JOSCM - Journal of Operations and Supply Chain Management – Vol. 10, n. 2 - Jul/Dec 2017

1 JOSCM | Journal of Operations and Supply Chain Management | FGV EAESP
FORUM
Invited article
DOI: http://dx.doi/10.12660/joscmv10n2p01-05
SUPPLY CHAIN AND OPERATIONS STRATEGIES
FOR PROBLEM-SOLVING IN LATIN AMERICAN
COUNTRIES: AN INTRODUCTION
Cristiane Biazzin
cristiane.biazzin@fgv.br
Professor at Fundação Getulio Vargas, Escola de Administração de Empresas de São Paulo – São Paulo –
SP, Brazil
Elyn L. Solano-Charris
erlyn.solano@unisabana.edu.co
Professor at Universidad de La Sabana, Escuela Internacional de Ciencias Económicas y Administrativas
– La Sabana, Colombia
Jairo Alberto Jarrín Quintero
jairo.jarrin@unisabana.edu.co
Professor at Universidad de La Sabana, Escuela Internacional de Ciencias Económicas y Administrativas
– La Sabana, Colombia
ISSN: 1984-3046© JOSCM | São Paulo | V. 10 | n. 2 | July-December 2017 | 01-05

2 AUTHORS | Cristiane Biazzin | Elyn L. Solano-Charris | Jairo Alberto Jarrín Quintero
INTRODUCTION
Companies are subject to external and internal con-
straints, and they constantly seek ways to respond
to these forces to survive and thrive (Chakravarty,
2014). Shifting globalization patterns and disrupt-
ing technologies call the feasibility of current op-
erations and supply chain strategies. Global value
chains remain concentrated among a relatively small
number of countries (Hallward-Driemeier & Nayyar,
2018). As reported by the Economic Commission for
Latin America and the Caribbean (ECLAC, 2016),
Foreign Direct Investment (FDI) flows in Latin
America decreased by 16% in 2014 and by 9.1% in
2015 driven by the prices decline and the economic
slowdown in the region. Meanwhile, FDI flows grew
by 90% to developed countries, even in a context of
high uncertainty in the global economy.
A variety of possible explanations have been dis-
cussed in the literature, including competition and
trade (Kehoe & Meza, 2011; De La Torre et al.,
2016), corruption (Gaviria, 2002) and infrastruc-
ture investments (Ramamurti& Doh, 2004; William,
2015; Fay et al., 2017). Unfortunately, few stud-
ies have looked at the supply chain and operations
management decision-making process in emerging
countries (Avittathur & Jayaram, 2016), specifically
in Latin American countries, and explored how they
overcome the barriers imposed by the institutional
environment. Rare exceptions like Williams (2015)
focused on how to capture FDI for Latin America by
expanding the stock of infrastructure and its qual-
ity. Due to the chronical issues of infrastructure, lo-
gistics, cultural and language limitations, managers
have been setting up creative ways of defining supply
chain and operations strategies to maintain its com-
petitiveness.
This Special Issue intends to open a broad agenda to
raise awareness among Operations and Supply Chain
researchers of the importance of exchanging experi-
ences from different fields of knowledge instead of
exploit findings, constraints, lessons learned at a
single perspective (Siegler et al., 2014). We intend
to motivate the possibility of stablishing new col-
laboration between researchers in Latin America,
exploring different contexts and aiming for comple-
mentary ideas on Supply Chain and Operation strat-
egies for problem solving. Consequently, it aims to
advance this discussion through some examples of
well-succeeded strategies adopted in Latin America
countries and contribute for addressing new direc-
tions for future researches.
DOING BUSINESS IN LATIN AMERI-
CAN COUNTRIES – NEW DIRECTIONS
TO OVERCOME CURRENT SUPPLY
CHAIN AND OPERATIONAL BARRIERS
ForOperationsManagement(OM)andSupplyChain
Management (SCM), one of the main challenges for
the economic growth of Latin America countries is
its current infrastructure, which is inferior to what is
needed. In one side, several specialists argue that the
solution of advancing it is to spend more. However,
Fay et al. (2017) argues that Latin American coun-
tries shall grasp its attention by spending efficiently
on the right things. According to the authors “[…]
there is sufficient evidence that spending better and
focusing scarce public resources on what matters
would significantly narrow the service gap”. In this
same direction, it is necessary that the governments
and institutions involved integrate serious studies
carried out by experts, and decisions regarding pub-
lic policies in logistics and operations, that benefit
the sustainable balance of logistics operations.
Figure 1 illustrates the impact of the infrastructure
for doing business through the comparison between
the “ease of doing business index” and the “quality
of port infrastructure” (both data available at World
Bank Database, 2017). Briefly, for the “ease of doing
business index” the lower the rate, the friendlier the
environment is for doing business. In the “quality of
port infrastructure” the ranges are defined from ex-
tremely underdeveloped (#1) to well developed (#7).
Based on the current situation of Latin America
countries, it is perceived that the higher the qual-
ity of port infrastructure is, the easier becomes do
business with organizations. Undoubtedly, the ef-
fort of improving infrastructure would support the
improvement of doing business with Latin Ameri-
can countries. However, according to World Bank
specialists, Latin America countries are unlikely to
have an increase investment in infrastructure in the
coming years.

3ARTICLES |Supply Chain and Operations Strategies for Problem-Solving in Latin American Countries: An Introduction
Figure 1. The Relation between Quality of port infrastructure and the ease of doing business with Latin American
countries
Source: Based on World Bank Database (2017).
Another issue that concerns the society and im-
pacts the firms and institutions competitiveness is
corruption, fraud and counterfeit. Corruption and
bribes raise operational costs, lower sales, impact
firm’ competitiveness and create uncertainty (Ga-
viria, 2002). Prior assumption is that bribes can in-
crease efficiency by granting companies to influence
governments from developing excessive and overly
restrictive regulations. However, Gaviria (2002)
argues that most of the cases, government regula-
tions are strategically used by bureaucrats to maxi-
mize bribe collection.
Besides, fake products and counterfeit is also quite
dangerous for society sustainability and business
competitiveness. According to Li (2013, p. 168)
“[…] Faking products has developed into an exis-
tential threat to the rights of both businesses and
consumers. This threat calls for anti-counterfeiting
technology to safeguard authentic products and
keep companies from unfair competition.”
Under such critical circumstances it is reasonable to
infer that practitioners should be in a position where
their hands are tied. Practitioners’ intentions and be-
liefs are closely related to the environment and their
narratives are justified in the adoption of a differ-
ent strategy or neglect of moving forward (Biazzin
et al, 2017). However, it is worth remembering that
inertia in supply chain management undermine the
operational efficiency and productivity of a com-
pany (Smith et al, 2015). Organizations must stand
against these illegal practices by neglecting to join
this “game” through robust reforms on Operations
and Supply Chain strategies, changing behaviours,
processes and implementing new technologies.

4 AUTHORS | Cristiane Biazzin | Elyn L. Solano-Charris | Jairo Alberto Jarrín Quintero
In fact, as it might be noticed, several constraints
limit Latin American competitiveness. On one
hand, it means that Latin America has been los-
ing ground. However, on the other hand, Cooper et
al. (1997, p. 5) claims that “[…] successful supply
chain management requires a change from man-
aging individual functions to integrating activities
into key supply chain processes. It calls for disrup-
tive, technological and even creative actions ad-
dressed by private sectors, to rescue and redefine
strategies for managing operations and supply
chain for value creation.
In this sense, one example of strategies implement-
ed by Public sector is the advancement of e-pro-
curement approach. E-procurement is a technology
solution that go through all procurement process,
including e-design of specifications until the suppli-
er performance evaluation (Presutti, 2003). Due to
its virtual characteristic by consolidating data with-
out face-to-face contact, e-Procurement is known as
a robust mechanism to fight against fraud and cor-
ruption. Chile and Mexico, for instance, achieved
superior results through strong procurement re-
forms. The electronic portal ChileCompra estimates
US$ 280 million in savings, while Mexico’s tender-
ing modernization generated about US$ 1 billion
savings in three years (Fay et al., 2017).
According to Hallward-Driemeier and Nayyar
(2018), there are three alternative to be prepared
for change. The first one is regarding the urgency
of reforms that reduce the unit labor costs, ensure
new business models formation, new ways to ad-
vance buyer-suppliers relationships as well as new
ways to structure production of goods and servic-
es. Then, in order to attend the previous scenario,
new capabilities must be identified and developed
i.e., their capacity to handle new technologies and
take smart and fast decisions through complex
data sets. Finally, authors argue that increasing
the connectivity will not only support clear and
open trading strategies both in product, service
and operations performance, but advance the in-
ternet of things implementation.
THIS SPECIAL ISSUE CONTRIBUTION
This special issue offers an important contribution
for advancing this dialogue in our field. It contains
a range of different approaches to present Supply
Chain and Operations Strategies for Problem-Solv-
ing in Latin American Countries. In particular, it
describes different strategies for enhance organi-
zational competitiveness through manufacturing,
technology, cooperation, among others; and its im-
pact in the supply chain management.
Social Sustainability in Supply Chains: A Latin
American Country Case. This paper explores how
to overcome social sustainability issues in Supply
Chain in Latin American context.
Simulation Analysis of a Tannery Fabrication Pro-
cess. The authors provide a Discrete Event Comput-
er Simulation to analyse the current performance
of a Tannery production system in order to propose
alternatives for improvement, as well as optimum
parameters for production.
An Implementation Framework for Additive Man-
ufacturing in Supply Chains. Analyse the existing
supply chain methods and frameworks of additive
manufacturing and its impact in supply chain man-
agement.
Technology, Production Paradigm and Operation:
Transformation of Brazilian Brewing Sector. The
article explores the technological transformation of
the brewing sector for creating innovative manage-
ment and operations in Brazil.
The “Indy Way”: Lessons from Brazilian Sugar-Cane
Biofuel Supply Chain. The authors study how the
Brazilian sugar-energetic processors used Indycar
racing to increase exports to the United States and
create value by transforming the Brazilian ethanol
from a commodity fuel to an advanced biofuel.
The Effect of Uncertainty and Cooperative Behaviour
on Operational Performance: Evidence from Bra-
zilian Firm. This study aims examines the effect of
manager’ uncertainty on cooperative behaviour in
interorganizational relations, and how this affects
operational performance.
ACKNOWLEGEMENTS
We would like to thank the previous Editor in
Chief, Juliana Bonomi, for supporting the initial
idea of this special issue and the current Editor
in Chief, Luciana Vieira for opening this space
for contributions in this important subject. We
thank all submitting authors and the reviewers
who allowed us to give relevant feedbacks for all
papers received (approved or not).

5ARTICLES |Supply Chain and Operations Strategies for Problem-Solving in Latin American Countries: An Introduction
REFERENCES
Biazzin, C., Sacomano Neto, M., Candido, S. E. A. & Paiva, E.L.
(2017). Why is so hard to disseminate operational capabilities?
Cultural and political conditioning factors in an intra-organisa-
tional network, Working Paper, EAESP/FGV and UFSCar.
Cooper, M.C., Lambert, D. M., & Pagh, J. D. (1997). Supply
chain management: More than a new name for logistics.
International Journal of Logistics Management, 8(1), 1-15.
De la Torre, A., Ize, A., Lederman, D., Bennett, F., & Sasson.
M. (2016). The big switch in Latin America: Restoring growth
through trade. Chief Economist Office, Latin America and
the Caribbean, the World Bank.
Fay, M., Andres, L. A., Fox, C., Narloch, U., Straub, S., & Slaw-
son, M. (2017). Rethinking Infraestructure in Latin America
and the Caribbean. The World Bank Report.
Gaviria, A. (2002). Assessing the effects of corruption and
crime on firm performance: Evidence from Latin America.
Emerging Markets Review, 3(3), 245-268.
Hallward-Driemeier, M., & Nayyar, G. (2017). Trouble in the
Making? World Bank Publications.
Kehoe, T. J., & Meza, F. (2011). Catch-up growth followed by
stagnation: Mexico, 1950-2010. Latin American Journal of
Economics, 48(2), 227-268.
Lewis-Faupel, S., Neggers, B. Y., & Pande, R. (2016). Can elec-
tronic procurement improve infrastructure provision? Evi-
dence from public works in India and Indonesia. American
Economic Journal: Economic Policy, 8(3), 258-283.
Presutti, W. D. (2003). Supply management and e-procure-
ment: Creating value added in the supply chain. Industrial
marketing management, 32(3), 219-226.
Ramamurti, R., & Doh, J. P. (2004). Rethinking foreign infra-
structure investment in developing countries. Journal of
World Business, 39(2), 151-167.
Siegler, J., Biazzin, C., & Fernandes, A. R. (2014). Fragmen-
tação do conhecimento científico em Administração: Uma
análise crítica. RAE-Revista de Administração de Empresas,
54(3), 254-267.
Smith, M. F., Lancioni, R. A., & Oliva, T. A. (2005). The effects
of management inertia on the supply chain performance of
produce-to-stock firms. Industrial Marketing Management,
34(6), 614-628
Williams, K. (2015). Foreign direct investment in Latin Ameri-
ca and the Caribbean: An empirical analysis. Latin American
Journal of Economics, 52(1), 57-77.

FORUM
Submitted 27.07.2017. Approved 02.11.2017.
Evaluated by double blind review process.
ScientificEditors:CristianeBiazzin,ElynL.SolanoCharris,andJairoAlbertoJarrínQuintero.
SIMULATION ANALYSIS OF A FABRICATION
PROCESS OF A TANNERY: CASE STUDY OF A
LATIN AMERICAN COMPANY
ABSTRACT
A large number of real-life optimization problems in economics and business are
complex and difficult to solve. Among the solutions techniques available in the
Management Science, Discrete-Event computer Simulation (DES) can be considered
as one of the most preferred by practitioners. DES has been used as an analysis tool
to evaluate new production system concepts, and has also been used in the op-
eration and planning of manufacturing facilities. In this paper, we propose to apply
DES for the analysis of a leather manufacturing facility. The objective is to analyze
the current performance of the production system in order to propose alternatives
for improvement, as well as optimum parameters for production. Results obtained
showed the advantages of using such a quantitative decision-aid technique by cap-
turing most of the complex characteristics of the production process.
KEYWORDS|Leatherfabrication,simulation,processimprovement,casestudy,Colombia.
Carolina Pirachicán-Mayorga
caropm23@gmail.com
Professor at Pontificia Universidad Javeriana, Departamento de Procesos Productivos – Bogotá, Colombia
Jairo R. Montoya-Torres
jairo.montoya@unisabana.edu.co
Professor at Universidad de La Sabana, Facultad de Ingeniería – Chía – Cundinamarca, Colombia

7 AUTHORS | Carolina Pirachicán-Mayorga | Jairo R. Montoya-Torres
INTRODUCTION
As explained in detail by Blanco and Paiva (2014),
Latin American countries have a booming middle
class increasingly demanding more sophisticated
products and services. Global companies are investing
in new plants in Latin American countries in order
to decrease logistics costs. Some local multinational
companies (“multi-latinas”) are strong global players
in diverse industries (e.g., airplanes, food, oil, cement,
beverages, banking and telecommunications, leather,
etc.). As a consequence, research works inspired in
real-life cases have enriched the fields of operations,
logistics and supply chain management.
The purpose of the current paper is to study a real-
life decision-making problem related to the opera-
tion of the fabrication process of a tannery in Co-
lombia. As most real-life optimization problems in
economics and business, this manufacturing process
is complex and difficult to solve. The Operations Re-
search community has developed efficient and effec-
tive solution techniques since its first applications in
industry in the 1940’s (Hillier and Lieberman 2005).
Traditional solution approaches include mathemati-
cal programming (linear, integer and even nonlinear
modeling), dynamic programming and exact algo-
rithms like branch-and-bound techniques. Because
of the current interest by researchers on consider-
ing more and more constraints during the modeling
process, problems in business under study nowadays
cannot be solved in an exact manner within a rea-
sonable amount of time (Talbi 2009). Among the so-
lutions techniques available in Operations Research
and the Management Sciences, discrete-event com-
puter simulation (DES) has proven to be very useful
for practitioners in real-life decision-making (Banks
et al. 2009). In today’s globalized environment, in-
dustries are calling for immediate action to develop
computational and simulation-based methods that
will lead to faster transactions, reduced operating
costs, and improved performance and customer ser-
vice. DES has been used as an analysis tool to evalu-
ate new production system concepts, and has also
been used in the operation and planning of manu-
facturing facilities. For several years, simulation has
been used in the long-term planning, design and
analysis of manufacturing systems (Solano-Charris
& Paternina-Arboleda, 2013). These models have
been termed as “throw away models” because they
are seldom used after the initial plans or when de-
signs are finalized (Son and Wysk 2001, Smith and
Brett 1996, Harmonosky 1995). Over the past de-
cade, however, researchers and practitioners have
taken advantage of the power of simulation technol-
ogy to develop models that can be fully integrated
into complex manufacturing systems and run in re-
al-time. The ability to automatically generate simu-
lation models for certain applications has also been
achieved (Son and Wysk 2001). Recent attempts to
use simulation modeling in the control and analysis
of production logistics and manufacturing systems
may be found in the works of (Mullarkey et al. 2000,
Rabbath et al. 2000, Lee et al. 2002, Dangelmaier et
al. 2006, Barra Montevechi et al. 2009, Zülch et al.
2009, Sharda and Bury 2010, Pawlewski and Fertsch
2010, Montoya-Torres 2010, Montoya-Torres et
al. 2012) just to mention a few. Note however that
most, not to say all, of these works have been per-
formed in developed countries, and very little ap-
plications and successful case studies are presented
in the scientific literature for small and medium en-
terprises in emerging economies. The focus of this
paper is hence to contribute to the formal analysis of
production processes in emerging countries, and in
particular in the tanning industry, towards the use
of computer simulation models in order to improve
daily decision-making processes. As stated before,
simulation modeling methodology is used on a real-
life case study from a medium enterprise located at
the north of Bogota, Colombia.
Whereas simulation has already been used for many
years as a tool for planning and controlling produc-
tion processes, very little attention has been given to
the improvement of production processes of a tan-
nery, to the best of our knowledge. The inexistent
use of simulation into the tanning industry may be
attributed to the complex and changing character of
both the product and the production process. In this
paper, we propose to apply discrete-event computer
simulation for the analysis of leather manufactur-
ing. The objective is to analyze the current behav-
ior of the production system in order to propose
alternatives for improvement, as well as optimum
parameters for production. The work presented in
this paper is an extension of the results presented
in Pirachicán-Mayorga et al. (2010). In comparison
with that paper, we present here more detailed re-
sults obtained from simulation runs and also extend
the analysis of the optimization approach.
This paper is organized as follows. The current manu-
facturing process is first described. Afterwards, the
proposed simulation model and the analysis of results
for the current situation are presented, followed by

8ARTICLES |Simulation Analysis of a Fabrication Process of a Tannery: Case Study of a Latin American Company
the analysis of some improvements and their manage-
rial implications. These scenarios are further analyzed
by using a simulation-optimization technique. This
paper ends by presenting some concluding remarks.
MANUFACTURING PROCESS AND
PROBLEM SETTING
Overview of the Manufacturing Process
The case study considered in this paper corresponds
to a medium company within the tanning industry,
whose name is kept confidential, dedicated to manu-
facture leather for furniture and cars. The company
currently has 15% of the market share, while its prin-
cipal competitor has 25% (see figure 1). The other
60% of the market is divided among many small en-
terprises (40%) and foreign companies (20%).
Figure 1. Distribution of the marketplace
Other small
competitors
40%
Foreign
companies
20%
Principal
competitor
25%
Company
under study
15%
The production process we are describing next is
based on the product named “Carioca black leather”.
We choose this product because it can provide suffi-
cient characteristics and complexities to understand
well the global manufacturing process. In addition,
its demand is the highest among the total produc-
tion of the market supplied by the company. The
process of leather preparation is quite wasteful and
time consuming. Figure 2 shows a diagram of the
process. The process begins with the arrival of the
skin to the factory. When the skin-pieces are not en-
tering the process immediately, sodium chloride is
added to them for dehydrate (salting process), and
then they pass to soaking (a pre-wash process with
water and wet), in order to afterwards remove hair
from the skin. The skin goes to fleshing, fixing and
split, where the tissue is separated in order to made
leather (dermis).
Transformation of the skin into leather is done by a
chemical process called tanning. The skin is cut in or-
der to reduce its thickness to an appropriate standard.
This process requires a large quantity of water and it is
hence necessary to drain it. Defects in the raw materi-
al have to be then corrected or mitigated. This is one of
the most important steps of the operation since it af-
fects the processes of greasing, staining, painting and
finishing that define the final features of the leather.
So the leather is conditioned in order to moisten for
an efficient implementation of those steps. Finally,
the leather is softened to break the adhesion between
the fibers and provide flexibility and softness.
Problem Description
At the moment of starting this simulation project,
the company presented various problems that all
together unfavorably interfere with the production
process, generating over cost and quality decrease.
By implementing a computer simulation model, we
represent the current production situation; carry
out a diagnosis of potential problems in the produc-
tive process and are able to quantify their impact on
the overall performance of the system. In particular,
the majority of problems identified concerns the
low quality of raw material received from suppliers,
which is difficult to identify at an early stage (i.e:
when received) since skin has to be chemically treat-
ed: such defects are detected when the product has
advanced nearly 40% of the stages. Table 1 shows
the categories of raw material selection according to
their acceptance level.
Figure 3 classifies in a Pareto chart the different
types of defects found in the product along the pro-
cess. It is important to clarify that such defects do
not necessary imply product non-conformities. That
is, defects in leather are inherent to the nature of raw
material. The fact of finding them only implies that
a series of additional steps are required throughout
the production process in order to obtain the final
quality level for the finished product. Another is-
sue to be addressed is the quantity of raw material
to be negotiated with suppliers. The problem here is
that the company under study does not have an ac-
curate procedure for demand forecasting and plan-
ning. Hence, the enterprise’s managers are not able
to negotiate in advance with suppliers in order to ob-
tain better quality of raw material. We argue that the
better the knowledge of the marketplace the better
the possibilities to establish long term relationships
with suppliers and therefore the better the quality of
raw material. At the end, this will lead to a decrease
of production costs, since, depending on the features
of the purchased leather (skin), the production pro-

cess and the quality will be affected, increasing or
decreasing its value. Figure 4 presents three types
of raw material received by the enterprise under
study. However, from the management standpoint,
we do believe that first the impact of having to pro-
cess the current raw material must be quantified to
afterwards propose an eventually different purchas-
ing policy to negotiate with suppliers. The proposed
simulation study will give us some insights about
this concern.
Figure 2. Process flow diagram
Sumary
Activity Number Time(Hrs)
PROCESS :
Since: Salting Until: Measurement
Totaly 29 124,01
Salting H2O Dirty
3 Hrs Wetting H2O Dirty
0,5 Hrs Wash H2O Dirty
4 Hrs Unhearing
Hair
0,03 Hrs Fleshing
Reamining
Meat and
Grease
0,02 Hrs Fixing
Pedazos de
piel
0,02 Hrs Split Gore
0,5 Hrs
Weighing Lod
Drum
8 Hrs Tannery
0,01 Hrs Selection 1
0,03 Hrs Drain
0,03 Hrs Thickness
0,02 Hrs Fixing 2
3 Hrs Retanning
3 Hrs Grease
3 Hrs Staining
94,5 Hrs
Enviromental
Dry
3 Hrs Fulling
0,02 Hrs Streaching
0,02 Hrs Schedule
0,03 Hrs Grinding Rubble
0,05 Hrs Undust Shaving
0,5 Hrs Unloading Skin
0,03 Hrs Ironing 1
0,08 Hrs Mat
OPERATION DIAGRAM
124
1 0,01
Leather Process
28
1
2
3
4
5
6
7
9
1
10
11
13
14
15
16
17
18
20
21
23
24
Raw Skin
Piel en Tripa
Wet blue
8
12
19
22

Table 1. Raw material selection categories
Selection category Accepted quality features
First selection (type 1A skin) Few scratches (patched in site) located in the center of the skin
Second selection Little holes of insects and low fixed scratches
Third selection Notorious insects holes and evident scratches
Fourth selection Any defect that could be ground
Fifth selection Any defect that could be ground and holes
Figure 3. Pareto chart of raw material
0
20
40
60
80
100
120
4 5 3 2 1
%
Selectioncategory of leather
% Cum.
Selection
category
% Cum.
4 44.44 44.44
5 27.78 72.22
3 16.67 88.89
2 8.89 97.78
1 2.22 100
Figure4.Typesofrawmaterialenteringtheproductionprocess
As explained before, raw material is received from
suppliers at different stages of processing. Figure 4
presents the three types of raw material received by
the enterprise under study. Depending on the pre-
vious state of processing, the quality of leather en-
tering the process will strongly affect the global cost
of manufacturing. For instance, for the case of raw
leather, since hair is still present not all defect are
visible and hence a processing is required in order
to remove it. About 45% of this type of raw mate-
rial does not accomplish quality specifications after
some processing. However, the decision-makers do
prefer buying leather with skin because it is pos-
sible to control the defects during manufacturing.
According to managers’ experience, the enterprise
estimates the following percentages of defects: 5%,
10% and 10% having, respectively one, two or three
holes at the lower part, 40% having more than three
perforations at the lower part and some in the cen-
ter and some scratches, 3% having more than three
holes located at the center of the leather, and 2%
corresponds to leather with multiple defects and
not possible to be repaired. These defects are only
detected once the product becomes gut leather, that
is, after eliminating the hair from the raw material.
Note that according to quality requirements by cus-
tomers, only leather fitting within the first three
categories can continue processing: 55% of the total
raw material. The other 45% of material is consid-
ered waste and does not generate any value-added
processing for the enterprise. However, 22% of this
waste product could be sold at a lower price than
bought by the enterprise.
For the case of gut leather, defects are clearly visible
at the time of purchasing. The range of selection is not
as big as when raw leather is purchased; it is assumed
that this leather has already been preselected. There
are three types of selection for the gut: Type A is used
for high quality furniture, type B is used for manu-
facturing standard quality furniture, and Type C is
devoted to make cars upholstery. When the raw mate-
rial is purchased as gut leather, the tanning process al-
lows it to become wet blue leather (or simply wet blue)
and it is ready for actual production. Another specific
selection can be made. From a lot of 100 pieces, the
distribution of leather is shown in figure 5, ordered

by decreasing quality level. Finally, wet blue leather is
bought in order to satisfy picks of demand when there
is not enough time to process the raw material since
the beginning of the production process. Even if this
supply strategy allows flexibility and agility to satisfy
demand, buying wet blue leather implies high cost
and hard difficulties to repair quality defects. Two cat-
egories of product are defined: first-class leather dedi-
cated to furniture (by decreasing order of quality: M1:
15%, M2: 40%, M3: 35% and M4: 5% of pieces), and
second-class (low quality) leather, which is devoted to
car upholstery (F1: 2% and F2: 3% of pieces).
Figure 5. Quality distribution of gut leather purchases
10
Fig. 5. Quality distribution of gut leather purchases
Note that the purchase of raw material has been the most significant problem for the
company. The inherent defects of the skin where at some time a key problem to be
solved. However, researchers working for the enterprise have generated very good
chemical processes that are the most valuable knowledge they have developed. The
challenge now is to understand both economic and productive implications of
continuing processing leather with those quality conditions. The proposed simulation
model is developed in order to attain such objective.
SIMULATION MODEL
As explained previously, discrete-event simulation (DES) was used to analyze the
behavior and global performance of the leather manufacturing system. The model was
built using Arena® software, which is a generic simulation package able to simulate a
large variety of systems (Fábregas et al. 2003). The model allows presenting the current
production situation and modeling different production scenarios seeking the
improvement of key performance metrics. The model was built to simulate the
operation flows of the Carioca black leather, the most representative product
manufactured by the company. It began with the entry of the raw leather and later other
types of purchase where introduced, gut leather and wet blue. The process flow,
described in Section 2 (see figure 2), was represented using logical blocks provided by
the software. Some complex operations had to be modeled using several blocks.
Raw material
Gut leather
A: 40%
B: 50%
C: 10%
Tannery
processing
Types A
and B
Type C
M1: 1%
M2: 6%
M3: 10%
M4: 30%
M5: 46%
M6: 5%
M7: 2%
F1: 2%
F2: 1%
F3: 1%
F4: 6%
M: leather for furniture
F: leather for car upholstery
Note that the purchase of raw material has been the
most significant problem for the company. The in-
herent defects of the skin where at some time a key
problem to be solved. However, researchers working
for the enterprise have generated very good chemical
processes that are the most valuable knowledge they
have developed. The challenge now is to understand
both economic and productive implications of con-
tinuing processing leather with those quality condi-
tions. The proposed simulation model is developed in
order to attain such objective.
SIMULATION MODEL
As explained previously, discrete-event simulation
(DES) was used to analyze the behavior and global
performance of the leather manufacturing system.
The model was built using Arena® software, which is
a generic simulation package able to simulate a large
variety of systems (Fábregas et al. 2003). The model
allows presenting the current production situation
and modeling different production scenarios seek-
ing the improvement of key performance metrics.
The model was built to simulate the operation flows
of the Carioca black leather, the most representa-
tive product manufactured by the company. It be-
gan with the entry of the raw leather and later other
types of purchase where introduced, gut leather and
wet blue. The process flow, described in Section 2
(see figure 2), was represented using logical blocks
provided by the software. Some complex operations
had to be modeled using several blocks.
Input data concerning the information about pro-
cessing times and time between arrivals was ob-
tained from two sources: historical data provided by
the company and data obtained after carrying out a
time study. Tests of fitness were carried out in or-
der to obtain the best probability distributions that
characterize those data. Since most of available data
about arrival and processing times were obtained
from small samples (30 or less), a Kolmogorov-
Smirnov test of fitness was chosen for all the pro-
cesses (Montoya-Torres 2006). For some processes,
uniform and constant distributions were considered
to be the most appropriate due to lack of data or be-
cause carrying out on-field sampling was too long
lasting and wasteful. In particular, this last was the
case of the tanning process: processing duration is
typically between 48.5 and 51.3 hours. Other input
information was taken from the enterprise data-
base, such as costs. Other operating conditions like
number of resources, input processes, assignment
of operators to machines, etc., were modeled exactly
as currently existing at the enterprise. The length of
simulation was set to be one year of production and
a total of 10 replications were performed. The whole
model is presented in figure 6. A warm-up period was
considered and statistics collected during this period
were discarded in order to eliminate initialization
bias. Model’s operational validation and verification
was done by using two independent and comple-
mentary techniques presented in Banks et al. 2005,
Sargent 2001, as employed in many simulation case
studies (see for example the work of Montoya-Torres
et al 2009). The first technique is the classical statis-
tical validation. The appropriate statistical test is a t-
test. The average total simulated processing time was
compared with the theoretical processing time (i.e.
the sum of processing times on all machines). The
obtained probability of no reject the null hypothesis
was 0.97 and hence this test provides no evidence of
model inadequacy. The second technique consisted
of a Turing test, for which the knowledge of experi-
enced engineers about the system behavior is used
to operationally validate the simulation model.

Figure 6. Structure of the simulation model
P ie l_ e n _ c r u d o
A p e la m b r a d o
S a lid a y E n t r a d a s _ p ie le s _ f a b r ic a
D e c id e 9
E n t it y . T y p e = = p ie l_ c u r t id a _ s ir v e
E n t it y . T y p e = = W e t _ b lu e _ s ir v e
E ls e
C o m p r a _ w e t _ b lu e
S e p a r a t e 8
E s c u r r id o
S e p a r a t e 9
c o m p r a _ e n _ t r ip a
S e le c c io n _ 1
T r u e
F a ls e
D e c id e 1 0
V e n d e
C la s if ic a c io n _ _ 2
T r u e
F a ls e
T R I P A
A lm a c e n a m ie n t o 1
C u r t id o A lm a c e n a m ie n t o _ 2
B A T C H
C la s if ic a c io n _ w e t _ b lu e
R e c u r t id o
S e c a d o _ a m b ie n t eE s t ir a d o E s t u c a d o E s m e r ila d o
_ E n t r a d a _ c u e r o
S a lid a
P la n c h a d o _ p o r o f in oF e lp e a d o T e m p la d o _ 2 P la n c h a d o _ p o r o f in o _ 2p in t u r a _ 1 p in t u r a _ 2 P la n c h a d o _ p o r o f in o _ 3
A b a t a n a d o t o p M e d ic io n C u e r o _ t e r m in a d o
B a t c h 1 2
R e b a ja d o
Ho ld 1
S ig n a l 1
T r u e
F a ls e
D e c id e 1 2
d e e n t id a d
T r a n s f o r m a c io n
C la s if ic a c io n _ 2
Ho ld 2
S ig n a l 2
L o t e _ 2
B a t c h 1 6
S e p a r a t e 1 2
S e p a r a t e 1 3
B a t c h 1 7
T r a n s f o r m a c io _ e n t id a d
S e p a r a t e 1 7
c r u d o
E n t r a d a p ie l a p e la m b r a d a s
P ie le s c r u d a s
P ie l c r u d a s ir v e
s ir v e
P ie le s t r ip a
s ir v e
P ie le s c u r t id a
s ir v e
P ie le s w e t b lu e
c o m p r a d a
P ie le s t r ip a
c o m p r a d a s
t e r m in a d o
U n id a d e s c u e r o
e l p r o c e s o
P ie le s t r ip a s e n
e n e l p r o c e s o
e n c u r t id a
t r a n s f o r m a d a s
P ie le s
e n r e c u r t id a
t r a n s f o r m a d a s
P ie le s
n o s ir v e
s ir v e
P ie le s c r u d a n o
s ir v e
P ie le s t r ip a n o
B a t c h 1 8
S e p a r a t e 1 8
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0 0 0
0
0 0 0
00 0
0
0 0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
ANALYSIS OF RESULTS: CURRENT
SITUATION
This section presents a summary of results obtained
from the simulation of the current situation at the
factory. The objective here is to identify the global
system bottleneck resource(s) and to identify pos-
sible scenarios for improvement, as well. Key perfor-
mance metrics were defined to be the average wait-
ing time of an entity in a queue (time an entity has
to wait before being processed by a resource), the
average number of entities in a given queue, and the
average resource utilization rate.
Values obtained for the first metric are presented
in figure 7. We can observe that the processes with
the longest average waiting time of entities in queue
are unhairing and re-tanning (re-tannery). This
is explained by the fact that the number of arriv-
ing entities is higher than at the other parts of the
process, and because these two operations require a
large amount of time when compared to the rest of
the process. This situation is verified by the results
obtained for the two other key indicators: average
number of entities in queue (figure 8) and average
resource utilization (figure 9). It has to be noted that
resources in this last figure are ordered alphabeti-
cally. Hence, the reader must remark that resource
named “Drum_3” is the resource that corresponds to
re-tanning operation.
Figure 7. Average waiting time in queue: all process stages
Figure 8. Average number of entities in queue
Another interesting analysis to be carried out con-
cerns the study of costs related to the production
process. Figure 10 is a comparative chart of utiliza-
tion costs of each resource (machines and operators).
The most expensive operator for the company is the
person performing the operations of thickening and
grinding (“Employ_4” in the figure), who is paid per
hour and per finished piece. On the other hand, the

most expensive machine is the “Press” which is used
three times during production.
Figure 9. Average resource utilization
Figure10. Comparison of operational costs per resource
ANALYSIS OF SCENARIOS
Once we have analyzed the current behavior of the
production system, we proceeded to explore several
alternatives in order to improve key performance
metrics. Different manufacturing scenarios were
studied and comparison between them was carried
out using statistical methods. As we could observe
in the simulation results, two critical resources are
present in the production line: the “drum” of tan-
nery process and the operator in charge of coat, flush
and measurement processes. Hence, considering this
situation, a first analysis was performed by adding
one or two drums to re-tanning processing. Results
for the first set of experiments are shown in table
2 and figure 11, for key performance metric named
resource utilization, number of entities processed
at this stage of the production process (i.e. entities
completely processed by the resource) and waiting
time in resource’s queue. Then, a second analysis was
carried out by adding one or two operators. Results
of this second analysis are presented in table 3.
As we can observe in table 2, there is a difference be-
tween average values of resource utilization, which is
explained by the fact that having more resources to
perform the re-tanning process, arriving entities will
be distributed among them. Hence, waiting times
in queue will diminish, and the number of entities
that are actually completely processed will increase.
By observing box-and-whistles diagrams in figure
11, generated using SPSS software, we can observe
that the values of both metrics resource utilization
and waiting times in queues are statistically differ-
ent when compared to the current situation and the
two new scenarios. However, this is not the case of
the number of entities finishing processing at this
stage of manufacturing. We can observe that box-
and-whistles diagram of the two proposed scenarios
overlap. Hence, there is no statistical difference in
having three drums in comparison with the perfor-
mance with two drums. The huge investment re-
quired for buying, installing and operating this third
drum will not be reflected in the number of finished
products. It is to note that similar results will then
be obtained later in Section 6 when performing the
optimization of the simulation model.
Table 2. Description of scenarios
Scenario properties Responses (average values)
Name
Control variable
(number of resources)
Resource utilization No. entities exiting
Waiting time in
resource’s queue
Current situation R e - t a n n i n g
process
1 95.1% 36 780.93
Scenario 1 R e - t a n n i n g
process
2 60.7% 46 357.26
Scenario 2 R e - t a n n i n g
process
3 42.6% 49 173.49

Figure 11. Comparison of scenarios
process
Scenario 2 Re-tanning
process
3 42.6% 49 173.49
Fig. 11. Comparison of scenarios
a) Utilization level c) Number of output entitiesb) Waiting time in queue
For the case of the operator in charge of coating,
flushing and measurement processes, experiments
were also carried out by adding one or two more op-
erators to help the execution of these tasks. Results
of the comparison of scenarios, average values, are
presented in table 3. A local analysis of this produc-
tion step will not give any interesting output by add-
ing one or two more operators because waiting times
in queues are zero and hence the number of enti-
ties actually finished at this step remains the same
whatever the scenario. We can observe that resource
utilization rate decreases by adding more operators,
which is logic. The interest of performing these sim-
ulations is to analyze the impact that the decision of
speeding this process step will have on the final stag-
es of the production process. By adding one opera-
tor at this stage, entities are finished faster and an
increase of the utilization level was observed at the
subsequent resources. This will be observed in detail
in the next section when performing optimization.
Table 3. Description of scenarios
Scenario properties Responses (average values)
Name Control variable
(number of resources)
Resource
utilization
No. entities exiting Waiting time in re-
source’s queue
Current situation Operator 1 15,5% 29 0
Scenario 1 Operator 2 7,8% 29 0
Scenario 2 Operator 3 5,2% 29 0
OPTIMIZATION USING SIMULATION
The problem of determining the best combination
of variables to use as input for a simulation model
often arises in practice (Paternina-Arboleda et al.
2008). Typically, the input values have to be chosen
such that the cost function is optimized, where the
latter is computed from the output variables of the
model. This problem has to be addressed in applica-
tion domains where the modeling of the system is
not possible by using a mathematical approach. In
the area of manufacturing systems, for instance,
simulation-optimization has been applied to opti-
mize several practical objective functions such as
productive machine hours, the cost of automated
transport/storage systems, the idle time of assem-
bly systems, or to tune the parameters of schedul-
ing heuristics or to configure Kanban systems (Klei-
jnen 1993, Rosenblatt et al. 1993, Mebarki 1995,
Paris et al. 1996, Paternina-Arboleda et al. 2008). A
simulation-optimization problem is an optimization
problem where the objective function is a response
evaluated by simulation (Andradottir 1998, Boesel
et al. 2001). It may be formulated as , where Z is the
criterion (or the vector of criteria) evaluated from
simulation, is the vector of variables and each vari-
able takes its values in a domain .
Several studies have been carried out on simulation-
optimization. These approaches can be categorized
in four major classes: gradient-based search meth-
ods, stochastic approximation methods, response

surface methodologies, and heuristic methods (An-
dradottir 1998, Fu 1994, April et al. 2003, 2004, Kim
2006). Basically, the aim of each of these approaches
is to propose a strategy to explore the solution space
D with a limited number of simulation experiments
(Pflug 1984). Two types of strategies exist. The first
consists in collecting a sample of interesting points
(e.g. using experimental design) and exploiting these
points in a second step (e.g. using a response sur-
face). The second strategy consists in searching it-
eratively the domain D, which requires a connection
between the optimization algorithm and the simula-
tion model (Haddock and Mittenthal 1992).
One of the optimization tools available in commercial
simulation packages is OptQuest®. This tool favors
the optimization procedure of the simulation model
by employing meta-heuristics optimization proce-
dures: the meta-heuristic optimizers chooses a set
of values for the input parameters and uses the re-
sponses generated by the simulation model to make
decisions. The meta-heuristics procedures employed
by OptQuest® are Scatter Search in conjunction with
the memory-based approach Tabu Search (April et al.
2006). OptQuest® finds the optimal solutions for the
simulation model through the generation of entries
(admissible values for the control variables) starting
on the recursive evaluation of the responses (April et
al. 2001). The control variables and minimum, sug-
gested and maximum values considered in the simu-
lation-optimization model are defined to be: the drum
with values 1, 2 and 3, respectively, and the number
of operators performing the processes of coating,
flushing and measurement with values 1, 2 and 2, re-
spectively. Responses (optimization objectives) were
defined to be the minimization of the total cost per
entity, the value added cost per entity and the total
accumulative cost of the process. Figure 12 presents
the evolution of the objective function of the three
cost metrics. Results recommended by OptQuest® for
the control variables according to the optimization of
costs functions are present in table 4. After running
the optimization, we observed that when the accumu-
lative total cost per process is taken into account, the
investment in an additional operator is not interest-
ing for the global performance of the manufacturing
system. This result was very surprising for managers
of the company since they believed that increasing the
speed of the processes of coating, flushing and mea-
surement will influence positively the global system
performance. On the other hand, from the point of
view of the entities, there is an increase in the amount
of outgoing entities from the resource called “drum”:
those entities are sharing the global cost and hence it
would be worth to invest in an additional resource of
this type. It is to note that the company currently has
one resource “drum” for the re-tanning process and
hence 1450 skins are processed during the production
period (one year). If another “drum” is purchased,
then 2100 pieces could be processed. Obviously, an
investment is required and there will be an increase in
fixed costs. However, this cost will be absorbed by the
additional amount of finished products exiting the
system during the production period. Hence, a posi-
tive profit is obtained by about 44.62% of increase.
This profit can be computed by considering the num-
ber of finished products sold versus the cost incurred
when installing the additional resources.
Figure 12. Converge of optimizations according to the
objective function 19
Fig. 12. Converge of optimizations according to the objective function
Table 4. Results recommended after optimization for the control variables
Configuration Value
added cost /
entity
Total cost /
entity
Output
entities
(1_Drum)
Output
entities
(2_Drums)
Total cost of output
entities
Total process
accumulative
cost
Sc. 1 (2 drums) $889,094 $14,893,00 - 2100 $1,,888,,097,,400 $31,275,300
Sc. 0 (1 drum) $919,419 $15,232,33 1450 - $1,,333,,157,,550 $22,066,086,5
Gap $554,939,850 $9,188,421,5
Gap (%) 40% 42%
CONCLUDING REMARKS
This paper considered a complex manufacturing process found in a tannery production.
a) Total cost per entity
c) Value added cost
per entity
b) Total process accumulative cost

Table 4. Results recommended after optimization for the control variables
Configuration Value added
cost / entity
Total cost /
entity
Output enti-
ties (1_Drum)
Output
entities
(2_Drums)
Total cost of out-
put entities
Total process ac-
cumulative cost
Sc. 1 (2 drums) $889,094 $14,893,00 - 2100 $1,,888,,097,,400 $31,275,300
Sc. 0 (1 drum) $919,419 $15,232,33 1450 - $1,,333,,157,,550 $22,066,086,5
Gap $554,939,850 $9,188,421,5
Gap (%) 40% 42%
CONCLUDING REMARKS
This paper considered a complex manufacturing pro-
cess found in a tannery production. We proposed
a simulation model in order to analyze the current
production process and to propose alternatives for
improvement. The model achieved good perfor-
mance in terms of confidence with respect to the real
situation in which the company operates. The simu-
lation found that the bottleneck resource is in the
process of re-tanning because of the lack of capacity
of the “drum” to process the amount of entities that
arrive. Among the nonphysical resources, operator
dedicated to the processes of coating, plushing and
measurement showed to restraint the global produc-
tion capacity.
An optimization routine, based on the standard
OptQuest® tool for Arena®, was also developed and
optimum parameters of the simulation model were
found. It was found that investing on a “drum” al-
lows the company to increase production by 44.83%.
But, to hire an additional operator to help the pro-
cesses of coating, flushing and measurement is sta-
tistically indifferent.
REFERENCES
Andradottir, S. (1998). Simulation optimization. In: Banks J
(ed), Handbook of Simulation, pp. 307-334.
April J., Glover F., Kelly J., & Laguna M. (2001). Simulation/
Optimization using “real-world” applications. In: Peters BA,
Smith JS, Medeiros DJ, Rohrer MW (eds), Proceedings of
the 2001 Winter Simulation Conference, pp. 134-138. IEEE,
Piscataway, USA.
April J., Glover F., Kelly J. P., & Laguna M. (2003). Practical intro-
duction to simulation optimization. In: Chick S, Sánchez PJ,
Ferrin D, Morrice DJ (eds), Proceedings of the 2003 Winter
Simulation Conference, pp. 71-78. IEEE, Piscataway, USA.
April J., Better M., Glover F., & Kelly J. (2004) New advances
and applications for marrying simulation and optimization.
In: Ingalls RG, Rossetti MD, Smith JS, Peters BA (eds), Pro-
ceedings of the 2004 Winter Simulation Conference, pp. 80-
86. IEEE, Piscataway, USA.
April J., Better M., Glover F., Kelly J., & Laguna M. (2004). En-
hancing business process management with simulation op-
timization. In: Perrone L. F., Weiland F. P., Liu J., Lawson B.
G., Nicol D. M., Fujimoto R. M. (Eds), Proceedings of the
2006 Winter Simulation Conference, pp. 642-649. IEEE,
Piscataway, USA.
Banks J., Carson J. S., Nelson B. L., & Nicol D. M. (2009). Dis-
crete-event system simulation. Prentice Hall.
Barra Montevechi J. A., da Silva Costa R. F., Leal F., Ferreira de
Pinho A., & Tadeu de Jesus J. (2009). Economic evaluation
of the increase in production capacity of a high technology
products manufacturing cell using discrete event simula-
tion. In: Rossetti M. D., Hill R. R., Johansson B., Dunkin A.,
Ingalls R. G. (Eds), Proceedings of the 2009 Winter Simula-
tion Conference, pp. 2185-2196, IEEE, Piscataway, USA.
Blanco E. E., & Paiva E. (2014) Supply chain management in
Latin America, International Journal of Physical Distribution
& Logistics Management, 44(7), editorial.
Boesel J., Bowden R.O., Glover F., Kelly J. P., Westwig E. (2001).
Future of simulation optimization. In: Peters BA, Smith JS,
Medeiros DJ, Rohrer MW (Eds), Proceedings of the 2001
Winter Simulation Conference, pp. 1466-1469. IEEE, Pisca-
taway, USA.
Dangelmaier W., Mahajan K. R., Seeger T., Klöpper B., & Auf-
enanger, M. (2006) Simulation assisted optimization and
real-time control aspects of flexible production systems sub-
ject to disturbances. In: Perrone L. F., Wieland F. P., Liu J.,
Lawson B. G., Nicol D. M., Fujimoto R. M. (Eds), Proceedings
of the 2006 Winter Simulation Conference, pp. 1785-1795.
IEEE, Piscataway, USA
Fábregas A., Wadnipar, R., Paternina, C., & Mancilla, A. (2003).
Simulación de sistemas productivos con Arena®. Ediciones
Uninorte, Barranquilla, Colombia
Fu, M. C. (1994). Optimization via simulation a review. Ann
Oper Res, 53, 199-247.
Haddock J., & Mittenthal J. (1992). Simulation optimization
using simulated annealing. Computers and Industrial Engi-
neering, 22, 387-395.

Harmonosky, M. C. (1995). Simulation-based scheduling: Re-
view of recent developments. In: Proceedings of the 1995
Winter Simulation Conference, pp 220-225. IEEE, Piscat-
away, USA
Hillier F., & Lieberman G. (2005). Introduction to Operations Re-
search. McGraw-Hill, USA.
Kim, S. (2006). Gradient-based simulation optimization. In:
Perrone L. F., Wieland, F. P., Liu J., Lawson, B. G., Nicol, D.
M., Fujimoto, R. M. (Eds), Proceedings of the 2006 Winter
Simulation Conference, pp. 159-167. IEEE, Piscataway, USA
Kleijnen, J. P. C. (1993). Simulation and optimization produc-
tion planning: A case study. Decis Support Syst, 9, 269-280.
Lee, S., Ramakrishnan, S., & Wysk, R. A. (2002). A federatopm
object coordinator for simulation based control and analy-
sis. In: Yücesan E., Chen C.H., Snowdon J. L., Charnes, J. M.
(Eds), Proceedings of the 2002 Winter Simulation Confer-
ence, pp. 1986-1994. IEEE, Piscataway, USA
Mebarki, N. (1995). Une approche d’ordonnancement temps réel
basée sur la sélection dynamique de règles de priorités. PhD the-
sis. Université Claude Bernand Lyon I, France.
Montoya-Torres, J. R. (2006). Discrete-event simulation. Lec-
ture notes, Department of Productive Processes, Pontificia
Universidad Javeriana, Bogota, Colombia
Montoya-Torres, J. R., Campagne, J. P., Rodriguez-Verjan, G.
(2009). Implementing an integrated framework for internal
logistics management in automated semiconductor manu-
facturing. International Journal of Logistics Systems and Man-
agement, 5(3-4), 418-435.
Montoya-Torres, J. R. (2010). A Monte-Carlo simulation-based
approach for single-machine job scheduling with release
dates and sequence-dependent setup times. In: Meja, G.
(Ed), Technologies in Logistics and Manufacturing for Small
and Medium Enterprises, Proceedings of the 5th International
Conference on Production Research Americas. CD-ROM Paper
8, July 21, 2010. Bogotá, Colombia.
Montoya-Torres J. R., González-Solano, F., & Soto-Ferrari, M.
(2012). Deterministic machine scheduling with release
times and sequence-dependent setups using random-inser-
tion heuristics. International Journal Advanced Operations
Management, 4(1-2), 4-26.
Mullarkey, P., Gavirneni S., & Morice, D. J. (2000). Dynamic
output analysis for simulation of manufacturing environ-
ments. In: Joines, J. A., Barton, R. R., Kang, K., Fishwick,
P. A. (Eds), Proceedings of the 2000 Winter Simulation Confer-
ence, pp. 1290-1296. IEEE, Piscataway, USA.
Paris, J. L., Pierreval, H., & Tautou L. (1996). Une approche
pour la configuration de systèmes Kanban multi-produit par
algorithme évolutionniste. Journal Européen des Systèmes
Automatisés, 30, 929-944.
Paternina-Arboleda, C. D., Montoya-Torres, J. R., Fábregas-Ari-
za, A. (2008). Simulation-optimization using a reinforce-
ment learning approach. In: Mason S. J., Hill, R., Moench
L., Rose, O. (Eds.), Proceedings of the 2008 Winter Simulation
Conference, pp. 1378-1383. IEEE, Piscataway, USA.
Pawlewski P., Fertsch, M. (2010). Modeling and Simulation
method to find and eliminate bottlenecks in production lo-
gistics systems. In B. Johansson, S. Jain, J. Montoya-Torres,
J. Hugan, & E. Yücesan. (Eds.). Proceedings of the 2010 Win-
ter Simulation Conference, pp. 1946-1956. IEEE, Piscataway,
USA.
Pirachicán Mayorga, D. C., Camacho, L. F., Montoya-Torres, J.
R. (2010). Simulación y análisis del proceso de fabricación
de curtiembres. In: G Mejia (Ed.) Technologies in Logistics
and Manufacturing for Small and Medium Enterprises, Pro-
ceedings of the 5th
International Conference on Production Re-
search Americas. CD-ROM Paper 67, July 21, 2010. Bogotá,
Colombia.
Pflug, G. C. (1984). Optimizing simulated systems. Simuletter,
15, 6-9.
Rabbath, C. A., Abdoune M., Belanger, J. (2000). Effective real-
time simulation of event-based systems. In: Joines, J. A.,
Barton, R. R., Kang, K., Fishwick, P. A. (Eds), Proceedings of
the 2000 Winter Simulation Conference, pp. 232-238, Piscat-
away, USA.
Rosenblatt, M. J., Roll, Y., Zyse, V. (1993). A combined optimiza-
tion and simulation approach for designing automated stor-
age/retrieval systems. IIE Transactions, 25, 40-50.
Sargent, R. G. (2001). Some approaches and paradigms for veri-
fying and validating simulation models. Proceedings of the
2001 Winter Simulation Conference, 106-114.
Sharda B., & Bury S. (2010). bottleneck analysis of a chemical
plant using discrete event simulation. In: Johansson, B.,
Jain, S., Montoya-Torres, J., Hugan, J., Yücesan, E. (Eds),
Proceedings of the 2010 Winter Simulation Conference, pp.
1547-1555. IEEE, Piscataway, USA.
Smith, J. S, & Brett, P. A. (1996). Short term scheduling using
discrete event simulation. Department of Industrial Engi-
neering, Texas A&M University, submitted to Southwestern
Bell-Technology Resources, Inc. Retrieved from http://tam-
cam.tamu.edu/Research/short.htm
Solano-Charris, E. L., & Paternina-Arboleda, C. D. (2013). Sim-
ulation model of the supply chain on a naval shipyard. Inter-
national Journal of Industrial and Systems Engineering, 13(3),
280-297.
Son, Y. J., & Wysk, R. A. (2001). Automated simulation model
generation for simulation-based, real-time shop floor con-
trol. Computers in Industry, 45, 291-308.
Talbi, E. G. (2009). Metaheuristics: from design to implementa-
tion. John Wiley & Sons, Inc, Hoboken, USA.
Zülch G., Steininger P., Gamber T., & Leupold, M. (2009). Gener-
ating, Benchmarking and Simulating Production Schedules
- From Formalisation to Real Problems. In: Rossetti MD, Hill
RR, Johansson B, Dunkin A, Ingalls RG (eds), Proceedings of
the 2009 Winter Simulation Conference, pp. 2238-2249, IEEE,
Piscataway, USA.

FORUM
Submitted 29.07.2017. Approved 05.11.2017.
Evaluated by double blind review process.
ScientificEditors:CristianeBiazzin,ElynL.SolanoCharris,andJairoAlbertoJarrínQuintero
AN IMPLEMENTATION FRAMEWORK FOR AD-
DITIVE MANUFACTURING IN SUPPLY CHAINS
ABSTRACT
Additive manufacturing has become one of the most important technologies in the
manufacturing field. Full implementation of additive manufacturing will change
many well-known management practices in the production sector. However, theo-
retical development in the field of additive manufacturing with regard to its impact
on supply chain management is rare. While additive manufacturing is believed to
revolutionize and enhance traditional manufacturing, there is no comprehensive
toolset developed in the manufacturing field to assess the impact of additive manu-
facturing and determine the best production method that suits the applied sup-
ply chain strategy. A significant portion of the existing supply chain methods and
frameworks were adopted in this study to examine the implementation of additive
manufacturing in supply chain management. The aim of this study is to develop a
framework to explain when additive manufacturing impacts supply chain manage-
ment efficiently.
KEYWORDS | Additive manufacturing, supply chain strategy, manufacturing strat-
egy, traditional manufacturing, theoretical framework.
Raed Handal
raedh@bethlehem.edu
Professor at Bethlehem University, Accounting Department - Bethlehem, Palestine

19 AUTHOR | Raed Handal
INTRODUCTION
Due to the global economic slowdown, Latin Ameri-
can countries have faced, like other countries, high
commodities prices and demand has faltered, partic-
ularly from China (de Barillas, 2014). “It has also im-
plied the substitution of locally manufactured goods
by imports, affecting the region’s manufacturing ca-
pacity and competitiveness” (de Barillas, 2014). This
opens a timely opportunity for the adoption of new
technologies to enhance customization, lower costs,
increase value added and improve value chains.
Appleton (2014) stated that improvements in addi-
tive manufacturing technology are growing rapidly.
Additive manufacturing has been dramatically de-
veloped through the past few years to overcome its
technical limitations and limited capabilities. How-
ever, manufacturers still underestimate additive
manufacturing ability to enhance manufacturing
processes or business operations, because additive
manufacturing is perceived not as cost effective as
repetitive processes of traditional manufacturing es-
pecially for large scale of production.
Literature shows a significant expansion in the ad-
ditive manufacturing market. However, it is not
easy for top managers to accept the adoption of this
technology in manufacturing (Cohen, 2014). That is
because the lack of existence of a clear model in lit-
erature to show which business strategy best fits the
adoption of additive manufacturing, and/or if addi-
tive manufacturing is applicable to all types of prod-
ucts and/or how additive manufacturing can change
or re-shape businesses and supply chains. Thus,
managers are facing some difficulties to implement
this technology in their manufacturing system.
Presently, manufacturers are trying to adopt addi-
tive manufacturing technology that is characterized
by being efficient in energy and material consump-
tion and, at the same time, being very flexible and
very fast with regards to:
1. Following the changes in the market demand
and
2. Delivering the product to the customer.
The adoption of this technology requires fundamen-
tal changes in the applied business models. Changing
production systems in manufacturers has to result in
the amendment of the business model’s operational
strategy. Optimizing operations in manufacturers
can be done by focusing on enhancing the main ele-
ments of operations which are: 1) decreasing costs,
2) increasing quality, 3) reducing both manufactur-
ing required time and lead time, 4) increasing pro-
duction flexibility and 5) increasing innovation.
Traditionally, companies are concerned with internal
performance improvements and keeping intensive
works. However, in this globalized market, customers
do not really differentiate a company from its suppli-
ers. Thus, companies have to worry about improve-
ments in their suppliers businesses in order to achieve
better performance in the market. The performance
of one company directly influences others in the same
supply chain. Literature suggests performance im-
provements through additive manufacturing (Cohen
et al,. 2014; Wohlers, 2014; Manners-Bell & Lyon,
2012). In addition, literature shows that additive
manufacturing affects the supply chain management.
Nyman and Sarlin (2014) argued that additive manu-
facturing is powerful and makes manufacturing pro-
cesses easier and customization less expensive. Wong
and Hernandez (2012) and Ashley (1991) assured
that additive manufacturing products are character-
ized by presenting higher quality, being lighter, cus-
tomizable, and stronger, already assembled and hav-
ing lower costs. Conerly (2014) confirmed that very
low volume of raw materials and work-in-process will
be in inventory, and no finished goods will be stored
in stock. Ugochukwu et al. (2012) stated that addi-
tive manufacturing technology helps in delivering
the right product, at the right time and at the right
price to customers. However, they all suggest a great
positive impact on supply chain management; addi-
tive manufacturing applications are still not fully ex-
panded to cover the supply chain management, so far.
The research problem is focused on the relationship
between supply chain strategies and product types.
Attention is particularly given to the specific condi-
tions that would make additive manufacturing ap-
plicable. It is because there is lack of contextualized,
structured and generalized framework that illustrates
the best supply chain strategy and product type man-
ufactured that make the adoption of additive manu-
facturing applicable. The existing literature has limit-
ed developments in terms of the conceptualization of
additive manufacturing in supply chain management.
In addition, previous studies fail in assessing and con-
solidating supply chain management and additive
manufacturing in terms of efficiency of production
and responsiveness to market strategies and to link it
with the type of products manufactured.

20ARTICLES |An Implementation Framework for Additive Manufacturing in Supply Chains
METHODOLOGY
Due to the exploratory nature of the research, ex-
ploratory interviews were adopted in this research.
To better understand the problem, two sets of induc-
tive interviews were held. The first one was conduct-
ed with a supply chain optimization consultancy.
The aim of this interview is to explore initiatives,
practices, problems and guidelines in managing sup-
ply chains in general. In addition, to refine the inter-
view questions that were set for the manufacturing
companies and to get benefit from their experience
in dealing with companies that already adopted ad-
ditive manufacturing.
The second set of interviews was conducted with
three different companies from different industries
in several geographical locations. The reason behind
this variety is that additive manufacturing became
famous in so many fields, such as but not limited to:
healthcare, aircraft, automotive, technology, food
sector, jewelry, and cloths and footwear.
The criterion followed in selecting the interviewees
was based on random sampling. We first checked
“All3DP Magazine”, a leading additive manufactur-
ing online magazine that ranks the additive manu-
facturing companies worldwide. Besides, All3DP
magazine clusters these companies into different
groups according to their geographical areas, indus-
tries, printing software, services, etc. From All3DP
magazine, we randomly collected contact informa-
tionofseveralcompaniesfromdifferentindustriesin
different geographical locations and different sizes.
Three companies and a consultancy firm specialized
in end-to-end supply chain optimization accepted to
be interviewed and each suggested a convenient date
and time for the interview according to their time
schedule. The names of participants and companies
are disclosed in the following table (Table 1):
Table 1: Interviewees’General Information
Interviewe Company JobTitle Industry Country
Years of
Experience
FraserGleekie FERCOLtd Senior Consultant
Supply-chain
Consultancy
United
Kingdom
40
Michael Lee Shapeways
Vendor Operations
Manager
Consumableproducts USA 9
GabrielAsfour ThreeAsfour Partner Fashion USA 18
AnnalisaNicola xybag CEOandCo-founder Fashion Italy 16
The interviews were aimed at exploring the guide-
lines in managing supply chains and exploring how
additive manufacturing is applied in these compa-
nies. On the other hand, the interviews helped in
pointing out and identifying the relevant elements
for designing the conceptual framework for adopting
the best fit manufacturing method based on supply
chain strategies.
The Proposed Research Framework
A conceptual model of factors influencing the imple-
mentation of additive manufacturing technologies
as a production method is presented in Figure 1. This
conceptual framework has been developed from the
literature review and a number of exploratory inter-
views and it is of a closed loop nature to illustrate
the interaction and dependency between the supply
chain strategy, manufacturing strategy, and manu-
facturing method that has to be implemented. Next,
the theories that ground our framework are pre-
sented and propositions behind the framework are
explained in detail.

Figure1: Conceptual Framework
Conceptual Framework Basis
The developed conceptual framework is based on the
following theories that are discussed in the literature:
Walter et al. (2004) discussed the effect of additive
manufacturing on the supply chain. The authors
suggest new solutions for supply chain based on
both centralized and decentralized applications of
additive manufacturing. In other words, they sug-
gest either implementing additive manufacturing
technologies on location or to stick with traditional
manufacturing method and outsource the produc-
tion of some parts through additive manufactur-
ing in locations close to customers. They explained
the advantages, as well as the disadvantages of both
centralized and decentralized application of additive
manufacturing. According to Walter et al. (2004)
implementation of additive manufacturing on loca-
tion have the advantage of cutting high inventory
costs and cutting production lead-times and deliv-
ery lead-times, and overcome of batch constraints.
They addressed these advantages to the use of ad-
ditive manufacturing since “it takes too much time
and costs too much to produce the required parts on
demand using conventional production technolo-
gies”. The authors also suggest that decentralized
application of additive manufacturing technologies
can be used to eliminate these costs. However, the
authors were concerned with the problem of having
enough demand to warrant additive manufacturing

machines on location which let the cost of outsourc-
ing much higher in that case. The authors depended
on a case study of an original equipment manufac-
turer operating in the aircraft industry. From their
findings, the authors suggest that to maximize the
benefit of additive manufacturing, a hybrid system
must be applied but concede that centralized appli-
cation of additive manufacturing will be the first to
be used, due to the significant changes the decen-
tralized manufacture will require. Based on Walter et
al. (2004) theory, we consider that additive manu-
facturing technologies are not suitable in all cases.
However, implementation of additive manufactur-
ing will definitely reduce the inventory level.
Besides, we also built our framework on Tuck and
Hague (2006) theory, which focuses on the cost effec-
tive production of customized products. Tuck and
Hague (2006) suggests that additive manufacturing
increase the customization level, and thus transport
costs are reduced and that the burden of part cost
will move from skilled labor operating machinery, to
the technology and material. This conclusion is sup-
ported by Ruffo et al. (2006). Tuck and Hague (2006)
also present and explain that additive manufacturing
influences supply chains in terms of lean, agile and
leagile supply chains. The authors claim that additive
manufacturing enhances lean supply chains as the
only requirements for producing a product are the
design CAD file and raw material. In addition, Tuck
and Hague (2006) also suggest that because additive
manufacturing can be used for economic low volume
production, there is no need to hold stock in inven-
tory. Therefore, a fully JIT system is applicable. They
conclude: “Additive manufacturing could offer the
first truly leagile supply chain paradigm, providing
goods at low cost through the benefits of lean prin-
ciples with the fast re-configurability and response
time required in volatile markets. The production of
goods through additive manufacturing could lead to
reductions in stock levels, logistics costs, component
costs (through reduction in assembled components)
and increase the flexibility of production, through
the ability to produce products to order in a timely
and cost effective fashion.” We base our framework
on the two supply chain strategies; lean and agile,
according to Tuck and Hague (2006) conclusion. Due
to the fact that additive manufacturing technologies
are being used for the production of personally cus-
tomized products, our framework illustrates the ne-
cessity to understand the strategy employed by the
companies, to integrate additive manufacturing and
customization.
Conceptual Framework Factors and the
Inter-relationship between Them
The key elements of a successful supply chain strat-
egy are the three Vs; Visibility, Variability and Ve-
locity (Walker, 2005). No matter what the specific
competitive priority for the organization is, the goal
of the supply chain management is to increase the
visibility and velocity while reducing the variability
(Narasimhan et al., 2008). The three Vs are defined
as follows:
• Visibility is the ability to view information in
all parts through the supply chain (Narasimhan
et al., 2008). Increasing visibility in the supply
chain benefits not only the suppliers and/or the
partners, but also, and most importantly, the
customers. That is because when visibility is in-
creased, managers in the supply chain can react
to change or eliminate unnecessary activities
that waste resources and thus focus on enhanc-
ing the performance of activities that add value
to the product.
• Velocity is the relative speed of all transactions
that have to be done along the supply chain (Nar-
asimhan et al., 2008). The higher the speed of
transactions, the better; it results in a higher as-
set turnover for stockholders and quick delivery
and response for customers. Velocity is similar
to visibility; both are enhanced by supply chain
management.
• Variability is the natural tendency of the results
of all business activities to fluctuate above and
below an average value along the supply chain.
Variability measures the fluctuation of average
values of time to completion, number of defects,
daily sales and production yields (Walker, 2005).
Contrary to visibility and velocity, variability
decreases with good supply chain management.
Supply chain management aims to reduce vari-
ability as much as possible.
Supply chain should match the degree of demand
uncertainty (Fisher, 1997). Implemented strategies
of supply chain can be either Pull or Push systems
(Cachon, 2004). The push strategy in the supply
chain is typically the method used to save customers’
waiting time. Ferguson et al. (2002) called this sys-
tem an “Early- commitment”. Adopting this method,
companies try to manufacture and deliver products
to the shelves before they get orders from customers,
in a way to let the final customers find their needs on

hand. Thus, customers get the product at the exact
time when they need it and can immediately have it.
There are three main types of supply chain strategies
within push strategy:
1. Stable strategy appropriate for a supply chain
which focuses on execution, efficiency and cost
performance. With this strategy, only simple
connectivity technologies are needed, and real
time information is not highly demanded either.
2. Reactive supply chain strategy works well
when the supply chain acts to fulfill the de-
mand from trade partner’s sales and market-
ing strategies.
3. Efficient reactive supply chain strategy is the
strategy that focuses on efficiency and cost
management.
Companies add value to their products in their cus-
tomers’ perspective by saving customer’s time of
waiting to satisfy their needs. However, push strat-
egy could not be perfect for all types of products and
that is because one critical point is missing in this
approach. Customization has not been taken into
consideration. Innovative and, sometimes, function-
al products need to be customized according to cus-
tomers’ preferences. Push supply chain does not give
the customers the opportunity to customize their
goods. However, pull supply chain is the preferable
strategy in such cases. Pull supply chain allows the
customer to ask first in order to manufacture what
he/she wants, and then the product is delivered to
them (Iyer & Bergen, 1997). Applying this strategy,
however, makes customers wait for some time to get
what they ask for.
Additive manufacturing implementation in a supply
chain provides the ability to enhance supply chain ef-
ficiency and effectiveness in terms of cost reduction,
and time saving (Tuck & Hague, 2006).
Even “Efficiency” and “Productivity” terms are
sometimes used interchangeably, as in Sengupta
(1995) or in Cooper et al. (2000). However, in this
research we differentiate the definition of efficiency
from productivity. Based on our understanding of
the literature, “Productivity” is defined as the ra-
tio between outputs and inputs. While, “Efficiency”
is defined as the proximity of a focal organization
to its benchmark within its cluster or industry de-
pending on:
1. The minimum cost of production in manufac-
turing and delivering a final product to a final
individual consumer, and
2. The velocity of the supply chain when trans-
forming inputs into outputs and delivering the
final product to its final customer. The manu-
facturing velocity is defined as the ratio of the
value added to the total throughout time.
In manufacturing, managers should think about
the two main cost drivers: direct and indirect costs,
which are summarized in material, labor and over-
head. At the same time, they should think about
producing goods to satisfy customers’ needs. In that
sense, an ideal product is one that consumes the
least direct and indirect costs of material, labor and
manufacturing overhead and, at the same time, sat-
isfies customers’ needs and wants (Sun, 2011).
Sun (2011) argues that in order to create a firm uses
the minimum possible inputs to produce the maxi-
mum possible value for customers, efficiency tool is
needed. In his opinion, lean production is that effi-
ciency tool.
In order to be efficient in delivering the right product
that satisfies customers’ needs, Value Specification
suggests that all non-value adding activities have to
be eliminated from the process (Gupta & Wilemon,
1990). Eliminating unnecessary steps will accelerate
the speed of production process while using fewer
resources and so improving both effectiveness and
efficiency (Iansiti, 1995b). In addition, Value Stream
helps to visualize the sequence of activities in the
whole process, thus making it easier to identify and
eliminate non-value adding activities. This ensures
increased efficiency.
Moreover, many authors, such as Sun (2011), Ian-
siti (1995a,b), Cordero (1991), Gupta and Wilemon
(1990), Rosenau Jr (1988), and Gold (1987) have
agreed on the basic idea of increasing and improving
efficiency through lean management, which refers
to the elimination of non-value adding activities.
Therefore, working on purely value adding activities
in less time, and with less resources, improves and
increases efficiency.
Besides that, and based on what has been discussed
earlier, we conclude that lean production is recog-
nized as an efficiency tool, because it focuses on
producing outputs with minimum cost by using the
least possible resources to deliver products that have
the maximum possible value for customers. As con-

sequence, lean production creates firms seeking to
add value to their products in all possible ways (Sun,
2011); which means that lean must not be for in-
door use only. It must be widespread across the en-
tire process, starting from getting raw materials and
continuing until the product is in customers’ hands.
It means that lean should go beyond production to
reach the entire organization, including the supply
chain. In that sense, firms that go beyond produc-
tion in implementing lean thinking can be termed
Lean Corporations, which is a more accurate concept
to be used (Sun, 2011). Moreover, their activities
could be acknowledged as a lean value chain.
In a lean value chain, manufacturers identify each
activity to check whether it adds value or it is un-
necessary and can be removed. Through lean tech-
niques, managers encounter dispensable activities
that create costs and eliminate them. For instance,
the JIT technique permits manufacturers to avoid
superfluous costs of shipping, receiving, inspection
and rework (Sun, 2011).
What is more, lean value chain elevates manufactur-
ers’ flexibility in pursuing the market’s changes due to
demand uncertainty and changing customers’ tastes
and preferences. JIT lean tool allows firms to change
outputs more quickly in response to demand changes,
compared to other manufacturing methods.
Lean management is the main bridge that links ad-
ditive manufacturing with supply chain. Lean man-
agement serves as a great linkage that connects both
topics by focusing on the efficiency of production.
Companies’ success or failure depends on getting the
right product at the right time, and at the right price
to customers (Nyman & Sarlin, 2014). As was clearly
visible when reviewing lean management and addi-
tive manufacturing literature, both share the follow-
ing two characteristics:
1. Eagerness to increase efficiency. Many authors,
such as Sezen and Erdogan (2009), explained
lean as a method used to reduce costs, as well
as to increase efficiency and quality. Moreover,
Shah and Ward (2007) defined it as a manage-
ment philosophy. Their definition was per-
fectly positioned on clear identification and
elimination of wastes not only within, but over
and above the production process to reach the
whole manufacture’s product value chain. Nev-
ertheless, from all the reviewed literature, we
concluded that all researchers agreed upon one
main opinion in defining the objective of lean
concepts. This objective is summarized in cost
reduction and production efficiency improve-
ment. In addition, researchers and authors in
the additive manufacturing field agreed that
additive manufacturing methods are able to cut
down manufacturing costs and save time. Based
on literature review, Wong and Hernandez
(2012) proved that additive manufacturing is
able to depreciate costs and save time. This has
been stated by many other researchers such as:
Noorani (2006), Herbert et al. (2005), Cooper
(2001) and Ashley (1991). Cost reduction and
time saving form the basis of doing things right
in terms of what is known as “efficiency.”
2. Better responsiveness to market changes in
both demand and supply. Globalization and
openness to the entire world’s markets cre-
ate rapid changes in natural conditions, tech-
nological progress, transport improvements,
customers’ income, customers’ tastes and
preferences, and future expectations of both
customers and suppliers. In this sense, lean
management uses practices and techniques
that make the manufacturing process very
responsive to these changes (Mohanty et al.,
2007; Nightingale, 2005). “Right amount at
the right time” practice, “Pull System” tool
and “JIT” tool are methods that technically en-
hance responsiveness to changes in the mar-
ket. These methods are based on having low
amount of raw materials inventory, as well
as, work-in-progress and finished goods. Low
inventory levels facilitate adapting to new
changes in the market easily with minimal
inventory costs. Moreover, additive manufac-
turing is based on producing small production
runs pulled from customers’ needs, in contrast
with traditional manufacturing (Campbell et
al., 2011). This feature in additive manufactur-
ing gives it the advantage to be able to quickly
adapt to market changes by not holding high
levels of inventory on hand.
Based on our previous discussion, we concluded that
additivemanufacturinghasfeaturesthatmakesitable
to work well with lean strategy in the supply chain,
where it fits perfectly under the following principles:
1. Value Specification: Specifying value, from the
end customers’ view, involves trying to find
out what customers desire from the product
(Womack & Jones, 1996a). Thus, in order to

specify the value, a lean practice of realizing
customers’ needs has to be applied. In that
sense, a lean tool has to be put into action in
order to translate customers’ needs into tan-
gible product. Here additive manufacturing
plays a role in transforming the specified value
of customers’ needs into real products (Nyman
& Sarlin, 2014; Wong & Hernandez 2012) be-
cause additive manufacturing is a very flexible
production tool. Products can be produced to
meet customer’s exact requirements for the
product. Materials used, shapes, sizes and any
other features can be adjusted on the spot to
meet what the customer requires, with mini-
mal costs of production compared to tradition-
al methods.
2. Identifying Value Stream: Organizations must
identify value stream of products at every step
of the supply chain, in order to enhance the
value added activities and eliminate the non-
value adding ones from the process (Womack
& Jones, 1996b; Womack et al., 1991). When
companies identify the value stream in their
supply chain, they will be able to reduce costs
which are synonymous with waste (Shah &
Ward, 2007; Ohno, 1988). Additionally, addi-
tive manufacturing has proven its effective-
ness in reducing costs to the minimum by re-
ducing waste from production (Berman, 2012;
Sealy, 2012). On that account, additive manu-
facturing could serve as an effective lean tool
to produce and deliver products that hold the
maximum value to customers with minimum
wastes and costs.
3. Pull Principle: Womack and Jones (1996a)
explained the pull principle as “production
should be done only when customers demand
the product.” That consecutively explains the
Right Amount at the Right Time practice in
lean management that calls to produce the
needed quantity only when it is needed (Shah
& Ward, 2007) because excess in production
leads to higher costs in inventory. Based on
these principles and practices, we can presume
that additive manufacturing perfectly per-
forms the needed duties to be a proper tool in
lean management. Based on the fact that ad-
ditive manufacturing makes it feasible to pro-
duce any product required by customers, at
the time it is demanded, without the need to
change production process or change or retrain
personnel as is often required with traditional
machinery. In addition, it allows for product
differentiation and customization, because of
its ability to flexibly produce any size or shape
required (Sealy, 2012).
With additive manufacturing, both customers and
businesses can benefit from designing and person-
ally customizing their final products. This new tech-
nological manufacturing method makes it possible
to modify the functionality of a product, from one
side, and physically from the other, in order to fit the
needs of the customer, in a way that was not avail-
able before. This, of course, has affected the sup-
ply chain. Businesses should keep pace with these
changes and keep modifying their supply chain to fit
the new requirements of the market.
Literature has showed that supply chain is not fixed
for all types of products or all types of businesses.
Supply chain differs from one production line to an-
other to match the uncertainties in both demand
and supply (Lee, 2002). Some businesses are look-
ing to shorten the supply chain by eliminating some
activities, while others are interested in having a re-
sponsive one and others like to hedge the risks stem-
ming from either supply or demand uncertainties.
When additive manufacturing is applied, the supply
chain takes a different shape. This is because tradi-
tional manufacturing methods depend mainly on
mass production, where products are made in batch-
es and stocked in inventories and have to be distrib-
uted to wholesalers and retailers in order to arrive
to final customers. With additive manufacturing,
responsiveness and the flexibility of both custom-
ization and delivery is more easily achieved while
eliminating all non-value adding activities such as
inventory and distribution.
Lee (2002) argued that agile supply chain is a strate-
gy that makes the supply chain capable of quickly re-
sponding to changes in the market and in customer
preferences, and diversify the product’s functional-
ity to perfectly match customers’ needs. In the con-
text of additive manufacturing, agile supply chain
is the strategy that is qualified to deliver a perfectly
customized product to customers, with the most ef-
ficient mode of delivery, at a minimized cost; this is
achieved by cutting all unnecessary activities that
add no value to the product. In addition, agile supply
chain is capable of responding quickly to any changes
in customers’ preferences while risks are minimized.

Thus, agile supply chain combines the characteristics
of efficiency, responsiveness, risk-hedging and custom-
ization. Likewise, additive manufacturing is based on
the same characteristics, while responding to custom-
ers’ requirements and perfectly customizing products
to fit their needs, cutting costs by reducing waste and
eliminatingnon-valueaddingactivities.Inthisfashion,
implementing additive manufacturing in focal organi-
zations of the supply chain requires the supply chain to
take the shape of an agile supply chain.
Previous studies found that technology has the abil-
ity to change and reshape businesses as well as im-
plemented strategies (de Jong & de Bruijn, 2014). It
is considered to be an internal strong point of the
business SWOT analysis when businesses know how
to properly employ these technologies to bring op-
portunities to their side. However, based on the held
interviews, interviewees such as Asfour from Three-
Asfour and Gleekie from FERCO LTD, claim that
additive manufacturing cannot be implemented in
businesses to produce all types of products and/or
all product components. They suggest that additive
manufacturing is more feasible when it is used with
high valued components or for complex products.
Additive manufacturing has been applied to low-
volume production, and the output can be of higher
rank than that of the traditionally manufactured
output; that is, additive manufactured products (es-
pecially consumer goods and health aids) are char-
acterized by presenting higher quality, being lighter,
more customizable, stronger, already assembled and
having lower cost (Wong & Hernandez, 2012; Ash-
ley, 1991) than items produced by traditional manu-
facturing methods. Additive manufacturing has the
ability to precisely control the quantity of material
used to make the product.
Nyman and Sarlin (2014) argued that additive man-
ufacturing is powerful and makes manufacturing
processes easier and customization less expensive
for customers. In traditional manufacturing meth-
ods, managers forecast future demand. Based on
that forecast, a sufficient amount of outputs, that
is in accordance with the management’s forecast, is
produced and stocked in inventory (Lee & Billing-
ton, 1992). However, when additive manufacturing
is implemented in a manufacturing method, real-
time demand manufacturing is set in motion. This
feature in additive manufacturing results in shorter
lead time from order to delivery and it gives the sup-
ply chain more flexibility in responding to changes
in product demand. Additive manufacturing allows
manufacturing to become more agile, more flexible,
abler to respond rapidly to shifts in market demand,
and more capable of introducing new products
quickly and inexpensively. As a result, both manu-
facturing and consumer behavior are affected. It also
affects the supply chain; it accelerates the shift from
“Push Supply Chains” to “Pull Supply Chains.” This
is because additive manufacturing makes it possible
to store products, parts and components on com-
puter files, with no need to have them physically in
warehouses. Each component can be pulled only at
the time it is needed. Contrast this with the JIT lean
management tool that let managers keep some in-
ventory on hand in warehouses to avoid the risk of
shortage (Conerly, 2014). Thus, a very low volume
of raw materials and work-in-progress will be in in-
ventory, and no finished goods will be stored in in-
ventory (Conerly, 2014). As a result, overall supply
chain management costs will be lower than those of
traditional manufacturing supply chains, because of
the reduced inventory costs and the reduced waste
of outdated products. However, the production cost
per one unit in traditional manufacturing methods,
where production runs for huge batches, is much
lower than in additive manufacturing (Conerly,
(2014). Conversely, the opposite is true for small
production runs; cost per unit in additive manu-
facturing for small batches is relatively low when
compared to traditional manufacturing. Figure 2 is
a hypothetical graph that explains the difference be-
tween production cost per unit when using additive
manufacturing methods and traditional manufac-
turing methods, with reference to number of units
produced in each method.
Figure 2: Hypothetical Cost per Unit in Both Additive
Manufacturing and Traditional Methods of Production

JOSCM - Journal of Operations and Supply Chain Management – Vol. 10, n. 2 - Jul/Dec 2017

JOSCM - Journal of Operations and Supply Chain Management – Vol. 10, n. 2 - Jul/Dec 2017

Recommended

Recommended

More Related Content

Similar to JOSCM - Journal of Operations and Supply Chain Management – Vol. 10, n. 2 - Jul/Dec 2017

Similar to JOSCM - Journal of Operations and Supply Chain Management – Vol. 10, n. 2 - Jul/Dec 2017 (20)

More from FGV | Fundação Getulio Vargas

More from FGV | Fundação Getulio Vargas (20)

Recently uploaded

Recently uploaded (20)

JOSCM - Journal of Operations and Supply Chain Management – Vol. 10, n. 2 - Jul/Dec 2017