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The problems of measuring efficiency in logistics

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Abstract and Figures

The efficiency is one of the key factors of company success. The importance of efficiency in logistics is recognized in literature and practice. In the efficiency measurement process different problems appear. Complexity and interdependence of logistics activities cause different problems like: indicator selection problem, conflicting goals, common resource problem, decomposition problem, etc. The mentioned problems are present on each measurement level: supply chain efficiency, logistics systems efficiency, logistics subsystem efficiency, logistics activity efficiency, etc. This paper gives the opportunities for overcoming mentioned problems. The proposed model is based on the Data Envelopment Analysis and the Principal Component Analysis methods. The case study results show that proposed model successfully overcome identified problems
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VOJNOTEHNIČKI GLASNIK/MILITARY TECHNICAL COURIER, 2013., Vol. LXI, No. 2
MEASURING EFFICIENCY
IN LOGISTICS
Milan M. Andrejić, Faculty of Transport and Traffic Engineering,
Logistics Department, University of Belgrade
Abstract:
Dynamic market and environmental changes greatly affect operating
of logistics systems. Logistics systems have to realize their activities and
processes in an efficient way. The main objective of this paper is to
analyze different aspects of efficiency measurement in logistics and to pr-
opose appropriate models of measurement. Measuring efficiency in logi-
stics is a complex process that requires consideration of all subsystems,
processes and activities as well as the impact of various financial, operati-
onal, environmental, quality and other factors. The proposed models have
a basis in the Data Envelopment Analysis method. They could help mana-
gers in decision making and corrective actions processes. The tests and
results of the model show the importance of input and output variables se-
lection.
Key words: efficiency, logistics, measuring methods, data envelopment
analysis.
Introduction
odern product and service markets are very dynamic with a
great number of participants. In order to stay in the market,
companies constantly have to measure and monitor their performances
as well as to define appropriate corrective actions. Depending on indu-
stries and types of systems, there are a large number of different perfor-
mances. One of the basic and frequently used performances is
efficiency. Efficiency is a very important indicator of the analysis of com-
panies’ operations.
There is no universal and generally accepted definition of efficiency.
Different authors define efficiency in different ways. In (Gleason, Barnum,
1982, pp. 379-386), the notion of effectiveness means the level of the
goals accomplishment ("doing the right things"), while under the notion of
efficiency they mean the accomplishment of these goals in the best pos-
sible way ("doing the right things in the right way"), i.e. accomplishing the
M
DOI: 10.5937/vojtehg61-1756
FIELD: Logistics, Operations Management
ARTICLE TYPE: Review Paper
m.andrejic@sf.bg.ac.rs
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Andrejić, M., Measuring efficiency in logistics, pp. 84–104
largest number of outputs while using the least amount of resources. In
the past, both in literature and in practice, the greatest attention was paid
to the operational efficiency. Operational efficiency can be defined as the
ratio between the exploited resources and the accomplished results.
The efficiency measurement process in manufacturing companies is
completely different from the efficiency measurement process in service
companies. Raw materials and components in the production process
are transformed in the final product. Tangibles and easy measurability of
the final product and resource usage greatly facilitate the efficiency mea-
surement of production processes. On the other hand, the final product in
service companies is a realized service that is by its nature transient, in-
tangible and quantity immeasurable, for which realization is often
necessary to employ different measurable resources such as space, ti-
me, labor, etc.
Products of logistics companies as well as typical service companies
are: transport services, warehouse services, material-handling services,
freight forwarding services and other services. For their realization, it is
necessary to use various resources such as transport and material han-
dling equipment, warehouse space, time, energy, labor, etc. whose usa-
ge is not easily measurable. An additional problem of measuring
efficiency in logistics is the complexity, integration and mutual dependen-
ce of both resources involved and realized services.
When defining performances of logistics systems, it is possible to
make very different and even conflicting aspects of performances. Defi-
ning and measuring the efficiency of logistics systems as one of the most
important performances in recent years is very significant. Existing mo-
dels for measuring and monitoring efficiency are not fully applicable in
logistics and contain a large number of constraints. The lack of models
for measuring and monitoring the efficiency in logistics systems applica-
ble in practice is evident. This confirms the lack of papers and models
tested on real examples. The aforementioned problem and the importan-
ce of monitoring and measuring the efficiency of logistics services for
practitioners and researchers are the main motive of this paper.
Recently, an increasing number of authors have been advocating the use
of approaches such as the Data Envelopment Analysis (DEA) method
(Min, Joo, 2006, pp. 259-65). The DEA method is used for estimating the
efficiency of homogeneous Decision Making Units (DMUs).
This paper provides an overview of models for measuring the
efficiency in logistics. A special emphasis is placed on DEA models, the
most frequently used method in efficiency measurement. The aspects of
efficiency measurement in logistics are also investigated. Different mo-
dels tested on real examples are described in this paper.
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VOJNOTEHNIČKI GLASNIK/MILITARY TECHNICAL COURIER, 2013., Vol. LXI, No. 2
Aspects of measuring efficiency in logistics
Measuring efficiency in logistics can be viewed from various
aspects. From the point of time and the significance of the decision-ma-
king, there are three hierarchical levels of efficiency measurement: stra-
tegic, tactical and operational level. In accordance with the mentioned
aspects, it is possible to make a difference among the following efficiency
measurement aspects in logistics: activities efficiency, processes effici-
ency, subsystems efficiency, systems efficiency and supply chain effici-
ency (Fig. 1).
DC
Retail
DC
DC
DC
Supply chain efficiency
Logistics system efficiency
Logistics subsystem efficiency
Logistics activities and processes efficiency
Figure 1 – Different perspectives on efficiency measuring
Slika 1 – Različite perspektive merenja efikasnosti
Measuring and monitoring the efficiency of logistic networks, chains,
systems, processes and activities primarily refers to the direct logistics
flows, but the efficiency of reverse logistics flows is also important. In ad-
dition to monitoring and measuring the efficiency in logistic systems (fre-
ight forwarding, logistic centres, distribution centres, warehouses, etc.), it
is necessary to analyze the efficiency of logistic activities and processes
in systems whose main function are not logistic services (retail chains,
hospitals, banks, etc.) For a successful measurement of the efficiency in
logistics, it is necessary to consider a large number of inputs and outputs
different in nature. Financial, technical, environmental, energy, social and
many other factors expressed in different units influence efficiency in lo-
gistics. In this sense, it is possible to measure: energy efficiency, eco
efficiency, cost efficiency, quality efficiency, etc. It can be concluded from
the previous discussion that measuring efficiency in logistics is a complex
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Andrejić, M., Measuring efficiency in logistics, pp. 84–104
process that requires consideration of all subsystems, processes and ac-
tivities as well as the impact of various financial, operational, environ-
mental, quality and other factors.
Efficiency measuring models in logistics
From the initial works (Farrell, 1957, pp. 253-290 and Charnes, et al,
1978, pp. 429-444) and setting the basis of the DEA method as well as
from introducing the notion of a DMU (Decision Making Unit), an expan-
sion of the works from this area occurs. The suitability of application and
the quality of the obtained results have influenced the application of this
method in different profit and non-profit organizations: banks, libraries,
hospitals, schools, universities, kindergartens, etc.
In the field of logistics, the DEA method is mostly used for the evalua-
tion of the efficiency of 3PL (Third Party Logistics) providers from the per-
spective of customers and the provider (Min, Joo, 2006, pp. 259-65). The
authors in (Zhou, et al, 2008, pp. 262-279), with the help of the DEA met-
hod, attempt to define the benchmark performances variables for 3PL pro-
viders in China. In their work, they consider the change of efficiency during
a period of time as well as the interaction of certain factors upon the per-
formances. In (Hamdan, Rogers, 2008, pp. 235–244), the authors apply
the DEA method to measure the efficiency of 3PL providers with the stress
upon the warehouse operations. Some papers refer to the estimation of
distribution centres (DC) and warehouse efficiency. Thus in (Ross, Droge,
2002, pp. 19-32), the authors examine the efficiency of 102 DCs as parts
of complex supply chains as well as the change of this efficiency over the
course of time. A fuzzy DEA model for measuring the efficiency of tran-
sport quality is proposed in (Pamučar, 2011, pp. 40-61). In (Ilić, Andrejić,
2011, pp. 93-115), the authors analyse the efficiency of possible queuing
models at gas stations using the large-scale queuing theory. Benchmar-
king and monitoring the international operators of the warehouse perfor-
mances by applying the DEA method was given in (de Koster, Balk, 2008,
pp. 1-10). The method has been in many ways applied to measuring
efficiency in supply chains (de Koster, Balk, 2008, pp. 1-10).
Supply chain efficiency measurement
Supply chains are complex systems composed of many interrelated and
conditioned processes. Supply chains and similar complex systems and pro-
cesses are viewed as "black boxes" for a long time. Their structure and ope-
rating are not considered properly. For a successful efficiency evaluation of
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VOJNOTEHNIČKI GLASNIK/MILITARY TECHNICAL COURIER, 2013., Vol. LXI, No. 2
supply chains, it is necessary to measure the performances of all participants
in the chain, including suppliers, manufacturers, traders and end users.
Some papers in the literature measure the efficiency of independent
participants in the supply chain using the DEA method. However, the
number of papers that analyse the efficiency of supply chains is very
small. The problem of measuring the efficiency of supply chains in the
literature has been recognized as a problem of measuring the efficiency
of multi-stage processes. The authors in (Cook, et al, 2010, pp. 423-430)
have categorized DEA models into four categories:
– standard DEA approach;
– efciency decomposition approach;
– network DEA approach;
– game-theoretic approach.
It is important to know that each supply chain member has its own
strategy of achieving efficiency. Sometimes, due to possible conflicts be-
tween supply chain members, one member’s inefficiency may be caused
by another’s efficient operations. For example, the supplier may increase
its raw material price to enhance its revenue and to achieve an efficient
performance. This increased revenue means increased cost to the manu-
facturer. Consequently, the manufacturer may become inefficient unless
it adjusts its current operating policy. Measuring supply chain performan-
ce becomes a difficult and challenging task because of the need to deal
with the multiple performance measures related to the supply chain
members and the need to integrate and coordinate the performance of
those members (Liang, et al, 2006, 35-49).
Two hurdles are present in measuring the performance of supply cha-
ins. One is the existence of multiple measures that characterize the perfor-
mance of each member in a supply chain. The other is the existence of con-
flicts between supply chain members with respect to specific measures.
The problem of input and output variables classification is a big pro-
blem in supply chain efficiency measuring. Some measures linked to re-
lated supply chain members cannot be simply classified as “outputs” or
“inputs” of the supply chain. For example, the supplier’s revenue is not
only an output of the supplier (the supplier wishes to maximize it), but
also an input to the manufacturer (the manufacturer wishes to minimize
it). Simply minimizing the total supply chain cost or maximizing the total
supply chain revenue (profit) does not model and solve the conflicts. The-
refore, the meaning of supply chain efficiency needs to be carefully defi-
ned and studied. Models need to define and measure the efficiency of a
supply chain as well as supply chain members. A standard DEA appro-
ach uses two separate DEA runs for two supply chain members and cal-
culates independent efficiency for each member. This approach does not
treat common measure in a coordinated manner (Fig. 2).
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Andrejić, M., Measuring efficiency in logistics, pp. 84–104
Figure 2 – Two-stage supply chain
Slika 2 – Dvoetapni lanac snabdevanja
Efficiency decomposition models define the efficiency of the overall
two-stage process. This approach takes into account the equality of in-
termediate measures. In literature, overall efficiency is defined as the
sum or the product of efficiencies of individual stages (members). Net-
work DEA models analyse a complex structure where intermediate mea-
sures are not only inputs to the second stage. These models are applied
in more general situations than two-stage processes.
The fourth type of approach uses game theory concepts. It origina-
tes from (Liang, et al, 2006, 35-49) where DEA is used to measure the
performance of supply chains with two members (as in a manufacturer–
retailer setting, for example). The concepts of the Stackelberg game (or
leader–follower) and the cooperative game are used to develop models
for measuring performance in supply chain settings. There are two main
approaches: non-cooperative and cooperative models. In non-
cooperative models, one-stage (member) is a leader while the other sta-
ge is a follower. The leader maximizes its efficiency regardless of the fol-
lower efficiency. The follower maximizes efficiency and with constraint
that leader’s efficiency is the same. The cooperative efficiency evaluation
model maximizes the joint efficiency of the both members and forces the
two players to agree on a common set of weights on the intermediate
measures. In (Andrejić, Kilibarda, 2011, pp. 237–242), a practical appli-
cation of this approach is tested.
Measuring efficiency of logistics systems
and subsystems
DCs represent complex systems with a large number of interconnec-
ted subsystems, processes and activities. They also represent the basic
hubs of the goods distribution process and connect a large number of
participants in supply chains. In order to maintain the competitive edge,
they have to monitor and measure the efficiency, but also define the sui-
Member 1
Member 2
Inputs
Intermediate measures
Outputs
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VOJNOTEHNIČKI GLASNIK/MILITARY TECHNICAL COURIER, 2013., Vol. LXI, No. 2
table corrective actions, if necessary. The managers and employees in
such systems are faced with several different tasks that are mainly con-
nected with determining the optimal amount of the resources (input) for
realizing a certain number of shipments, of a satisfactory level of quality.
Only such systems can satisfy both the demands of the customers and
their own demands. Apart from a DC as a system, this section describes
in more detail two basic subsystems, which are the transport and ware-
house subsystems. Regardless of the strong connection, these
subsystems have different goals, in different cases even conflicts. Just as
DCs, these subsystems strive to maximize the efficiency. In this sense,
DC managers must at the same time take into account the efficiency of a
DC as a system together with its subsystems.
Model formulation for logistics systems
and subsystems efficiency measurement
In the process of applying the DEA and Multiple Objective Data Envel-
opment Analysis (MODEA) approaches, one of the most important steps re-
presents the selection of the input and output variables (Boussofiane, et al,
1991, pp. 1-15). This work has evaluated the efficiency of 20 DCs of three
trading companies who operate in the region of Serbia and who have similar
sale networks, products ranges and distribution systems (Kilibarda, et al,
2011, pp. 996-1010). In order to estimate the DC and warehouse and tran-
sport subsystem efficiency, the variables describing their functioning in the
best way have been used. The number of employees, the number of pallet
places and the number of distribution vehicles at the DC disposal represent
the main input variables, while the number of realized deliveries, the number
of errors in the warehouse subsystem and the number of errors in the tran-
sport subsystem represent the output variables used in this work. The num-
ber of employees in a DC represents the common input variable, while the
number of realized deliveries represents the common output variable in the
transport and warehouse subsystem. It is assumed that DCs realize the deli-
veries the sizes of which are approximately the same. As the second input
variable to estimate the warehouse subsystem efficiency, the number of pal-
let places in the DC has been used. On the other hand, the number of vehic-
les at DC disposal represents an input variable in the transport subsystem.
The mentioned variables primarily refer to the estimation of the operational
efficiency. In order to obtain the results whose quality and reliability are
higher, as additional output variables, the quality indicators have been used.
DCs of trading companies, but also DCs in general are characterized by a
large number of indicators of realized deliveries. Errors in the transport and
warehouse subsystems represent qualitative indicators which may be the
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Andrejić, M., Measuring efficiency in logistics, pp. 84–104
cause of dissatisfaction and complaints on the part of the customer, i.e. a low
service quality level. Errors in the warehouse subsystem are mostly related to
the errors in the order-picking process (shortage/excess in the delivery, arti-
cles mix-up, damage), but also to other processes such as bad inventory
management, etc. Errors in transport primarily concern the delivery that is
falling behind schedule, as well as the damaging and losing goods in the
transport process. As it can be seen in Fig. 3, in order to estimate the DC ef-
ficiency, six variables have been used (three input and three output vari-
ables), while, in order to estimate the efficiency of its subsystems, four vari-
ables have been used per each system (two input and two output ones).
Figure 3 – DC and basic subsystems
Slika 3 – DC i osnovni podsistemi
The variables of input and output variables are given in Table 1. Table
1 also offers the descriptive statistics of the variables used in this paper. In
order to obtain the results of higher quality and reliability, on the recommen-
dation in (Klimberg, et al, 2010, pp. 79-93), the normalization of all variables
has been performed. The number of errors in the transport and warehouse
subsystems represents the output variables of the negative orientation, i.e.
smaller variables are favorable. Regarding the fact that the DEA method
perceives all output variables as positively oriented (larger variables are fa-
vorable), the change in the orientation of the mentioned variables has been
performed. Prior to defining the model to estimate the DC and its
subsystems efficiency, it is necessary to mention that the model proposed in
this paper does not consider the external factors which may influence their
efficiency, and do not come within the competence of the management of
the company. The mentioned factors may be: weather conditions, market
situation, industry branch, competition behavior, etc.
Number of
pallete places
Employees
Vehichles
Warehouse
flaws
Transport
flaws
Realized
Services
WAREHOUSE
SUBSYITEM
TRANSPORT
SUBSYSTEM
DISTRIBUTION
CENTER
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VOJNOTEHNIČKI GLASNIK/MILITARY TECHNICAL COURIER, 2013., Vol. LXI, No. 2
Table 1
Inputs and output variables in DC
Tabela 1
Ulazne i izlazne promenljive distributivnog centra
DMU Employees
Palette
places
Vehicles
Realized
deliveries
Warehouse
errors
Transport
errors
Mean 98.26 3941.10 27.96 4544.95 483.65 749.25
Standard 56.10 1898.57 10.45 2730.54 375.48 444.78
Max 186 6793 45 9006 1336 1426
Min 34 684 13 1207 36 133
In this particular case, a DC (with warehouse and transport
subsystems) represents a DMU whose efficiency is estimated. This work
estimates the efficiency of 20 DCs and their transport and warehouse
subsystems by means of applying the DEA and MODEA approach. The
DEA method is a mathematical programming technique which gives an
opportunity to compare different DMUs based on multiple inputs and out-
puts. This paper utilizes the DEA approach in order to estimate the
efficiency of DCs and warehouse and transport subsystems. According to
this approach, a logistic system or subsystem represents a "black box",
i.e. the processes and activities that are not realized within the
subsystem are not taken into consideration. From the group of DEA mo-
dels, the basic CCR model has been chosen (Charnes, et al, pp. 429-
444). By applying the CCR models, an independent estimation of the DC,
warehouse and transport subsystems has been performed.
The described model belongs to the group of standard approaches
which perform an independent estimation of the system efficiency without
entering into the structure of the very system. In this manner, it is not pos-
sible to measure the DC efficiency taking into account the efficiency of the
transport and warehouse subsystems, regarding the fact that these two
subsystems possess common input and output variables. The CCR DEA
models are not "sensitive" enough when evaluating the DMU efficiency,
regarding the fact that they do not take into consideration the compromises
and conflicts of independent DC subsystems goals. The MODEA approach
offers the possibility of overcoming the mentioned problem (Klimberg,
Puddicombe, 1999, pp. 201-232). The MODEA does not represent a sim-
ple set of independent standard CCR DEA models, but takes into account
the common resources and multiple goals of the subsystems. In the model
suggested in this section, the common input variable is the number of
employees, while the common output variable is the number of realized
deliveries. In most of the cases, the contribution of the common variable in
one subsystem depends upon the contribution of the same variable in the
other subsystem. In the ideal case, the ratio of the common variables sho-
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Andrejić, M., Measuring efficiency in logistics, pp. 84–104
uld be approximately 1 (the contribution of the common variable is identi-
cal). By further limitations upon the influence of the common variables in
different goals on the part of the decision maker, the model becomes "clo-
ser" to the real state of affairs and good operational practice. The examina-
tion of the weights of the variables and their influence within the goals
enables the decision makers to reach a suitable compromise. In the
analyzed example, the set of decision making units makes 20 DC (DMU)
(k = 1, 2, ..., 20), with two subsystems each (p=1,2). The warehouse and
transport subsystems are characterized by i input variables (i=1,2, ..., m
p
)
and r output variables (r=1,2, ..., s
p
). In this particular case, the transport
and warehouse subsystems have two input and output variables each
(m
1
= m
2
= s
1
= s
2
=2). The variable of the r-th output of the p-th subsystem of
the k-th DC is marked by y
rkp
, while x
ikp
marks the variable of the i-th output
of the p-th subsystem of the k-th DC. The weight coefficient assigned to
the r-th output of the p-th subsystem u
rp
and the weight coefficient assig-
ned to the i-th output of the p-th subsystem v
ip
represent the unknown vari-
ables (decision making variables). The coefficient
ϕ
represents the para-
meter of closeness of the common resources, assigned by the decision
maker. In the concrete case, the MODEA model for measuring the
efficiency of the k-th DC has the following form:
221121
2
1
rkrrkrkk
p
kp
yuyuwww Max +=+=
=
(1)
1,2p xv
p
m
i
ikpip
==
=
,1
1
(2)
==
==
pp
m
i
ikpip
s
r
rkprp
k1,2;pxvyu
11
20210 ,...,,, (3)
ϕϕ
+ 11
2
2
1
1
s
u
s
u
r
r
(4)
ϕϕ
+ 11
2
2
1
1
m
v
m
v
i
i
(5)
210 ,, =
i1,2;pv
ip
(6)
210 ,, =
=
r1,2;pu
rp
(7)
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VOJNOTEHNIČKI GLASNIK/MILITARY TECHNICAL COURIER, 2013., Vol. LXI, No. 2
In this paper, the efficiency of 20 DCs and their transport and ware-
house subsystems has been analyzed. The set of the observed DCs can
be deemed homogenous due to the fact these DCs operate in the same
way and under the same conditions. The efficiency estimation has been
carried out by applying the CCR DEA and MODEA approaches. The first
three models refer to the MODEA approach, in which the coefficient φ
(closeness parameter) takes the variables of 10%, 20% and 50%. By
comparing the number of DC efficiencies obtained by applying the MO-
DEA and CCR DEA approaches, it is pointed to a considerably larger
number of efficient DMUs by applying the latter approach. This can be
explained by the fact that the CCR model joins all indicators into a unique
measure of efficiency with no separation or observation of the efficiency
of its subsystems, i.e. it "overrates" the efficiency of DC.
By applying the MODEA approach, only 15% efficient DC can be ob-
tained, while the CCR approach yields even 45% efficient DC. Such re-
sults can be explained by the fact that the CCR model does not take into
consideration the efficiency of subsystems in a DC. On the other hand,
according to the MODEA approach, a DC is efficient if the transport and
warehouse subsystems are efficient. The average DC efficiency by
applying the MODEA approach amounts to 1.1388 with the average
efficiency of the warehouse subsystem amounting to 0.5158, and the
transport one to 0.6229. The observed DC set can be considered as
relatively inefficient and the largest portion of this inefficiency is a conse-
quence of the warehouse subsystem inefficiency (Kilibarda, et al, 2011,
pp. 996-1010).
The efficiency of one DC subsystem (warehouse or transport one)
does not entail the DC efficiency. It can easily be concluded that the wa-
rehouse subsystem efficiencies are smaller than the transport subsystem
efficiencies regardless of the variable of the parameter φ. By analyzing
the obtained variables, it can be concluded that there is a certain number
of DCs whose subsystems do not change the efficiency regardless of the
approach. DMU 2, DMU 12 and DMU 17 represent the DCs with stable
performances and efficient according to both the MODEA and the CCR
DEA approach. Stable efficiency of these centres can be explained by a
relatively small quantity of the used resources for the realization of a gre-
at number of deliveries with the number of errors in the transport and wa-
rehouse subsystems DMU 2 and DMU 12 considerably lower than the
average (Kilibarda, et al, 2011, pp. 996-1010).
By analyzing Fig. 4 in more detail, three characteristic DC groups
can be selected. The first group is comprised of the formerly mentioned
efficient units. This group includes DMU 2, DMU 12 and DMU 17 whose
transport and warehouse subsystems are efficient. The second group
includes the units whose warehouse subsystems are significantly more
95
Andrejić, M., Measuring efficiency in logistics, pp. 84–104
efficient than the transport subsystems. Such units are DMU 1 and DMU
11. As opposed to them, DMU 3, DMU 6, DMU 8, DMU 9 and DMU 13
have significantly more efficient transport subsystems. The last two DC
groups, apart from inefficiency of at least one subsystem are characteri-
zed by the overall DC inefficiency. This confirms the claim that no DC
can be efficient if one of its subsystems is inefficient.
Figure 4 – Efficiency of a DC and its subsystems
Slika 4 – Efikasnost DC i osnovnih podsistema
Measuring efficiency of refrigerated warehouses
Energy efficient warehouses and distribution centers are integral
components of supply chain strategies, especially nowadays in competi-
tive global economy. Warehouses are moving beyond a simple storage
function and today they offer high-tech inventory tracking and value-
Distributivni centar
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
DMU
Efikasnost
MODEA 10%
MODEA 20%
MODEA 50%
Skladišni podsistem
0
0.2
0.4
0.6
0.8
1
1.2
1234567891011121314151617181920
DMU
Efikasnost
MODEA 10%
MODEA 20%
MODEA 50%
Single objective
Distributivni centar
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
DMU
Efikasnost
MODEA 10%
MODEA 20%
MODEA 50%
Transportni podsistem
0
0.2
0.4
0.6
0.8
1
1.2
1 2 3 4 5 6 7 8 9 1011121314151617181920
DMU
Efikasnost
MODEA 10%
MODEA 20%
MODEA 50%
Single objective
a) DC Efficiency
b) Warehouse efficiency
c) Transport efficiency
Efficiency
Efficiency
Efficiency
CCR
CCR
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VOJNOTEHNIČKI GLASNIK/MILITARY TECHNICAL COURIER, 2013., Vol. LXI, No. 2
added services as quality-control testing and repackaging. As warehou-
ses grow more sophisticated, their energy consumption grows too. For
example, in the U.S., warehouses only for lighting spend an average of
10$/m
2
. Energy costs for some warehouses are more than 10% of their
total revenue. It does not matter whether a production, warehouse or di-
stribution center is 1000 square meters or 500000 square meters, it is
necessary to cut costs and to improve operating efficiencies. Refrigera-
ted warehouses (RWs) represent special types of warehouses that use
2.8 times more energy for operating than conventional warehouses (Roy,
2010, pp. 64-70). RWs are very important links in a supply chain (AS-
HRAE, 2006). They are also large energy consumers.
Logistic systems, especially RWs, demand more energy for process ope-
ration. In a situation of increasing energy demand and rising energy costs,
energy efficiency measures are becoming more important. The need for
energy efficiency measuring and monitoring becomes a necessity. This paper
uses one of the most frequently used methods for measuring efficiency – the
DEA method for RWs energy efficiency measuring. According to this method,
RWs are viewed as systems that use inputs (labor, electricity, fuel, water,
space, etc.) to produce products and deliver services with a high level of qua-
lity. Management plays the key role in the energy consumption reduction. This
paper presents a model for evaluating RWs energy efficiency, which should
help managers to reduce energy consumption.
The constant return to scale (CRS) assumption is appropriate when all
firms operate on an optimal scale. The CCR gives relative technical
efficiency (TE). However, imperfect competition, government regulations,
constraints on finance, etc., may cause a firm not to operate on an optimal
scale. In contrast to the CCR, the model that assumes CRS (proportional
increase in inputs results in a proportional increase in outputs), (Coelli, Pra-
sada, O'Donnell, Battese, 2005) extends the original DEA model to variable
returns to scale (VRS - increase in inputs does not result in a proportional
change in output). This model is known in the literature as the BCC model.
The BCC model measures the pure technical efficiency (PTE), and provides
a measure of efficiency which ignores the influence of the volume of busi-
ness, because RWs are compared only with other RWs of a similar scale.
Scale efficiency (SE) indicates whether the observed RW operates with an
optimal volume of operations. SE can be obtained for each RW by conduc-
ting both CCR and BCC models, and then by decomposing the TE scores
obtained from the CCR model into two components, one due to scale
inefficiency and one due to pure technical inefficiency. If there is a difference
in CCR and BCC TE scores for a particular RW, then this indicates that the
firm has scale inefficiency (Coelli, Prasada, O'Donnell, Battese, 2005):
TE (Technical Efficiency - CCR) = PTE (Pure Technical Efficiency -
BCC) x SE (Scale Efficiency)
97
Andrejić, M., Measuring efficiency in logistics, pp. 84–104
It is important to note that the overall efciency of energy consump-
tion consists of two factors: environmental factors and energy manage-
ment factors. The environmental factors include weather conditions, oc-
cupant intensity, imperfect competition, government regulations, constra-
ints on finance, etc. The energy management factors include equipment
efciency, operating strategy, etc. Due to the difculty of separating the
effect of the environmental factors from overall energy efciency, there is
little research analysis of the effect of energy management of RWs with
the DEA. Therefore, we use the DEA to measure the overall energy
efciency in detail by examining the environmental factors and the ma-
nagement factors. Considering the fact that RWs can control energy con-
sumption and reduce it in order to become more efficient, in this paper,
we adopted the input orientation of DEA models.
Five inputs and two outputs are used for evaluating the efficiency of
15 RWs. The data are generated in accordance with the actual values of
RWs in real systems (Andrejić, et al, 2011, pp. 313-319). The following
inputs are taken into consideration (Table 2): RW size (m
3
), labor, facility
utilization (%), electricity consumption, other energy costs (water, fuel,
gas, etc). The RW size presents the facility volume. It is better to use m
3
rather than m
2
when describing the warehouse space. The percentage of
facility utilization is a good indicator for the rationalization of energy con-
sumption. Electricity consumption is a basic measure for energy efficient
measuring. As mentioned before, electricity costs are more than 50% of
all costs in RWs, so a detailed analysis of this input is necessary. This
measure is expressed in MWh/year. All other costs of water, fuel, gas,
etc., consumption are incorporated in one measure – other energy costs,
and they are expressed in thousands of monetary units (m.u.).
On the other hand, the proposed model uses two outputs:
successfully realized services and goods deterioration. Successfully rea-
lized services are services that are realized according to customer requi-
rements (right time, right place, right quality, etc). Goods deterioration is
caused by poor handling and storing of goods (i.e. inappropriate tempe-
rature, pressure and humidity).
In accordance with the defined input and output values, as mentio-
ned, we used the input minimizing orientation of CCR and BCC models,
where the emphasis is on the maintenance or improvement of the level of
service provided with the minimum use of inputs. In this paper, the
analysis is carried out with the DEA software efficiency measurement
system (EMS), developed by the Operations Research Department at the
University of Dortmund ((http://www.ub.uni-dortmund.de/service/benu-
ord.htm.en). Table 2 presents the descriptive statistics for the outputs
and inputs of the 15 RWs.
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VOJNOTEHNIČKI GLASNIK/MILITARY TECHNICAL COURIER, 2013., Vol. LXI, No. 2
Table 2
Descriptive statistics for the inputs and outputs in refrigerated warehouses
Tabela 2
Deskriptivna statistika ulaznih i izlaznih promenljivih skladišta hladnjača
Inputs/outputs Mean
Standard
deviation
Max Min
Size (m3) 51.67 23.29 92.72 26.36
Employees 74.68 29.72 132.69 31.95
Facility utilization (%) 69.20 17.64 98.20 44.48
Electricity consumption (MWh/year) 3993.44 843.75 5078.87 2420.65
Other energy costs (1000 m.u.) 26.66 11.04 48.01 11.73
Realized services (1000) 535.79 215.94 997.13 298.57
Goods deterioration (%) 9.54 5.89 19.37 1.12
The results of the DEA analysis, TE and SE scores for each RW are
given in Table 3. Out of the 15 RWs, 8 (53%) were found TE with a score
of 100%. The remaining 7 RWs (47%) were technically inefficient since
they had a TE score lower than 100%. The average efficiency of 15
analyzed RWs is 85%, which roughly means that each RW can store and
deliver the current quantities of goods up to 15% less resources during the
observed period (in this case one year). In this way, the RWs can realize
potential savings of 600 MWh in electricity consumption and 4000 m.u. in
other energy costs in average. In average, the RWs could operate with 11
employees less. The average technical inefficiency of the observed RWs is
15%. Most of the inefficiency is in SE, about 14%, while about 1% of
inefficiency corresponds to PTE. These results show the fact that most of
the inefficiency is the result of the inadequate size of the facility, and the
environmental factor rather than management (i.e. an RW does not opera-
te on an optimal scale). In real systems, the inefficiency is often caused by
bad management. The detailed analysis of the obtained results shows that
the RWs can be classified into certain groups. Namely, the DEA analyses
have found that there are 8 technically efficient and 7 inefficient RWs. As
mentioned before, most RWs do not operate on the optimal scale. A rea-
son for their inefficiency can be found in the size of RWs and the environ-
mental factors (Andrejić, et al, 2011, pp. 313-319).
Table 3
RWs efficiency scores
Tabela 3
Efikasnost skladišta hladnjača
RW TE (CRS) PTE (VRS) SE
RW 1 0.54 1.00 0.54
RW 2 1.00 1.00 1.00
RW 3 1.00 1.00 1.00
99
Andrejić, M., Measuring efficiency in logistics, pp. 84–104
RW TE (CRS) PTE (VRS) SE
RW 4 0.53 0.98 0.54
RW 5 1.00 1.00 1.00
RW 6 1.00 1.00 1.00
RW 7 0.80 1.00 0.80
RW 8 1.00 1.00 1.00
RW 9 1.00 1.00 1.00
RW 10 0.61 1.00 0.61
RW 11 0.97 0.98 0.99
RW 12 1.00 1.00 1.00
RW 13 0.50 0.91 0.55
RW 14 0.86 1.00 0.86
RW 15 1,00 1,00 1,00
Average efficiency 0.85 0.99 0.86
Efficient units 8 (53%) 12 (80%) 8 (53%)
Inefficient units 7 (47%) 3 (20%) 7 (47%)
The slack movements,which arise because of the sections of the pi-
ece-wise linear frontier that runs parallelly to the axes are reported in or-
der to give an accurate indication of RWs TE. The analysis of slack valu-
es (Table 4) shows that there is a significant slack in the input variable.
Much of the inefficiency is the result of scale inefficiency of the RW ope-
rating. Potential improvements are related to the average decrease in
inputs, which range from 0.7% for other energy costs up to 11.21% for
employees while maintaining RW current level of outputs.
Table 4
Potential savings in inputs
Tabela 4
Potencijalne uštede u ulazima
Actual average
Average
slack
Potential
reduce
Size 51.67 3.51 6.79%
Employees 74.68 8.37 11.21%
Facility utilization 69.20 5.39 7.78%
Electricity consumption 3993.44 254.85 6.38%
Other energy costs 26.66 0.19 0.70%
Namely, RW energy efficiency heavily depends on the organization
management, technologies, size of facility and awareness of employees
in RWs, as well as of a number of external factors.
100
VOJNOTEHNIČKI GLASNIK/MILITARY TECHNICAL COURIER, 2013., Vol. LXI, No. 2
Conclusions
Measuring and monitoring the efficiency in logistics is one of the cru-
cial success factors in the market. The main objective of this paper is to
define some kind of efficiency measurement models review. This paper
describes different aspects of measuring efficiency in logistics. The main
efficiency measurement aspects in logistics described in this paper are:
activities efficiency, processes efficiency, subsystems efficiency, systems
efficiency and supply chain efficiency. The paper further describes the
fundamental issues and models for measuring efficiency in certain areas.
The tests and results of the model show remarkable importance of input
and output variables selection. In this paper, the models for measuring
supply chain, systems, and subsystems are described in more detail.
They are also tested on real examples. The methodology for measuring
refrigerated warehouses is also described in this paper.
In literature, there is a lack of case studies, i.e.model testing in con-
crete examples. This fact indicates the insufficient amount of research in
this area. The efficiency of supply chains and logistci systems is also in-
fluenced by a great number of factors upon which the company mana-
gement have no influence. In future models, it would be desirable to in-
troduce certain indicators which, to a certain extent, can describe exter-
nal factors such as: weather conditions, market situation, competition be-
havior, etc. The presented models are a good basis for the development
of future models. Incorporating different performance indicators (quality,
cost, environmental, social, etc.) in efficiency measurement models sho-
uld be a subject of future research.
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VOJNOTEHNIČKI GLASNIK/MILITARY TECHNICAL COURIER, 2013., Vol. LXI, No. 2
MERENJE EFIKASNOSTI U LOGISTICI
OBLAST: logistika, operativni menadžment
VRSTA ČLANKA: pregledni članak
Sažetak:
Dinamično tržište i promene u okruženju u velikoj meri utiču na funkci-
onisanje logističkih sistema. Logistički sistemi moraju realizovati aktivnosti i
procese na efikasan način. U radu se analiziraju različiti aspekti merenja efi-
kasnosti u logistici i predlažu odgovarajući modeli merenja. Merenje efika-
snosti u logistici predstavlja kompleksan proces koji zahteva sagledavanje
svih podsistema, procesa i aktivnosti, kao i uticaja različitih finansijskih, ope-
rativnih, ekoloških, kvalitativnih i drugih faktora. Predloženi modeli bazirani
su na Data Envelopment Analysis metodi. Oni mogu pomoći menadžerima
u procesu odlučivanja i sprovođenja korektivnih akcija. Analizom rezultata
testiranih modela potvrđen je značaj izbora ulaznih i izlaznih veličina.
Uvod
Savremeno poslovanje prvenstveno podrazumeva izrazito zahtevnu
tržišnu borbu, bez obzira na to da li se radi o proizvodnji ili pružanju usluga
u bilo kojoj grani industrije. Postupak merenja efikasnosti u proizvodnim
kompanijama u velikoj meri se razlikuje od postupka merenja efikasnosti u
uslužnim kompanijama. Za proizvodnju materijalnog proizvoda koriste se
materijali, sirovine i komponente od kojih u proizvodnom procesu nastaje
finalni proizvod. Opipljivost i laka merljivost finalnog proizvoda, kao i upo-
trebljenih resursa, u velikoj meri olakšavaju merenje efikasnosti proizvod-
nih procesa. Nasuprot tome, kod uslužnih kompanija finalni proizvod je re-
alizovana usluga koja je po svojoj prirodi prolaznog karaktera, neopipljiva i
kvantitativno nemerljiva, za čiju upotrebu se angažuju različiti, često teško
merljivi resursi, kao što su prostor, vreme i angažovana radna snaga. Pro-
izvod logističkih kompanija, kao tipičnih uslužnih kompanija, najčće su
transportne, skladišne, pretovarno-manipulativne, špediterske i druge
usluge, u čijoj realizaciji se angažuju različiti resursi, kao što su transport-
na i pretovarno- manipulativna sredstva, skladišni prostor, vreme, radna
snaga, itd. čiju upotrebu nije jednostavno izmeriti. Dodatni problem mere-
nja efikasnosti u logistici predstavlja kompleksnost, integrisanost i među-
sobna uslovljenost, kako angažovanih resursa, tako i realizovanih usluga.
Aspekti merenja efikasnosti u logistici
Sa vremenskog aspekta razlikuje se merenje efikasnosti na strate-
škom, taktičkom i operativnom nivou. Sa aspekta nivoa merenja efikasnosti
moguće je razlikovati: efikasnost aktivnosti, efikasnost procesa, efikasnost
podsistema, efikasnost sistema i efikasnost lanca. Kada je reč o merenju i
praćenju efikasnosti logističkih mreža, lanaca, sistema, procesa i aktivnosti
moguć
e je razlikovati efikasnost direktnih i povratnih logističkih tokova. Osim
praćenja i merenja efikasnosti u logističkim sistemima (špedicije, logistički
centri, distributivni centri, skladišta itd...) neophodno je analizirati efikasnost
103
Andrejić, M., Measuring efficiency in logistics, pp. 84–104
logističkih aktivnosti i procesa u sistemima čija osnovna funkcija nije pruža-
nje logističkih usluga (trgovinski lanci, zdravstvene ustanove itd.). Za uspe-
šno merenje efikasnosti u logistici neophodno je sagledati veliki broj ulaza i
izlaza koji su po svojoj prirodi raznorodni (finansijski, tehnički, ekološki, ener-
getski, socijalni itd.) i izražavaju se u različitim mernim jedinicama. U tom
smislu moguće je meriti energetsku, ekološku, troškovnu i druge tipove efi-
kasnosti u logistici. Sa druge strane, često je neophodno sagledati pomenu-
te aspekte i definisati jedinstvenu meru efikasnosti.
Modeli merenja efikasnosti u logistici
Za procenu efikasnosti distributivnih centara (DC) dugo su korće-
ni „single ratio” pokazatelji, kao što su iskorišćenost opreme i produktiv-
nost radne snage. S obzirom na to da pomenuti izmeritelji nisu najbolji
pokazatelji efikasnosti, u (Min, Joo, 2006, pp. 259-65) ističu da se veliki
broj autora zalaže za upotrebu Data Envelopment Analysis (DEA) meto-
de. DEA metoda predstavlja jedan od najšire korišćenih alata za mere-
nje efikasnosti. U oblasti logistike DEA metoda se najčće koristi za pr-
ocenu efikasnosti 3PL (Third Party Logistics) provajdera, kako iz per-
spektive provajdera (Min, Joo, 2006, pp. 259-65), tako i iz perspektive
korisnika. U (Zhou, et al, 2008, pp. 262-279), uz pomoć DEA metode,
pokušavaju da definišu benčmark vrednosti performansi za 3PL provaj-
dere u Kini. Manji broj radova odnosi se na procenu efikasnosti DC i
skladišta. Tako u (Ross, Droge, 2002, pp. 19-32) istražuju efikasnost
102 DC, kao delova kompleksnih lanaca snabdevanja i promenu te efi-
kasnosti u vremenu. Metoda je višestruko primenjivana za merenje efi-
kasnosti u lancima snabdevanja (Cook, et al, 2010, pp. 423-430).
Merenje efikasnosti u lancima snabdevanja
Iz perspektive DEA metode, kao najčće korišćene metode za
merenje efikasnosti višefaznih procesa, postoji veliki broj modela i meto-
da koje se direktno ili uz izvesne modifikacije mogu primeniti na lance
snabdevanja. U ovom radu izrađen je pregled DEA modela za merenje
efikasnosti lanaca snabdevanja. Prema (Cook, et al, 2010, pp. 423-430)
pomenuti modeli mogu se klasifikovati u četiri grupe:
standardni DEA modeli;
• modeli dekompozicije efikasnosti;
• mrežni DEA modeli;
modeli bazirani na teoriji igara.
Merenje efikasnosti logističkih sistema i podsistema
Distributivni centri predstavljaju kompleksne sisteme sa velikim brojem
međusobno povezanih podsistema, procesa i aktivnosti. Radi održavanja
konkurentnosti, oni moraju pratiti i meriti efikasnost, ali i definisati odgovara-
juće korektivne akcije, ukoliko je to potrebno. Pred menadžerima i zaposle-
nima u ovakvim sistemima postavlja se više različitih zadataka koji su uglav-
nom vezani za odre
đivanje optimalne količine resursa (ulaza) za realizova-
nje određenog broja isporuka, odgovarajućeg nivoa kvaliteta. Pored DC kao
sistema u radu su detaljnije analizirana i dva osnovna podsistema i to tran-
104
VOJNOTEHNIČKI GLASNIK/MILITARY TECHNICAL COURIER, 2013., Vol. LXI, No. 2
sportni i skladišni podsistem. Bez obzira na jaku povezanost ovi podsistemi
imaju različite ciljeve, u određenim slučajevima i konfliktne. Kao i DC i podsi-
stemi teže maksimizaciji efikasnosti. U tom smislu menadžeri DC moraju
istovremeno voditi računa o efikasnosti DC, kao sistema, ali i njegovih pod-
sistema. U radu je analizirana efikasnost 20 DC i njegovih transportnih i
skladišnih podsistema. Skup posmatranih DC može se smatrati homogenim
s obzirom na to da DC posluju na isti način i pod istim uslovima. Procena
efikasnosti sprovedena je primenom CCR DEA i Multiple Objective Data
Envelopment Analysis (MODEA) pristupa. Primenom MODEA pristupa do-
bija se svega 15% efikasnih DC, dok se CCR pristupom dobija čak 45% efi-
kasnih DC. Ovakvi rezultati mogu se objasniti činjenicom da CCR model ne
razmatra efikasnost podsistema u DC. Sa druge strane, prema MODEA pri-
stupu (Kilibarda, et al, 2011, pp. 996-1010) DC je efikasan ako su i tran-
sportni i skladišni podsistem efikasni.
Merenje efikasnosti skladišta hladnjača
Energetski efikasna skladišta i distributivni centri sastavni su delovi
strategije lanca snabdevanja, posebno danas u konkurentnoj globalnoj
ekonomiji. U situaciji povećanja potrošnje energije i rasta troškova ener-
gije, energetska efikasnost postaje sve važnija. Potreba za merenjem i
praćenjem energetske efikasnosti postaje nužnost.
Pet ulaznih i dve izlazne veličine korćene su za procenu efikasnosti
15 skladišta hladnjača. Ulazne veličine korišćene u ovom modelu su: veličina
objekta, iskorišćenost objekta, potrošnja energije, ostali energetski troškovi
(naftni derivati, gas, voda itd.). Izlazne veličine, razmatrane u predloženom
modelu, jesu: uspešno realizovane usluge i stepen kvarenja robe. Analizom
rezultata zaključeno je da efikasnost skladišta hladnjača u velikoj meri zavisi
od menadžmenta, tehnološke razvijenosti i odgovornosti zaposlenih, a mno-
go manje od spoljnih faktora (Andrejić, et al, 2011, pp. 313-319).
Zaključak
Merenje i praćenje efikasnosti u logistici jedan je od presudnih fak-
tora uspeha na tržištu. Osnovni cilj ovog rada je pregled modela merenja
efikasnosti. U radu se opisuju različiti aspekti merenja efikasnosti u logi-
stici,, od kojih su osnovni: efikasnost aktivnosti, efikasnost procesa, efi-
kasnost podsistema, efikasnost sistema i efikasnost lanca snabdevanja.
Rad detaljnije opisuje osnovne probleme i modele za merenje efi-
kasnosti u pojedinim oblastima. Testovi i rezultati modela pokazuju izu-
zetan značaj izbora ulaznih i izlaznih promenljivih.
Ključne reči: efikasnost, logistika, metode merenja, Data Envelopment
Analysis.
Datum prijema članka/Paper received on: 31. 03. 2012.
Datum dostavljanja ispravki rukopisa/Manuscript corrections submitted on:
23. 04. 2012.
Datum konačnog prihvatanja članka za objavljivanje/ Paper accepted for publis-
hing on: 25. 04. 2012.
... A-Importance of logistic efficiency on customer satisfaction Efficiency is a very important indicator of the analysis of companies' operations ( Andrejić, 2013). Competitive advantage can be gained by a company that finds ways of performing strategically logistics activities, or ensuring that these activities are performed, more efficiently than its competitors. ...
... Keywords -Logistic efficiency, service quality, customer satisfaction, customer profitability , customer satisfaction measurement , efficiency measurement In order to stay in the market, companies constantly have to measure and monitor their performances as well as to define appropriate corrective actions. One of the basic and frequently used performances is efficiency, and it is a very important indicator of the analysis of companies' operations ( Andrejić, 2013). Efficiency is the ratio of resources utilized against the results derived (Mentzer & Konrad, 1991). ...
... Accordingly to Andrejić (2013), measuring efficiency in logistics is a complex process that requires consideration of all subsystems, processes, and activities as well as the impact of various financial, operational, environmental, quality and other factors. From the point of time and the significance of the decision-making, there are three hierarchical levels of efficiency measurement: strategic, tactical and operational level. ...
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The purpose of this paper was to assess the importance of logistics efficiency on customer satisfaction. Key research findings in logistics efficiency and service quality, logistics performance, customer service, and satisfaction are noted, and the views of the field leaders are identified and assessed, and research outcomes are compared as a basis for how logistics efficiency and customer satisfaction are viewed today. The analysis of the different literature provided strong indications that confirmed the following: that there is a relationship between logistics efficiency, customer's perceived quality and the level of customer satisfaction and that there is a relationship between the customer satisfaction and the level of customer loyalty and profitability.
... o f outputs]) [10] only suggests the number of DMUs, which further influences the number of inputs/outputs, but does not indicate the ratio between the number of inputs or outputs nor any guidance regarding how to select the relevant inputs and outputs. Studies have revealed that the availability of data and suggestions by the authors are factors that most often assist in identifying inputs and outputs [11][12][13][14][15][16][17]. This, however, raises doubt about the value of the selected inputs/outputs and consequently the robustness and reliability of the results. ...
... The proposed, non-separable SBM model is based on the inseparability of desirable and undesirable outputs in equation [12]. In other words, a reduction in undesirable outputs will inevitably require a cut in desirable outputs [50]. ...
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Sustainable concerns are reputed to be of the utmost priority among governments. Consequently, they have become more and more of a concern among supply chain partners. Logistics service providers (LPs), as significant contributors to supply chain success but also one of the greatest generator of emissions, play a significant role in reducing the negative environmental impact. Thus, the performance evaluations of LPs should necessarily involve such a measure which, firstly, represents a balance between all three pillars of sustainability and, secondly, consider the desirable and undesirable performance criteria. This paper proposes an integrated analytic hierarchy process (AHP) and slack-based measure (SBM) data envelopment analysis (DEA) model, based on the assumption of a variable return to scale (VRS). An AHP pairwise comparison enables selecting the most influential input/output variables. Output-oriented SBM DEA provides simultaneously evaluation of both the undesirable and desirable outputs. The proposed model was tested on a numerical example of 18 LPs. The comparison of output Charnes, Cooper and Rhodes (CCR) and SBM DEA models resulted in a higher number of inefficient LPs when the SBM DEA model was applied. Moreover, efficiency scores of inefficient LPs were lower in SBM DEA model. The proposed model is fair to those LPs that are environmentally friendly.
... However, there are a number of factors that explain it, since, as it is an endogenous variable, there are numerous elements that determine its quality. Milan (2013) notes that logistical quality is based on four pillars, which are: the quality of the transport infrastructure, the storage of the goods, the handling service and the provision of freight. However, according to the study, it is not easy to measure these variables. ...
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The quality of the logistical network is an important aspect of commercial relations between countries, especially in the case of agricultural goods, as speed and care in delivery are of extreme importance because their greater perishability. Thus, this work aims to assess the impact of logistical efficiency on agricultural goods' exports, in the period between 2007 and 2018, for a group of 168 countries. For that, it was proposed to carry out a descriptive analysis of a logistic efficiency indicator and to estimate a regression based on the gravity model. The descriptive analysis showed that the countries with the worst logistic performance are the poorest and those on the African continent, while those with the best results are those with high income. The estimated regression revealed that greater logistical quality positively affects agricultural exports. Thus, it is necessary to discuss which policies and actions should be taken in order to improve the logistics network in the countries, stimulating projects aimed at improving infrastructure, traceability and making customs procedures more efficient.
... As defined by Milan (2013) efficiency is one of the key factors in the success of the company. Throughout works and training, the value of logistics quality is recognized; different problems arise in the process of output calculation. ...
... Entretanto, há um conjunto de fatores que a explicam, pois, como se trata de uma variável endógena, existem inúmeros elementos que determinam a sua qualidade. Milan (2013) observa que a qualidade logística se baseia em quatro pilares, que são: a qualidade da infraestrutura de transporte, a armazenagem da mercadoria, o serviço de manuseio e a prestação de frete. Contudo, segundo o estudo, não é fácil realizar a mensuração destas variáveis. ...
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... When it comes to efficiency and its measurement, there are widespread applications in the same areas of human activity, such as healthcare [30], finance and banking [31], environmental conservation [32], construction and manufacturing [33], computer science and robotics [34], tourism [35], emergency management [36], logistics [37], electricity supply [38] and others [39,40]. ...
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Local self-government has the task of enabling stable economic development, in addition to enabling a normal quality of life for citizens. This is why the state government should provide guidelines that will improve the local business climate, and by doing so enable local economic development. This can be done through the introduction of a business-friendly certification procedure, which is influenced by uncertain inputs and influences many output factors. Each local government has the important task of determining its rank of efficiency in this process. A number of methodologies developed to solve this problem are generally divided into two groups: Parametric and non-parametric. These two groups of methodologies could provide quite different results. Therefore, the purpose of this paper was to create a model using both approaches to achieve a balanced symmetrical approach that produces better results than each approach individually. For this purpose, the paper describes a multicriteria decision aid-based model of optimization to evaluate the effectiveness of this process, integrating classification, data envelopment analysis, and stochastic frontier analysis, as well as its application in a case study of business-friendly certification in the Republic of Serbia.
... Kulović (2012),y Samanta y Banerjee (2004) lo usan como índice de calidad para medir efectividad global de los equipos mineros [10]. Cruijssen (2010) lo trabaja como factor de entrada para la evaluación de la eficiencia de las flotas flamencas de transporte de carga por carretera [11], al igual que Bhadra (2009). Referido al desempeño de flotas aéreas [12] y Andrejić y Kilibarda (2012) en el estudio de la eficiencia de centros de distribución [13] y más tarde como factor de salida [14], en todos los casos se aplica el análisis de datos de contorno (DEA por sus siglas en inglés). ...
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The exploitation use of vehicles is influenced by several factors implying down time days, which affects the economic results of fleet operation. If these factors grow beyond acceptable levels, it shows a situation required of decision-making. That is the reason for a mathematical expression to determine the limiting exploitation value of freight motor vehicles, based on a balance between working benefits and down time economic effects. The relation obtained is simple and easy to use by freight fleets operators. The applicability was confirmed by its use in a Cuban company. The concordance between the applied model and the actual economic evaluation is almost perfect.
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A qualidade da rede logística é um importante aspecto das relações comerciais entre países, principalmente no caso dos bens agrícolas, pois a rapidez e cuidado na entrega são de extrema importância por sua maior perecibilidade. Assim, esse trabalho tem por objetivo avaliar o impacto da eficiência logística nas exportações de bens agrícolas, no período entre 2007 e 2018, para um grupo de 168 países. Para tanto, propõe a realização de uma análise descritiva de um indicador de eficiência logística e a estimação de uma regressão baseada no modelo gravitacional. A análise descritiva mostrou que os países com pior desempenho são aqueles mais pobres e os do continente africano, enquanto os de melhores resultados são aqueles de alta renda. A regressão estimada revelou que uma maior qualidade logística afeta positivamente as exportações agrícolas. Desta forma, é necessário discutir quais políticas e ações devem ser feitas com o objetivo de aprimorar a rede de logística nos países, estimulando projetos que visem melhorar a infraestrutura, a rastreabilidade e tornar mais eficientes os procedimentos aduaneiros.
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The development of global supply chains, market liberalization, and new business trends have contributed to creating more competition in the market and increasing demand. Logistics companies strive to generate revenue by providing services of a higher scope and level of quality, while cost minimization is often associated with a system of calculating salaries and employee benefits. The fluctuation of workers is one of the current problems faced by a large number of companies, so the development of a system that will improve conditions and motivate workers is very important. The paper proposes a model for evaluating work performance and calculating bonuses for employees in the warehouse. The model considers the main performance indicators that refer to work engagement in terms of the number of picked items and presence at the workplace. The sensitivity analysis of the model established the convenience and universality of the application.
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Outbound logistics channels are of crucial importance for an efficient construction materials logistics management and impacts on customer satisfaction. However, there is limited knowledge of the outbound logistics channels for construction material in Nigeria. This study aims to identify and examine the current outbound logistics channels used by the Nigerian construction material manufacturing industries. A quantitative research method using a case study approach was adopted in this research. The purposive sampling technique was chosen, where six construction material manufactured and distributed within five states capital and Abuja in the North-central region of Nigeria were selected for this study. A research instrument was developed and used in conjunction with an observation protocol in the form of a template. The data were collected through observations, direct measurement onsite and archival records of transactions. A descriptive method of data analysis was employed to analyse the data. Our findings indicate that there exist six alternative outbound logistics channels that can be used separately or in combination with each other to deliver materials to end users. The study concludes that the research finding provides a potential knowledge and understanding of the manufacturers’ outbound logistics channels that can be used at the start of a project to accomplish effective planning and delivery of the whole project. The study also established the average transportation cost per average ton and average transportation cost per average distance driven for construction material delivery. This information can be used for construction material transportation management.
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The efficiency of distribution systems is largely affected by the performances of distribution centres. The main objective of this paper is to develop and propose a DEA model for distribution centres efficiency measuring that can help managers in decision making and improving the efficiency. Due to numerous indicators that describe DCs operating, the main problem is indicators selection. In order to improve discriminatory power of classical DEA models PCA–DEA approach is used. This paper analysis the efficiency of distribution centres of one trading company in Serbia. Proposed models integrate operational, quality, energy, utilisation and equipment warehouse and transport indicators. Several hypotheses are tested in this paper. The results showed that small distribution centres are more efficient than large.
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Purpose – In an era of downsizing and financial cutbacks, the operational efficiency of third party logistics providers (3PLs) dictates their competitiveness and/or survival. In an effort to help 3PLs enhance productivity and price leverage in the increasingly competitive third party logistics market, this paper aims to develop a meaningful set of financial benchmarks that will dictate best practices. In particular, the paper proposes a data envelopment analysis (DEA) that is proven to be useful for measuring the operational efficiency of various profit or non‐profit organizations. Design/methodology/approach – Using the examples of major 3PLs in the USA, this paper is the first to illustrate the usefulness of DEA for measuring the competitiveness of third party logistics services. The proposed DEA model also helps 3PLs identify potential sources of inefficiency and provide useful hindsight for the continuous improvement of operational efficiency. Furthermore, the proposed DEA model not only helps 3PLs establish detailed policy guidelines in prioritizing the use of financial resources, but also helps them evaluate the effects of financial investment on the profitability of 3PLs. Findings – DEA study finds that the strength of 3PL service performances and the breadth of diverse 3PL services is somewhat correlated to the long‐term financial strength of 3PLs. Originality/value – The main contribution of this paper includes the novel application of DEA to the performance measurement of 3PLS. The proposed DEA model not only helps 3PLS establish detailed policy guidelines in prioritizing the use of financial resources, but also helps them evaluate the effects of financial investment on the profitability of 3PLS.
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A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs. A scalar measure of the efficiency of each participating unit is thereby provided, along with methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs. Equivalences are established to ordinary linear programming models for effecting computations. The duals to these linear programming models provide a new way for estimating extremal relations from observational data. Connections between engineering and economic approaches to efficiency are delineated along with new interpretations and ways of using them in evaluating and controlling managerial behavior in public programs.
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