Department of Marketing and Supply Chain Management
Michigan State University
Donald J. Bowersox
The John H. McConnell University Professor of Business Administration
Department of Marketing and Supply Chain Management
Michigan State University
and
Roger J. Calantone
The Eli Broad University Professor of Business
Department of Marketing and Supply Chain Management
Michigan State University
Working Paper: Please do not reference without authors’ permission
Contact: Dr. Alexandre M. Rodrigues
Department of Marketing and Supply Chain Management Michigan State University N370 North Business Complex East Lansing, Michigan 48824-1122 Phone: 517-432-5535 x230, FAX: 517-432-1112 Email: [email protected]
Many firms have a significant and growing presence in markets outside their
country of origin. Global operations have substantially increased transportation and
inventory required to accomplish international commerce. Logistics is one of the largest
costs involved in international trade. However, the sizing of global logistic expenditure is
a difficult task.
The objective of this research is to estimate logistic expenditure for the global
economy. An Artificial Neural Network is used to generate national estimates. The
estimation set constitutes 24 countries that represent approximately 75% of the global
Gross Domestic Product. The model utilizes variables that capture economic activity,
transportation activity, income level, country size, and geographic location. Cluster
Analysis and ANOVA are used to validate the neural network estimates.
There are two main contributions from this research. The first is an update of
previous estimates. This study uses 2002 available data and as such represents the most
current estimation of global logistic expenditure. The second is continuous improvement
of the estimation method, including new input variables, better information quality,
mathematical model refinement, and methods of model validation.
1
ESTIMATION OF GLOBAL AND NATIONAL
LOGISTICS EXPENDITURES: 2002 DATA UPDATE
INTRODUCTION
Many firms have a significant and growing presence in markets outside their
country of origin. In fact, global business transactions grew during the 1990s at a rate at
least three times as great as typical domestic economies throughout the world (Bowersox
and Calantone 1998). Global operations have substantially increased transportation and
inventory required to accomplish international commerce.
Global logistics management requires understanding of the relative transportation
efficiencies in different countries. It requires that managers understand the transportation
capabilities and characteristics of primary trading countries. Logistics is one of the largest
costs involved in international trade. However, the measurement of national logistics
expenditures is difficult. The objective of this research is to estimate logistics
expenditures for the global economy. Information regarding logistics expenditures is
relevant to both corporate managers and government administrators. Identification of
transportation inefficiencies is essential to the development and promotion of
infrastructure enhancements to improve logistics performance.
This paper begins with the relevant literature review. Next, the methodology used
for estimation and the related variables are described. The third section contains three
parts: estimated national and global expenditures; model validation based on Cluster
Analysis and ANOVA; and a comparison of trends across different groups and regions.
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The final section concludes this article with the presentation of key findings, limitations,
and potential paths for future research.
LITERATURE REVIEW
Heskett, Glaskowsky, and Ivie (1973) presented the first published research for
logistical cost estimation. The authors developed a methodology for estimating total
logistics cost and applied it to the United States. Their methodology considers total
logistics cost as the sum of four types of commercial activities: Transportation, Inventory,
Warehousing, and Order Processing.
This basic methodology, with adjustments, has been used by Cass Information
Systems and now the Council of Supply Chain Management Professionals (CSCMP) to
estimate annual logistical expenditures in the United States (Wilson 2004). The study
combines data related to three key components to estimate logistics expenditures:
Inventory Carrying Cost, Transportation Cost, and Administrative Cost. The process
includes Warehousing Cost as part of Inventory Carrying Cost.
The challenge in estimating global logistic expenditure, as contrasted to the
United States, is that the data to perform a direct measurement or roll-up summation
methodology are not available. Although the data are available to varying degrees in most
developed nations, they are not available in most other countries. Therefore, a country-
by-country estimation requires the use of available primary and secondary data to project
expenditures. The availability of such data varies extensively by country and regions.
The first study to estimate global logistic expenditure was published by Bowersox
(1992). The author presented an estimation of global logistics costs based on four
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components: Total Gross Domestic Product (GDP), Government Sector Product,
Industrial Sector Product, and Total Trade Ratio.
In a later study, Bowersox and Calantone (1998) refined the estimation method by
introducing an Artificial Neural Network (ANN) model. It expanded the scope of the
previous approach by including infrastructure variables related to cost and information
systems. The model was used to predict individual country logistics expenditures using
1996 data. The study was later updated using 1997 data (Bowersox, Closs, and Stank
1999).
The most recent estimation of logistics expenditures was published by Bowersox,
Calantone, and Rodrigues (2003). The study considered twenty-seven variables that
captured information regarding geographic region, income level, country size, economy
level, and transportation activity. The neural network model was used to estimate national
logistics expenditures using 2000 data.
This article presents an update of the process using 2002 data. In addition, this
research represents continuous improvement in the estimation method, as well as the
inclusion of new input variables, better information quality, mathematical model
refinement, and model validation.
The authors acknowledge the limitations of this research. However, the
importance of sizing the global logistics market to the development of business strategy
and national policy serve to justify a continuous effort to develop and refine the
methodology. The fact that sufficient data do not exist to apply even the crude
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methodology deployed to the critical Middle East and North Africa regions serves to
illustrate the need for continued methodological development.
METHODOLOGY
Artificial Neural Networks
Artificial neural networks are collections of mathematical models that emulate
some of the observed properties of biological nervous systems and draw on the analogies
of adaptive biological learning. An artificial neural network is composed of a large
number of highly interconnected processing elements that are analogous to neurons and
are tied together with weighted connections that are analogous to synapses (Haykin
1999).
The starting point for most networks is a model neuron. This model neural
consists of multiple inputs and a single output. Each input is modified by a weight, which
multiplies with the input value. The neuron will combine these weighted inputs and, with
reference to a threshold value and activation function, use these to estimate an output.
One can use several model neurons and array them to form a layer. A layer either
has all of its inputs connected to a preceding layer or to external inputs. In turn, all
outputs are connected to either a succeeding layer or to external outputs. Next, layers can
be structured one succeeding the other so that there is an input layer, multiple
intermediate layers, and finally an output layer. Intermediate layers, those having no
inputs or outputs to the external world, are called hidden layers.
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Learning in a neural network is called training. The training is conducted by the
utilization of a training set, a group of cases that contain both input variables and known
output variables from the actual experiment under study. From the difference between the
desired response (known outputs) and the actual response (neural network outputs), the
error is determined and is propagated through the network. At each neuron in the
network an error adjustment is made to the weights and threshold values. This is
analogous to fitting parameters in a non-linear regression. This corrective procedure is
called back propagation and it is applied continuously and repetitively as long as the
individual or total errors in the responses exceed a specified level. At this point, the
neural network has completed the training process and can be used to estimate based on
new input data.
This methodology has three major advantages. First, neural networks can build
models when more conventional approaches fail. Because neural networks learn to
recognize patterns in the dataset they can easily model data that are too complex for
traditional approaches. Second, neural networks can generalize. In other words, they can
respond appropriately to patterns that are only somewhat similar to patterns in the
original training data. Finally, neural networks are flexible in changing environments.
Although neural networks may take some time to learn a sudden change, they are
excellent at adapting to dynamic information.
Dataset
Data availability and reliability is one of the challenges to developing country
research. To minimize these problems, and to be able to make reliable comparisons
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across countries, available economic and infrastructure information was used to capture
the relationship between activities and characteristics that generate national logistics
expenditures. The main source of data was the World Development Indicators database
(The World Bank 2004). Other important sources of data were: United Nations
Conference on Trade and Development (UNCTAD) (2004), Organization for Economic
Co-operation and Development (OECD) (2004), EUROSTAT (2003), and USDOT
(2003). These data sources contained information for the year 2002.
The input variables were selected from previous studies (Bowersox and Calantone
1998; Bowersox, Calantone, and Rodrigues 2003). An attempt was made to control not
only for country size, but also for geographic region and income level. Variables that
capture economic activity and freight activity were also included. The output variable
was the ratio between National Logistic Expenditure and Gross Domestic Product (GDP).
Figure 1 summarizes the variables used in this current estimation.
***** Insert Figure 1 about here *****
Training Set and Estimation Set
The utilization of artificial neural networks to estimate requires the definition of a
training set, a group of cases that could be used to train and define a neural network
structure.
The information presented by the annual report developed by Wilson (2004) was
used as part of training information (Training Set A). The report contains the projected
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logistics cost for the United States from 1981 to 2003, measured as a percentage of Gross
Domestic Product (GDP). Information from 1981 to 2001 was used for Training Set A.
In addition to this information, the estimates provided by Bowersox and
Calantone (1998) were used as part of the training set (Training Set B). The study reports
National Logistics Expenditures estimates for 19 countries, using 1996 data. The
estimates published by Bowersox, Closs, and Stank (1999) were also considered as part
of the training set (Training Set C). The study presents estimates for 24 countries, using
1997 available data.
Finally, Training Set D considers the estimation results published by Bowersox,
Calantone, and Rodrigues (2003). The study provides estimates for 24 countries, using
2000 data. The study also presents detailed tables of Training Sets A, B, and C.
Table 1 presents the 24 countries used as the estimation prediction set. This group
of countries was chosen to allow direct comparisons with previous estimates. In addition,
quality and availability of secondary data is higher for these countries. They constitute a
representative subset of countries, accounting for approximately 75% of the global Gross
Domestic Product in 2002. The notable absence is the Middle East and North Africa
regions. As noted earlier, data related to the infrastructure and economic activity of this
important area is not sufficient at this time to meet the minimum requirements of the
estimation model.
***** Insert Table 1 about here *****
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Parameters of the Artificial Neural Network
The Artificial Neural Network utilized six layers: one input layer, four hidden
layers, and one output layer. The number of neurons in each layer was set respectively as:
29, 25, 20, 15, 10, and 1.
The training parameters were set as follows: Learning Rate 0.5, Momentum 0.6,
Training Tolerance 0.05 and Testing Tolerance 0.1. Using the back propagation
algorithm, the Artificial Neural Network presented a Mean Average Percentual Error
(MAPE) difference between the target values and output values of 1.87%. This represents
an improvement in accuracy from the 2.8% MAPE reported in the previous study
(Bowersox, Calantone, and Rodrigues 2003). The fitted Artificial Neural Network was
then used to obtain National Logistics Expenditures as a percentage of Gross Domestic
Product. A summary of the parameters used to train the Artificial Neural Network is
presented on Table 2.
***** Insert Table 2 about here *****
9
GLOBAL AND NATIONAL ESTIMATES
Presentation of Results
Results are presented using the 24 country structure of earlier estimates. Table 3
presents the 2002 estimated national logistics expenditures, as well as a comparative
analysis with the previous 1997 and 2000 estimations. Table 4 includes a comparative
analysis of the logistics expenditures aggregated by geographic region. Using World
Bank definitions, Table 5 presents a comparative analysis of the estimated expenditures
aggregated by income level.
***** Insert Table 3 about here *****
***** Insert Table 4 about here *****
***** Insert Table 5 about here *****
The conservative estimate of global requirement for logistics expenditures is
estimated as US$ 5.1 trillion in 1997 and US$ 6.4 trillion in 2000. The 2002 estimate is
US$ 6.7 trillion. This represents a 32% increase from 1997, and a 5% increase from
2000. The 2002 estimate represents 13.8% of the world Gross Domestic Product.
Validation of Results
The set of logistics expenditures presented in Table 3 are estimates based on past
patterns of behavior. A logical objection to any estimate is the veracity of such macro-
estimates. To attempt to tie the logic of how a country’s particular and unique economic
10
development and trade patterns devolve into particular levels of logistics expenditures, an
analysis of relationships was performed between groupings of countries. These groupings
are based on economic activity, trade volume patterns, and geographic location. These
groupings were then tested to identify differences in logistics expenditures measured both
in absolute dollars and in percentage of GDP.
The first step to understand the relationships between input variables and the
estimated expenditures is through a two-step cluster analysis. The two-step cluster
analysis is an exploratory tool designed to reveal natural groupings (or clusters) within a
data set that would otherwise not be apparent. This technique was used to provide a
grouping based on economic activity, trade volume patterns, and geographic location as a
basis for differential economic activity at a macro level. These variables represent a
snapshot of trade activity both within and between all the countries. Since this approach
groups countries on activities that create the need for logistics services, rather than on the
logistics services themselves, an independent check of ranges of logistics costs can be
validated. Using the cluster analysis classification, the logistics expenditures are
exogenous to the grouping procedure. Therefore, the validation test is: Does the grouping
of countries provided by the 2-step cluster analysis significantly differ from logistics
expenditures estimates?
All the training sets and the estimation set were used in this analysis, for a total of
108 cases. These cases represent the country set presented in Table 1 across different
time periods.
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Three different clusters were obtained from this validation procedure (Table 6).
Figure 2 and Figure 3 present the resulting clusters by geographic location and income
level. The procedure allocated the United States as the sole country in the first cluster.
The second cluster is composed by developed countries in the Pacific Rim, developing
countries in South America, and Mexico. The last group is composed by European
countries and Canada.
***** Insert Table 6 about here *****
***** Insert Figure 2 about here *****
***** Insert Figure 3 about here *****
These three groups were then used to determine if the logistics expenditure
estimates were significantly different. A one-way ANOVA procedure was used. Results
of the comparison tests between the groups are presented on Table 7. The three groups
significantly differ at the p<0.05 level in their logistics expenditures, measured as both
percentage of GDP and total US$. Therefore, the validation test is confirmed: the
grouping of countries provided by the 2-step cluster analysis significantly differ on
logistics expenditures.
***** Insert Table 7 about here *****
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Discussion of Results
An interesting analysis is a comparison of trends across different geographic
regions (Table 4) and income levels (Table 5). Whereas logistics efficiency has increased
in developed nations, this is not true in the balance of the world.
One possible explanation is that globalization has created greater operational
pressures in developing markets. During the 90s, the developed countries experienced
output growth of 2.4%, while developing nations recorded an average increase of 4.1%
(United Nations Conference on Trade and Development (UNCTAD) 2004). Decreases in
logistics efficiency in developing nations are potentially due to the fact that their current
logistics infrastructure is not adequate to support this higher growth rate.
Other possible explanation is the characteristics of products transported in each
country. Transportation cost efficiencies are substantially affected by the value and
density of products moved. Density is a combination of weight and volume. Typically,
vehicles are constrained more by cubic capacity than by weight. While in developed
nations the majority of freight activity is related to products with high value and low
density, in developing nations the majority is related to products with low value and high
density.
CONCLUSION
This paper reports the most recent effort to quantify the size of logistics
expenditures in a global economy. Using secondary data and neural network
methodology, selecting variables according to previous frameworks, the magnitude of
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global logistics cost and trends were estimated. Results estimate that global logistics
expenditures represent 13.8% of the world Gross Domestic Product for the year of 2002.
An interesting result is a comparison of trends across different groups and
regions. Whereas logistics efficiency has increased in developed regions, this is not true
in the balance of the world. These results highlight the necessity for logistics
infrastructure investment and efficiency improvements throughout developing nations.
Any macro estimation study such as this must come with some caveats. The input
data accuracy is the best that can be achieved using available data from the World Bank,
the United Nations, and similar organizations. Nevertheless, they are not perfect. The
number of steps between the original collected data and these compendia vary widely and
are subject to political intrigue in several areas. That means that absolute values for
expenditures, provided in current US$, may be biased in unknown ways. Where
appropriate, the relative measures (% GDP) are more reliable.
There are three potential paths for future research. The first is to expand the
estimation set by including additional countries, particularly in important regions such as
North Africa and Middle East. This would enhance model generalizability and provide
comparisons across additional geographic regions. The second is to improve model
estimation by adding relevant variables. For example, investments in infrastructure areas
such as telecommunications and transportation can be incorporated in the model. The
third is to further understand the knowledge acquired by the neural network model.
Auxiliary methodologies such as Decision Tree and Neural Networks Rule Extraction
can be used to assist in the difficult interpretation of the fitted neural network. These
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techniques can generate rules with approximately the same predictive power as the neural
network itself and may provide improved interpretations of the relationship between
input variables and estimated results.
NOTES
Bowersox, Donald J., "Framing Global Logistics Requirements," in Proceedings of the Council of Logistics Management. (Cincinnati, OH: Council of Logistics Management, 1992), pp. 267-277.
Bowersox, Donald J. and Roger J. Calantone (1998), "Executive insights: Global logistics," Journal of International Marketing, Vol. 6, No. 4, pp. 83-93.
Bowersox, Donald J., Roger J. Calantone, and Alexandre M. Rodrigues (2003), "Estimation of Global Logistics Expenditures Using Neural Networks," Journal of Business Logistics, Vol. 24, No. 2, pp. 21-36.
Bowersox, Donald J., David J. Closs, and Theodore P. Stank, "Sizing Global Logistics Expenditures," in 21st century logistics: making supply chain integration a reality. (Oak Brook, IL: Council of Logistics Management, 1999), pp. 211-214.
EUROSTAT, European Union Energy & Transport in Figures (Brussels, Belgium: Directorate-General for Energy and Transport, 2003).
Haykin, Simon S., Neural Networks: A Comprehensive Foundation (Upper Saddle River, N.J.: Prentice Hall, 1999), pp. xxi, 842.
Heskett, James L., Nicholas A. Glaskowsky, and Robert M. Ivie, Business Logistics - Physical Distribution and Materials Management (New York: Ronald Press Co., 1973), pp. xii, 789.
Organization for Economic Co-operation and Development (OECD), OECD in Figures (Paris: OECD Publications, 2004).
The World Bank, World development indicators (Washington, D.C.: The World Bank, 2004), pp. v.
United Nations Conference on Trade and Development (UNCTAD), Review of Maritime Transportation (New York, N.Y.: United Nations Publications, 2004).
USDOT, U.S. Department of Transportation, Transportation Statistics Annual Report (Washington, DC: Bureau of Transportation Statistics, 2003).
Wilson, Rosalyn, 15th Annual State of Logistics Report: Globalization (Washington, DC: Cass Information Systems, 2004).
15
FIGURE 1
VARIABLES USED IN THE STUDY
Geographic Region (8) Economy (9)- South America - GDP, PPP (current international $)- Central America - Inflation, consumer prices (annual %)- North America - Unemployment, total (% of total labor force)- Asia - Imports ($)- Europe and Central Asia - Exports ($)- Middle East and North Africa - Trade Openness=(Imports+Exports)/GDP- Sub-Saharan Africa - Agriculture, value added (% of GDP)- USA Flag - Industry, value added (% of GDP)
Country Size (3)- Area (km2)- Urban population- Coastline (km)
INPUT VARIABLES
OUTPUTVARIABLE
Logistics Expenditures(% of GDP)
Geographic Region (8) Economy (9)- South America - GDP, PPP (current international $)- Central America - Inflation, consumer prices (annual %)- North America - Unemployment, total (% of total labor force)- Asia - Imports ($)- Europe and Central Asia - Exports ($)- Middle East and North Africa - Trade Openness=(Imports+Exports)/GDP- Sub-Saharan Africa - Agriculture, value added (% of GDP)- USA Flag - Industry, value added (% of GDP)
Country Size (3)- Area (km2)- Urban population- Coastline (km)
INPUT VARIABLES
Geographic Region (8) Economy (9)- South America - GDP, PPP (current international $)- Central America - Inflation, consumer prices (annual %)- North America - Unemployment, total (% of total labor force)- Asia - Imports ($)- Europe and Central Asia - Exports ($)- Middle East and North Africa - Trade Openness=(Imports+Exports)/GDP- Sub-Saharan Africa - Agriculture, value added (% of GDP)- USA Flag - Industry, value added (% of GDP)
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