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Business logistics: importance and some research opportunities

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Business logistics is defined and reasons are given as to why it is a vital area of management. Political and economic trends are highlighted to show that it is even increasing in importance. Current research in business logistics is discussed with a focus on the design of the logistics network as it is aided by computer modeling. Research opportunities are identified to both improve modeling for network design and better specify the information inputs to the design process.Logística empresarial é definida e algumas razões são apontadas do porquê ela é uma área vital de administração. Tendências políticas e econômicas são evidenciadas para mostrar que ela está ainda crescendo em importância. A pesquisa atual em logística empresarial é discutida enfocando o projeto da rede logística e como ele é auxiliado por modelagem computacional. Oportunidades de pesquisa são identificadas para melhorar a modelagem do projeto da rede e melhor especificar as necessidades de informação para o processo de elaboração do projeto.
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v.4, n.2, p. 117-129, ago. 1997
BUSINESS LOGISTICS
IMPORTANCE
AND SOME RESEARCH
OPPORTUNITIES
Invited Paper
Ronald H. Ballou
Department of Operations Research and Operations
Management
Weatherhead School of Management
Case Western Reserve University
Cleveland, Ohio U.S.A.
Abstract
Business logistics is defined and reasons are given as to why it is a vital area of
management. Political and economic trends are highlighted to show that it is even increasing
in importance. Current research in business logistics is discussed with a focus on the design
of the logistics network as it is aided by computer modeling. Research opportunities are
identified to both improve modeling for network design and better specify the information
inputs to the design process.
Key words: business logistics, survey, logistics network design.
1. Introduction
upply chain management, physical
distribution, materials management,
and even rhocrematics are names that
have been given to the field of business
logistics. Regardless of the name, business
logistics is a vital area of management
within most firms, whether they are
manufacturing or service firms. Logistics
has been defined by the Council of Logistics
Management as
...the process of planning, implementing,
and controlling the efficient, cost-
effective flow and storage of raw
materials, in-process inventory, finished
goods and related information from point
of origin to point of consumption for the
purpose of conforming to customer
requirements.
S
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118
The stated mission for business logistics is
...to get the right goods or services to the
right place, at the right time, and in the
desired condition, while making the
greatest contribution to the firm.
This mission is accomplished by careful
management of those activities that
significantly contribute to logistics customer
service and that are in cost tradeoff with
each other. The typical logistics activities
are shown in Figure 1.
Physical distribution
Physical supply
(Materials management)
Business logistics
Sources of
supply
Plants/
operations
Customers
• Transportation
• Inventory maintenance
• Order processing
• Acquistion
• Protective packaging
• Warehousing
• Materials handling
• Information maintenance
• Transportation
• Inventory maintenance
• Order processing
• Product scheduling
• Protective packaging
• Warehousing
• Materials handling
• Information maintenance
Figure 1 - Typical Activities in a Firm’s Immediate Supply Chain
2. Importance of Business Logistics
ogistics is important because it
creates valuevalue for customers
and suppliers of the firm, and value
for the firm’s stakeholders. Value in
logistics is expressed in terms of time and
place. Products and services have little or no
value unless they are in the possession of
customers when (time) and where (place)
they wish to consume them. To many firms
throughout the world, logistics has become
an increasingly important value-adding
process for a number of reasons.
2.1 Costs Are Significant
According to the International Monetary
Fund, logistics costs average about 12
percent of the world’s gross domestic
product. Examples of logistics costs within
individual economies show variation. The
United Kingdom has logistics costs of 16
percent of sales (MURPHY, 1972, p. 7).
Japan has physical distribution costs of 26.5
percent of sales (KOBAYASHI, 1973, p. 9).
Australia has estimated average physical
distribution costs of 14.1 percent of sales
(STEPHENSON, 1975). In the European
Economic Community, logistics costs were
21 percent on a value added basis
(KEARNEY, 1987). In the United States,
logistics costs for the typical firm are about
10.5 percent of sales (DAVIS & DRUMM,
1995). Depending on the particular industry,
logistics costs may range from 4 percent of
sales (pharmaceuticals) to over 30 percent of
sales (food and food products). It has been
noted that for many firms, after the cost of
goods sold, logistics represents the highest
cost of doing business.
L
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119
The economic forces of change are further
acting to alter logistics cost relationships and
force careful replanning of logistics systems
around the world. Trade barriers are falling
as free trade is encouraged in countries that
previously had strictly managed economies.
Tariffs are being eliminated to allow the free
flow of goods across political boundaries,
giving firms the opportunity to reposition
their logistics networks for lower costs and
higher customer service. Finally, the world
economies seem to be on a wave of
economic deregulation that will heighten
competition. Since transportation is
frequently a target for deregulation, logistics
system costs will be affected.
2.2 Globalization of Industries
The trend is towards an integrated world
economy. Firms are seeking, or have
developed, global strategies where either
their products are designed for a world
market and they are produced wherever the
low-cost raw materials, components, and
labor can be found, or they simply produce
locally and sell internationally. In either
case, supply and distribution lines are
stretched, as compared with the producer
who wishes to manufacture and sell locally.
Not only has the trend occurred naturally by
firms seeking to cut costs or expand
markets, but it is also being encouraged by
political arrangements that promote trade.
Examples are the formation of the European
Economic Community (EC92), the signing
of the North America Free Trade Agreement
(NAFTA) between Canada, United States,
and Mexico, and the creation of a new
economic trade agreement among several
countries of South America (MERCOSUL).
Globalization/internationalization of in-
dustries everywhere will depend heavily on
logistics performance and costs, as
companies take more of a world view of
their operations. As this happens, logistics
takes on increased importance within the
firm since logistics costs, especially the
transportation component, become a
significant part of the total cost structure.
For example, if a firm seeks foreign
suppliers for the materials entering its
product or foreign locations to build its
products, the motivation is to increase profit.
Material and labor costs may be reduced, but
logistics costs are likely to increase due to
increased transportation and inventory costs.
The tradeoff, as shown in Figure 2, may lead
to higher profit by reducing materials, labor,
and overhead costs at the expense of
logistics costs and tariffs. Outsourcing adds
value, but it requires more careful
management of logistics costs and product
flow times in the supply channel.
2.3 Logistics is important to strategy
Firms spend a great deal of time finding
ways to differentiate their product offerings
from those of their competitors. When
management recognizes that logistics
impacts on a significant portion of a firm’s
costs and that the result of decisions made
about the supply chain yields different levels
of customer service, it is in a position to use
this information effectively to penetrate new
markets, increase market share, and increase
profits.
To illustrate, a company selling its
merchandise through a catalog has its
inventory and operations centralized at one
location in the country. It wishes to compete
effectively with retail stores operating in
local market areas. Although the company
benefits from low overhead, buying
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120
economies, and good product availability, it
was at a disadvantage in being time
responsive to customers. To overcome the
disadvantage, the company developed a
marketing strategy based primarily on
logistics. Delivery time and its variability
were at the heart of the strategy. First, a toll-
free telephone number and 24-hour order
taking were established so that customers
could place their orders free of charge any
time of the day or night. Next, orders were
filled the same day that they were received,
even as late as 6:30PM. Finally, orders were
shipped using an overnight delivery service
such as FedEx. The result was that
customers could place orders late in the day
and receive them at their home or business
by 10:30AM the next morning. The
company was able to compete effectively on
both price and service dimensions.
Profit
G & A
Marketing
Logistics
Overhead
Materials
Labor
Tariffs
Overhead
Materials
Labor
Logistics
Marketing
G & A
Profit
Domestic sources Foreign sources
Figure 2 - Economic Benefit of Sourcing from Low-Cost Offshore Locations Rather then
from Higher-Cost Local Suppliers - Source: “International Logistics: Battleground of the
‘90s” (Chicago: A.T. Kearney, 1988)
2.4 Logistics Is Key to Customer Service
Research over the years has shown that
logistics variables are dominant in the minds
of customers when they evaluate the service
offerings for a product; see STERLING &
LAMBERT (1989), HARRINGTON &
LAMBERT (1989), LALONDE &
ZINSZER (1976), MARR (1994), BARITZ
& ZISSMAN (1983), JACKSON et al.
(1986). Frequently, one-half of the customer
service variables are logistics related and
delivery time typically ranks the highest
among all service variables. Since customers
respond to a company’s service offerings
with their patronage, revenues are a
frequently determined by logistics variables.
2.5 Customers Increasingly Want Quick Customized Response
Customers have been increasingly
sensitized to expect quick response to their
demands. Fast food restaurants, overnight
package delivery, and instant access to
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121
information on the Internet are examples of
what customers might anticipate in the way
of service from a wide range of product and
service offerings. In addition, improved
information technology and flexible manu-
facturing systems have led the marketplace
towards mass customization. Rather than
consumers having to accept the “one size
fits all” philosophy in their purchases,
suppliers are increasingly offering products
that meet individual customer needs. This
has placed growing demands on production
and logistics systems to achieve ever higher
performance levels.
3. Status of Logistics Research
ith the growing significance of
logistics among the other
activities of the firm, research
has been conducted over a broad front to
support and enhance logistics management.
Research has ranged from quantitative
modeling for decision making to
philosophical works that set the tone for
good logistics management. Within this
range, one of the most important research
areas has been the strategic planning of the
logistics network. Network design involves
establishing customer service levels, the
deployment of inventories, the location of
facilities, and the selection of the modes of
transportation. It sets the structure that
determines the overall level of logistics cost
and customer service. Because it impacts on
a significant level of company costs and it
affects the revenue-generating capabilities of
the firm, logistics network design is
typically of top management concern.
Experience shows that good network
redesign can save a company between 5 and
15 percent of its annual logistics costs.
Logistics network design can be viewed
as a triangle of logistics strategy (see Figure
3). Logistics customer service is the goal
and the result of decisions made about the
network design. Establishing the level of
customer service to be achieved in turn
defines the revenues to be generated through
logistics activities. It also defines the
combined effect of three structural
variableslocation strategy, inventory
strategy, and transportation strategy.
Customer
service goals
•Modes of
transport
•Carrier routing/
scheduling
•Shipment size/
consolidation
Transport Strategy
•Inventory
levels
•Deployment
of inventories
•Control
methods
Inventory Strategy
•Number, size, and location
of facilities
•Assignment of stocking
points to sourcing points
•Assignment of demand to
stocking points or sourcing
points
•Private/public warehousing
Location Strategy
Figure 3 - The Triangle of Logistics Strategy
W
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122
The three strategies are in tradeoff with
each other. That is, a good location strategy
is dependent on the manner in which
inventories are managed and on the resulting
inventory levels, and on the transportation
service selected. Inventory levels are
dependent on the number and location of
facilities as well as the transportation service
selected. And so the interdependence goes.
Hence, a triangle of logistics strategy.
Logistics network design is based on
three objectivescost minimization, capital
minimization, and logistics customer service
maximization. Not all of these objectives
can be achieved simultaneously since they
may be in conflict. For example, minimizing
costs and simultaneously maximizing
service are incompatible.
Research through the years, coupled with
advancements in computer technology, has
provided the capability to design logistics
networks with the aid of mathematical
modeling. This modeling is now so popular
with consultants and managers that few
networks are designed without the aid of
such sophisticated tools (BALLOU &
MASTERS, 1993, p. 83). The logistics
network design problem is captured in the
abstract diagram of Figure 4. The strategic
questions revolve around locating the
facilities intermediate to source and demand
points, determining the modes of
transportation, projecting the amount of
aggregate inventory in the logistics system,
and controlling the design impact on
logistics customer service.
Supply
Demand
L
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d
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s
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p
p
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n
g
Destination nodes
Customers
Retail
outlets
Warehouses
Origin nodes
Raw material
sources
Plants
Intermediate nodes
General direction of information flow
E
l
e
c
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r
o
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Figure 4 - An Abstract Diagram for a Logistics Network
Network models used commercially are
generally of two types: mathematical
programming and computer simulation.
These models follow the two basic
characteristics of the network planning
problemthe spatial and the temporal
orientations (HESKETT, 1966). Spatially
(geographical) based models have been
much more popular than temporally (time)
based ones.
GESTÃO & PRODUÇÃO v.4, n.2, p. 117-129, ago. 1997
123
3.1 Spatial Models
Spatial models have primarily been used
to locate the facilities in the logistics network
on a geographical plane. They answer such
questions as: How many facilities are needed,
where should they be located, and what size
should they be? Logistics-related costs are
minimized, or profits maximized, subject to
geographical constraints on customer service
and restrictions on facility capacities.
A survey of commercial-grade location
model developers showed that deterministic
approaches such as linear programming,
mixed integer programming and heuristic
methods were the methods of choice
(BALLOU & MASTERS, 1993). Continuous
location methods (e.g., center-of-gravity
approaches) have been relegated to offering
suggestions for possible locations within the
deterministic algorithms. The reason for this
would seem to be that the deterministic
methods can handle most of the costs of
location with a great deal of realism and also
that restrictions can be introduced that are
not easily handled within the continuous
framework. Much of the development in the
last 20 years has focused on making location
models more user-friendly, adding features
that enhance communication, and adding
programs that facilitate data conversion to
model format. Extended solution capabili-
ties, such as adding more echelons to the
network and more facilities, source points,
and customers to be analyzed, have resulted
primarily from greater memory and
computational speed of computers. Basic
algorithmic development remains slow since
the mid-1970s (for highlights on location
model evolution in the last two decades, see
GEOFFRION & POWERS (1995)),
however, facility location remains an active
area of research, probably because manage-
ment continues to use these models and they
help solve such an important problem to top
management.
3.2 Temporal Models
When time becomes the important
variable to be managed, as is the case when
controlling inventories, choosing a trans-
portation service, or scheduling product
flow, computer simulation has been a good
model choice. Such models permit the flow
of product in the supply channel to be
observed in simulated time. Complex
product flow interactions among activities
taking place between multiple echelons can
be observed. Inventory levels, vehicle
loading and shipping patterns, out-of-stock
percentages and cost profiles are a few of
the results obtained when the pattern of
customer orders is placed on the simulated
system. The extensive information detail for
logistics channel simulators is generally not
available in spatial models where products
are grouped, costs are averaged, and the time
frame for analysis is from a month to a year.
A few simulations specifically designed
for logistics analysis have appeared over the
years (BOWERSOX et al. (1972), RONEN
(1988)). However, they have not been used
as extensively as spatial models. It also may
be the case that the general computer
simulation packages that can be adapted to
deal with logistics problems fill the need.
Little research is being conducted to develop
channel simulators specifically designed for
logistics planning. Significant data
requirements, lack of understanding of the
value of simulation results compared to
spatial model results, and limited promotion
of the methodology are some of the reasons
for the underutilization of this important
methodology.
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124
4. Research Opportunities
he spatial and temporal aspects of
logistics network design have not
been effectively merged into one
solution platform, although it is possible to
run such models separately and sequentially
to converge on network solutions that satisfy
both strategic and tactical dimensions.
Neither a location model nor a channel
simulator model by itself is a complete
model for logistics network design. Location
models rely on generalized inventory-
throughput relationships and assumptions
about the methods of transportation used as
inputs and give good results on location
issues. On the other hand, channel
simulators take facility locations as inputs
and provide good results on inventory and
transportation issues. The two models are
inter-related, as shown in Figure 5. Research
should be directed at bringing spatial and
temporal dimensions together, probably
within the location model framework since
it is the most popular modeling platform.
Location
model
Channel
simulator
Facility locations
Inventory/throughput
relationships
Transport modes
Figure 5 - Inter-relationship Between Location and Channel Simulator Models
4.1 Logistics Customer Service
Perhaps the research needed most in
logistics network design is to find methods
for determining the relationship between the
level of logistics customer service provided
and the revenues generated by the firm.
Current practice is to treat customer service
as a constraint on network design and
minimize costs subject to the constraint.
However, the preferred practice is to
maximize profit when both revenue and cost
are variables, since the customer service
level will be set at optimal based on
economic factors. The network design can
be quite different depending on the objective
used, that is, profit maximization versus cost
minimization.
Developing the revenue-logistics service
relationship for a particular firm can be as
difficult as determining the effectiveness of
its advertising budget or other sales efforts.
Some methods have been proposed, but
none seems very satisfactory (BALLOU,
1992, pp. 94-96). For location models, the
revenue-service relationship can be
expressed as a price function where price
declines as customers are farther from their
sourcing points. For channel simulators, the
appropriate variable might be customer
order cycle time. Research could begin with
the methods used for similar problems in
marketing.
T
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125
4.2 Inventory
Inventory represents a key economic
factor in network design that forces
consolidation of inventories into a small
number of locations. There has been
substantial research over the years on
controlling individual product item
inventory levels but relatively little about
estimating inventory levels when there is
more than one product item taken at a time.
The practical concerns of network design
require that many items be collected into
product families and dealt with as an
aggregate group. What is needed is to be
able to estimate inventory levels as demand
is assigned to facilities. The famous square-
root rule (for a discussion of the square-root
rule, see BALLOU (1992, pp. 447-449)) is a
good, first approximation of inventory
effects in network design which has been
refined to include safety stock effects
(BALLOU, 1981). However, these rules are
generally based on inventory control
procedures formulated around the economic
order concept. Estimating inventory levels
based on other rules found in practice would
be beneficial.
The relationship between inventory levels
in facilities and the allocated demand to
them is frequently nonlinear and concave.
Since the solution platform for location
models is typically linear or mixed integer
programming, the nonlinear relationship
causes computational difficulties. Better
methods, besides decomposing the function
into piecewise linear elements, are needed
so that the inventory relationship can easily
be incorporated into the computational
process. Too often, the inventory
consolidation effects of network design are
computed outside of the solution process to
avoid these computational difficulties.
4.3 Transportation
Two transportation problems arise in
logistics network planning as a result of
using mathematical programming to design
the network. These are the handling of
private trucking and the selection of
transportation services.
When using a linear programming-based
solution method for network planning, it is
assumed that transportation between points
on the network is one-way. For-hire
transportation fits this assumption since
rates are quoted between two specified
points. However, when transportation
involves more than one stop before the
delivery vehicle returns to its depot,
transportation costs may be in error. Since
equivalent rates must be calculated for each
transport leg from the multiple-stop routes,
the stops (customers) on the route and the
depot (facility) to which they are assigned
must be known. However, assigning
customers to facilities is the result of the
location analysis. Rates are a result of
customer allocation and customer allocation
depends on rates. Therefore, research needs
to be conducted on combining transport
routing and location-allocation analysis.
In a channel simulator, the transportation
service can be selected based on the size and
characteristics of the order when it is
presented for delivery. In a locator where
products are grouped into families of items
and transportation rates are constructed
based on average shipment size,
transportation modes are represented within
the rate data. It is then assumed that the
mode mix remains constant as reallocation
of demand occurs among facilities during
optimization. Since the various modes used
are combined into a weighted rate for
GESTÃO & PRODUÇÃO v.4, n.2, p. 117-129, ago. 1997
126
product volume flowing from a facility, the
percentage (weights) of shipments by the
various modes should change as the network
design is being calculated. This
interdependence is an area for research in
location models.
4.4 Location
The use of location modeling has been a
dominant approach for logistics network
design. At this point in time, the
methodology which is based on linear
programming concepts is well refined and
quite robust in handling a wide range of
practical network design problems.
Problems having numerous product families,
thousands of customers, hundreds of
intermediate facilities, and 4 or more
network echelons are routinely solved. Most
of the relevant costs can be handled by the
methodology. However, designers are
always wanting to press the model’s limits
which leaves opportunities for further
research. Several of these are discussed
below.
A problem not easily handled within the
standard network model occurs when
vendors, as opposed to plants, supply the
network. Plants are usually represented by a
location that can supply product up to its
limit of capacity. In contrast, vendors supply
only a portion of their total capacity to a
particular firm’s network and ship to all
intermediate facilities a percentage of that
firm’s demand. In addition, a vendor may
ship to the network from a number of
locations. A network design involving many
vendors generally takes place for retail
oriented companies. A network design
problem occurs when optimization
methodology will allocate to some vendors
and not to others according to their costs and
capacity limitations. The allocation results
do not represent the actual flow patterns for
the vendors. Adjustments are needed in the
computational methodology.
Although logistics networks are designed
primarily with the aid of mixed integer
programming and heuristic (deterministic)
procedures (BALLOU & MASTERS, 1993),
there has been a resurgence of interest in
continuous location methods such as the
center-of-gravity approach to location. Users
seem to like the feature that the continuous
location model will give locational choices
without preselection of candidate facilities,
as is the case for deterministic methods. It is
well known that it is difficult to obtain exact
solutions to continuous models having
multiple facilities and a rich environment of
logistics costs and constraints. Currently,
continuous location models provide
candidate locations for deterministic models
to further evaluate. Research might be
directed at improving the continuous
location models for commercial-grade use
and integrating them into the deterministic
methods for facility location.
4.5 Data Issues
Much can be said about the need for
improved understanding of the data
relationships that are presented to the
network analyzers. Whereas a great deal of
research has been conducted on the methods
by which to design the network, very little
attention has been given to data elements
that are the inputs to the design process. Yet,
the solution quality of the network design is
probably more sensitive to variations in data
inputs than differences among solution
methods. Consider some of the more
important data issues (for a general
discussion of the informational issues in
GESTÃO & PRODUÇÃO v.4, n.2, p. 117-129, ago. 1997
127
network design, see BALLOU (1987) and
BALLOU (1995)).
Transport rates can be presented to a
network model as specific rates by particular
mode between defined points in the
network. For practical-size problems, this
may involve millions of rates. Alternately,
transport rate curves can be developed that
can be used for estimating rates for various
distances from a facility. These rate curves
can be developed from a sample of the
actual rates and are usually a function of
distance alone. Although a linear
relationship between rates and distance work
well for the for-hire carriers, it is not clear
that it is the best fit for small package
shipments or full vehicle load rates. Non-
uniformly applied discounts and tariffs
potentially can distort the linear relationship.
Research to find good rate relationships and
project the error that is involved in such
estimating procedures is needed (BALLOU,
1991).
Costs associated with facilities are
available through accounting reports. For
network analysis, the costs need to be
separated into fixed, storage, and handling
categories. Since the separation is arbitrary,
the network design may be dramatically
influenced by the cost allocation. For
example, while one analyst may view such
costs as trash removal, fire protection, and
telephone charges as variable with the
volume flowing through the facility, others
may see these as fixed costs. Of course, the
number of facilities in a network may be
greatly influenced by this arbitrary allocation
of expenses. Expense allocation rules need
to be tested to show just how they affect
network design and developed to provide
reasonable and consistent treatment of these
facility expenses.
When thousands of customer locations
are involved in the network design, it is
practical to aggregate them into a smaller
number of geographical clusters. This
reduces the amount of data to be handled
and the computational and computer
memory requirements. Demand clustering
can result in errors when estimating the
transportation cost to customers, since the
basis for transportation rates is the center of
the clusters and not the actual location of
each customer. Research has shown the
number of clusters needed in a network
design problem to control the transport
costing error to a given percentage
(BALLOU, 1994). More research is needed
to find improved customer clustering
algorithms to minimize the costing error.
5. Concluding Comments
usiness logistics is a vital area of
management within most firms.
Costs are a significant portion of
sales, and logistics variables are often
dominant in the company’s customer service
mix. With trends that are occurring, such as
growing international trade, increasing
customers’ desire for quick customized
response, and dismantling of trade barriers, a
good logistics strategy has become ever
more critical to maintaining a competitive
edge and penetrating new markets.
Good design of the logistics network is
one of the keys to good logistics
performance. Great strides have been made
in using the computer and mathematical
models to construct these networks.
Research opportunities exist not only to
improve the methodology for solving
network designs, but also to further improve
the cost and customer service relationships
that are the inputs to the design process.
B
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LOGÍSTICA EMPRESARIAL — IMPORTÂNCIA E ALGUMAS
OPORTUNIDADES DE PESQUISA
Artigo Convidado
Resumo
Logística empresarial é definida e algumas razões são apontadas do porquê ela é uma
área vital de administração. Tendências políticas e econômicas são evidenciadas para
mostrar que ela está ainda crescendo em importância. A pesquisa atual em logística
empresarial é discutida enfocando o projeto da rede logística e como ele é auxiliado por
modelagem computacional. Oportunidades de pesquisa são identificadas para melhorar a
modelagem do projeto da rede e melhor especificar as necessidades de informação para o
processo de elaboração do projeto.
Palavras-chave: logística empresarial, visão geral, projeto de rede logística.
... Lojistik, müşterilerin isteğiyle başlayan ve talep edilenin karşılanmasıyla nihai bir sürecin verimli bir şekilde koordinasyonunu sağlayan bir sistem olduğundan dolayı; gerçekleştirilen her türlü faaliyetin sonucunda ortaya çıkan maliyetle ürün fiyatılarında bir orana sahip olması sebebiyle örgütlerde mali açıdan önem arz etmektedir. Bu kapsamda lojistik yönetimi, örgütler için ekonomik etkileri olan, zaman ve mekân faydası yaratan, işletmelere rekabet avantajı sağlayan son derece önemli bir işletme stratejisidir (Ballou, 1992;Ballou, 1997). ...
Article
Full-text available
Günümüz dünyası, küreselleşme faaliyetlerinin ve ticari uygulamaların hızla yaşandığı bir dönemdedir. Bu sebeple lojistik, tüm küresel tüketicilerin ihtiyaç duyduğu her türlü ürün ve hizmetlerin zamanlaması, yeri, maliyetleri uygun ve doğru şartlarda müşteriye yöneltebilmek için önemli bir işletme işlevidir. İşletmeler açısından ise lojistik, işletmeye girdilerin sağlanmasında, işletmenin süreçlerindeki bilgi akışında ve işletme çıktılarının hedef pazarlarda uygun dağıtımının sağlanmasında stratejik öneme sahiptir ve gerek işletme maliyetlerinin planmasında gerekse işletmenin değer kazanması açısından önem arz etmektedir. Ayrıca lojistik, işletmeler arasında ürün ve bilgi akışını sağlayarak tedarik zinciri olarak tanımlanan yapının gerçekleşmesine imkân sağlamaktadır. Küreselleşmenin günden güne hız kazandığı yüzyılın son çeyreğinde işletmeler hem faaliyetlerinde devamlılığı sağlamak ve kazançlarının en önemli kısmını oluşturan müşterilerini kaybetmemek amacıyla lojistik uygulamalarını her geçen gün geliştirerek şirket süreçlerine dâhil etmektedirler. Fakat lojistik genellikle salt bir ulaştırma süreciyle bazen de bilgisayar yazılımları ile yapılan yapılan sayısal uygulamalar olarak tanımlanmaya çalışılmıştır. Ancak lojistiğin birçok sayısal işlemi içeren mali bir yanı olmakla birlikte her adımında planlama, organize etme ve denetim sağlayan yönetimsel bir alanı de bulunmaktadır. İşletmeler açısında oldukça öneme sahip olan lojistik faaliyetlerin ortaya çıkış sürecine ve gelişimine değinilerek bu faaliyetlerin işletmelerdeki lojistik yönetimi uygulamaları, işletmelere olan faydaları ve işletmelerde lojistik yönetimin çalışma alanları incelenmiştir.
... According to, logistics is an effective and efficient planning, implementing and controlling process for the flow and storage of raw materials, work-in-process inventory, finished products and other related information from point of origin to point of consumption according to customer needs [11]. Rushton et al., stated that logistics is the management of the flow between marketing and production, where there are activities between the point and time of production (supply) and the point and time of product purchase (demand) [12]. ...
Conference Paper
Full-text available
This study is aimed to investigate the relationship between logistics performance and environmental performance as measured by carbon emissions in ASEAN countries. The study was conducted in 10 ASEAN countries using panel data analysis from 2007 – 2018. Logistic performance is measured by the logistic performance index (LPI) and carbon emissions are measured by CO2 (carbon dioxide) emissions per capita. Data published by The World Bank Database. The results show that LPI and carbon emissions has a negative and significant correlation. This means that the higher the performance of logistics, the lower emissions of carbon. The results of the analysis of the relationship between the six LPI indicators on carbon emissions, show that only infrastructure and international shipments are related to carbon emissions, but in different directions. Meanwhile, customs, logistics competence, tracking and tracing and timeliness are not related to carbon emissions. Keywords: Logistics Performance; Carbon Emissions; Trade; Urban Population
... A certain product will have a value for the customer only when they can consume it in a particular place and at a particular time. In order to achieve this goal, organizations should carefully organize product delivery, i.e. they should pay appropriate attention to the question of logistics (Ballou, 1997). An adequate selection of the transportation mode and a transportation company will not only add value to the organization's performance with respect to its quality, but it will also contribute to a reduction in costs. ...
Article
Full-text available
Nowadays, customers are not only interested in the quality of products, but they also want to have these products in a timely manner. The managers of an organization are faced with two problems when the distribution of products is in question, namely: (1) customers are usually geographically dispersed and (2) transportation should be performed in a cost-effective way. Although managers may have a significant experience and formal knowledge, decisions connected with the selection of an appropriate transportation company may very often be biased. For the purpose of avoiding making the inadequate decisions that might harm the operation of the organization, the application of a hybrid MCDM model is proposed in this paper. The proposed model consists of three fuzzy MCDM methods, including: the PIPRECIA, the PSI, and the CoCoSo methods. The fuzzy-PIPRECIA method is used to achieve the subjective weights of criteria, whereas the fuzzy-PSI method is used to obtain the objective weights of criteria. Fuzzy-CoCoSo is utilized to rank alternative transportation companies according to their performances. The possibilities of the proposed hybrid model are tested on a real case study pointed at the selection of an appropriate company for the transportation of ready-garments to retailers in Turkey. First published online 07 July 2021
... Global markets, mass customization, reduced product life cycles and cost reduction are among the drivers behind an increased interest in logistics activities worldwide (Kherbach et al, 2016;Lee, 2016;Ballou, 1997). Hence teaching logistics concepts according to traditional teaching methods in logistics allows students to comprehend the concepts of an adequate operations management; however, through these methods, students lack the capabilities to put these concepts into practice (E. A. Pacheco-Velazquez, Palma-Mendoza, & Valdelamar-Dewar, 2020). ...
Conference Paper
The Texas construction market is the second-largest hub inside the U.S. Nearly 750 thousand people are working in different sectors of the Texas construction industry. Although the big picture indicates steady growth in Texas hiring size in the last 30 years, the Texas construction market's volatility has been an issue for construction companies and their hiring plans. Rather than seasonal patterns inherent to construction activities, factors such as economic recessions and crises, tropical hurricanes, and outbreaks of pandemics are potential reasons for fluctuations in construction companies' demand to hire. The impact of each factor on the cities varies due to geographical and demographical diversity inside Texas. This paper focuses on understanding workforce migration behaviors following local disasters because it relies heavily on the local workforce. To determine each factor's significance is to find if they created an anomaly in the dataset after they occurred. This research implemented an outlier detection analysis on Texas cities and compared the resulting outlier dates with the timeline of Texas's extreme events in the last 30 years. The results show that economic crises with national scales such as the dot-com bubble at the start of the century and the 2008 economic crisis mostly affected four major cities (Austin, Houston, Dallas/Fort Worth, and San Antonio) of Texas. Multi-state local disasters such as hurricane Harvey impacted both major cities and their satellite cities, suggesting the migration of the workforce to the disaster-areas. The research found that low population cities have been affected by local disasters.
... Nowadays, there is more focus on how companies can optimize their resources and do more with less, a perfect balance between efficiency and customer service. According to [1] logistics are vital because it creates value, for customers and suppliers of the firm, and value for the firm's stakeholders. Value in logistics is expressed in terms of time and place. ...
Article
A distribution company in Mexico covers the travel expenses for 21 sales representatives. Currently, the routes they follow are not established clearly, which can lead to high costs in this subject. A reduction of such cost is sought after, by optimizing the routes for each one of them. The following research finds an improvement on the routes for the sales representative of a distribution company in Mexico. It was done by using the Traveling Salesman Problem with Hotel Selection or Base selections via a Simulated Annealing algorithm. The results show an improvement in a reasonable timeframe by using the Simulated Annealing. It also shows that the maximum process time was of 156.63 minutes, and the least amount of improvement was 24.44% over the current route selection. Applying this model will be beneficial for the company as the company is trying to reduce costs related to the sales representatives such as; fuel cost, hotel cost, and travel expenses.
Article
This study was carried out to perform bibliometric analysis on e-logistics, which could not be found to be examined before in the literature, and create bibliometric networks through visualizations. The VOSviewer (ver. 1.6.17) program was used in the creation and visualization of bibliometric networks. According to the analysis results of this study, most publications in the field of e-logistics were published in 2004, the languages of the publications were mostly English, the country with the most publications was China, the most cited authors were Feng-Cheng Tung, Su-Chao Chang, and Chi-Min Chou, 2008 and according to Web of Science (WoS) categories, the most publications were understood to be in the computer science information systems category. According to the results of the bibliometric analysis using the creation and visualization of bibliometric networks, the most cited author was Ulieru, M. based on the co-citation author analysis, the most cited source was the International Journal of Physical Distribution & Logistics Management based on the co-citation source analysis, the country with the most documents was China based on the bibliographic coupling country analysis, and the most repeated keywords were e-logistics, e-commerce, logistics, supply chain management and reverse logistics based on the keyword co-occurrence analysis.
Book
Full-text available
Preface . We are very pleased to have you participate in the 1st Conference on Quality Innovation and Sustainability – ICQIS2019 in Valença, Viana do Castelo, Portugal. The conference is being organized together with Business Sciences School of the Polytechnic Institute of Viana do Castelo. It takes place in Valença on the 6th and 7th of June 2019. Our main goal is to join together in this event academics and practitioners from a variety of fields and the dissemination of knowledge and the exchange of good practices among in the main domain of quality management, but also in innovative practices and sustainability. We expect to provide a forum of debate for researchers and practitioners, contributing to support the sharing of experiences, to promote cross-knowledge and strengthen the academic-industry relationship. This book compiles the papers presented at the 1st Conference on Quality Innovation and Sustainability (ICQIS2019) The Editors would like to thank all the authors and reviewers for their valuable contribution and for making ICQIS2019 such a big success. Thank you very much for your important participation and collaboration in ICQIS2019!
Article
Purpose – It has become a requirement for businesses to follow the changes occurring both in its inside and outside environment in a competitive environment where there is a transformation from craft production mode to lean manufacturing, from lean manufacturing to agile manufacturing. For this reason, businesses must have a “proactive” structure. As a matter of fact, businesses that carry out their activities by adapting to these changes and successfully manage or execute businessenvironment integration in this process will be able to be successful in the current competition. In this process, particular factors such as customer satisfaction, timely delivery, and delivery speed can be considered as an important factor of performance. For this reason, businesses are required to fulfill the demands from themselves within the framework of the “agile business” approach. In this context, the main purpose of this study is to determine the effect of agile manufacturing on logistics performance. Design/methodology/approach – In the study, firstly, the conceptual framework was created by literature review. Subsequently, in order to determine the causal relationship between the agile manufacturing and logistcs performance, a survey was conducted on businesses operating in the textile and its derivative industries. Statistical Package for Social Science (SPSS) 23.0 was used to analyze the data obtained from the survey. Findings – As a result of the analysis; it has been concluded that agile manufacturing practices and logistics performances of enterprises are found above average. First of all, correlation analysis was performed and the relationship between the agile manufacturing and logistcs performance was determined a positive and moderate (0.553) relationship was found between those variables. As a result of the analysis, agile manufacturing has been found to have a positive effect (0.295) on logistics performance. Discussion – It has been determined that logistics performance can be explained by agile manufacturing. The result of this research is not generalizable. But, it is considered that the study will contribute to the literature as the studies on this subject are limited. When evaluated, it can be suggested that future studies may contribute to the literature performing the study on different sectors or on the same sector but on the country level.
Article
This study, by considering the time-dependent demand (TDD) characteristic, investigates the location-routing problem with time-dependent demands (LRPTDD) as an extension of the location-routing problem (LRP). The demand in each customer site is represented by a constant demand rate over a known production period. In the LRPTDD, the locations are selected, and the routes are constructed to pick up all the demands and minimize the total distance. The picked load depends on the vehicle arrival time at the site; thus, the “time-dependency” characteristic of the LRPTDD is based on the vehicle arrival time. A mixed-integer nonlinear programming (MINLP) formulation is presented. A simulated annealing (SA) algorithm for the LRPTDD is developed. The computational study demonstrates the competitiveness of the proposed SA heuristic against other well-known algorithms for LRPs, and most importantly, its effectiveness for the LRPTDD.
Article
Full-text available
Truck class rates are usually needed in strategic logistical planning, especially for facility location. Using point-to-point transport rates is exact, but for practical reasons it is often necessary to estimate them from distance. Two sources of rate estimating error are examined: (a) the error resulting from the linear approximation of rates from distances; and, (b) the error associated with estimating distances from shipment origin-destination coordinate points. Suggestions are made as to how the rate estimating error may be minimized.
Article
Full-text available
Relatively little attention has been given to methods for estimating and auditing distribution network inventories in the aggregate. This article shows that a simple polynomial equation whose coefficients can be determined by means of regression analysis techniques is a good, basic tool for estimating overall inventory levels and can be used to provide insight into the effectiveness of inventory control policies at different stocking points. It also serves as the basis for auditing and controlling current inventory policies on an ongoing basis. Tests of the model in a number of actual cases revealed a predictable association between the inventory policy being used and the coefficient values in the polynomial equation.
Article
Spatial concepts of system design have outlived their usefulness, says the author of this article. He holds that a more useful framework for future analysis can be based on temporal measures—that it offers the opportunity to develop an analytic approach devoid of crippling assumptions.
Article
Using optimization to design distribution systems became technically feasible a little more than two decades ago, and developments have occurred at a rapid rate ever since. These developments can be understood in terms of six evolutionary processes. Four are core: evolution of algorithms, data development tools, model features and software capabilities, and how companies actually use software for designing distribution systems. The other two are environmental: evolution of logistics as a corporate function and of computer and communications technology.
Article
This article provides an overview of the practical application of modeling to the business logistics network design problem. Various location models are categorized and selected ones are illustrated that represent an example of the class and/or that have been used extensively in practice. Suggestions are made as to how data can be aggregated to facilitate the modeling process. Numerous examples are given as to how and where these location models have been applied.
Although significant advances have been made in customer service research, a majority of this research has concentrated on defining and measuring the importance of customer service in isolation from the other components of the marketing mix. In order to achieve a competitive advantage from customer service, it is necessary to establish service levels as part of the firm's overall marketing strategy. This monograph reviews the development of customer service; evaluates past customer service research; presents a methodology for integrating customer service and marketing strategy, and provides some suggestions for future research.
Dynamic Simulation of Physical Distribution Systems
  • S G Baritz
  • L Zissman
  • D J Bowersox
  • O K Helferich
  • P F Gimour
  • W Morgan
  • E J Marien
  • M L Lawrence
  • R T Rogers
BARITZ, S.G. & ZISSMAN, L.: "Researching Customer Service: The Right Way". Proceedings of The National Council of Physical Distribution Management, vol. II, New Orleans, pp. 608-619, October 25, 1983. BOWERSOX, D.J.; HELFERICH, O.K.; GIMOUR, P.F.; MORGAN Jr. W., MARIEN, E.J.; LAWRENCE, M.L. & ROGERS, R.T.: Dynamic Simulation of Physical Distribution Systems, East Lansing, MI: Division of Research, Graduate School of Business Administration, Michigan State University, 1972; PIPELINE MANAGER by Andersen Consulting.
Physical Distribution Cost and Service
  • W Davis H
  • H Drumm W
DAVIS H.W. & DRUMM W.H.: "Physical Distribution Cost and Service 1995". Annual Conference Proceedings, San Diego: Council of Logistics Management, p. 221, 1995.