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Nowadays, in order for wine companies to reach a world-class standard, it is necessary to implement the industry best practices and continuously adapt their logistics processes. Through benchmarking, these enterprises can find opportunities for improvement. So far, little research in benchmarking and performance measurement has been developed for the wine industry. In this paper a logistics benchmarking framework for the wine industry is proposed. A benchmarking study considering several wineries from Mendoza (Argentina) is presented as a case study, in order to demonstrate the validity of the developed framework.
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A framework for measuring logistics performance in the wine industry
Fernanda A. Garcia
, Martin G. Marchetta
, Mauricio Camargo
, Laure Morel
Raymundo Q. Forradellas
School of Engineering, National University of Cuyo, Centro Universitario, CC405 (M5500AAT) Mendoza, Argentina
Research Team in Innovative Processes (EA N3767 ERPI), ENSGSI, Institut National Polytechnique de Lorraine, 8 rue Bastien Lepage, BP 90647 (54010) Nancy Cedex, France
article info
Article history:
Received 11 August 2010
Accepted 1 August 2011
Available online 17 August 2011
Logistics benchmarking
Performance measurement systems
Wine industry
Wine supply chain
Nowadays, in order for wine companies to reach a world-class standard, it is necessary to implement
the industry best practices and continuously adapt their logistics processes. Through benchmarking,
these enterprises can find opportunities for improvement. So far, little research in benchmarking and
performance measurement has been developed for the wine industry. In this paper a logistics
benchmarking framework for the wine industry is proposed. A benchmarking study considering
several wineries from Mendoza (Argentina) is presented as a case study, in order to demonstrate the
validity of the developed framework.
&2011 Elsevier B.V. All rights reserved.
1. Introduction
In a global economy and competitive and dynamic environ-
ment, Supply Chain Management (SCM) is a key strategic factor
for increasing organizational effectiveness. Wine companies
around the world are realizing the importance of supply chains
and the impact of their performance on the business.
The importance of the wine industry worldwide can be
measured by its business and operations volume. According to a
study of the International Wine and Spirit Record (IWSR, 2010), in
2010 the wine world market reached 23.6 billion liters, repre-
senting 183.1 billion dollars, a growth of 4.5% from 2005. For 2014
an increase of 3.2% is expected.
Logistics activities in the wine industry are becoming more
and more important. New markets are appearing as a conse-
quence of the economic development of some emerging countries
such as China, Russia and the Asia-Pacific region. Moreover, the
IWSR (2010) report indicates that today 25% of wine bottles
consumed are imported wines, and this proportion will be higher
in the next years. Therefore, an increase of the logistics operations
in the wine industry is expected.
Additionally, different segments of wines, from table to super
premium, need particular logistics activities. For example Dollet
and Diaz (2010), studied the premium and super-premium wines,
a market driven by time-to-market and customization, and they
proposed a multi-level network orchestration SCM model for this
specific market. On the other hand, for commodity wines the
fierce competition for existent and new markets leads to search
for supply chain strategies in order to reduce transportation cost.
Roy and Cordery (2010) proposed a collective procurement
approach for growers and wine producers, and export in bulk
rather in bottles. Furthermore, Wen et al. (2010) applied the
Quality Function Deployment methodology to identify customer
segments and infer wine taste but also scale operation and supply
chain strategy for this growing market.
Supply Chain Management is important for wineries world-
wide because they compete in an international marketplace
where, even though the production is rather flat (Fig. 1), the
‘‘old world wine producers’’ tend to decrease their wine produc-
tion and consumption while ‘‘new world wine producers’’ are
becoming more aggressive, offering very high quality wines at
more competitive prices. Moreover, due to the complexity of the
different segments, the traditional dichotomy ‘‘old world’’/‘‘new
world’’ does not have sense anymore (Banks and Overton, 2010),
as each production country has different supply integration levels,
winery sizes and production styles (traditional, modern, artisanal,
closely tied to place and vintage, large-scale industrial production
for a mass market). Therefore, wine producers have to be able to
better meet their customers’ demands at a more affordable cost
and ensure few stock outs on store shelves. This evolution has
resulted in a growing necessity to higher inventory turns, service
level and improved customer satisfaction.
Therefore, improving supply chain’s effectiveness and effi-
ciency becomes a critical factor to remain competitive in a
marketplace that is more and more global, and where competi-
tion is tougher and tougher. The Wine Supply Chain (WSC) is
a very complex system due to several aspects: the nature of the
product (which forces the use of a mixed push/pull schema), the
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Int. J. Production Economics
0925-5273/$ - see front matter &2011 Elsevier B.V. All rights reserved.
Corresponding author. Tel.: þ54 261 4135000x2128; fax: þ54 261 4380120.
E-mail addresses: (F.A. Garcia), (M.G. Marchetta), (M. Camargo), (L. Morel), (R.Q. Forradellas).
Int. J. Production Economics 135 (2012) 284–298
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number of actors and relationships between them, the multi-tier
systems in distribution cycle of some countries, the requirements
of final customers, the continuous pressure of local and external
competitors in the market and the legal constraints of distribu-
tion, among others. Some of these characteristics, like the mixed
push/pull schemas, apply to other industries as well, particularly
alcoholic beverage industries and agro-food industries.
For wine companies it is increasingly important to integrate
logistics processes along the supply chain and to improve the
performance of each process to reach a world-class standard. In
order to improve the performance, it needs to be measured, so the
definition of a consistent and world-class performance measure-
ment framework, and the execution of benchmarking studies to
acquire knowledge about the organization’s performance related to
its competitors and to the leadersoftheindustry,isanimportant
tool for reaching world-class standards (Frazelle, 2002).
Many organizations have improved their logistics processes per-
formance through the implementation of the industry best practices.
However, little attention has been given so far to the performance
evaluation, and hence, to the measures and metrics in the wine
industry. Benchmarking is the search of those best practices that will
lead to the superior performance of a company (Camp, 1989).
In this paper a description of the WSC is presented along with
a hierarchical benchmarking framework for measuring the per-
formance of logistics processes along the WSC. Additionally, a list
of potential problems and the relation between these problems
and the corresponding indicators of the hierarchy is presented.
Finally, the results of a benchmarking study conducted on a
sample of wineries from Mendoza (Argentina), is described.
2. Benchmarking background
During the past years, different works related to supply chain
performance measurement have been developed following dif-
ferent approaches, e.g. different scopes (all logistics activities vs.
individual logistics processes), different techniques (grouping
indicators within dimensions, Data Envelopment Analysis,
multi-criteria analysis, etc.). These works have different objec-
tives (e.g. measuring internal performance, benchmarking,
extracting knowledge in the form of dependency relationships
between indicators, etc.). The proposed models and techniques
have been applied in different industries and company sizes (e.g.
manufacturing, hardware and software, textile and garment, etc.).
In this section, a review of the recent literature is presented to
illustrate the use of performance measurement in different
industries, settings and with different objectives.
Bhagwat and Sharma (2007) developed a balance scorecard
approach for supply chain management focused on small and
medium sized enterprises. The availability of indicators that this
kind of dashboards provides has made possible to obtain a
quantitative perspective of the dynamics of distributed logistics
chains. This has opened the possibility of implementing tools
already developed for internal logistics chains, such us the
implementation of information systems, costs management, opti-
mization systems or multi-criteria decision support.
In recent years, the impact of information systems on logistics
activities has grown. Performance of information systems activities
has been measured following several perspectives (Martinsons et al.,
1999), and the relation between IT implementation, Supply Chain
Integration and Supply Chain Performance has also been researched
(Li et al., 2009). Bayraktar et al. (2009) presentedastudyonwhich
they have empirically tested a framework identifying the causal
links among SCM and information systems practices.
Finding the exact performance evaluation of the SCM in inven-
tory level minimizing the total cost has also been studied (Kojima
et al., 2008), and performance measurement systems have been
applied to particular cases, such as manufacturing organizations
(Lohman et al., 2004). Muchiri et al. (2010) presented a conceptual
framework for guiding the definition of performance indicators for
supporting the alignment of maintenance objectives with manufac-
turing and corporate objectives. Neely et al. (1996) identified
different aspects that define a performance measurement system,
and surveyed the use of structured processes for defining such
systems, including more than 850 SMEs in the UK, which indicates
that using structured approaches simplifies this task and improves
the quality of the systems obtained.
Danese and Kalchschmidt (in press) investigated the impact of
forecasting variables on companies’ performance. Pinheiro de Lima
et al. (2009) presented a process to integrate operations strategy to
the design of operations performance measurement systems. Xu
et al. (2009) studied the main uncertainty factors affecting the
supply chain performance evaluation, and developed a performance
evaluation model based on Rough Data Envelopment Analysis.
Kulmala et al. (2009) propose to include leadership behavior in
performance measurement in order to improve that area.
Outsourcing of logistics activities has become a common
practice applied by companies to focus on their core competen-
cies, what has yielded the need to measure performance of service
providers. Krakovics et al. (2008) discussed the definition and
design of a quantitative system to measure logistics performance
when logistics activities are outsourced to 3PL (Third-Party-
Logistics). Wong and Karia (2009) identified strategic logistics
resources acquired and bundled by logistics service providers to
achieve competitive advantage. Bustinza et al. (2010) presented a
model to study the impact of outsourcing on the firm’s compe-
titive capabilities. Hsiao et al. (2010) presented a research frame-
work to assess the effect of the outsourcing decision at 4 levels
Fig. 1. New/Old World wine countries average production (Source: OIV – International Organization of Vine and Wine).
F.A. Garcia et al. / Int. J. Production Economics 135 (2012) 284–298 285
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(transportation, packing, transportation management and distri-
bution network management). Awasthi et al. (2010) proposed a
fuzzy multi-criteria approach for evaluating environmental per-
formance of suppliers.
Many benchmarking studies have been carried out in the past
years in the integrated supply chain (Andersen et al., 1999)in
several industries like manufacturing (Stewart, 1995;Voss et al.,
1995;Collins et al., 1996;Voss et al., 1997;Hines, 1998;Geary
and Zonnenberg, 2000;Choy, 2002;Cooper and Edgett, 2003),
software and hardware (Cohen et al., 1997;Beitz and Wieczorek,
2000;Hamilton, 2006), transport (BizSys, 2006), port (Bichou,
2007) and textile and garment (Jolly-Desodt et al., 2006), among
others. Lai et al. (2004) conducted a benchmarking study of
companies in the transport logistics industry of Hong Kong,
considering efficiency (economic use of resources) and effective-
ness (fulfillment of customer requirements) measures, using a
framework based on the SCOR model.
Pestana and Peypoch (2009) applied a two step Data Envelop-
ment Analysis (DEA) procedure to evaluate the operational perfor-
mance of a sample of the Association of European Airlines.
Schmidberger et al. (2009) developed a holistic performance mea-
surement system (PMS) for airport ramp service providers with a
process-based perspective, and conducted a benchmarking study in
several European hub airports. The authors followed the action
research approach for defining the PMS, which associates weights to
the measures in an Analytical Hierarchical Process, and groups
measures into the perspectives of the Balanced Scorecard (BSC).
Benchmarking studies developed follow a wide range of
approaches, including definition of measurement indicators for
specific domains, defining best practices for improving efficiency
in a specific industry, modeling different processes to measure its
performance and the use of a tool to benchmark a specific domain.
In the particular case of the wine industry there are some bench-
marking studies focusing on particular aspects, such as the financial
and economic performance of the enterprises (Deloitte and
Winemakers’ Federation of Australia, 2004;Deloitte and New
Zealand Winegrowers, 2008), the energy efficiency opportunities
(Galitsky et al., 2005), and the changes that have happened in the
wine industry and their impact in old world and new world wine
countries, from the demand, innovation, supply chain structure and
institutional framework perspectives (Cusmano et al., 2010).
Although there are works related to logistics benchmarking in
others industries such as in warehousing and distribution opera-
tions (Hackman et al., 2001) and in manufacturing industry
(IAC, 2000), there are few works on logistics benchmarking in
the wine industry. On the other hand, aspects other than logistics
have been researched within the wine industry in previous
works: supply cycle, production and distribution cycle, analysis
of the situation of different countries, sustainability, etc. (Duraj
et al., 2000;Adamo, 2004;Sheu et al., 2005;Colman and P¨
2007;Musee et al., 2007;Alturria et al., 2008;Dunstall et al.,
2008;Ferrer et al., 2008;Gabzdylova et al., 2009).
Therefore, there are no integrated supply-chain-wide frameworks
to measure logistics performance in the wine industry. Only sepa-
rated measures have been presented (Bailey, 2003), but no inte-
grated framework covering the whole supply chain has been found
in the literature. The definition of an integrated and consistent
framework for measuring the logistics performance in the wine
industry, and its evaluation in a case study, is the focus of this article.
3. Wine supply chain
Considering the complexities of the WSC detailed in Section 1,itis
difficult for managers to make appropriate decisions and to measure
and improve logistics performance without a model of the supply
chain including its actors and relationships (Lambertetal.,2008).
Therefore, it is necessary to count with a formal and generic model of
the WSC, which represents all the possible instances.
Every SC consists of several nodes which can be called ‘‘actors’’
(Gigler et al., 2002). Each actor is a character, a link of the chain, a
part played by a performer. Fig. 2 shows the actors of a generic WSC,
who are connected through material flows (represented by contin-
uous lines) and information flows (represented by dotted lines).
Even though different products have different customer require-
ments and cannot be satisfied by a single SC strategy, a generic
representation of the WSC is presented, which means that it
contains the more general SC, and it can be instantiated into many
particular cases. This representative model has been defined follow-
ing the supply chain modeling approach described by Lambert et al.
(2008), and considering: (a) literature review and analysis,
(b) through extensive observation of real wineries and other actors
in the WSC, and (c) using information gathered by means of
questionnaires and interviews made to people of different wineries
and other WSC actors in Mendoza, Argentina (Garcia, 2009).
Grape Grower: The Grape Grower is responsible for the
production and harvest of the grapes (GS1, 2005). This node
Fig. 2. Wine supply chain.
F.A. Garcia et al. / Int. J. Production Economics 135 (2012) 284–298286
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is one of the most important within the WSC because the final
quality of the wine is directly related to the quality of the
grapes (Adamo, 2004). The main activities of grape growers
are: planting the grapes, cultivating and pruning the vines,
eliminating the inadequate vineyards, fertilizing the vineyards,
controlling plagues, harvesting grapes, etc.
Raw Materials Supplier: This actor provides Wine Producers
and Fillers/Packers with all the supplies needed for wine-
making or filling and packing. The main activities are: receive
new orders from wineries and/or Fillers/Packers, prepare
orders, send supplies to the wineries and Fillers/Packers, store
supplies, etc.
Wine Producer: Wine producers are responsible for receiving
grapes, the elaboration, manufacture and/or blending of wine
products. Depending on the type of wine that is going to be
elaborated, the process is different (Llera and Martinengo, 2004).
In general, the main activities to elaborate wine are: receiving
and weighing the grapes, crushing, stemming and pressing juice,
addition of sulfite and decanting, addition of yeast, fermentation,
refrigeration, clarification and stabilization, temperature control,
storage in stainless steels tanks or oak barrels, filtration, pre-
paration for bottling, maturation in bottle, etc. Several aspects of
the WSC will be different depending on the segment of wine
produced, such as the number and quality of vineyards used, the
number and the quality of supplies, the packing and labels of the
wine, etc. (Adamo, 2004).
Filler/Packer: Fillers/Packers are responsible for the reception,
analysis, filling, packing and dispatch of finished goods (GS1,
2005). The Filler/Packer receives containers of bulk wine from
the Wine Producer, and then the wine is filled into different
kinds of packages. Consumer units, such as bottles, bag-in-box,
tetra packs, etc. are produced from the wine batches supplied.
The next step is packing into consumer units into cartons and
pallets or other logistic units.
Freight Forwarder: This actor organizes the shipment planning,
which is the process of choosing shipment frequencies and
deciding for each shipment which orders should be assigned. It
also includes the safe and efficient movement of goods on behalf
of an exporter, importer or another company or person, some-
times including dealing with packing and storage. Typical
activities include (Frazelle, 2002): researching and planning the
most appropriate route for a shipment (taking into account the
nature of the goods, cost, transit time and security), arranging
appropriate packing (taking into account climate, terrain, weight,
nature of goods and cost) and delivering or warehousing of goods
at their final destination.
Freight operators: They supply service for transporting goods
from the Winery to the Importer or to other actors (distributor,
wholesaler, retailer, etc.), by air, through airline services, by
sea through shipping lines or by road and rail through different
operators. The courier could be an express/parcel carrier
trucking company, an ocean liner, a railroad or an air carrier/
integrator (Frazelle, 2002).
Importer: This actor buys goods from the Wine Producer and
is responsible for the reception, storage, inventory manage-
ment and dispatch of finished goods, which receives from the
Freight Forwarder through the Freight Operator. The Importer
sales and delivers finished goods to the Wholesaler or Dis-
tributor of the destination country depending on the distribu-
tion channel used in the country.
Finished Goods Distributor: This actor is responsible for the
reception, storage, inventory management and dispatch of
finished goods, as well as re-packing and re-labeling as per
specific customer requirements required (GS1, 2005).
Wholesaler: The Wholesaler receives pallets and cartons from
the Finished Goods Distributor and picks and dispatches goods
to the retails stores. They put new orders to the Finished
Goods Distributor, to the Importer and may also buy directly
from the winery.
Retailer: The Retailer receives finished goods from the Fin-
ished Goods Distributor or the Wholesaler depending on the
distribution channel. The retailer sells consumer units (bottles,
cartons) to the Final Consumer. The different sales’ channels
are: hyper/supermarket, liquor stores, drugs stores, specialist
store, hotels, restaurants, catering, clubs, etc.
Final Consumer: This is the final customer of the SC. Final
customers may buy finished goods directly from some wine-
ries, or they can make an indirect order of new products when
they go to the store or supermarket and chose some kind of
wine. These orders are almost always placed in-site during the
customer’s visits to the retailer’s shop (Adamo, 2004).
4. Proposed framework
The term benchmarking implies the measurement through a
collection of metrics to adequately quantify the performance of
processes. These quantitative analyses may be complemented
with qualitative ones in order to support decision making pro-
cesses, for example by exploring good or bad practices to imple-
ment or improve once the values of the metrics have been
obtained and contrasted against target values.
These metrics should be selected and maintained as a system.
For an organization to arrive to a world-class standard it is
necessary to implement a set of world-class logistics per-
formance indicators (Frazelle, 2002). Through the measurement
of logistics performance along the WSC it is possible to under-
stand the industry’s best practices through which it is easier
for a winery to fulfill the customers’ requirements, to better
understand the WSC dynamics, which helps to find bottlenecks
all along the chain, and finally to have a diagnosis of the
wineries with respect to their competitors and the industry
leaders, which helps to develop new strategies to become more
In order to define a set of key logistics performance indicators
for a winery, it is important to specify classification dimensions
for these indicators. The SCOR model (Supply Chain Council,
2010) defines a single classification dimension for metrics: the
performance attributes. In this work, following the approach
described by Frazelle (2002), we propose two classification
dimensions, namely the performance attributes and the logistics
processes. Both attributes and processes are guides to apply the
relevant framework indicators for a particular situation, since
they group performance metrics.
4.1. Performance attributes
The SCOR model (Supply Chain Council, 2010) defines 5 per-
formance attributes (reliability, responsiveness, agility, costs and
assets). Some of these performance attributes are also considered
in the approach proposed by Frazelle (2002), who states that from
the logistics point of view every business competes on the basis of
financial performance, productivity performance, quality perfor-
mance, and cycle time performance. Garcia et al. (2009) adapted
these categories for defining the following 4 performance attri-
butes related to logistics processes in the WSC, which we adopt in
this work: Quality,Timeliness,Logistics Cost,Productivity and
Capacity. Quality is related to both process and product quality
along the WSC. Measuring the quality performance of logistics
processes and products is the way to improve these processes
and at the same time insure the customer’s satisfaction level.
Timeliness is related to the response time of the supply chain to
F.A. Garcia et al. / Int. J. Production Economics 135 (2012) 284–298 287
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satisfy the customer’s requirements; Logistics Cost is related to
logistics financial performance; and Productivity & Capacity is
related to the efficiency of the resources usage.
4.2. Logistics processes
Logistics performance is measured along Logistics Processes.
Based on the logistics activities identified by Frazelle (2002),we
defined the following 6 logistics processes: Supply,Production
and Bottling,Inventory Management,Warehousing,Transpor-
tation and Distribution and Customer Response. Supply includes
all activities related to the purchase of supplies, management of
suppliers, products income, etc. Production and Bottling includes
all the activities related to the wine elaboration process, bottling
(including bottles, bag-in-box, etc.) and packing. Inventory Man-
agement, includes all the activities of planning, inventory admin-
istration, inventory moving, etc. Warehousing involves all
warehouse management from product reception and picking to
container loading. Transportation and Distribution includes all
the activities of distribution and transportation of the wine orders
to arrive to the customer location. Finally, Customer Response
includes all the activities related to customer services, order
entry, order processing, follow up, etc. Fig. 3 shows the relation
between these two classification dimensions.
The figure shows how a performance attribute can be measured
through all the logistics process. For example, considering the
response time, it represents the lead time of suppliers in the supply
process, the elaboration and bottling time in the production process,
the transportation time in the distribution process, etc.
4.3. Key performance indicators hierarchy
Within this work, we consider the winery point of view, i.e. we
consider the winery as the focal company (Lambert et al., 2008).
In order to make the performance measurement framework
manageable, considering the complexity of the WSC, the number
of actors and its dynamics, the generic catalog of indicators is
structured in a hierarchy of three levels. Although the classifica-
tion dimensions are not the same, nor the specific metrics defined
on each level, the idea of including several aggregation levels was
inspired in the SCOR model (Supply Chain Council, 2010).
The first level contains indicators that reflect the global
performance of the winery as well as the whole WSC. These first
level indicators will show the result of the efficiency of several
activities performed along the WSC by different actors, and they
represent high level aggregated results. The combined use of
these indicators will help to further understand the overall
logistics performance of the enterprise taking into account qual-
ity, logistics costs, time, and productivity.
As we go down through the hierarchy, the number of indica-
tors at each level grows, thus providing more detailed measures
for each combination of performance attribute and logistics
process. The second level contains indicators to measure perfor-
mance of the enterprise in the same performance attributes and
logistics processes previously described, but the information is
shown in more detail than it is in the first level.
The third level measures the performance of the operations of
the organization. This level is related to the everyday operations
of the enterprise, with the purpose of improving its performance.
As the previous level, the lower level indicators complement the
high levels of the hierarchy with further details.
This generic catalog is composed of a great number of
indicators (the second and third levels of the hierarchy are shown
in the appendices). This does not mean that every actor of the
WSC will use the whole hierarchy. Each actor will instantiate
the framework depending on the strategy of the enterprise and
the structure of its supply chain, that is, depending on the place
the company occupies within the supply chain.
Some KPI (Key Performance Indicators) of the framework have
been adapted from different authors’ frameworks (Frazelle, 2002;
Choy, 2002;Lo et al., 2005) and others have been defined
specifically for measuring the integrated WSC performance.
The selection and adaptation of indicators from the other
frameworks, as well as the definition of the new indicators
not included in previous works were done through question-
naires, observation and interviews to people of different actors
of the WSC in Argentina. The adaptation of metrics included
in the other frameworks, as well as the new indicators added
convey much of the particularities of the wine industry that
might be generalized to other alcoholic beverage or agri-food
Fig. 4 shows the KPI hierarchy, including the three aggregation
levels, the four performance attributes defined, and the six
logistics processes performed within the winery where each
indicator must be measured.
This figure also shows the indicators’ relation from one level to
the other. For example, for measuring the time consumed in the
warehousing process, there is one key performance indicator in
the first level which shows the average time of the process, and if
a deeper analysis is required, the second and the third level
indicators can be analyzed to find the causes of the performance
problems. One first level key performance indicator can be related
to one or more second level indicators (though this is not a
taxonomic decomposition; the lower level indicators give more
details of high level indicators, but they do not define them
directly), and the same applies to the second and third levels of
the hierarchy. Table 1 shows all the first level indicators, a
description and the formula.
Fig. 3. Logistics processes and performance attributes (Source: elaborated by authors).
F.A. Garcia et al. / Int. J. Production Economics 135 (2012) 284–298288
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All the formulas have to be used considering a specific period
of time, defined at the moment of the implementation of the
framework, (e.g. annually, monthly, daily, etc).
Tables 2 and 3 present the details of the second level
indicators related to the Total Logistics Cycle Time and Resources
Utilization Percentage first level indicators, which are used in the
case study.
Fig. 5 graphically shows all the periods measured within the
Total Logistic Cycle Time.
4.4. Different uses of the framework
Considering the wine supply chain described earlier, several
instantiations are possible depending on the wine segment
Fig. 4. Key performance indicators hierarchy.
Table 1
Key performance indicators1st level of indicators.
Indicator name Description Formula
Quality Supplier performance index It measures the supplier’s performance (including the
average of claims made by the winery to the supplier
in a specific period of time) PNumber of perfect purchase orders/number of
placed purchase orders (1)
Right quality grapes
It is a rate of the quantity of grapes obtained with the
right quality during harvest (in a specific period of
time) PQuantity of grapes of right quality obtained/total
quantity of grapes obtained (2)
Production performance
It is a rate of perfectly produced units in a specific
period of time POrders produced as planned without failures and
rejections/total orders from customer (3)
Inventory performance index It shows the global performance of all activities of
inventory in a specific period of time PLower level indicators performance/total number
of lower level indicators (4)
Warehousing performance
It is related to the performance of all activities of
warehousing processes performed in a specific period
of time PNumbers of items received or put away or picked
or shipped correctly and without damages/number
of items manipulated (5)
Customer satisfaction index It measures the customer’s satisfaction during a
specific period of time P[PQuantity of perfect customer
/total orders
entered for customer
]/number of customers (6)
Perfect order percentage Is the rate of orders that were perfectly produced,
bottled, without damages, with perfect
documentation, and with no claims from the
customer received in a specific period of time
PQuantity order without any problems/total
orders (7)
Timeliness New demand response time It is the average time the supplier takes to respond to
demand of new supplies P[Reception date–new demand confirmation
date]/total number of new demands (8)
Total production cycle time It is the average time needed for elaborating and
aging the product, including quality tasting and
bottling time PQuality tasting cycle timeþelaboration cycle
timeþaging cycle time þbottling cycle time/total
number of order produced (9)
Delivery cycle time It is the average freight transport time. From the
moment the order is ready in the warehouse to the
reception by the customer. P[Reception date by customer–order ready date in
the Warehouse]/total number of delivered orders
Total logistics cycle time It is the average time elapsed between the customer
order placement and the moment the order is
delivered in the customer’s location P[Reception datetransaction confirmation date]/
total number of orders (11)
Total logistics cost It is the aggregated cost of all logistics activities
considered in a specific period of time
Supply log. costþproduction log. cost þinventory
log. costþwarehouse log. cost þtransportation log.
costþlog. cost of returns from
customersþcustomer response logistics cost (12)
Total logistics cost
It is the contribution of the Total Logistics Cost to the
enterprise’s total operational costs considered in a
specific period of time
Total logistics cost/total operational cost (19)
Resources utilization
It measures the average utilization level of the
winery’s resources, within a specific period of time,
as compared with the total capacity of each resource PUtilization % of resource i/number of resources
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produced, the market, the actor in the supply chain, etc. For
example, a winery that delivers in the overseas market under a
FOB (Free-On-Board) schema has a particular supply chain, as
shown in Fig. 6.
According to the definition appeared in the INCOTERMS,
means that the winery sells, delivers and is responsible for the
finished goods up to the boarding port, from where the goods will
be transported to the destination country.
Fig. 7 shows another example, the supply chain of the Whole-
saler, on which the visibility is restricted up to the Importer (the
wholesaler usually has not access to information and material
flows between wineries, freight operators, etc.)
These figures show a great difference between the wine
producer and the actors in the distribution cycle. For the whole-
saler the application of the framework is different than it is for the
winery. For example, if the lead-time of the importer is measured
considering the wholesaler’s supply chain, it will include the
Importer to Final Distributor cycle time and Finished Goods
Table 2
Second level indicators related to the total logistics cycle Time.
1st level indicator 2nd Level
Description Formula
Total logistic cycle
Order processing
Is the period elapsed from the moment the order is entered in
the winery until the moment the order is released to the
warehouse P[Order prepared at warehouse
dateconfirmation transaction date]/total
orders requested (21)
Purchase order CT If the supplies are not available from stock and a purchase is
needed it measures the average time of the procurement
process P[Purchase orders reception datepurchase
order confirmation date]/total purchase orders
Bottling CT It measures the average time needed for bottling the wine. It
includes the scheduling, filling, covering, labeling and packing
of the order. P[Finished bottling date/timestart bottling
date/time]/total orders produced (23)
Warehouse CT It measures the average time required to prepare the order in
the warehouse, including picking, packing and shipping
MTS (Make-to-Stock) P[Order prepared at the
warehouse dateorder transaction confirmed
date]/total orders prepared
MTO (Make-to-Order) P[Order prepared at the
warehouse datefinished bottling date]/total
orders prepared (24)
Delivery CT It includes waiting for loading, travel time and unloading time
in the customer’s site. P[Received date in customer locationorder
prepared in the warehouse date]/total orders
delivered (25)
Table 3
Second level indicators related to the resources utilization percentage.
1st level indicator 2nd level indicator Description Formula
Resources utilization
Capacity utilization bottling
Used capacity of bottling machines
compared with their full capacity
[Quantity bottles/product bottled]/
bottling machines full capacity (bottle/
product per hour) (26)
Warehouse utilization
Storage location that are occupied compared
with full capacity
Location occupied/Total number of
location in warehouse (27)
Cellar utilization capacity Storage for wine aging that are occupied in
comparison with cellar full capacity
Used capacity/cellar full capacity(28)
Fig. 5. Total logistics CT.
The Incoterms are formulated and updated by the International Chamber of
Commerce which regulates the norms, obligations and rights on international
commerce trades.
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Distributor to Wholesaler cycle time in order to arrive to the
wholesaler’s warehouse. Therefore, it covers the time elapsed
from the moment the order is prepared in the importer’s ware-
house until the order is delivered in the wholesaler’s location.
Considering the winery’s supply chain, the Lead-time covers the
Internal Lead-time of the winery, the Freight Operator cycle time,
the Port-to-Port cycle time, and the Port-to-Importer cycle time in
order to deliver the product to the importer (which is the
customer for the company because it follows a FOB sales strat-
egy). This lead-time covers the time elapsed from the moment the
order is prepared in the winery’s warehouse until the order
arrives to the importer’s location.
Fig. 8 shows an overall schema of how the framework is
instantiated for different actors of the WSC. The framework is the
same for all of them, but depending on the actor and its strategy,
the instantiation is different. Some indicators will be useful for
some actors, while others will be useful for other actors. The key
is that the hierarchy counts with several indicators in the three
levels which, depending on the actor and the strategy followed,
will be ‘‘activated’’ (i.e. be meaningful) on each case. In some
cases the majority of these indicators are used together following
the relations that exist between them in order to facilitate the
decision making process and to have a better understanding of
the processes as a whole.
4.5. Applying the framework
A concrete example of instantiation (i.e. selection of relevant
indicators) is shown in order to illustrate the application of the
framework. Consider the Lead-time (Total Logistic Cycle Time)
indicator in the context of the winery’s and the wholesaler’s
supply chains previously described.
For the Winery’s supply chain the lead-time includes: the
Production Cycle Time (including order processing, procurement
and bottling), and the Freight Forwarder Cycle Time, including the
Freight Operator Cycle Time (order picking in the winery, trans-
portation, freight consolidation, etc. up to the origin port); the
Port-to-Port Cycle Time; and the Port-to-Importer Cycle Time (up to
the importer’s warehouse). In the overseas market, the Importer
is usually the winery’s customer. Therefore, in some cases the
winery’s lead-time is considered only up to the origin port since
under FOB, it is usually the importer who takes care of the
transportation operations carried out from that point on.
For the Wholesaler’s supply chain the lead-time includes: Final
Distributor to Wholesaler Cycle Time, which is the elapsed time
between the moment the Wholesaler puts an order to the Dis-
tributor and the moment on which finished goods arrive to whole-
saler’s facility; the Wholesaler to Retailer Cycle Time (time between
order entering and arrival of finished goods to retailer’s facility).
This example aimed at highlighting the differences in the
application of the framework depending on the actor in the
WSC who uses the framework.
5. Case study
A comparison of the logistics performance of 6 wineries from
Mendoza (Argentina) is presented as a case study. There are more
Fig. 6. Winery’s supply chain.
Fig. 7. Wholesaler’s supply chain.
Fig. 8. Framework instantiation for different actors along the WSC.
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than 1000 wineries at Mendoza, with different sizes and markets,
and only some of the KPI defined in the framework (Section 4)
were measured and evaluated. Therefore, this study was con-
ducted as an initial validation of the proposed framework and it is
not a complete benchmarking study of the wine industry in
Argentina, neither considering the wineries compared, nor from
the point of view of the indicators measured.
These wineries belong to different segments considering the
production volume and size of the organization. However, we
selected them because an important part of their production goes
to the overseas market and because they produce wines of similar
quality and price segments, which make their logistics operations
comparable. Additionally, the aspects measured were normalized
and compared in order to obtain performance gaps from their
logistics performance. Tables with the main results are shown in
the appendices.
The required data was collected by means of questionnaires.
These questionnaires were prepared and tested with people of the
participant wineries, in order to make relevant questions and to
get the appropriate information.
5.1. Wineries background information
The percentage of the production volume of the wineries of the
study for overseas markets ranges from 45% to 98%. The wine
segment delivered to these markets is of high quality wines. Some
super premium wines are only available for overseas markets. The
main markets are USA, Brazil, Mexico, China, Holland, United
Kingdom and Canada.
Different production strategies are followed by wineries when an
order arrives: bottle to order, label to order, packing to order or make to
stock (deliver to order) (Garcia, 2009). Winery 1, winery 2, and winery
4followalabel to order strategy. They use the previously bottled wine
and they label the order when the confirmation from the customer
arrives. This strategy is followed because of several reasons. Premium
wines need extra aging process in bottle, which helps to increase
wine quality. Additionally, this strategy helps wineries to save time in
order to have a faster response to demand. Another reason for using
this strategy is that when exporting, labels need to be customized
because they must contain importers’ information, and some specific
data depending on the destination country (language, regulations,
dates, etc). Therefore, labels (sometimes just second labels) need to be
printed as late as possible (once all these parameters are known).
After this process, the preparation and transportation processes begin.
Winery 5 follows a make to stock strategy, because it can
forecast the demand and plan its production and bottling pro-
cesses for the most important customers.
Winery 3 and Winery 6 follow a bottle to order strategy when
the order arrives. Although this strategy implies an extra time for
bottling process and for the procurement of supplies, it reduces
the risks related to obsolescence of the labels.
5.2. Performance comparison
5.2.1. KPI
As exposed in Section 4.4, not every actor in the WSC uses all the
indicators for measuring the performance or doing a benchmark
study. The indicators selected (i.e. the framework instantiation) may
depend on the segment of the wines produced, the market, etc.
Therefore, only some indicators of the framework were evaluated in
this case study, which were selected according to the feedback
received from the wineries that participated in the study. These
wineries indicated that the impact of logistics in their business was
mainly related to the timeliness and resources utilization perfor-
mance attributes.
The first and second level KPI measured and benchmarked in
the study are:
1. Total Logistics Cycle Time
3.1 Order Processing Cycle Time
3.2 Purchase Order Cycle Time
3.3 Bottling Cycle Time
3.4 Delivery Cycle Time (partially)
2. Resources Utilization Percentage
5.1 Capacity Utilization Bottling Machines
5.2 Warehouse Utilization Percentage
5.3 Cellar Utilization Capacity
Regarding the Delivery Cycle Time indicator, the transporta-
tion time beyond the origin port was not included in the
evaluation since, according to the wineries that participated in
the study, they did not have control over the subsequent stages,
and their visibility was restricted.
In the following sections, the values measured for these KPI,
along with an analysis of the gaps found between the wineries is
5.2.2. Timeliness
Fig. 9a shows the average lead-time of all wineries. As said
before, the transportation time from the origin port to the
reception of the order in the importer’s warehouse was not
considered. The lead-time shown in Fig. 9a includes the values
of the first 4 indicators of the previous section, namely: Total
Logistics Cycle Time (Order Processing Cycle Time, Purchase
Order Cycle Time, Bottling Cycle Time and Delivery Cycle Time
until the origin port).
Fig. 9b shows how each cycle influences the total lead-time of
each winery. Winery 4 has the shorter lead-time, with 11 days to
prepare and put the order in the ship. Some good practices for
having this result are: work with a make to stock strategy for
highly rotating products, and work with a label to order strategy
for the other products, but only putting second labels when the
order arrives. Bottling and labeling processes are scheduled
following sales forecasting, and considering delivery dates for
confirmed orders.
The average lead time is 28 days. The BIC (Best-in-Class) is 11
days and the WIC (Worst-in-Class) is 40 days.
Fig. 10 shows the average delay of local suppliers for each type
of key supply. This is an alternative view of the purchase order
cycle time, in which the purchase time has been split across
different supplies averaging each winery’s average. As can be
seen, labels are the most critical supply, and they have the longest
delay. This is an aspect that affects wineries which follow a JIT
(Just in Time) strategy, because they have to buy supplies when
the order is confirmed. In order to reduce the lead-time, it is
better to have a security stock of some supplies. However,
maintaining a stock of labels conveys a risk of obsolescence
because the specific information related to regulations of the
destination countries may change. Similarly, bottles are very
fragile which may cause losses due to breakage.
Wineries which have long lead-times are mainly influenced by
the suppliers’ lead-times. For those wineries who buy supplies JIT
this becomes a problem and a different strategy must be imple-
mented in order to reduce delivery times, such as collaborative
Fig. 11 shows the number of clients of each winery from overseas
market. It is important to mention that Winery 4, the one who has
the shortest lead-time, has a small number of customers in the
overseas market. Winery 6, whose 98% of production is devoted to
overseas market, has also a small number of clients.
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Fig. 10. Suppliers’ delays.
Fig. 9. (a) Average internal lead-time and (b) internal lead-time composition.
Fig. 11. Number of clients from overseas market.
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5.2.3. Productivity and capacity
All wineries, except Winery 4 have their own bottling and
labeling lines. Winery 4 rents an external line for bottling and
labeling for the first label. Second labels are put manually.
Fig. 12 shows used and wasted capacity of filling lines. Winery
5 has six filling and labeling integrated lines and 4 extra labeling
lines. Although each winery has different bottling capacities, a
large percentage of wasted capacity can be observed.
All wineries have warehouses for supplies and finished goods.
The utilization percentage of finished goods warehouses is shown
in Fig. 13.
Some wineries use finished goods warehouses to age the
bottled wine. Others have specific spaces devoted to age the
wine. Fig. 14 shows aging capacity in bottle (i.e. Cellar Utilization
Capacity the indicator).
If we consider the first level KPI ‘‘Resources Utilization Per-
centage’’ for each winery (Table 4), it is possible to summarize
the level of utilization of the described resources (filling lines,
warehouses, aging capacity and vat capacity), as shown in table
and Fig. 15.
From this information it can be observed that winery 2 is
getting more than 80% of utilization of its resources in average.
Wineries 1, 3 and 5 are near it, with more than 70%. Wineries
4 and 6, on the other hand, should implement and consider
different strategies in order to improve their resources’ utilization
(which is below 60%).
Fig. 12. Filling lines used and wasted capacity.
Fig. 13. Finished goods warehouse used and wasted capacity.
Fig. 14. Used and wasted capacity of aging in bottle.
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5.3. Analysis of the performance gaps
In order to illustrate how this analysis may be useful for an
organization, consider the Winery 4. It has the shortest lead-time
of all the wineries analyzed, so it is the BIC of this study regarding
that indicator. In order to achieve this, it implements different
strategies for fulfilling customers’ orders. However, the resources
utilization percentage is 50%. Therefore, this is a potential
improvement area for the winery.
Through the study conducted, some good practices were
discovered. In order to get a good average performance, it is
convenient to define different strategies for each product, custo-
mer, destination country, rotation level, etc. This allows the
winery to give the most suitable treatment to products, custo-
mers and markets with similar conditions, such as stability of the
demand, seasonality and buying volumes. Another strategy is to
maintain security stocks of those key supplies which have long
lead times and that have no obsolescence problems.
Standardization of data of first labels allows the winery to
avoid modifying them for each kind of customer. This makes it
possible to keep stock of first labels and to reduce the impact of
forecasting deviations regarding this supply. Additionally, second
labels (which contain customizable data) may also contain some
fixed (common) data, and be reprinted with the customized data
as needed. Making collaborative forecasting with customers and
suppliers is another desirable practice, although it is almost not
In each case, it is necessary to determine the logistics cost (e.g.
supplies warehousing costs) in order to identify the optimum
strategy. The same analysis may be extended to the other
indicators in the framework, and more good practices may be
identified in a similar way.
6. Conclusions and future work
In this paper a framework for performance measurement and
benchmarking in the wine industry was presented. A descriptive
model of the WSC was proposed including a representation of all
actorswhoworktobringtheproduct to the final consumer. For each
actor in the supply chain, a description of the main activities was
presented. Material flows and information flows were identified
along the WSC. A framework composed of KPI for measuring logistics
performance was presented. Additionally, formulae, description and
different scenarios for implementing the framework were explained.
(Argentina), in order to illustrate the framework application and to
compare logistics performance. Results of the comparison were
exposed and explained along with a description of wineries’ good
practices found during the study.
The contributions of this research include the definition and
representation of a model for the WSC, and a framework of KPI for
measuring logistics performance along the wine supply chain.
Additionally, a set of guidelines were described as part of the case
study in order to illustrate the instantiation of the framework, and
a benchmarking study conducted over a sample of 6 wineries
from Mendoza (Argentina) was presented for illustrating the
application of the framework.
With this model and the proposed framework, companies in
the wine industry can have a better understanding of the relations
and the complex dynamics present in the WSC. This could
help them to focus on processes to improve, on new strategies
or goals, on supply chain and resources optimization to increment
final consumer’s satisfaction level, and to lower costs and
delivery times.
Future work will be carried out along three main directions.
First, a more comprehensive benchmarking study will be per-
formed in the Argentine wine industry using this model and the
framework defined, in order to identify a wider scope of indus-
try’s best practices. Additionally, a deeper verification will be
done and a comparison of the WSC of different countries will be
carried out, to show the variety of instances and diversity of
Second, additional aspects of the descriptive model of the WSC
presented in this work, which includes actors, relations and flows,
will be formalized. Simulation and formal mathematical models
Fig. 15. Resource utilization percentage.
Table 4
Computation of the resources utilization percentage.
Winery 1 Winery 2 Winery 3 Winery 4 Winery 5 Winery 6
Filling and labeling line utilization 50 60 90 75 80 80
Wine aging in bottle capacity 100 100 50 50 50
Warehouse utilization 80 90 90 25 70 50
Resources utilization (%) 77 83 77 50 75 60
F.A. Garcia et al. / Int. J. Production Economics 135 (2012) 284–298 295
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of different logistics aspects will be developed based on this
descriptive model, as tools for wineries to evaluate different
scenarios and make better decisions. Different aspects will be
included in these models, such as timing, inventory, capacity and
costs. Therefore, the descriptive model presented in this work will
be a guide for creating and integrating such mathematical and
simulation models.
Moreover, from the data collected in the study, different
production strategies were observed (bottle to order, label to
order, packing to order and make to stock). A correlation study of
metrics performance, context situation (target market, wine
segment, etc.) and production strategy will give more information
on how each strategy is and should be determined. Advanced
multi-criteria techniques will be used such as Choquet integral or
AHP (Analytic Hierarchy Process), thus enabling the wineries to
setup optimization models for each case.
Finally, the third line for future work will include general-
ization of the model (both the descriptive supply chain model and
the performance measurement framework) to other industries,
especially other alcoholic beverage and agro-food industries (e.g.
olive oil).
This research was jointly developed by the School of Engineer-
ing (UNCuyo, Argentina) and E
´cole Nationale Supe
´rieure en Ge
des Syst
emes Industriels (INPL, France), under the PREMER F-599
and ARFITEC ARF-08-04 programs. We would like to thank our
colleagues from the Wine Supply Chain Council for their com-
ments and discussions.
Appendix A
See Tables A1–A6.
Table A1
First and second level indicatorsquality.
First level indicators Second level indicators
Supplier satisfaction index Claims due to quality fails
Claims due to out of time deliveries
Claims due to costs
Right quality grapes percentage Bad quality due to transport of grapes
Bad quality due to storage of grapes
Bad quality due to harvest
Bad quality due to climate
Production performance index Product unit perfectly produced
Inventory performance index Forecast accuracy
Inventory obsolescence
Out of stock occurrences
Inventory accuracy
Warehousing performance index Receiving performance index
Shipping performance Index
Warehouse damage percentage
Customer satisfaction index Claims due to quality fails
Claims due to out of time deliveries
Claims due to costs
Perfect order percentage Perfect purchase order percentage
Product unit perfectly bottled percentage
Order perfectly fillable percentagefill rate
Order perfectly picked and packed
Orders perfectly delivered percentage
Orders perfectly received
Table A2
First and second level indicatorstimeliness.
First level indicators Second level indicators
Total logistic cycle time Order processing cycle time
Return processing cycle time
Backorder duration
Purchase order cycle time
Total bottling cycle time
Warehouse order cycle time
Deliver cycle time
Total production cycle time Quality tasting cycle time
Manufacturing and aging cycle time
Total bottling and label cycle time
Deliver cycle time Lead time for overseas market
Lead time for domestic market
Vehicle load/unload time
Delayed in traffic time
New demand response time
Table A3
First and second level indicatorslogistics cost.
First level indicators Second level indicators
Logistic costs
Total logistic cost Supplier total logistic cost
Production total logistics cost
Inventory cost
Total cost of warehouse
Transportation total logistics cost
Cost to return from customers
Total customer response cost
Total logistic cost
Supply cost as contribution to supply chain total
logistics cost
Production cost contribution as to supply chain
total logistics cost
Inventory cost as contribution to supply chain
total logistics cost
Warehouse cost as contribution to supply chain
total logistics cost
Transportation cost as contribution to supply
chain total logistics cost
Urgencies cost as contribution to supply chain
total logistics cost
Return cost as contribution to supply chain total
logistics cost
Customer response cost as contribution to
supply chain total logistics cost
Table A4
First and second level indicatorsproduction and capacity.
First level indicators Second level indicators
Production and capacity
Resources utilization
Winery reception capacity
Purchase Order launched per person-hour
Number of suppliers managed
Capacity utilization filling/labeling machines
Inventory turnover
Inventory turnover of supply
Storage density
Warehouse utilization percentage
Cellar utilization capacity
Material handling equipment utilization
Percentage of full-load trailer/container capacity
utilized per shipment
Transport/vehicle productivity
Customer Orders processed per vendorhour
Requirements fill percentage
Repalletizing of cartons percentage
Re-pack bottles percentage
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Table A5
Benchmarking results. General information and lead-times.
Winery 1 Winery 2 Winery 3 Winery 4 Winery 5 Winery 6
Annual production capacity (l) 1,000,000 12,000,000 3,750,000 1,300,000 2,700,000
Real annual production (l) 1,000,000 12,000,000 3,750,000 840,000 6,000,000 1,000,000
Percentage of production for
Exportation (%)
60 45 60 90 60 98
Main Production Strategy Label to order Label to order Bottle to order Make to stock and
label to order
(second label)
Make to stock
(bottleþlabel þ
Bottle to order
Average size of orders (l) 9000 9900 9000 4000 3750 9900
Percentage of wasted space in
containers (%)
9.09 0 9.09 59.60 62.12 0
Internal lead-time (days) 18 21 30 7 23 35
Winery to port cycle time
2232 23
Waiting time in port (days) 5 5 2 2 2 2
Supplies transport cycle time National suppliers
Bottles 7 30 2 15 15 30
Corks 30 3 15 10
Capsules 7 30 5 15 15 17
Labels 20 20 19 25 15 23
Number of customers
60 60 40 25 70 25
Most important destination
Mexico, Brazil,
USA, China
USA, Holland,
Brazil USA UK, Canada,
USA, Mexico,
USA, Brazil
Table A6
Benchmarking results. Resources utilization.
Winery 1 Winery 2 Winery 3 Winery 4 Winery 5 Winery 6
Filling line capacity (bottles/hour) 2000 12,000 4500 2500 30,000 2500
Percentage of work-time (filling) 50 60 90 75 80 80
Percentage of idle time (filling) 10 40 10 17 12 12
Labeling line capacity 1000
Percentage of work-time (labeling) 90
Percentage of idle time (labeling) 25
Finished products’ warehouse capacity (bottles) 72,000 1,200,000 660,000 600,000 92,400
Percentage of finished goods warehouse used capacity 80 90 90 25 70 50
Wine aging in bottle capacity used (bottles) 300,000 60,000 352,500 600,000 – 40,000
Vat capacity 1,600.000 10,800,000 2,000,000 1,690,000 242,000,000 3,000,000
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... The "responsiveness" category includes indices such as fill rate or customer's response time, while finally, the "food quality" category includes KPIs that evaluate the characteristics of the production system, environmental impact and marketing (Li et al., 2021). The analysis carried out by Garcia et al. (2012) aimed at measuring the logistics performance of a wine supply chain. As many food systems, a wine supply chain is a complex system, in that the nature of the product calls for the application of a mixed push/pull strategy; also, the number of players at the various levels is wide, involving difficulties in their relationships. ...
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The study aims to delineate a framework for the measurement and evaluation of supply chain performance in the food sector. The proposed approach grounds on the well-known LARG (lean, agile, resilient, and green) perspectives, for which the relating literature is analysed, and the available metrics are reviewed with a specific focus on the food sector. This allows for the development of an appropriate performance measurement system for the food supply chain.
... Operational accuracy How the process in accurately measured to be close to the target Garcia et al. (2012) and Schönsleben (2012) • The survey will take around 8 to 10 minutes and your responses are completely anonymous. ...
... Previous efforts have studied the wine supply chain holistically, focusing mainly on logistics performance indicators (Chandes et al. 2003;Garcia et al. 2012;Smit et al. 2017;Díaz-Reza et al. 2018;Varas et al. 2020b) and sustainability issues (Cholette and Venkat 2009;Rugani et al. 2013;Varsei and Polyakovskiy 2017;Ponstein et al. 2019;Fragoso and Figueira 2020). In all wine supply chain stages, operations research models and methodologies have been used to improve efficiency and reduce costs (Moccia 2013). ...
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Horizontal collaboration is a strategy that has increasingly been used for improving supply chain operations. In this paper, we analyze the benefits of using a collaborative approach when optimally planning the wine grape harvesting process. Particularly, we assess how labor and machinery collaborative planning impacts harvesting costs. We model cooperation among wineries as a coalitional game with transferable costs for which the characteristic function vector is computed by solving a new formulation for planning the wine grape harvesting. In order to obtain stable coalitions, we devise an optimization problem that incorporates both rationality and efficiency conditions and uses the Gini index as a fairness criterion. Focusing on an illustrative case, we develop several computational experiments that show the positive effect of collaboration in the harvesting process. Moreover, our computational results reveal that the results depend strongly on the fairness criteria used. The Gini index, for example, favors the formation of smaller coalitions compared to other fairness criteria such as entropy.
... Most researchers (e.g., Garcia et al., 2012;Supply Chain Council, 2017) find that the primary measure of the reliability of the supply chain is a perfect order, which is calculated using Eq. (1): ...
Supplier reliability and order fulfilment performance are usually assessed using a perfect-order calculation. Information management of perfect-order estimation is frequently reduced to expert estimates and to the multiplication of probabilities of failure-free performance of some logistics operations. Moreover, perfect-order estimation is calculated without consideration of supply chain structure, possible combinations of failures, and operational policies (e.g., safety stock levels and alternative transportation routes). As a result, the existing methods frequently provide different estimates for the same statistics and cannot be consistently used in the allocation of companies’ resources to improve the order fulfilment process. This paper considers different variants of probabilistic assessment of a perfect order and proposes an approach to assess the impact of changes in parameter probabilities and number of parameters on the value of a perfect order. The proposed models are based on an analytical approach using discrete distributions of random variables. We illustrate the applicability of our approach to several numerical examples to confirm the adequacy of the proposed method. Our approach can be immediately applied in practice to assess supply and order fulfilment process reliability and to evaluate the effectiveness of various operational policies (safety stock levels or modes of transportation) to achieve some planned values of a perfect order in the supply chain.
... In order to showcase a more realistic case study of the agrifood sector, we have considered a wine supply chain network. Although a typical wine supply chain may consist of suppliers, wineries, bottling plants, distribution centers, and demand points [59], this strategic study includes three discrete echelons: (i) suppliers (i.e., vine growers), (ii) manufacturing sites, where wine making/ageing and bottling are taking place, and (iii) markets, in which regional warehousing/distribution centers fulfil the bottled wine demand of nearby points ( Figure 2). In order to build the OR model, a multi-objective MILP methodology was employed, including both continuous (i.e., quantity-related) and binary (i.e., location-or mode-related) variables [60]. ...
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As agriculture and industry exploit more than 90% of the global freshwater resources, water overuse and degradation have emerged as critical socio-environmental challenges for both nations and corporations. In this context, the water footprint concept was introduced in order to quantify the freshwater consumption and pollution of a territory or across a product's life cycle. As research on water management in supply chains is growing, this work aims to integrate the perspective of freshwater resources into supply network configuration. Focusing on the agrifood sector, we have developed a mixed-integer linear programming model that can be used to minimize the operational costs under a water footprint cap in a wine supply chain network by selecting the optimal suppliers (vine growers), manufacturing sites (winemakers), and transportation modes (fuel-powered trucks). The optimization outcomes unveil that the wine network's configurations (struc-ture and fuel type) vary significantly depending on the values of the water footprint cap so as to balance the trade-off between economic and water-related environmental efficiency. Beyond the viticulture sector, the proposed model is anticipated to act as a paradigm for setting joint sustainable targets or caps to limit water use across supply chains.
... Customer service, an essential measure of logistics performance, was often mentioned in previous logistics studies (Fawcett and Cooper, 1998;Wang, 2018). Cost is always used as a key indicator of logistics performance (Christopher, 1998;Fawcett and Cooper, 1998;Garcia et al., 2011), while some other studies focus on service quality such as environment, reliability and accuracy (e.g. on-time delivery, damage, disruptions) (Irene Gil et al., 2008;Stank et al., 1999;Khan, 2019). ...
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The study aims to provide an in-depth analysis of a transportation capacity shortage issue affecting Australian logistics service providers. Transportation capacity shortage is an important issue in all transportation modes. In this study, the driver shortage is viewed as an antecedent variable to estimate the impact of transportation capacity shortage on logistics performance. This study investigates the underlying relationships between driver shortage, logistics capability, and logistics performance according to resource-based theory. Structural equation modeling (SEM) was used to analyze the measurement models and structural model. The empirical results illustrate that driver shortage indirectly influences logistics performance, the logistics capability is a mediator factor in the relationship between driver shortage and logistics performance in logistics service providers. We argue that this provides valuable insights for transportation capacity shortage management.
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The relevant literature on logistics is largely centred on advanced countries and research on logistics outsourcing (LO) in developing countries in general with African ones, sparse in particular. This paper addresses this gap by shedding light on the usage of LO by Moroccan manufacturing firms. Thus, a qualitative study is conducted to know the extent to which they outsource their logistics: factors influencing the LO decision, benefits and risks of LO, logistics activities outsourced, impact of LO on their performance, logistics services providers' (LSPs) types, determinants and sources used to select the appropriate LSPs, and finally their expectations regarding the supply of their LSPs. The results denote that the usage of LO reveals positive impact on firm's performance although it still at a nascent stage in Morocco. This study contributes to the growing body of knowledge of LO practices. Research limitations are highlighted and potential future research avenues are proposed.
The global wine industry has recognized the benefits of embracing a more sustainable approach to be more competitive and resilient. However, evaluation of complex winegrowing systems and assessment of their sustainability performance remains a difficult task. In this paper, we use the Douro wine region in Portugal as a case study to test different benchmarking approaches to assess sustainability performance. Our findings revealed a greater sustainability of social and environmental components of the Douro vineyard farms, but the economic pillar shows weaknesses. We may assume that the Douro region demands a strategy to improve its economic stability, resilience, and adaptability to face future challenges (more adverse climate conditions, labor shortage, and increased costs). This work also emphasizes the need of the wine sector to increase and improve research on sustainability issues and evaluation frameworks to assess it, having always present the context where the winegrowing system operates.
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The costs associated with quality planning, monitoring, evaluation of conformity and the costs incurred by failing to meet the requirements of satisfaction of the factory or customer (products or services with defects). Objectives of the study are to identify the causes the absence or loss of quality (not quality), propose corrective actions in the process of developing wines, by measuring the frequency and classifying the costs of not quality. Knowing the frequency of the causes of non-quality stages of Wine making process will optimize the management and cut costs by reducing its correction. The study identifies the factors that alter the quality of wines. The higher frequency of defects has origin in fermentation or in stages prior to this, along with the breeding, conservation and originated in the vineyard explaining 79% of them. Defects identified are costs of prevention and internal evaluation, but will fail if the cellar is integrated and will be allocated to external fault in the case of non-integrated producer. Wineries in which was able to identify and establish frequency of the defects that affect wine quality are those who have some system of quality management which is a cost of prevention and assessment, as they have standardized records that serve as a management tool. Those responsible for managing the wineries should be aware of the critical points of the process for conducting the preventive management of potential defects. The costs that have to do with the quality of the product to market, must be registered, controlled and monitored forward to know the impact of the same in the final prices and backward so that in planning the next development is contemplated prevention.
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From a development perspective an investigation of the changes that have occurred in the wine industry is of particular interest because it provides evidence on how emerging economies have been able to acquire significant shares of the international market in a dynamic sector. Based on novel empirical evidence, this paper shows that emerging countries with diverse institutional models and innovation strategies have actively participated in the process of technological modernization and product standardization. These newcomers in the wine sector have responded particularly effectively to changes in demand, aligning emerging scientific approaches with institutional building efforts and successful marketing strategies.
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This conceptual paper contrasts wine production in the three most important regions of New Zealand. Some of the key issues faced in one of these regions are then identified through published data, personal interviews, data analysis and discussion. The broad objective of this paper is to suggest some qualitative and quantitative solutions to mitigate the problems identified. The issues discussed are: should growers and wine producers go for a collective procurement approach to buy some of their prime inputs; should producers/exporters consider changes in wine packaging to reduce the supply chain cost and should there be more emphasis on exporting wine in bulk rather in bottles? The paper identifies potential packaging alternatives and suggests that a clustered procurement model and a scientific transport model can reduce the supply chain cost.
Consumers are constantly demanding better products, at lower prices, along with better overall services and customer support. Organizations, on the other hand, are struggling with shorter product life cycles, increased product variety and lower profit margins due to fierce global competition and faster commoditization of products and services. In a new, global world, targeting local markets does not seem to be enough for long-term company survival. Good supply chain management and design is becoming a key factor for resource optimization, overall user experience enhancement and to achieve a competitive strategic advantage in order to gain sustainable growth ratios. This thesis analyses how current trends in Supply Chain Management are affecting the global wine supply chain, and builds on the specific case of Argentinean wineries that sell their products in the U.S. market. I start by analyzing each tier of the supply chain using Porter's Five Forces model in order to understand the characteristics of each tier, how these forces impact the supply chain as a whole, and how companies interact between tiers. While doing so, I also analyze how current trends in Supply Chain Management are affecting the current state of the supply chain. Finally, I describe possible changes in the supply chain configuration due to the adoption of these new trends by organizations along the chain, and describe some of the major aspects that Argentinean wineries should take into account in order to gain a better competitive advantage along the chain.
Conference Paper
Purpose - This paper illustrates an investigation of wine demand chain management. It explores what influence does the different consumer prefer, consumer behavior and traditional culture have on the wine-buying decisions in China, aiming to design a demand chain for new world wineries entering the Chinese wine market. Design/methodology/approach - This paper draws its conclusions based upon a literature review including desk-based research from many organizations and researchers. The proposed quality function deployment (QFD) approach provides a midway to designers of the demand chain and helps the wineries to build a system for entering and developing the wine market in China. Findings -Obvious advantages of new world wine are rational price, scale operation, and popular taste. Among all links of the demand chain, consumer segments, pricing, and promotion are key to the success of the demand chain for new world winery. Originality/value -This paper is probably the first attempt to present a systematic Demand Chain Management in China for new world wineries and provide a framework that guides the wineries to improve the strategic demand in China. Research limitations/implications - Researcher put more emphasis on consumer demand and ignore other links of demand chain.
We develop an input-output model of a warehouse system to assess operational efficiency. Our model simultaneously accounts for all of the critical resources (labor, space, storage and handling equipment) and the different workload requirements (broken case, full case and pallet picking, storage and order accumulation) of a warehouse. We collected extensive data on 57 warehouse and distribution facilities from a variety of industries, including auto parts, dental and office supplies, electronics, fine papers, hardware, health care, industrial packaging, mail order apparel, office machines, photographic supplies, and wholesale drugs, and used the model to assess and compare their efficiencies. We offer 3 conclusions based on a statistical analysis of the operating efficiencies obtained from several models: Smaller warehouses tend to be more efficient than larger warehouses. Warehouses using lower levels of automation tend to be more efficient. This association is more pronounced in small firms. Unionization is not negatively associated with efficiency and in fact may actually contribute to higher efficiency.
Describes a comprehensive set of fact-based performance measures that can be used to describe accurately a world-class supply chain of plan, source, make and deliver activities. Aims to help companies take a broad supply-chain-process perspective by quantifying performance improvement opportunities across the entire supply chain. Includes quantitative measures such as cash-to-cash cycle time and supply chain response time, as well as qualitative analysis of best-in-class performance. Best practice benchmarks provide more insight into how to achieve world-class performance.