the previous case,ranging between 6.20 ecent/case in the efficient supply chain configuration and 23.21ecent/case in the inefficient base-line configuration. As shown in Figure 7, it is now the
Pay-back (years) NPV (millione)Base-linesupplychain
Unit tagcost Manufacturer Retailer
Supplychain Manufacturer Retailer
Supplychain
Efficient Current .10 1 1 20.75 21.1 21.8Mid-term .10 1 .10 20.65 21 21.65Long-term . 1 .10 20.5 20.8 21.3
Average Current 9 8 8.5 20.22 20.06 20.28Mid-term 8 6.5 7.5 20.11 0.07 20.04Long-term 7 5.5 6 0.035 0.25 0.285
Inefficient Current 5.5 3 4 0.37 1.1 1.47Mid-term 5 3 4 0.48 1.25 1.73Long-term 4.5 3 3.5 0.62 1.43 2.05
Table XIV.The results of the model– NPV and pay-back timein the RFId on palletloads scenario R1)
ActivityCosts in the base-line scenario
(ecent/case)Costs in the R2 scenario
(ecent/case)Benefits
(ecent/case)
Retailer DC – receivingTruck acceptance 0.04 0.04 0Truck unloading 0.44 0.44 0Re-labelling 0.06 0 0.06Controls 0.36 0.06 0.30Re-palletisation 0.81 0.82 20.01Definition of the palletload location 0.08 0 0.08Total 1.75 1.36 0.39
Table XV.The results of the model– the receiving activity inthe retailer DC – efficientbase-line scenario
IJOPM29,10
1074
retailer that obtains most of the total benefit. In addition to the previously describedadvantages, there are also significant gains in the handling activities performed oncases (e.g. picking, controls on mixed pallet loads, and receiving in the retailer DC andat the point of sale) and in visibility-related benefits (i.e. mainly shrinkage and stockouts), which account for 25-35 percent of the overall benefits.
Results for CapEx and OpEx are reported in Table XVII. Table XVIII gives detailsof the NPV and pay-back time.
Once again, if we consider an efficient base-line supply chain, the results areanything but good from a supply chain perspective. Better results can be obtained forbase-line supply chains (average, inefficient) that perform less well. Unlike the resultsin the R1 scenario, profitability differs significantly between the two actors in thesupply chain. While the manufacturer always has a negative NPV irrespective of the
Figure 7.The results of the model –the benefits in the RFId on
pallet loads and casesscenario (R2)
Benefits: Breakdown per SC player
10.26
0
4
8
12
16
20
24
Efficient Average Inefficient
Supply chain
Efficient Average Inefficient
Supply chain
cent
/cas
e
6.20
20.1812.44
23.21
Benefits: Breakdown per activity
2.731.17
1.98
0.88
0.440.61
10.83
2.19
4.66
0.93
–0.07–0.06
2.14
0.09
0.10
0.036.20
12.44
–0.07
1.550.060.55
3.100.090.85
4.65
0.151.21
23.21
1.412.18 3.03
4.79
–4
0
4
8
12
16
20
24
0.34
cent
/cas
e
Packaging
Receiving
Putting away
Order preparing
Shipping
Other activities
ShrinkageContentious issuesOut-of-stocks
Retailer
Manufacturer
Efficient Average Inefficient
Traditional base-line scenario (B1)(COST per case (ecent/case)) 90.79 105.61 117.46RFId on pallet loads and cases scenario (R2)(COST per case (ecent/case)) 84.59 93.17 94.25RFId on pallet loads and cases scenario (R2)(DCOST ( ¼ BENEFIT) per case (ecent/case)) 6.20 12.44 23.21RFId on pallet loads and cases scenario (R2)(%DCOST ( ¼ % BENEFIT)) 7.0 11.8 19.8
Table XVI.The results of the model
– the benefits in the RFIdon pallet loads and cases
scenario (R2)
The introductionof RFId in FMCG
supply chain
1075
Man
ufa
ctu
rer
Ret
aile
rS
up
ply
chai
n
Cap
ital
exp
end
itu
res
(e)
1,00
0,00
02,
650,
000
3,65
0,00
0H
ard
war
e(e
)65
0,00
01,
700,
000
2,35
0,00
0S
oftw
are
(e)
100,
000
150,
000
250,
000
Pro
ject
man
agem
ent
(e)
250,
000
800,
000
1,05
0,00
0Current
tagcosts
Mid-term
tagcosts
Long-term
tagcosts
Current
tagcosts
Mid-term
tagcosts
Long-term
tagcosts
Current
tagcosts
Mid-term
tagcosts
Long-term
tagcosts
Op
erat
ion
alE
xp
end
itu
res
(e/y
)3,
095,
000
2,29
5,00
01,
595,
000
375,
000
355,
000
335,
000
3,47
0,00
02,
650,
000
1,93
0,00
0M
ain
ten
ance
(e/y
)65
,000
65,0
0065
,000
170,
000
170,
000
170,
000
235,
000
235,
000
235,
000
RF
Idta
gs
(e/y
)3,
000,
000
2,20
0,00
01,
500,
000
105,
000
85,0
0065
,000
3,10
5,00
02,
285,
000
1,56
5,00
0E
DI
(e/y
)30
,000
30,0
0030
,000
100,
000
100,
000
100,
000
130,
000
130,
000
130,
000
Table XVII.The results of the model– CapEx and OpEx in theRFId on pallet loads andcases scenario (R2)
IJOPM29,10
1076
base-line supply chain and the costs of RFId tags, the investment is always profitablefor the retailer. These findings emphasise that when the RFId tags are applied to bothpallet loads and cases, the sharing of the benefits and costs becomes the significantissue.
7. ConclusionsThere is a contrasting situation regarding RFId technology in the FMCG supply chain.On the one hand, there is considerable interest in the question, and large retailersworldwide are pushing and fostering its adoption. On the other, manufacturers andlogistics operators are still doubtful, questioning what RFId promises in terms of ROIand actual process improvements. In order to bring together these contrasting opinionsand to reduce the information asymmetry between the actors involved with the issue,this paper presents an RFId profitability assessment model and the results of itsapplication to the FMCG supply chain.
The main numerical outcomes of the model have been addressed in the previoussections. All the results have been extensively discussed with the industry expertsinvolved in the research project both to verify the model accuracy, i.e. compliance withthe real processes, and the robustness of the numerical outcomes. The expertsappreciated the possibility of having a comprehensive overview of the real issues inimplementing RFId technology in the FMCG supply chain today: the relationshipbetween the overall benefits and the tag costs, the importance of the base-line scenario,the most feasible implementation scenarios, the most impacted activities, and the mostprofitable actor in the chain (to name just a few). In fact, the relationship between eachsingle element is easily understood from a theoretical point of view, and the developedmodel provides a significant contribution to quantifying the weight of each factor inthe investment profitability indicators. The validity of the model is guaranteed by themethodology which ensures coherence between the model and its input parameters andthe real FMCG supply chain processes.
In addition to the specific numerical results, practitioners can also benefit from thestrategic perspective deriving from the overall picture. This picture brings together allthe above factors in a closely inter-connected way, explaining the situation seen in theindustry worldwide, which we would call “the RFId paradox”. If an application to palletloads were pursued, numbers would probably be sustainable at present. However,
Pay-back (years) NPV (million e)Base-linesupplychain
Unit tagcost Manufacturer Retailer
Supplychain Manufacturer Retailer
Supplychain
Efficient Current 1 3.1 1 221.2 4.4 216.8Mid-term 1 3 1 214.5 4.6 29.9Long-term 1 2.9 1 210.1 4.8 25.3
Average Current 1 1.5 .10 219.6 15.5 24.1Mid-term 1 1.5 4.8 213.0 15.6 2.6Long-term 1 1.5 3.1 28.5 15.8 7.3
Inefficient Current 1 1.0 2.0 217.9 35.6 17.7Mid-term 1 1.0 1.6 211.3 35.7 24.4Long-term 1 1.0 1.4 26.8 35.9 29.1
Table XVIII.The results of the model
– NPV and pay-back timein the RFId on pallet
loads and cases scenario(R2)
The introductionof RFId in FMCG
supply chain
1077
significant investments would be needed to achieve limited benefits (with respect to themoney spent and to the full potential of RFId). Moreover, as the model testifies, profitswould be concentrated in the hands of manufacturers, as their processes are probablyeasier to optimise. In this scenario, retailers would see very few benefits. Consequently,the R1 scenario is undermined from the outset and will probably never be deployed.Conversely, the R2 scenario (RFId application on pallets and cases) has the potential tooffer greater benefits to retailers, who are, in fact, strongly fostering its adoptionworldwide (see Wal-Mart, Tesco, Marks & Spencer, Metro). However, substantial costs(CapEx and OpEx) will have to be borne by manufacturers, who may be reluctant to doso, as they will see little benefit in such applications, given that their part of the process isthe simplest and most optimised.
As apparent from the paper, there are two means to avoid this situation. One is to cutthe tag cost (thus reducing the financial burden on manufacturers), and technology willprobably make this possible. The critical issue is to use cheaper tags withoutcompromising the 100 percent reading reliability necessary for the administrative usage ofthe technology (as that described here). The second approach is to design a cost-sharingagreement between manufacturers and retailers to divide the tag costs in proportion to theachieved benefits. This is a challenging situation, since sensitive data will have to beshared and information asymmetry is part of the conventional way of doing business inthis industry. Nevertheless, the model could reasonably help in this direction.
The work presented in this paper has a few limitations. The most important is thatit does not consider ILT. Even though currently economically (and technologically)infeasible on a large-scale, there is unanimous agreement that ILT would enable newscenarios in terms of dynamic stock management and advanced customer interaction.However, ILT appears to be a more viable option for particular products in particularsupply chains (e.g. textile, consumer electronics). A more focused study, whichconsiders specific supply chains and processes would therefore be needed. Indeed, to beexplored in full, this scenario could require using simulation tools which are bettersuited to assessing the complex dynamic processes that would result from ILTadoption (e.g. reaction to product unavailability, dynamic order allocation, promotionsmanagement, etc.). Our model shares the same pros (i.e. flexibility and visibility) andcons (i.e. approximation and staticity) of any analytical model. Finally, even if multiplevalues have been considered for the most uncertain parameters, a more extensivesensitivity analysis could provide additional insight, e.g. the impact of the labour coston the investment profitability.
Three main directions for future research can be outlined. First, the benefits relatedto the improved effectiveness and service (e.g. prompt information) could be betterinvestigated, since there is clear evidence that a positive return on investment at caselevel is heavily dependent on these variables. Second, all our results have beenobtained under the assumption of the perfect reliability of the technology (100 percentreading rate in each scenario). This is a result that has still to be consolidated,especially in the R2 scenario, even though extremely positive outcomes have beenreported by EPC research centres in the USA and Europe. In this regard, it might beinteresting to introduce a further variable into the model, i.e. the degree to which someprocesses will have to be modified (with the corresponding costs), if 100 percentreliability is to be achieved. Third, the model currently considers all the activities fromthe end of the production line to receipt at the point of sale, but does not evaluate the
IJOPM29,10
1078
benefits within the store, which might represent a significant part of the advantages forretailers and, if shared, for manufacturers. All these research lines are currently beingdeveloped out of the initial model.
References
Agarwal, V. (2001), “Assessing the benefits of auto-ID technology in the consumer goodsindustry”, White Paper, Cambridge, Cambridge University Auto-ID Centre.
Alexander, K., Birkhofer, G., Gramling, K., Kleinberger, H., Leng, S., Moogimane, D. and Woods, M.(2002), “Focus on retail: applying auto-ID to improve product availability at the retail shelf”,White Paper, Auto-ID Center, MIT, Cambridge, MA.
Alexander, K., Gilliam, T., Gramling, K., Kindy, M., Moogimane, D., Schultz, M. and Woods, M.(2003a), “Focus on the supply chain: applying auto-ID within the distribution center”,White Paper, Auto-ID Center, MIT, Cambridge, MA.
Alexander, K., Gilliam, T., Gramling, K., Grubelic, C., Kleinberger, H., Leng, S., Moogimane, D.and Sheedy, C. (2003b), “Applying auto-ID to reduce losses associated with productobsolescence”, White Paper, Auto-ID Center, MIT, Cambridge, MA.
Alexander, K., Gilliam, T., Gramling, K., Grubelic, C., Kleinberger, H., Leng, S., Moogimane, D.and Sheedy, C. (2003c), “Applying auto-ID to reduce losses associated with shrink”, WhitePaper, Auto-ID Center, MIT, Cambridge, MA.
Angeles, R. (2005), “RFID technologies: supply-chain applications and implementation issues”,Information Systems Management, Vol. 22 No. 1, pp. 51-65.
Atali, A., Lee, H. and Ozer, O. (2004), “If the inventory manager knew: value of RFID underimperfect inventory information”, working paper, Stanford University, Stanford, CA.
Attaran, M. (2007), “RFID: an enabler of supply chain operations”, Supply Chain Management:An International Journal, Vol. 12 No. 4, pp. 249-57.
Barratt, M. and Choi, T. (2007), “Mandated RFID and institutional responses: cases of decentralizedbusiness units”, Production and Operations Management, Vol. 16 No. 5, pp. 569-85.
BCG-ECR (1996), “Redesign the supply chain”, research report.
Bendavid, Y., Lefebvre, E., Lefebvre, L.A. and Fosso Wamba, S. (2007), “B-to-B e-commerce:assessing the impacts of RFId technology in a five layer supply chain”, Proceedings of the40th Hawaii International Conference on System Sciences, 3-6 January, Big Island, Hawaii.
Bhattacharya, M., Chu, C. and Mullen, T. (2007), “RFID implementation in retail industry: currentstatus, issues, and challenges”, College of Information Sciences and Technology – ThePennsylvania State University.
Boone, T. and Ganeshan, R. (2007), “The frontiers of eBusiness technology and supply chains”,Journal of Operations Management, Vol. 25, pp. 1195-8.
Cannon, A.R., Reyes, P., Frazier, G.V. and Prater, E.L. (2008), “RFID in the contemporary supplychain: multiple perspectives on its benefits and risks”, International Journal of Operations& Production Management, Vol. 28 No. 5, pp. 433-54.
Cantor, D.E. and Macdonald, J.R. (2009), “Decision-making in the supply chain: examiningproblem solving approaches and information availability”, Journal of OperationsManagement, Vol. 27, pp. 220-32.
Chappell, G., Ginsburg, L., Schmidt, P., Smith, J. and Tobolski, J. (2002a), “Auto-ID on demand:the value of auto-ID technology in consumer packaged goods demand planning”, WhitePaper, Auto-ID Center, MIT, Cambridge, MA.
The introductionof RFId in FMCG
supply chain
1079
Chappell, G., Ginsburg, L., Schmidt, P., Smith, J. and Tobolski, J. (2003b), “Auto-ID on the line: thevalue of auto-ID technology in manufacturing”, White Paper, Auto-ID Center, MIT,Cambridge, MA.
Chappell, G., Durdan, D., Gilbert, G., Ginsburg, L., Smith, J. and Tobolski, J. (2003a), “Auto-ID inthe Box: the value of auto-ID technology in retail stores”, White Paper, Auto-ID Center,MIT, Cambridge, MA.
Chappell, G., Durdan, D., Gilbert, G., Ginsburg, L., Smith, J. and Tobolski, J. (2002b), “Auto-ID ondelivery: the value of auto-ID technology in the retail supply chain”, White Paper, Auto-IDCenter, MIT, Cambridge, MA.
Chuang, M.L. and Shaw, W.H. (2005), “How RFID will impact supply chain networks”,Proceedings of the Engineering Management Conference, September 11-13, IEEEInternational.
Curtin, J., Kauffman, R.J. and Riggins, F.J. (2007), “Making the ‘MOST’ out of RFID technology: aresearch agenda for the study of the adoption, usage and impact of RFID”, InformationTechnology and Management, Vol. 8, pp. 87-110.
de Kok, A.G., van Donselaar, K.H. and van Woensel, T. (2008), “A break-even analysis of RFIdtechnology for inventory sensitive to shrinkage”, International Journal of ProductionEconomics, Vol. 112 No. 2, pp. 521-31.
Delen, D., Hardgrave, B.C. and Sharda, R. (2007), “RFID for better supply-chain managementthrough enhanced information visibility”, Production and OperationsManagement, Vol. 16No. 5, pp. 613-24.
Dutta, A., Lee, H.L. and Whang, S. (2007), “RFID and operations management: technology, valueand incentives”, Production and Operations Management, Vol. 16 No. 5, pp. 646-55.
ECR Europe (2003), Optimal Shelf Availability, ECR Europe, Kontich.
EPC Lab (2008), “Experimentation of RFId technology in EPC standard”, available at: www.indicod-ecr.it
Fleisch, E. and Tellkamp, C. (2005), “Inventory inaccuracy and supply chain performance: asimulation study of a retail supply chain”, International Journal of Production Economics,Vol. 95 No. 3, pp. 373-85.
Fosso Wamba, S., Lefebvre, L.A., Bendavid, Y. and Lefebvre, E. (2008), “Exploring the impact ofRFId technology and the EPC network on mobile B2B eCommerce: a case study in theretail industry”, International Journal of Production Economics, Vol. 112 No. 2, pp. 614-29.
Gale, T., Rajamani, D. and Sriskandarajah, C. (2004), The Impact of RFID on Supply ChainPerformance, University of Texas at Dallas, Richardson, TX.
Gaukler, G., Seifert, R. and Hausman, W. (2007), “Item-level RFID in the retail supply chain”,Production and Operations Management, Vol. 16 No. 1, pp. 65-76.
Gruen, T.W., Corsten, D.S. and Bharadwaj, S. (2002), “Retail out-of-stock: a worldwide examinationof extent, causes and consumer responses”, Research paper, Emory University, Atlanta, GA(GMA et al.).
Gunasekaran, A. and Ngai, E.W.T. (2005), “Build-to-order supply chain management: a literaturereview and framework for development”, Journal of OperationsManagement, Vol. 23 No. 5,pp. 423-51.
Hardgrave, B.C., Waller, M. and Miller, R. (2006), Does RFID Reduce Out of Stocks? A PreliminaryAnalysis, Information Technology Research Institute, University of Arkansas,Fayetteville, AR.
Helders, B. and Vethman, A.J. (2003), “Beyond 2005: how RFID will change the global supplychain”, Chain Store Age, Vol. 79 No. 12, pp. 39-48.
IJOPM29,10
1080
Johansson, M. (2005), “Identification of the main factors influencing an RFID implementation inthe automotive and pharmaceutical industries”, Masters thesis, Department of Science andTechnology, Linkoping University, Linkoping.
Jones, P., Clarke-Hill, C., Hiller, D. and Comfort, D. (2005), “The benefits, challenges and impactsof radio frequency identification technology (RFId) for retailers in the UK”, MarketingIntelligence & Planning, Vol. 23 No. 4, pp. 395-402.
Kaplan, R.S. and Bruns, W. (1987), Accounting and Management: A Field Study Perspective,Harvard Business School Press, Boston, MA.
Karkkainen, M. (2003), “Increasing efficiency in the supply chain for short shelf life goods usingRFID tagging”, International Journal of Retail & Distribution Management, Vol. 31 No. 10,pp. 529-36.
Keen, P. and Mackinttosh, R. (2001), The Freedom Economy: Gaining the M-commerce Edge inthe Era of the Wireless Internet, McGraw-Hill, New York, NY.
Kim, E.Y., Ko, E., Kim, H. and Koh, C.E. (2008), “Comparison of benefits of radio frequencyidentification: implications for business strategic performance in the US and Koreanretailers”, Industrial Marketing Management, Vol. 37 No. 7, pp. 797-806.
Langer, N., Forman, C., Kekre, S. and Scheller-Wolf, A. (2007), “Assessing the impact of RFId onreturn center logistics”, Interfaces, Vol. 37 No. 6, pp. 510-4.
Lapide, L. (2004), “RFID: what’s in it for the forecaster?”, The Journal of Business ForecastingMethods & Systems, Vol. 23 No. 2, pp. 16-19.
Laubacher, R., Kothari, S.P., Malone, T. and Subirana, B. (2005), What is RFID Worth to YourCompany? Measuring Performance at the Activity Level, research brief, MIT Center foreBusiness, Cambridge, MA.
Lee, H. and Ozer, O. (2007), “Unlocking the value of RFId”, Production and OperationsManagement, Vol. 16 No. 1, pp. 40-64.
Liu, X., Tang, O. and Huang, P. (2008), “Dynamic pricing and ordering decision for the perishablefood of the supermarket using RFId technology”, Asia Pacific Journal of Marketing andLogistics, Vol. 20 No. 1, pp. 7-22.
Loebbecke, C. (2005), “RFID technology and application in the retail supply chain: the early metrogroup pilot”, Proceedings of the 18th Bled Conference on e-Integration in Action, Bled,Slovenia.
Michael, K. and McCathie, L. (2005), “The pros and cons of RFID in supply chain management”,Mobile Transformation – Enterprise Transformation Through Mobile Technologies andApplications 2005 Proceedings of the International Conference on Mobile Business(ICMB’05) in Sydney, Australia, IEEE Computer Society, Washington, DC, pp. 623-9.
Miragliotta, G., Perego, A. and Tumino, A. (2007), “RFId technology in a container port: anactivity-based model to assess costs and benefits”, in Lalwani, C.S. (Ed.), Global SupplyChains: Developing Skills, Capabilities and Networks, Proceedings of the Logistics ResearchNetwork (LRN) Annual Conference, Hull, UK, The Chartered Institute of Logistics andTransport, Corby, pp. 101-6.
Miragliotta, G., Perego, A. and Tumino, A. (2008), “RFId in the pharmaceutical industry: anactivity-based model to assess the costs & benefits for the wholesalers”, in Lyons, A.C.(Ed.), Supply Chain Innovations: People, Practice and Performance, Proceedings of theLogistics Research Network (LRN) Annual Conference, Liverpool, UK, The CharteredInstitute of Logistics and Transport, Corby, pp. 551-6.
The introductionof RFId in FMCG
supply chain
1081
Pramatari, K.C., Doukidis, G.I. and Kourouthanassis, P. (2005), “Towards ‘Smarter’ supply anddemand-chain collaboration practices enabled by RFID technology”, Consumer DrivenElectronic Transformation, Berlin, Springer, Heidelberg, pp. 241-56.
Rekik, Y., Sahin, E. and Dallery, Y. (2008a), “Analysis of the impact of the RFId technology onreducing product misplacement errors at retail stores”, International Journal of ProductionEconomics, Vol. 112 No. 1, pp. 264-78.
Rekik, Y., Sahin, E. and Dallery, Y. (2008b), “Inventory inaccuracy in retail stores due to theft: ananalysis of the benefits of RFId”, International Journal of Production Economics, Vol. 118No. 1, pp. 189-98.
Reyes, P.M. and Jaska, P. (2007), “Is RFID right for your organization or application?”,Management Research News, Vol. 30 No. 8, pp. 570-80.
RFId-IPO, Italian permanent Observatory on RFId Technologies (2005), “RFId From present toFuture”, research report (English version available), Italian Permanent Observatory onRFId Technologies.
RFId-IPO, Italian Permanent Observatory on RFId Technologies (2006), “RFId alla prova deifatti”, research report (in Italian).
RFId-IPO, Italian Permanent Observatory on RFId Technologies (2007), “RFId: alla ricerca delvalore”, research report (in Italian).
Sellitto, C., Burgess, S. and Hawking, P. (2007), “Information quality attributes associated withRFID-derived benefits in the retail supply chain”, International Journal of Retail &Distribution Management, Vol. 35 No. 1, pp. 69-87.
Srivastava, B. (2004), “Radio frequency ID technology: the next revolution in SCM”, BusinessHorizons, Vol. 47 No. 6, pp. 60-8.
Subirana, B., Eckes, C., Herman, G., Sarma, S. and Barrett, M. (2003), “Measuring the impactof information technology on value and productivity using a process-based approach:the case of RFId technology”, MIT Sloan Working Paper No. 4450-03, available at: http://ssrn.com/abstract¼478582 or DOI: 10.2139/ssrn.478582.
Szmerekovsky, J.G. and Zhang, J. (2008), “Coordination and adoption of item-level RFId withvendor managed inventory”, International Journal of Production Economics, Vol. 114 No. 1,pp. 388-98.
Tellkamp, C. (2003), “The auto-ID calculator: an overview”, White Paper, Auto-ID Center, MIT,Cambridge, MA.
Veeramani, D., Tang, J. and Gutierrez, A. (2008), “A framework for assessing the value of RFIDimplementation by tier-one suppliers to major retailers”, Journal of Theoretical and AppliedElectronic Commerce Research, Vol. 3 No. 1, pp. 55-70.
Wang, S., Liu, S. and Wang, W. (2008), “The simulated impact of RFId-enabled supply chain onpull-based inventory replenishment in TFT-LCD industry”, International Journal ofProduction Economics, Vol. 112 No. 2, pp. 570-86.
Wu, N.C., Nystrom, M.A., Lin, T.R. and Yu, H.C. (2006), “Challenges to global RFID adoption”,Technovation, Vol. 26 No. 12, pp. 1317-23.
Corresponding authorAngela Tumino can be contacted at: [email protected]
IJOPM29,10
1082
To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints