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Combining make-to-order and make-to-stock inventorypolicies: an empirical application to a manufacturingSMEMarco Perona a , Nicola Saccani a & Simone Zanoni aa Department of Mechanical and Industrial Engineering , University of Brescia , Via Branze38, 25123 Brescia, ItalyPublished online: 15 Sep 2009.

To cite this article: Marco Perona , Nicola Saccani & Simone Zanoni (2009) Combining make-to-order and make-to-stockinventory policies: an empirical application to a manufacturing SME, Production Planning & Control: The Management ofOperations, 20:7, 559-575, DOI: 10.1080/09537280903034271

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Production Planning & ControlVol. 20, No. 7, October 2009, 559–575

Combining make-to-order and make-to-stock inventory policies:

an empirical application to a manufacturing SME

Marco Perona, Nicola Saccani* and Simone Zanoni

Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy

(Final version received 17 April 2009)

This article focuses on decoupling point and inventory policy decisions in manufacturing companies supplyingproducts with different demand patterns and customisation levels. In such a context, adopting a pure make-to-order (MTO) approach may severely affect the response time for standard and regular products while, on theother hand, a pure make-to-stock (MTS) policy may result in excess inventory. To overcome this, companies tendto adopt hybrid and dynamic MTO–MTS policies, but decisions are often taken without the support of a rationalmodel. In this article we develop a rational model to support inventory management decisions in a MTO–MTScontext and bridge the gap between theory and practice. Starting from a real-life case study, we develop adecision-making approach that employs simple models, methods and tools, thus making it suitable for practicalimplementation in small and medium sized enterprises (SMEs). Different product characteristics are analysed inorder to develop a framework for choosing the most suitable decoupling point and replenishment policy (such aseconomic order quantity, EOQ) and for determining the parameters of the chosen policy (such as lot size).The simplicity of the procedure together with the positive results achieved in this first case study implementationsuggest that the new framework has the potential to improve the inventory policies adopted by SMEs in aMTO–MTS context and should be refined and developed through further case study research.

Keywords: make-to-order; make-to-stock; small and medium sized enterprises; implementation; case study

1. Introduction

Inventory planning in multi-product and multi-stagemanufacturing systems is challenging – the stage atwhich the decoupling (or order penetration) point(Hoekstra and Romme 1992, Olhager 2003) is posi-tioned has to be determined for each product. Oncedetermined, an inventory policy has to be chosen inorder to set the inventory level and reordering policy atthe chosen decoupling point. Moreover, each inventorypolicy comes with a set of parameters to be tuned: forinstance, an economic order quantity (EOQ) and areorder point (OP) have to be set in order to apply theEOQ–OP model (Mennel 1961, Silver et al. 1998). Thisis a multi-dimensional problem: differences in the maindecisional drivers (for instance, the cost structure orthe demand pattern) occur along the transformationprocess, across the product mix and over time.

As a consequence, one single policy is unlikelyto fit all these features: a hybrid policy combiningdifferent decoupling points, inventory models and/orparameters for different products could instead beadvisable (Soman et al. 2004, 2007). Yet, on this point,the approaches adopted by researchers and practi-tioners tend to diverge. The theoretical approaches

focus on joint multi-item and multi-stage cost optimi-

sation but are either too complex to be effectively usedin real cases or fail to consider all relevant constraints.Therefore, practitioners tend to base their planningdecisions on common sense and experience without anunderlying rational model (Federgruen and Katalan

1995). These flexible approaches allow the practitionerto consider all relevant parameters and constraintsbut they often lack the required consistency. Hence,there is a gap which very few have tried to bridge(Van Donk 2001).

This article aims at bridging the aforementionedgap between theoretical models and current industrial

practice. The majority of academic research on pro-duction planning and control (PPC) starts from theoryand then tries to put it into practice (see for instanceStevenson and Silva (2008)); yet theoretical methodsmight be at odds with reality. This may happen

because the required data are not available, the shop-floor managers are not skilled enough, some relevantconstraint has not been taken into consideration, orsimply the addressed problem is not commonly foundin reality (Perona and Miragliotta 2000). In this study,

instead, we start from the shop floor and attempt to

*Corresponding author. Email: [email protected]

ISSN 0953–7287 print/ISSN 1366–5871 online

� 2009 Taylor & Francis

DOI: 10.1080/09537280903034271

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determine the incremental improvements needed to

bridge the quantitative and rational decision-making

gap between the theoretical methods and everyday

practice.Given the above, the remainder of this article is

organised as follows: Section 2 provides a critical

assessment of the available literature in light of the

above; Section 3 presents the methodological approachthat has been followed in this study; Section 4

contains the description of the company where the

case study has been developed; Section 5 describes the

new planning method proposed in this article; and

Section 6 illustrates the comparative advantages it

achieves in the specific case considered. Finally,

Section 7 contains a critical discussion of the workundertaken and suggestions for future research.

2. Literature review

A major problem faced by managers in multi-stage

manufacturing contexts is the positioning of the

decoupling point for each product (Hoekstra and

Romme 1992). As highlighted by Olhager (2003),

different positions of the decoupling point define

different ways of fulfilling commercial demand, withan impact on such performances as customer service

level, manufacturing efficiency and working capital

tied up in inventory. In the literature review presented

in this section we consider papers which explicitly

address the decoupling point positioning problem in

hybrid make-to-order–make-to-stock (MTO–MTS)

production systems, while we do not consider thosefocusing only on production planning or scheduling

problems, such as Federgruen and Katalan (1999),

Tsubone et al. (2002), Wu et al. (2008). We categorise

the relevant papers according to the main approach

adopted to analyse the decoupling point positioning

problem as analytical models, frameworks, or case

studies, with some belonging tomore than one category.An overview of the literature is shown in Table 1.

Although in real life many multi-stage manufacturing

contexts adopt hybrid MTO–MTS inventory policies,

Soman et al. (2004) point out that literature addressing

these systems and their peculiar problems is still scarce,

while most research deals with pure MTS or MTO

systems. This is also our finding, demonstrated by the

fact that Table 1 encompasses only 11 contributions,scattered over 16 years. Table 1 highlights the main

contribution to theory and/or practice of each paper,

as well as the main limitations preventing the real-life

application of the solutions proposed or making it

difficult.

Six papers in Table 1 propose an analyticalmodel, pursuing a monetary objective function, suchas the minimisation of a cost function (Arreola-Risaand DeCroix 1998, Rajagopalan 2002, Gupta andBenjafaar 2004, Kaminsky and Kaya 2008) or themaximisation of the expected profit (Li 1992). Servicelevel is considered either in the objective function(through backorder cost) or as an external constraint(maximum response time or average delay). Althoughthese papers provide exact or numerical solutions tothe problem and give some important insights, theirassumptions often limit their applicability to real cases,as summarised in Table 1. Soman et al. (2004) assertthe insufficiency of mathematical approaches to solvethe decoupling point positioning problem, the mainreason being the difficulty in modelling the problemaccurately. For instance, many models in Table 1 donot consider set-up times or cost (Li 1992, Arreola-Risa and DeCroix 1998, Gupta and Benjafaar 2004,Kaminsky and Kaya 2008), or the possibility tostock intermediate items as an alternative to MTO orMTS for finished products (Li 1992, Arreola-Risaand DeCroix 1998, Rajagopalan 2002, Kaminsky andKaya 2008). Moreover, these models may: (i) rely ona complex procedure not easily understandable by asmall and medium sized enterprise (SME) supervisor(Gupta and Benjafaar 2004, Kaminsky and Kaya2008, Sun et al. 2008); (ii) require data difficultto collect or estimate (Gupta and Benjafaar 2004,Sun et al. 2008) or (iii) be based on simplisticassumptions about the number of products (Sunet al. 2008), the production system (Arreola-Risa andDeCroix 1998) or the demand process (Rajagopalan2002). Moreover, most of these models fail to providedetailed support for practical implementation: onlythe model by Rajagopalan (2002) is accompanied byan application to a real case.

Two papers in Table 1 propose qualitative frame-works, encompassing sets of decisions. Huiskonenet al. (2003) propose a four-step framework inwhich decoupling point determination is driven byproduct analysis (sales volumes and demand stability),complemented by customer analysis and grouping(on attractiveness and judgemental recommendationsabout the opportunity for maintaining, improvingor reducing the level of service). The outcome is aproduct/customer specific policy: therefore, the sameproduct might have different decoupling points for dif-ferent customers. Soman et al. (2004) indicate decouplingpoint setting as the first of three hierarchical decisionallevels, followed by production planning (due datepolicies for MTO products, lot sizes for MTS products,monthly production volumes) and scheduling and con-trol (daily/weekly production volumes and sequence).

560 M. Perona et al.

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Table

1.Summary

ofresearchondecouplingpointdecisionsin

hybridMTO–MTSsystem

s.

Paper

Approach

Main

contribution

Main

limitations

Analyticalmodels

Li1992

Analyticalmodel(foronefirm

andforn

firm

s)aim

edatprofitmaxim

isation.

New

sboy-likeform

ulasallow

theoptimalchoice

betweenMTO

andMTSoperationsandthe

optimalinventory

level

tobedetermined.

Single

product

system

.Set-upcostsnotconsidered.

Interm

ediate

productsnotconsidered.

Considerscost

structure

andspeedofdelivery

asadecisionaldriver.

Arreola-R

isaand

DeC

roix

(1998)

Analyticalmodel

aim

edatminim

ising

thetotalcost

(inventory

holding,

backordering).

Findsoptimalandexact

solutions.

Identifies

severaldecisionaldrivers(ratiobetween

unitholdingandbackorderingcost,capacity,

variabilityofprocessingtimes).

Single

stagemanufacturingsystem

.Set-upcostsnotconsidered.

Interm

ediate


Rajagopalan

(2002)

Analyticalmodel

minim

isingthe

inventory

holdingcost

ofMTSitem

s,subject

toaresponse

timeconstraint

forMTO

item

s.Case

studyofaplastic

productionfirm

with123products.

Findsheuristic

solutions.Proposesanem

pirical

application.

MTO

item

sperturb

MTSitem

production,increasing

congestion,leadtimeandinventory.In

thecase

analysed,verylowandveryhighdem

anditem

sare

MTO,theothersare

MTS.

Considersasdecisionaldrivers:dem

and,unit

processingtime,unitholdingcostandset-uptime.

Interm

ediate


Verylow

orsporadic

dem

andproducts

notconsidered.

Gupta

and

Benjafaar

(2004)

Analyticalmodel

minim

isinginventory

costsunder

amaxim

um

average

deliverydelayconstraint.

Solutionsare

given

throughnumerical

examples.Thecost

ofmodular

product

re-design(tohaveaunique

interm

ediate

forallitem

s)isalso

considered.

Analysestherelationamongcapacity

constraints,

decouplingthroughdelayed

differentiation

(interm

ediate

products),inventory

costandservice

level:in

general,thedesirabilityofdelayed

differentiationdependsontheamountofslack

capacity

available

(thelower,themore

aMTS

productionisfavoured).

Relatesdecouplingpointdecisionsto

product

design

decisions.

Themodel

hypotheses

makeitsuitable

forthe

PC

assem

bly

industry.

Decisionaldrivers:inventory

cost,backorder

cost,

product

re-designcost,timeperform

ance,

available

capacity

atdifferentproductionstages,

workload.

Set-upcostsnotconsidered.

Only

‘pure’MTSorMTO

situations

considered.

Themodel

usesaunitamortised

product/process

redesigncost

that

should

beestimatedforeach

possible

decouplingpoint.

Kaminskyand

Kaya(2008)

Analyticalmodel

minim

isinginventory

costs,leadtimeandtardinessin

the

whole

system

.Extensionto

atw

o-tieredsupply

chain.

Findsheuristic

solutions.Relatesthedecoupling

pointdecisionto

thejobsequencingrule

andthe

evaluationofwaitingtimes

asawayto

jointly

minim

isewaitingtimes

andinventory

holding

costs.Extendstheproblem

toasupply

chain

contextfortheevaluationofoverallcost

perform

ance.

Decisionaldrivers:timeparameters,cost

structure.

Set-uptimes

andcostsnotconsidered.

Interm

ediate


Verycomplexprocedure.

Complexdecisionalgorithm. (c

ontinued

)

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Table

1.Continued.

Paper

Approach

Main

contribution

Main

limitations

Sunet

al.(2008)

Analyticalmodel

minim

isingtheoverall

cost

(inventory,set-up,stock-outand

asset

specificity)in

asupply

network,

subject

tosatisfyingcustomer

delivery

time.

Applies

theproblem

toanassem

bly

rather

than

manufacturingcontext,within

asupply

network

wheremultiple

possible

decouplingpoints

exist.

Takes

into

considerationBOM

relationships

amongdifferentmaterials.Provides

numerical

solutions.Decisionaldrivers:componentand

product

cost

structure,dem

andvariance

and

customer

deliverytime.

Single

product

model.

Themodel

applicationrequires

the

estimationoftheasset

specificitycost

foreach

component.

Framew

orks

Huiskonen

etal.

(2003)

Framew

ork

inwhichproduct-based

inform

ationissupplementedby

customer-specificcharacteristics

when

decidingontheinventory

policy

fora

specific

product.

Case

studyofapackagingindustry

firm

with185productsand384customers.

Decouplingdecisionsare

relatedto

customer

aspects,

andare

madeforeach

product/customer

combinationonthebasisofquantitativeand

judgem

entalsteps.Decisionaldrivers:product-

basedinform

ation(dem

andvolumes

and

variation),customer

inform

ation(purchase

volumes,profitcontribution,growth

potential,

impact

ofservicelevel

oncustomer

volumes).

Nooptimisationapproach

todecision-

making.

Interm

ediate


Theproduct/customer

couplinganalysis

includes

judgem

ental(m

anual)steps:

itbecomes

verytimeconsumingfor

firm

swithawideproduct

rangeanda

largeportfolioofcustomers.

Somanet

al.

(2004)

Hierarchicalplanningframew

ork

developed

forthefoodprocessing

industry.

Points

outtheinsufficiency

ofmathem

atical

approaches

insolvingtheMTO–MTSdecision,

themain

reasonsbeingthedifficultyofmodelling

theproblem

accurately.Theconceptual

framew

ork

suggestedisanattem

ptto

structure

the

productionplanningdecisionsin

acombined

MTO–MTSproductionsituation.

Decisionaldriversconsidered:dem

andvolumes

and

variability,inventory

vs.set-upcosts,sequence-

dependency

ofset-upcosts,product

customisation

andperishability.

Interm

ediate


Theproposedframew

ork

provides

astructuredandhierarchicalsetof

decisionsto

bemade,

butonly

suggestionsonhow

differentdrivers

influence

thedecisionsare

provided.


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Case

studies

VanDonk(2001)

Case

studyofafoodindustry

firm

buildinganew

facility,withmore

than200products.

Highlightsthecontrastingim

pact

ofdifferentmarket

andprocess

characteristics

onthedecoupling

pointin

thefoodindustry.Easily

understandable

bymanagers.

Decisionaldriversconsidered:(a)Product

and

market

characteristics:deliverytimeand

reliability,dem

andpredictabilityandspecificity;

(b)Process

characteristics:leadtime,coststructure

(includingset-up,control,stock

holdingand

obsolescence).

How

tosolvethetrade-offsisnot

explained.

Verylimited

quantitativeinsights

(even

inthecase

study).

Nooptimisationapproach

todecision-

making.

Theadoptedrulesfordecouplingpoint

decisionsare

case

specific.

Somanet

al.

(2007)

Case

studyapplyingtheframew

ork

of

Somanet

al.(2004)to

afoodindustry

firm

with230products.

Inorder

todefinethedecouplingpointforproducts,

adem

andvariabilityanalysis,andthen

aproduct-

process

analysisare

perform

ed.

Points

outtherelevance

ofqualitativeandshared

decision-m

aking:jointmeetingsofsalesand

productionpeopleshould

bearranged

toshowthe

resultofdetailed

analysisandto

discuss

the

implicationsofvariousscenarios.

Lim

ited

quantitativeinsights

onhow

toundertakethedecouplingpoint

positioning:analyticaldecisionaids

are

provided

only

forscheduling

problems.

Kerkkanen

(2007)

Case

studyofasm

allMTO

steelmill

planningto

movetowardsahybrid

MTO–MTSsystem

.

When

alargeamountofinform

ationthatisneeded

formakingthedecisionisin

anintangible

and

qualitativeform

,organisationalfactors

will

dominate.

Thereisaneedto

combinequalitativeand

quantitativeinform

ationandto

increase

communicationsurroundingcriticalissues.

Thedecisionprocess

illustratedis

specific

totheproblem

andcase

companyanalysed.

Thefocusofthepaper

isonfinding

factors

affectingcommonality

among

thepotentialinterm

ediate

item

s(i.e.

grades

ofsteel,billetdim

ensions,

dem

and),rather

thanonanalysingthe

MTO–MTSissueatastrategic

level.


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Their conceptual framework can be used by managersas a starting point for designing or redesigning theplanning and scheduling hierarchy structure for aparticular situation. The framework, especially devel-oped for the food processing industry, is appliedto a case study company, as described in Somanet al. (2007).

The referred frameworks have advantages overanalytical models, as they are more easily understoodby managers and easier to implement in real cases.On the other hand, they do not lead to optimalsolutions for the cost-service trade-off, and theyprovide suggestions or guidelines rather than detailedprocedures or quantitative decision aids. Finally, casestudies such as those by Kerkkanen (2007) and VanDonk (2001) are rich in interesting insights arisingfrom the company analysed, but the decision problemis solved through very specific rules or procedures,which are difficult to generalise beyond the specificcontext considered.

The papers in Table 1 help to identify a list offactors that can be considered as decisional driverswhen setting the decoupling point. One class of driverscorresponds to the product cost structure, encom-passing inventory holding, backordering, set-up, con-trol and obsolescence costs, and several ratios amongthem. Products with high inventory and/or obsoles-cence costs are more suitable for MTO production,while MTS production best suits products affectedby relevant backordering, set-up and control costs.A second category concerns time parameters, includinglead time, delivery time, processing times and theirvariability and/or predictability: long and unpredict-able times require companies to buffer demand withstock (i.e. MTS production) while MTO productioncan suit products with short and more reliable times.A third category of drivers concerns demand in itsabsolute volume, variability over time, specificity andpredictability: the more one product is affected bya high, regular, general and predictable demand,the more it makes sense to fulfil it from stock, andvice versa. A further relevant aspect in some sectors isproduct ‘perishability’, as Soman et al. (2004) reportfor food processing.

Most of the aforementioned papers acknowledgethat these characteristics are related with the decou-pling point positioning. Yet some papers underlineother aspects as well. For instance, both Arreola-Risa and DeCroix (1998), and Gupta and Benjafaar(2004) refer to the relationship between availableshop floor capacity and demand and its effect on thepositioning of the decoupling point. Furthermore,Soman et al. (2004) consider the sequence-dependency

of set-ups as a relevant factor. It is suggested that

when a company is short of capacity, it should

resort to large lots (and therefore MTS rather than

MTO production) in order to free capacity by

reducing machine time lost to changeovers; however,

when set-up times are sequence-dependent, a set-up

time reduction could also be achieved by means of a

sensible dispatching policy, as noted by Perona and

Portioli (1993).Even the product structure and bill of materials

(BOM) could influence whether an item is most

suitable to be produced to order or to stock, as noted

by Sun et al. (2008). In assembly systems, common

components or subgroups might be kept in stock

and assembled to order, and much the same might

happen in manufacturing when a semi-finished item

can evolve into several different finished products.

Finally, Huiskonen et al. (2003) underline that spe-

cific customer requirements could dictate that the

same product is produced differently for different

customers.Overall, the above discussion shows that decou-

pling point decisions have not been adequately

addressed in the available literature, and points to

limitations in theoretical approaches, as well as to the

scarcity of practical applications. On the one hand,

analytical approaches use rational and quantitative

models, but do not seem suited for being easily

implemented in practice, and actually have not been

implemented (with one exception). On the other hand,

case studies show that, in reality, managers tend to

base their decision-making mostly on qualitative and

intangible aspects. This is the theory–practice gap first

noted by Van Donk (2001), for which several reasons

exist. As pointed out by Kerkkanen (2007), inventory/

production decisions have cross-functional effects

on functions that have different levels of knowledge

of the problems treated. Moreover, SMEs might

find it difficult to deploy the (financial and human)

resources needed to implement complex decision

models. Finally, rational and formal models do not

take into account the effects of most contextual

factors that accompany decision-making in real-world

settings (Cohen 1993). Therefore, experienced decision-

makers operating in their ‘natural’ environment are

‘more concerned about sizing up the situation and

refreshing their awareness through feedback, rather

than developing multiple options to compare one another’

(Zsambok 1997). Consistent with this, they tend to

base their decisions on experience and common

sense. All these observations call for approaches

that are simple and illustrative, without giving up the


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ability to support the decision-making process in arational way.

3. Research methodology

To bridge the theory–practice gap, the research couldeither start with a theoretical model and refine it tobetter suit practice or vice versa. The first, ‘technologypush’ approach, might be the best choice when theorybuilding is the main objective, as argued by Stevensonand Silva (2008). In contrast, the second ‘marketdriven’ approach seems better suited to pursue thespecific objectives of this study. This article, in fact,marks the first step in a wider research program whichaims to design a theoretically sound planning frame-work through a refining process that starts withpractice, by adding to and/or improving rational andquantitative decision-making procedures. Consistently,our article does not propose any new planning policy:rather it drafts some new ideas on how establishedpolicies should be selected, combined and assignedto various products in order to fit their most relevantand sometimes conflicting requirements. This articleonly considers inventory planning issues: productionand capacity, as well as purchasing, are left forfuture development. The new framework proposed inSection 5 has been put into practice in the companydescribed in the next section.

Although our new framework does not pursue anyexplicit objective function, its general objective can bepractically defined as the search for inventory policiesthat are able to reduce the total inventory and set-upcost, without deteriorating (or better, improvingat the same time) the customer order response timeand the timeliness performance. Thus, in order tomeasure the potential of our framework, cost, time andtimeliness performances achieved under the old deci-sional system had to be compared with those gatheredusing the new decisional framework. Despite theavailability of results achieved by using the newplanning framework in practice, we considered thatthe comparison of these data with the old approachwould not provide a clear indication of the newframework’s potential, since the new data stem notonly from new methods but also from new anddifferent demand, and are also affected by the newinformation system and a new production manager.As a consequence we performed a static simulationanalysis based on a spreadsheet, so that the old andnew approaches could be compared under the sameconditions. Historical data on customer and produc-tion orders for the past 3 years were used as the

starting point. We divided this time frame into twointervals: an ex-ante period, consisting of the formertwo years, which was used for the item segmentation,planning policy setting and parameters calculation,following the framework described in Section 5, and anex-post period, corresponding to the last year, used toperform the static simulation of the new decisionalenvironment (NEW situation) and the comparisonwith the historical performances (OLD situation)achieved in the same year.

The direct output of the ex-post period simulationwas the entire set of production orders issued foreach product. Thus, the performances that could becomputed and used as key indicators in the compar-ison of the two planning environments are: theaverage lot size; the average stock; the average delayin customer order fulfilment; the set-up costs incurredin the planning horizon; and the holding costs inthe planning horizon. The performances achievedby the new planning environment were assessed byfirstly simulating them for a sample of representativeproducts, and then comparing them with the corre-sponding performance actually achieved by the sameproducts in the same ex-post year, but with the olddecisional context. Starting from the comparativeperformances achieved by the sampled items, weextrapolated the whole production mix behaviour byextending the average advantage achieved by thesampled products to all of the products, weightedbased on the demand volume of each product.Although this might not be the most scientificallyrigorous method to do this comparison, we foundit to be the most practical given the vast amountof products that should be evaluated (Section 4).Moreover, several cross-checks confirmed the overallsoundness of projected results.

4. The case study company

The company considered in this study manufacturessteel wires. It is a small–medium sized enterprise,employing more than 100 people and with annualrevenues of approximately E35 million. The productrange consists of more than 3000 finished products,differing by: chemical composition, diameter andtolerance, mechanical characteristics, surface finishingand packaging. The overall production volume is inexcess of 13,500 tons of finished wire per year. Thenumber of active customers is more than 300, 13% ofwhom account for 80% of the overall turnover.Demand is irregularly distributed: while a smallrange of standard products have a rather high and


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predictable demand, many other products experience

an unpredictable and even ‘lumpy’ consumption.The manufacturing process consists of the

following phases, illustrated in Figure 1:

(a) An initial surface treatment prepares the

material for the following drawing phase;(b) The wire drawing is performed until the target

diameter is reached;(c) An annealing treatment and, if needed;(d) other surface treatments, allow the required

strength and tolerance to be obtained;(e) The final activities are quality control and

packing.

If a very high diameter reduction is needed, the sequence

(surface treatment! drawing) can be repeated up to

three times until the required diameter is obtained.Cycle routings may vary significantly, as a function

of: the diameter reduction ratio, the type of material,

and the required mechanical characteristics. Machines

incur set-up activities that may last from 20min to

several hours according to the product sequence.

Given the extremely wide product range supplied,

the ‘default’ demand fulfilment policy adopted was

MTO. By exception, some particularly high and regular

demand products were produced for stock, with a target

stock level issued per week for each item and their

availability updated and faxed to key customers.No formalised or quantitative criteria were

adopted to support decision-making: for instance,

items considered appropriate for MTS production

were identified based on common sense and experi-

ence, rather than by numerically examining demand

patterns. Likewise, lot sizes were set by considering

the space available in the warehouse, as well as the

raw material coil dimension; the classical trade-off

between inventory and set-up costs was not considered.

Moreover, MTS orders were issued to keep machines

running rather than to replenish low inventories.No specific, formalised tool for inventory planningwas established within the company’s informationsystem. As a result, delivery times were long andunpredictable (ranging from 5 to 80 days), with a pooron-time delivery record. Many orders were deliveredearly and many others delivered late; the averagelateness was almost 6 days.

5. The new framework

5.1. Overview

Given the specific objectives pursued in this article, ouraim is not to suggest new inventory policies but ratherto devise a new framework to select and assign existingpolicies to the different products of the company toimprove efficiency (inventory and set-up costs) andeffectiveness (delivery performances). The new frame-work proposed in this article consists of four maindecisional steps:

(i) Products are segmented into homogeneousgroups with regard to demand volume andspecificity to customers (Section 5.2).

(ii) Next, an appropriate decoupling point isdetermined for each group on the basis ofdemand and customer analysis, as proposed inHuiskonen et al. (2003) and Soman et al. (2007),by considering the option of delayed differen-tiation as in Gupta and Benjafaar (2004).

(iii) Then, a specific replenishment policy is assignedto each product group on the basis of specificcustomer requirements (Section 5.4).

(iv) And finally, the parameters (lot sizes andtarget stock availability) needed to apply eachreplenishment policy to each considered prod-uct are computed by considering set-up andinventory holding costs and time parameters(Section 5.5).

Figure 1. Scheme of the production process in the case study company.


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Table 2 gathers the link between each frameworkstep and the existing literature.

Some of the decisional drivers identified in theliterature review described in Section 2 were notconsidered. For example, given that the companymanufactures steel wiring, perishability was not arelevant issue. Also, we did not have to consider therelationship between demand and available shop floorcapacity, since the company did not experience capac-ity constraints. However, capacity should be consid-ered in later versions of our decisional framework inorder to make it more general. Likewise, although set-up times for drawing benches are (moderately) affectedby the production sequence, this was not considered.In fact, given the medium term horizon consideredby our framework, we decided for the sake of sim-plicity to base computations on an average, sequence-independent set-up time.

5.2. Step 1: segmentation into homogenousproduct groups

This first decisional step identifies groups ofhomogenous products with regard to their sugg-ested decoupling point and inventory planning policy.It is performed through Pareto multi-criteria analysis(Flores and Whybark 1986), combining physicalvolumes with the number of customer orders(according to the two digit ABC analysis proposedby Krupp (1994)). The cross-analysis structure isshown in Figure 2.

The analysis generates five groups. Group 1consists of finished products with a very high andregular demand, resulting from the combination ofmany small orders issued by a large number ofdifferent customers. Group 2 encompasses finishedproducts with an almost equally high demand, but onlya few large orders: they are special products custom

made in large quantities for specific large scale

customers. Finished products with a medium level of

demand and number of orders are classified as Group

3. The BOM for the remaining (and very low demand)

finished items is analysed in order to identify common

intermediates. Intermediates can be examined using

the same bi-dimensional Pareto analysis as for fin-

ished products (production volumes and orders are

considered instead of sales and customer orders).

The common intermediates with a large and regular

demand, are assigned to Group 4, while the remaining

finished products (with no common intermediates) are

placed in Group 5.The segmentation process is based on demand

history, while a new product would fall under one of

the following three scenarios: (a) it is phased-in as a

variant of a pre-existing product that is phased-out

accordingly: in this case the new product is inserted

into the same group as the phased-out old product;

(b) it is introduced to fulfil a custom request of a

large scale customer with a shared order portfolio,

covering several months or even years: in this case

it fits the requirements of Group 2; (c) otherwise, the

Table 2. Link between the steps of our framework and existing literature.

Framework step Related literature Considered aspect

Productsegmentation

Flores and Whybark (1986)Krupp (1994)Huiskonen et al. (2005)

Multi-criteria Pareto analysis

Decoupling pointdetermination

Huiskonen et al. (2003)Soman et al. (2007)

Decoupling point set on the basis of demandand customer analysis

Gupta and Benjafaar (2004) Delayed differentiation option considered(e.g. MTS intermediate items)

Inventory policyassignment

Silver et al. (1998) Criteria for policy assignment

Parameter setting Silver et al. (1998) Basic criteria for parameters settingsSilver and Robb (2008) Advanced criteria for parameters settings

Figure 2. Pareto cross matrix for finished products andintermediate items.


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new product is firstly inserted into Group 5. Sincethe whole procedure is periodically reiterated in orderto refresh the various decisions on the ground ofnew data, the assignment of new products can bere-assessed as soon as sufficient historical demand datais available.

5.3. Step 2: decoupling point determination

Three possible decoupling points are considered in ourframework (as exemplified in Figure 1): raw materials,intermediates and finished products in the warehouse.In accordance with Silver et al. (1998), products withingroups 1 and 2 are MTS. Therefore, their decouplingpoint was positioned at the finished product level.Products belonging to Group 4 are decoupled at theintermediate level. Many products in the group sharethe same intermediate: as a consequence, althougheach finished item has low and unpredictable demand,the corresponding intermediates share a rather highand regular production volume. This way of fulfillingcustomer demand was named make-to-stock-finish-to-order (MTSFTO): for further details on the finish-to-order policy, see Dobson and Stavrulaki (2003).Finally, the decoupling point for items belonging togroups 3 and 5 is positioned at the raw material level(i.e. MTO), given their unpredictable and irregulardemand. This is logical because each raw materialcan be used to manufacture on average around 50–60finished products, so all raw materials tend to have apredictable and regular demand pattern.

5.4. Step 3: replenishment policy assignment

Table 3 illustrates the replenishment policies assignedto each group, combined with the respective decou-pling point.

An MTS policy was considered appropriate forproducts in group 1, characterised by regular andrepetitive demand consisting of many small ordersissued by several independent customers. The replen-ishment policy proposed is a variation of the (R, s, S)that Scarf (1960) proved, under quite general assump-tions, to minimise the total review, replenishment,carrying and shortage costs. More precisely, thereplenishment policy selected for items within Group1 is a fixed reorder time model (R, S) (Silver et al.1998). Replenishment orders are released at a fixedtime interval (R), with a quantity that allows the totalavailable stock to be restored to the order-up-to level(S). In accordance with Silver et al. (1998), the safetystock was set so as to guarantee a given fill rate and tocover demand variability during the replenishment leadtime. The simple periodic stock review required inorder to implement this inventory policy was sup-ported with a semi-automated spreadsheet procedure.A more detailed discussion on the (R, S) periodicreview inventory system and its optimality conditions,together with some managerial interpretations, can befound in Silver and Robb (2008).

Products in Group 2 are characterised by a highand regular demand and are purchased by only onecustomer: hence, customer–supplier collaboration iskey for inventory planning. Customers should providea periodic consumption plan, with a single item detailover a horizon of some periods, with different levelsof flexibility on the quantities indicated. A rollingcollaborative requirement planning policy has tobe activated: customer demand plans allow the netrequirements of finished products to be computed,and production and raw material replenishment ordersto be issued.

Products within Group 3 have medium sales withsome regularity. Demand is not high enough to justifya pure MTS policy, while the existence of severalcustomers and orders for these items suggested thatreplenishment order quantities could be planned muchlarger than the average customer order, to save onchangeover time and cost. Therefore, production ofthese items is triggered by a customer order, but theminimum production quantity has been set to theeconomic order quantity (EOQ; see Harris (1913)). Asa consequence, each time a replenishment order isissued, part of it serves the related customer orderwhile the remainder is stored and used to meet futurecustomer orders. Group 4 encompasses semi-finisheditems with high and regular volumes, used for a largenumber of finished products and managed using amake-to-order policy. Consistently, their decouplingpoint is set at the intermediate item level. Thereplenishment policy chosen for intermediate items

Table 3. Inventory policies combined with decoupling pointpositioning.

Decoupling point positioning

Replenishmentpolicy

Rawmaterials Intermediate Finished

L 4 L Group 5 Group 4(intermediateto finished)

–

EOQ Group 3 –Monthlyrolling

Group 2

R, S Group 4 (rawto intermediate)

Group 1


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within this group is similar to that chosen for finisheditems within Group 1, since the demand characteristicsof these two groups match. Moreover, the correspond-ing finished items are replenished in a lot-for-lot (L4L)fashion, given the absence of repetitiveness in theirdemand patterns. Finally, products in Group 5 arealso replenished by means of a L4L policy becauseof their very low and sporadic demand. Therefore,these products can be produced only on the basis ofcustomer orders, and production matches the customerorder quantity.

5.5. Step 4: parameter setting

The setting, monitoring and periodic updating ofparameters can be supported by a simple informationtechnology tool, which gathers data from a centraldatabase, loads it into a preconfigured spreadsheet andenables the parameters for each product in each groupto be updated. Given the inventory policies describedabove, and according to Silver et al. (1998), thefollowing parameters have to be set:

(i) The EOQ, used both directly in Group 3 toset the replenishment quantity and indirectlyin groups 1 and 4 to set the review period (r).The EOQ is calculated according to theHarris–Wilson EOQ formulation (Harris 1913, Wilson1934) that requires three main data, as follows:

. Expected demand, which is extrapolated onthe grounds of historical demand.

. Set-up costs, that consider both physicaland opportunity components. The oppor-tunity cost connected with the capacity lossdetermined by each set-up is computedas the contribution margin. The physicalset-up cost is, instead, the sum of all directcosts connected with changeover activities,such as specific materials.

. Holding costs, computed as the sum ofa financial and a physical component.The financial part depends on the need toanticipate the working capital connected toraw material, WIP and finished productsstill on hand. It is evaluated by takinginto account the marginal interest rate onfinancial debt. The physical storage costis evaluated on the basis of a third-partywarehousing facility pricelist.

(ii) The review period, R, used to schedule stockreviews and the issuing of replenishmentorders in groups 1 and 4 was set equal to thenumber of weeks that best approximates

the EOQ model under average demandconditions.

(iii) The order-up-to level, S, used to determine thereordering quantity in Groups 1 and 4, was setto the sum of the average demand during thereview period and production lead time plussafety stock (computed according to the vari-ability of the demand during the lead time).

6. Empirical application

6.1. Projected results

The framework illustrated in Section 5 was appliedto the case company described in Section 4. The resultsof product segmentation and the related inventorypolicies assigned are reported in Figure 3. The smallnumber of rather repetitive products clustered inGroups 1, 2 and 3 produce almost 70% of thevolume and sales despite accounting for less than15% of products. They define a ‘standard and repet-itive factory’ within the overall factory in contrast tothe ‘custom and lumpy factory’ encompassing the pureMTO products in Groups 4 and 5. Products belongingto the ‘standard and repetitive factory’ should bemanaged with a focus on efficiency, while effectiveness(achieved with a short delivery time) should be consid-ered as given. By contrast, custom products should bemanaged by focusing on effectiveness (especially designquality, conformity and delivery time).

In order to evaluate the benefits that can beachieved with the framework application by the casecompany, we performed a simulation of the newinventory policies under the ex-post period of oneyear, as described in Section 3. In the following,we report the results of the comparison between theold and new decisional context for three products,one from each of Groups 1, 2 and 3, chosen for being

Figure 3. Product segmentation results.


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particularly representative of their groups. It wasnot possible to evaluate Group 4 because no histori-cal data was held in the old information systemabout intermediate items, while the inventory policyfor Group 5 did not change substantially and soa comparison was not necessary. Table 4 presentsthe exogenous and endogenous parameters for theselected products while the comparison is reportedin Table 5.

Table 5 reveals how the new planning frameworkdramatically reduced the number of production orders,yielding an increase in average stock and correspond-ing holding costs. However, this increase is more thanoffset by the reduction in set-up costs, meaning totalcosts are also reduced.

Note that holding costs in the NEW scenarioare computed considering a third-party warehousingservice, so as to take into account the overall inventorycost regardless of the amount of additional spacerequired. This explains the difference between theaverage increase for the quantity in stock (þ40%) andthe increase in holding costs (þ200%). For timeliness,items in groups 1 and 2 (accounting for 63% of theoverall production volume) are supplied from stockunder the NEW scenario, so their delivery time anddelay are equal to zero. Moreover, items in Group 3

can be supplied with a much reduced delay in theNEW scenario, given the lot sizing policy adopted. Thesample item chosen in this group had 16 customerorders in the considered ex-post year: in the OLDscenario as many as 11 production orders were issued,meaning that only 4 out of 16 customer orders(25% of the total amount) were fulfilled from stock.In the NEW scenario, only 4 production orders wereissued to cope with the same number of customerorders: therefore the percentage of customer ordersfulfilled from stock has increased to 75%, whichexplains the average delay reduction.

Results aggregated and projected (as explained inSection 3) are reported in Figure 4, where a decrease inthe total cost is achieved due to the balancing effect ofset-up and holding costs that leads to an overall savingin excess of 36%. According to historical data, themain planning policy used to be an almost pure MTO,which is confirmed by a very low level of holding costs,and by set-up costs which account for more than92% of the total costs within the old decisionalenvironment. However, this almost pure MTO policyis significantly outperformed by the mixed policiesconsidered by the new decisional context, because ofthe better balance achieved between inventory holdingand set-up costs.

Table 5. OLD vs NEW performance comparison for the three selected items.

Group 1 Group 2 Group 3

Parameter OLD NEW D% OLD NEW D% OLD NEW D%

Number of production orders 22 5 �77 21 11 �48 11 4 �64Average production order [ton] 1.9 11.8 þ520 4.6 9 þ95 3.1 8.4 þ171Average stock [ton] 5 7 þ40 1.63 1.27 �22 0.8 2.8 þ250Average delay [days] �0.9 (max¼ 49!) 0 �100 3.1 0 �100 4.2 2.3 �45Set-up costs [E/year] 4044 920 �85 928 486 �48 2021 735 �64Holding costs [E/year] 486 1456 þ200 162 126 �22 144 504 +250Total cost 4530E 2376E �48 1090E 612E �44 2165E 1239E �43

Table 4. Product parameters.

Representative items from each group

Group 1 Group 2 Group 3

Demand 63 [tons/year] 100 [tons/year] 34.6 [tons/year]133 [customer orders] 23 [customer orders] 16 [orders]59 customers 1 customer 4 customers

Planning policy Review period¼ 9 [week] N/A EOQ¼ 8.4 [tons]Reorder level¼ 14.6 [tons]Safety Stock¼ 3.4 [tons]


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Note that the results for the NEW decisionalenvironment for products within Groups 4 and 5 wereassumed to be unchanged from the OLD environ-ment. While this is reasonable for products in Group5, it does not account for the reduction in set-up andinventory costs gained by shifting the decouplingpoint for Group 4 products from the raw materialto the intermediate product stage. Moreover, stockholding costs within the NEW decisional scenariowere computed by considering the prices of anexternal warehousing facility, given the sharp increasein inventory suggested by the new framework: never-theless it was later discovered that enough space wasavailable to cope with the increase in inventory (seeSection 6.4. for more details). Thus, results presentedin Figure 4 are likely to underestimate the overall costreduction obtained.

6.2. Implementation issues

Once the benefits achievable thanks to the newdecisional environment were estimated through simu-lation, the company decided to implement the newdecisional framework in practice. The new planningenvironment has been transferred to the companymanagers and supervisors (especially in sales andproduction). The transfer process encompassed thefollowing steps, which took approximately 6 monthsto complete:

(i) First, the various decisional drivers identifiedin the literature were introduced and dis-cussed, in order to find the best fit with theenvironment.

(ii) Then, the segmentation methods proposedwere explained to managers and supervisorsand an appropriate means of implementingthem was explored.

(iii) Next, the choice of decoupling points andinventory policies was discussed in depth. Forproducts in Group 2 a monthly plan with afour month horizon was arranged, with dif-ferent levels of flexibility allocated to differentmonths: quantities forecast for month 1 wereconsidered as fixed orders; quantities forecastfor month 2 were allowed a review margin of�10%; and quantities indicated for months 3and 4 were considered as only indicative andopen to change by the customer. Moreover,the MTSFTO policy selected for productsand intermediates within Group 4 requiredBOM to be updated. This was a long anddelicate task.

(iv) Then, we developed a set of spreadsheetapplications, in order to download customerorders and technical data from the centraldatabase and to make all the computationsneeded in order to segment products anddetermine their inventory policy parameters.

(v) And finally, these applications were trans-ferred to managers. At the end of the trans-fer process, the staff involved could use thespreadsheet-based applications and all appre-ciated their ease of use and transparency.

As for the results, it is difficult to compare themto any of the performances illustrated in Section 6.1because they were not only due to the new planningdecisional framework but also to the different decis-ional style of a new production manager, the differ-ent informational and decisional support of the newinformation system, and (above all) a new set of inputdata (for example, a different production mix withdifferent customer orders). Nevertheless, in the yearfollowing our project, the company’s sales soared by20%, due to the combined effect of an increase in theprice of finished products (due in turn to an increasein the price of steel, and a shift in the production mixfrom carbon to stainless steel wire) and an increasein overall output. An increase in output was facilitatedby more capacity at the drawing benches as a resultof fewer changeovers, made possible by larger lot sizescombined with the switch from pure MTO to someforms of MTS.

Meanwhile, the quantity of finished productsin stock more than doubled because of the largerproportion of products and volumes that were pro-duced to stock. The availability of some free spaceallowed the additional inventory to be stocked withinthe plant. Moreover, the warehousing of finishedproducts was further simplified by the adoption ofthe new decisional framework due to the sharp

Figure 4. Cost comparison generalisation.


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reduction in the number of handling operationsconnected with larger production lots. However, thiscost reduction has not been quantitatively evaluatedand considered in Figure 4 because both personnel andequipment remained within the company and were notoutsourced.

7. Discussion and conclusion

In Section 5 we presented a new, easy-to-use andsufficiently straightforward decisional framework toplan inventory in a generic multi-stage manufacturingcompany. We described a field implementation as ameans of developing a theoretically sound, yet practi-cally usable, method. The test, performed at a small steelwire producer, supplied evidence that the new frame-work can provide a significant performance improve-ment when compared with a less formalised planningapproach. An improvement in the trade-off betweeninventory and set-up costs was achieved, improvingon-time delivery performance at the same time.

The reasons why this positive result was achievedare threefold. Firstly, this method addresses a hierar-chical gap in the operations decisional frameworkof the targeted company. Before the implementationof the new method, decisions tended to be taken eitherat the long-term (budget), or short-term (scheduling)level, while the mid-term (planning) level was almostdisregarded: this led to a poor alignment betweenlong and short term plans and resulted in an ‘all isurgent’ syndrome. Secondly, the framework identifiedtwo different ‘virtual factories’ co-existing within thesame manufacturing system. Products with a high andsteady demand are few but represent a large percentageof volume and sales. By offering these to customersfrom stock (or by producing them in larger lots),not only has the company achieved an unprecedentedlevel of efficiency, but also has the opportunity tofulfil demand that previously was lost to competitors.Improving efficiency also freed up capacity that couldbe used in a reactive manner when producing otherproducts to order or when attempting to accommodatelarger production volumes. Thirdly, planning decisionsare now based on logical and quantitative reasoning,supported by formalised methods and tools, and aimedat improving rational and quantitative objectives.The lack of formalised and straightforward planningactivities, together with the tendency to use the sameplanning policies for all items and products simplybecause they are produced in the same way, isconsidered commonplace in many manufacturingSMEs. Hence, the proposed method could also beuseful in other similar companies.

It should be noted that the projected results showa severe increase in holding costs (þ200%); this isconsidered appropriate for the particular setting ofthe case study selected but the proposed frame-work does not necessarily lead to an inventoryincrease or decrease. Rather, it attempts to improveoverall performance by better balancing inventoryand changeover costs. In line with this remark,Cannon (2008) underlines that inventory reductionis not always a solid indicator of overall improvedperformance. It is also important to note that set-upcost savings are connected with the sharp reduction inchangeover operations obtained by setting largerproduction lots. As explained in Section 5.5, part ofthe set-up costs was computed as the opportunitycost connected with the loss of production capac-ity consumed by changeover operations. Therefore,a part of set-up savings achieved refers to poten-tial savings in lost capacity (a detailed discussion on thiseffect can be found in Spence and Porteus (1987)).

This article adds to the available literature bycontributing to bridging the theory–practice gap,proposing a rational and quantitative inventory plan-ning approach which retains its usability in practicalenvironments. We chose to ground our study in prac-tice and proceed towards theory, instead of doing thereverse. Thus, the new decisional framework proposedwas developed using standard, well-established andeasy-to-use methods, requiring only simple and readilyavailable data and being supported by standard tools.Nonetheless, the proposed approach incorporatesseveral contributions from the theoretical literaturediscussed in Section 2. In particular, our frameworkadopts decisional drivers proposed in previous works,as highlighted in Section 5.1: the demand and customeranalysis, the delayed differentiation option, and thecost structure. However, in Section 2 we pointed outthat the analytical models in the literature have limita-tions that prevent real-life application. Comparing ournew approach with the six analytical models analysedin Section 2, we believe that our framework overcomesmost of their limitations, in that:

(i) it handles, without major problems, a realenvironment with fivemajor production stages,three different decoupling points and morethan 3000 final products. On the contrary, thereviewed models are single-product (Li 1992,Sun et al. 2008), single-stage (Arreola-Risaand Decroix 1998) and/or do not considerintermediate products (Rajagopalan 2002,Kaminsky and Kaya 2008);

(ii) it considers set-up costs as a relevant decisio-nal driver, accommodating decision support


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for products with high and stable demandalongside products with sporadic and irregulardemand. Set-up costs are neglected by four outof six models analysed, and low demand itemsare not considered in some other models(Rajagopalan 2002);

(iii) it accommodates the choice of a differentdecoupling point, replenishment model andconnected parameters for each finishedproduct, intermediate item or raw material.Models in the literature allowing for a decou-pling point at an intermediate level consideronly pure MTS or pure MTO inventorypolicies (Gupta and Benjafaar 2004), or over-simplify the product range to a single-productcase (Sun et al. 2008);

(iv) finally, and perhaps most importantly, it doesnot require a PhD laureate for it to be imple-mented and run. Most analytical models in theliterature are arguably too complex for an SMEproductionmanager, or require data difficult tocollect or estimate, as highlighted in Section 2.

Although our new framework, differently from theanalytical models in the literature, does not optimiseany explicit objective function, we find that it supportsa large amount of decision-making with quantitativeand rational methods as compared to the otherframeworks and case studies reported in Section 2.In fact, the cost structure as well as time performances(timeliness of deliveries) are quantitatively assessed as away to define the thresholds for product segmentation(and therefore the decoupling point and inventorypolicy decisions) through a trial-and-error approach,in order to find a ‘good compromise’ between costsand service level. Moreover, since its quantitativemethods can be supported by automated and easy-to-use tools, the application of our framework mightimply less judgemental and time-consuming steps thansuggested, for instance in the case studies presentedby Van Donk (2001), Huiskonen et al. (2003) orKerkkanen (2007). In our case application, mostjudgmental analysis on the product segmentationresults was devoted to Groups 1 and 2, whichaccounted for a small fraction of the whole productmix. Furthermore, our approach suggests a rathergeneral framework that could be applied to severalindustrial contexts, while part of the analysed literaturecalls for an application to a very specific context, suchas the food industry (Van Donk 2001, Soman et al.2004, 2007) or a steel mill (Kerkkanen 2007).

The proposed approach has some limitations thatfuture research should address. Firstly, our decisionalframework does not consider such relevant planning

activities as capacity and purchasing: these were notcritical tasks within the considered case study butshould be added before our method can be consideredsufficiently general, as suggested by Arreola-Risa andDeCroix (1998) and Gupta and Benjafaar (2004).Secondly, despite the fact that the decisional frame-work is generally more rational and quantitative thanthat previously used in the considered company, wewould like to increase its quantitative and rationaldecision-making support. For instance, it would beinteresting to formulate an explicit optimal objectivefunction for costs or profit; an equally interesting newfeature would be to set thresholds for the segmentationmatrices depicted in Figure 2 in a way that optimisesthe objective function – this would be an improvementon the current trial-and-error approach. Thirdly, manymore real-life cases should be tested before any suchmethod can be declared generally applicable. Andfinally, although this research program aims to developpractically usable methods, quantitative comparisonsshould be made with scientific methods found in theliterature before we can be satisfied with the resultsachieved.

Acknowledgements

The authors wish to thank the (anonymous) referees for theirdemanding comments that indicated how to obtain, in ourview, a sharp increase in the quality of this article.

Notes on contributors

Marco Perona is full professor ofIndustrial Logistics at the Universityof Brescia (Italy). His research activityis devoted to operations management,supply chain management, IT man-agement and service management.In these fields he has published todate more than 40 papers in highlyregarded international journals. He is

Scientific Director of the Research Centre on ServiceManagement at the University of Brescia. He leads a smallteam of researchers and consultants with a portfolio ofprojects within leading international companies such asNestle, Procter & Gamble, Kone, Mediamarket and Ferrari.

Nicola Saccani (Department ofMechanical & Industrial Engineering,University of Brescia, [email protected]) holds a PhD inOperations Management, and isAssistant Professor at the Universityof Brescia (Italy). His research andpublications concern service opera-tions and management, inventory


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management, and buyer–supplier relationships. He isa member of the Research Centre on ServiceManagement at the University of Brescia, and of the ASAPService Management Forum initiative (http://www.progettoasap.org/).

Simone Zanoni ([email protected])is currently Assistant Professor at theMechanical & Industrial EngineeringDepartment at the University ofBrescia (Italy). He graduated (withdistinction) in 2001 in MechanicalEngineering and took his PhD atthe University of Brescia (Italy) in2005. His main research interests are:

Layout Design, Inventory Management and Closed-loopSupply Chains.

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