The Productivity Effects of Truck Size and Weight Policies
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ORNL- 6840
FINAL REPORT
THE PRODUCTIVITY EFFECTS OF TRUCK SIZE AND WEIGHT POLICIES
David P. Middendorf
Michael S. Bronzini
Center for Transportation Analysis
Energy Division
Oak Ridge National Laboratory
Oak Ridge, Tennessee 37831
November 1994
Managed by
Martin Marietta Energy Systems, Inc.
for the
Department of Energy
under Contract No. DE-AC05-850R21400
Prepared for
Federal Highway Administration
U.S. Department of Transportation
Washington, D.C. 2059
TABLE OF CONTENTS
Chapter Page
EXECUTIVE SUMMARY xv
1. INTRODUCTION 1
BACKGROUND 1
RESEARCH OBJECTIVES 1
ORGANIZATION OF THE REPORT 2
2. DATA COLLECTION AND LOGISTICS COST ANALYSIS METHODOLOGY 5
INTRODUCTION 5
SHIPPER SURVEY 5
Information Collected 5
Data Collection Methodology 7
Sample Size and Characteristics 10
FREIGHT TRANSPORTATION ANALYZER 13
3. EFFECTS OF LCV USAGE ON THE LOGISTICS COSTS OF TRUCK
SHIPPERS 19
INTRODUCTION 19
OVERALL EFFECTS 19
INFLUENCE OF VARIOUS COST, PRODUCT,AND LANE VARIABLES 22
Ratio of Freight to Inventory Costs 22
Product Value 25
Annual Lane Volume 26
Lane Distance 29
Annual Lane Ton-Mileage 29
Product Value and Annual Lane Volume 31
Product Value and Annual Lane Ton-Mileage 37
INTRAMODAL DIVERSION TO LCVs 38
4. EFFECTS OF LCV USAGE ON THE LOGISTICS COSTS OF RAIL AND
INTERMODAL SHIPPERS 45
INTRODUCTION 45
RAIL BOXCAR SHIPPERS 45
INTERMODAL SHIPPERS 51
5. ESTIMATION OF NATIONWIDE LCV USAGE 59
INTRODUCTION 59
INTRAMODAL DIVERSION MODEL 59
ESTIMATION PROCEDURE 60
iii
TABLE OF CONTENTS (Continued)
Chapter PAGE
5. ESTIMATION OF NATIONWIDE LCV USAGE (continued)
ESTIMATED INTRAMODAL DIVERSION 66
COMPARISON WITH OTHER ESTIMATES 71
6. CONCLUSIONS 75
REFERENCES 79
iv
LIST OF FIGURES
Figure No. Page
1 Percent reduction in total logistics cost using 23
LCVs under low GVW limits as a function of freight
to inventory cost ratio for single trailers
2 Percent reduction in total logistics cost using 24
LCVs under high GVW limits as a function of freight
to inventory cost ratio for single trailers
3 Rail freight rate versus percent cost reduction 47
using Rocky Mountain doubles under existing
GVW limits
4. Rail freight rate versus percent cost reduction 48
using Rocky Mountain doubles under higher GVW limits
5. Rail freight rate versus percent cost reduction 49
using turnpike doubles under existing GVW limits
6. Rail freight rate versus percent cost reduction using 50
turnpike doubles under higher GVW limits
7. Intermodal freight rate versus percent cost reduction 54
using Rocky Mountain doubles under existing GVW limits
8. Intermodal freight rate versus percent cost reduction 55
using Rocky Mountain doubles under higher GVW limits
9. Intermodal freight rate versus percent cost reduction 56
using turnpike doubles under existing GVW limits
10. Intermodal freight rate versus percent cost 57
reduction using turnpike doubles under higher
GVW limits
v
vi
LIST OF TABLES
Table No. Page
1. Distribution of traffic lane observations in the shipper 11
survey by type of product
2. Distribution of traffic lane observations in the shipper 12
survey by mode of transportation and type of equipment
3. Weight capacity of longer combination vehicles under two 14
gross vehicle weight (GVW) limit scenarios
4. Cubic capacity of longer combination vehicles compared 15
to single trailers of various sizes
5. LCV rate adjustment factors based on a comparison of 16
operating costs per loaded mile
6. Breakdown of FTA observations by current principal mode 17
7. Overall effect of LCVs on the total logistics cost of 20
single trailer truck shippers by type of LCV, GVW
limits, and relative LCV rates
8. Truck configuration resulting in the lowest total 21
logistics cost by GVW limits and relative LCV rates
9. Correlation between product value and the percent 26
reduction in total logistics cost resulting from
LCV usage
10. Correlation between percent cost reduction from using 27
LCVs and the composite variable formed by multiplying
product value by the inventory carrying cost expressed
as a percentage of inventory value
11. Average percent reduction in total logistics cost 28
from use of LCVs for different levels of annual lane
volume
12. Correlation between annual lane volume and the percent 29
reduction in total logistics cost resulting from LCV
usage
13. Average percent reduction in total logistics cost 30
from use of LCVs for different traffic lane distances
vii
LIST OF TABLES (continued)
Table No. PAGE
14. Average percent reduction in total logistics cost from 31
use of LCVs for different levels of annual lane
ton-mileage
15. Correlation between annual lane ton-mileage and the 32
percent reduction in total logistics cost resulting
from LCV usage
16. Combined effect of annual lane volume and product value 33
on total logistics cost using Rocky Mountain doubles
under existing GVW limits
17. Combined effect of annual lane volume and product value 34
on total logistics cost using Rocky Mountain doubles
under higher GVW limits
18. Combined effect of annual lane volume and product value 35
on total logistics cost using turnpike doubles under
existing GVW limits
19. Combined effect of annual lane volume and product value 36
on total logistics cost using turnpike doubles under
higher GVW limits
20. Combined effect of annual lane ton-mileage and product 39
value on total logistics cost using Rocky Mountain
doubles under existing GVW limits
21. Combined effect of annual lane ton-mileage and product 40
value on total logistics cost using Rocky Mountain
doubles under higher GVW limits
22. Combined effect of annual lane ton-mileage and product 41
value on total logistics cost using turnpike doubles
under existing GVW limits
23. Combined effect of annual lane ton-mileage and product 42
value on total logistics cost using turnpike doubles
under higher GVW limits
24. Percent of FTA cases and ton-mileage assumed to divert 43
to LCVs under different GVW limits, cost savings
thresholds, and relative LCV freight rates
25. Overall effect of LCVs on the total logistics cost of 46
rail boxcar shippers
26. Transportation mode with the lowest total logistics 46
cost for rail boxcar shippers
viii
LIST OF TABLES (continued)
Table No. Page
27. Correlation between rail freight charge per mile 51
and percent reduction in total logistics cost from
switching to LCVs
28. Overall effect of LCVs on the total logistics cost 52
of intermodal shippers
29. Transportation mode with the lowest total logistics 53
cost for intermodal shippers
30. Correlation between intermodal freight charge per 53
mile and percent reduction in total logistics cost
using LCVs
31. Percent of annual traffic lane ton-mileage that 61
would divert to LCVs
32. Definitions of relevant data items selected from 1987 63
TIUS public use records
33. Estimated truck vehicle-miles (in millions) diverting 67
to LCVs under existing GVW limits with LCV freight
rates same as current single trailer truckload rates
34. Estimated truck vehicle-miles (in millions) diverting 68
to LCVs under existing GVW limits with LCV freight
rates higher than current single trailer truckload rates
35. Estimated truck vehicle-miles (in millions) diverting 69
to LCVs under higher GVW limits with LCV freight
rates same as current single trailer truckload rates
36. Estimated truck vehicle-miles (in millions) diverting 70
to LCVs under higher GVW limits with LCV freight
rates higher than current single trailer truckload rates
37. Estimated billions of truck miles diverting to Turner 73
prototypes in the intercity dry van truckload
market section.
ix
x
LIST OF ABBREVIATIONS AND SYMBOLS
AASHTO American Association of State Highway and
Transportation Officials
CFS Commodity Flow Survey
cwt hundredweight (hundreds of pounds)
EOQ Economic Order Quantity
FAK Freight All Kinds
FTA Freight Transportation Analyzer computer program
GVW Gross Vehicle Weight
LCV Longer Combination Vehicle
LTL Less Than Truckload
SIC Standard Industrial Classification
TIUS Truck Inventory and Use Survey
TOFC/COFC Trailer-on-flatcar/container-on-flatcar
TRB Transportation Research Board
xi
xii
ACKNOWLEDGEMENTS
The authors are grateful to the Center for Logistics Research
at The Pennsylvania State University, which conducted the
shipper survey and ran the Freight Transportation Analyzer (FTA)
computerprogram. The results of the survey and the FTA program
provided the data for the research described in this report. In
particular, the authors would like to thank Mr. Paul Poissant
and Ms. Rosemarie Greaser for compiling and editing the data
from the shipper survey and the output from the FTA; Dr. Gary L.
Gittings, Assistant Professor of Business Logistics, for
providing technical assistance and a description of the survey
methodology; and Dr. Alan J. Stenger, Associate Director of the
Center for Logistics Research, for granting permission to use
his FTA model for this study.
The authors are also grateful to the Federal Highway
Administration, which sponsored the research described in this
report, and in particular, Mr. Jake Jacoby for his valuable
technical guidance.
xiii
EXECUTIVE SUMMARY
While previous studies have indicated that increases in truck
size and weight limits could improve motor carrier productivity,
the question of whether or not freight shippers will also
benefit has not been adequately addressed. It is generally
assumed that competitive conditions in the motor carrier
industry will result in cost savings being passed to shippers in
the form of lower freight rates. Transportation costs, however,
are only one component of shipper total logistics cost.
Warehousing cost, inventory holding cost, order processing
cost, and other categories of business logistics cost may also
change as a result of the less frequent but larger shipments
typically associated with the use of longer combination
vehicles (LCVs). If switching from single trailer truckload
shipments to LCVs causes shipper non-transport logistics costs
to increase more than the savings available from lower freight
rates, then productivity gains may be lost to the firm and the
economy as a whole. This research was undertaken to determine
the net effect of truck size and weight policy changes on
shipper total logistics cost and how these effects might
influence the demand for alternative tractor-trailer
configurations.
One of the more difficult tasks in this study was the
collection of logistics cost data on which to perform the
analysis. Original data of a highly confidential nature was
required to fulfill the study's objectives. Firms are naturally
reluctant to divulge sensitive data which might compromise their
competitive position. Many of the firms contacted in this study
were willing to provide freight flow data for their principal
products; however, even when assured of confidentiality, they
frequently remained either unwilling or unable to specify
critical logistics costs such as order processing cost and
inventory carrying cost. Some of the contacted firms also
lacked the sophisticated logistics management systems necessary
to respond fully to the detailed questions that were asked. As
a result, this research is based on a limited sample of 297
product-specific traffic lane (origin-destination) movements
obtained from a total of 72 companies.
The data on product characteristics, lane volumes,
transportation cost, and other logistics costs gathered in the
shipper survey were entered into a computer program called the
Freight Transportation Analyzer (FTA). Developed by Dr. Alan J.
Stenger of the Pennsylvania State University Department of
Business Logistics, the FTA implements a deterministic economic
order quantity (EOQ) model adapted to incorporate transportation
costs. For each lane observation in the survey dataset, the
FTA calculated the shipper's annual freight, order, and
inventory carrying costs for the shipper's current mode of
transport as well as for two types of LCVs: the Rocky Mountain
double combination consisting of a tractor pulling a 48-ft
(14.6-m) trailer followed by a 28-ft (8.5-m) trailer and the
turnpike double combination which is a tractor pulling two
48-ft (14.6-m) trailers.
A major finding of the study is that, in most cases, use of
LCVs would have a significant favorable impact on the annual
total logistics cost of truckload shippers. Savings in annual
total logistics cost as high as 59 percent for turnpike doubles
and 52 percent for
xv
Rocky Mountain doubles were observed. Average savings depend
on the type of LCV used, gross vehicle weight (GVW) limits, and
the difference between the rates charged by motor carriers for
LCV and single trailer transport. For example, the study
predicted average savings of 32 percent for shippers switching
to turnpike doubles operating at 131,000-lb (59,422-kg) GVW
limits under LCV freight rates that are the same as current
single trailer freight rates. The average savings drop to 21
percent when turnpike double freight charges are 30 percent
higher than current single trailer freight rates.
The greater the flow of a company's product in the lane and
the longer the lane, the more likely it is that a shipper would
benefit from using some type of LCV. When annual lane volumes
are below 5000 cwt (226,795 kg), LCVs tend to increase the
shipper's annual total logistics cost. When annual lane volumes
exceed 25,000 cwt (1,133,975 kg), however, LCVs nearly always
produce significant cost savings.
An excellent indicator of whether or not a truckload shipper
would benefit from switching to LCVs is the ratio of the
shipper's current annual single trailer freight costs to annual
inventory carrying costs. The research indicates that, when
single trailer freight costs are two or more times greater than
the inventory carrying costs, switching from single trailers to
LCVs will in all likelihood greatly reduce the shipper's annual
total logistics cost. On the other hand, when inventory carrying
costs are roughly the same as or greater than the single
trailer freight costs, the chances are good that switching from
single trailers to LCVs will increase the shipper's annual total
logistics cost.
No single variable or combination of variables among the ones
considered in this study appears to be highly effective at
predicting how much or to what degree an individual shipper's
annual total logistics cost would change as a result of
switching to some type of LCV. The influence of product value,
in particular, is much smaller than is commonly expected.
Product value is significant only when annual traffic lane
volumes fall below 15,000 cwt (680,385 kg) or 350,000 ton-mi
(510,650 metric ton-km). Only at low annual shipment volumes do
higher product values significantly increase the chances that
LCV use will increase the shipper's total logistics cost. Other
factors such as annual lane volume and lane distance are good
indicators of whether or not a shipper would benefit from using
LCVs, but they are not highly significant estimators of the
amount that would be saved or lost. Further research with more
detailed shipper data will be needed to produce better logistics
cost models for alternative truck sizes and weights.
Given the significant potential benefits of LCVs to truckload
shippers as indicated by the analysis, the next question is to
what extent would shippers nationwide switch from using single
trailers to some type of LCV. Using data from the 1987 Truck
Inventory and Use Survey (TIUS), an attempt was made to answer
this question. The resulting estimates of nationwide intramodal
diversion to LCVs vary depending on GVW limits, the difference
between LCV and single trailer freight rates, and the minimum
percent logistic cost savings necessary before shippers would
switch to LCVs. For example, given weight limits of 115,000 lb
(52,164 kg) for Rocky Mountain doubles and 131,000 lb (59,422
kg) for turnpike
xvi
doubles and LCV freight rates equal to current single trailer
freight rates, the estimated nationwide intramodal diversion to
LCVs range from 28 percent to 36 percent for weight-limited or
high density freight and from 38 percent to 51 percent for
cube-limited or low density freight. The magnitude of this
diversion is estimated to be 1.1 billion to 1.5 billion truck-
mi (1.8 billion to 2.4 billion truck-km) for high density
freight and 1.S billion to 2.1 billion truck-mi (2.4 billion to
3.4 billion truck-km) for low density freight out of an
estimated base of 4.1 billion truck-mi (6.5 billion truck-km) in
1987. The base figure represents the number of miles operated by
truck tractors pulling loaded single enclosed dry van semi-
trailers with at least two axles on trips at least 200 mi (322
km) long. More research with better data and more
robust logistics cost models is needed to determine whether this
much diversion would actually occur and what the cumulative
nationwide impact on shippers' total logistics cost would be.
Because of the small number of rail boxcar and intermodal
observations in the shipper survey data, it was not possible to
estimate the amount of diversion that might occur from rail to
LCVs. The research indicates, however, that turnpike doubles
operating under higher than existing GVW limits could reduce
shippers' annual total logistics cost enough to induce some
shippers to switch from rail boxcars and intermodal to LCVs.
Additional research is needed to determine how much rail boxcar
and truck-rail intermodal freight might be diverted.
xvii
1. INTRODUCTION
BACKGROUND
Allowable limits on truck size and weight has been a recurring
issue at both the State and Federal level since the earliest
days of motor carriers and public roadbuilding. Lately the issue
has been raised once again. The Surface Transportation
Assistance Act of 1982 increased the allowable width and length
of tractor-trailer combinations and permitted the use of double
trailer combinations on Interstates and designated Federal aid
primary highways. The Surface Transportation and Uniform
Relocation Assistance Act of 1987 provided for studies to be
undertaken that would investigate the effects of even longer and
heavier tractor-trailer configurations. The American Association
of State Highway and Transportation Officials (AASHTO) also
requested a study of new approaches to regulating truck size and
eight. The resulting studies examined the effects of longer
combination vehicles (LCVs) on motor carrier productivity,
highway safety, highway capital and maintenance costs, and the
transportation costs of shippers and receivers.(1,2,3) Although
some of these studies have considered the effects of LCVs on
shippers' transportation costs, most have not explicitly looked
at each component of a shipper's total logistics cost and how it
might affect the demand for l CV service.
Recent preliminary research provided some initial insight
into the relationship between a shipper's logistics costs and
the economic advantages of LCVs.(4) The study revealed that the
net benefits of larger truck sizes and weights to shippers were
sensitive to product value, length of haul, and lane volume. In
particular, the study found that the user's inventory carrying
costs comprised an increasingly larger proportion of the total
logistics cost as product value increased and as shipping
distance and lane volume decreased. The results indicated that
certain combinations of product value, shipment distance, and
annual lane volume can increase inventory carrying costs to the
point where they offset the transportation cost savings offered
by longer combination vehicles.
The results of the above study were based on data from a
small sample of shippers and covered only a few types of
products. A need existed to extend this firm-level research to a
broader range of industries, product values and densities,
shipment distances, and annual volumes. There also existed a
need to develop, test, and implement models to scale the
research findings from the level of the individual shipper or
firm to the national level so as to provide nationwide estimates
of the effects of Federal truck size and weight policy options.
The research described in this report was undertaken to satisfy
these needs.
RESEARCH OBJECTIVES
The main objectives of this research were the following:
1
. Analyze the effects of Federal truck size and weight
policy options on shippers'component logistics costs.
. Estimate the nationwide diversion of freight from
conventional, single trailer truck configurations and
from other modes to LCVs, based on the results of the
above analysis.
The study attempted to complement the knowledge gained from
previous studies by looking explicitly at how different truck
sizes and weights can affect the component logistics costs of
individual shippers. The intent was to acquire a broader
perspective on the productivity gains and economic
implications of changes in Federal truck size and weight
policy.
ORGANIZATION OF THE REPORT
The research findings were based on a mail and telephone
survey of shippers and a mathematical model for computing a
firm's logistics costs. Chapter 2 discusses what information
was collected from shippers and how the sample was obtained.
It also describes the Freight Transportation Analyzer (FTA), a
computer program which implements the model for calculating
logistics costs. Chapter 2 also contains statistics on the
size and characteristics of the sample.
Chapter 3 presents the results of the FTA model for
those companies in the survey sample who shipped their
products in single semi-trailers of various sizes. It
describes the overall effects of LCVs on the total logistics
cost of these shippers as well as the influence of such
factors as product value, shipment distance, annual lane
volume, and combinations of these factors. It also provides
estimates of LCV diversion rates under various situations.
Chapter 4 covers the results of the FTA model for
companies in the survey sample that shipped via rail boxcar or
trailer-on-flatcar (TOFC) intermodal service. Because of the
much smaller sample sizes involved, the discussion is not as
extensive as that of Chapter 3. Nevertheless, Chapter 4
discusses the overall effects of LCVs on the logistics costs
of rail and intermodal shippers as well as the influence of
rail and intermodal freight rates on intermodal diversion to
LCVs.
In Chapter 5, the results of the firm-level analysis are
applied to data from the 1987 Truck Inventory and Use Survey
(TIUS) to estimate the nationwide diversion of truck traffic
to LCVs. Chapter 5 describes the estimation methodology and
compares the resulting estimates with those from a previous
study.
Chapter 6 summarizes the major findings and limitations of
the research.
Throughout this report the terms "rate", "freight rate", and
"freight charge" are used interchangeably. They refer to a
carrier's price for shipping a certain quantity of cargo
2
between an origin and a destination. In practice this price
could be quoted per unit, per hundredweight (cwt), or per mile.
In this report, unless noted otherwise, carrier rates or charges
are specified in dollars per mile for a truckload or a
boxcarload shipment. The terms "transportation cost" and
"freight cost" refer to the total amount that a shipper pays for
a shipment between an origin and a destination. It is equal to
the carrier's rate per mile times the shipment distance or the
carrier's rate per hundredweight times the weight of the
shipment. A shipper's annual freight cost is equal to the annual
number of shipments times the carrier's freight charge per
shipment.
3
2. DATA COLLECTION AND LOGISTICS COST ANALYSIS METHODOLOGY
INTRODUCTION
The firm-level analysis was based on data from a mail
and telephone survey of companies in selected industries. The
information provided by the surveyed companies was then
entered into a computer program called the Freight
Transportation Analyzer (FTA) to calculate component logistics
costs for alternative types of longer combination vehicles as
well as for the shipper's current primary mode of
transportation.
This chapter addresses the survey methodology and the
FTA. It identifies the kinds of information requested from the
surveyed companies, describes how the sample of shippers was
obtained, and presents statistics on the size and
characteristics of the resulting sample. The discussion then
turns to the FTA, including a description of its input and
output. The chapter concludes with a description of the size,
structure, and characteristics of the resulting FTA datasets.
SHIPPER SURVEY
Information Collected
The survey was designed to collect product information,
freight flow data, and logistics cost information from each
company.
Shippers were first asked to select and describe two of
their company's primary products or product groups. The
following data were requested for each principal product:
. Standard Industrial Classification (SIC) code or a
written description of the product.
. Unit weight of the product in pounds where a unit
represented the wholesale or shipping package such as a
case, sack, bale, or tote.
. Cost of the product per pound or unit.
. Whether the product would cube-out or weigh-out; that
is, whether the product would use up the cubic capacity
of a 48-ft (14.6-m) long, 102-in (2.6-m) wide semi-
trailer before reaching the maximum weight limit or
whether it would reach the weight limit before filling
up the cargo space of such a trailer.
5
The surveyed companies were then asked to select a
representative short-distance (less than 500 mi [805 km]),
medium-distance (500 to 1,000 mi [805 to 1,609 km]), and long-
distance (over 1,000 mi [1,609 km]) lane or origin-destination
pair for each of the two principal products and to provide the
following freight flow data for each combination of lane and
product:
. Origin city.
. Destination city.
. Lane distance in miles.
. Annual volume in pounds or number of shipments.
. Primary mode or type of carrier.
. Freight rate.
. Typical size of trailer, container, or rail boxcar used.
. Average weight per shipment.
In selecting representative lanes and freight flows, shippers
were asked to observe the following guidelines:
. Consider only outbound movements where the shipper
controlled the volume and route. such as shipments between
a plant and a distribution center.
. Include only direct movements, not movements involving
stop-offs or special handling.
. Include only movements in trailers, intermodal containers,
or rail boxcars.
. Exclude shipments involving specialized equipment such
as bulk hoppers or tankers.
. Include only full-sized shipments, defined as having one
or more of the following characteristics:
. At least 14,000 pounds (6,350 kg).
. At least 18 pallets or slipsheets.
. At least 50 percent of the cubic capacity of a 40-ft
(12.2-m) long trailer or container or a rail boxcar.
6
Finally, shippers were asked to specify their company's
average cost of processing an order or shipment and their
company's inventory carrying cost. The latter was reported as
a percentage of inventory value. Order costs involve the cost
of handling the paperwork or electronic data interchange for
an order or a shipment. Inventory carrying costs typically
cover the cost of capital, warehousing, taxes, insurance,
depreciation, and obsolescence. These two cost categories,
along with transportation costs, form the primary components
of shippers’total logistics cost.
Data Collection Methodology
The nature of the data to be collected posed some
challenging problems in devising an effective data collection
method. First, cost information is closely guarded in most
firms. Some companies go so far as to impose corporate
policies against disseminating any sensitive cost information.
Second, four types of cost information were required: product
costs, inventory carrying costs, order costs, and freight
transportation costs (as well as traffic volume data).
Information on all four of these cost categories is not
necessarily maintained or controlled by one individual or even
one functional unit within an organization. To provide all of
the requested information, a company contact would in many
cases have to coordinate with other functional groups, greatly
increasing the likelihood of either an incomplete response or
no response at all.
While a broad mix of products, company sizes, and
shipment volumes was desirable, including all possible
products and shipping patterns in the sample would have
greatly increased the time and cost of running the FTA program
and analyzing the results. Therefore, an effort was made to
target the survey at companies matching the following profile:
. Ship in truckload or carload quantities.
. Use dry transportation equipment such as dry vans,
containers, or boxcars instead of flatbeds, tankers,
reefers, or bulk hoppers.
. Make direct shipments with little or no special handling
requirements.
Because of these and other obstacles, three separate
data collection efforts were eventually required to obtain an
adequate sample size. Each effort employed a different
sampling frame.
Phase One
The initial effort involved a mail and telephone survey,
using as the sampling frame the 1992 edition of The Official
Directory of Industrial & Commercial Traffic Executives
7
(commonly referred to as the Bluebook). Companies were randomly
selected. To increase efficiency and ensure broad geographic
coverage, samples were drawn from five base business regions:
. East: eastern New York, northern New Jersey, and
southern Connecticut.
. South: Georgia and southern South Carolina.
. Midwest: northeastern Illinois, southern Wisconsin,
northwestern Michigan, and northern Indiana.
. Southwest: Texas.
. West: southern California.
The sample was further stratified by two-digit SIC code to
ensure a broad mix of basic commodities.
The randomly chosen companies were contacted by
telephone and questioned about their current shipment patterns
and their willingness to participate in the research.
Approximately 3,700 phone calls were made in the effort to
screen potential participants. Eligible companies received a
five-page questionnaire along with a cover letter and a
postage-paid, self-addressed return envelope. Follow-up phone
calls were made when necessary two to three weeks after the
initial contact to answer questions and improve the response
rate.
The results of the initial data collection effort were
as follows:
. 731 randomly selected companies were contacted
and screened for eligibility.
. 251 companies were sent a questionnaire.
. 11 companies completed the survey satisfactorily,
resulting in a response rate of only 4.38 percent.
Several factors contributed to the extremely low
response rate. A major factor was the complexity of the
information being requested, especially the logistics cost
information. Many companies were able to provide freight flow
data but were either unwilling or unable to specify their
order processing and inventory carrying costs. Many of the
small firms lacked the sophisticated logistics management
necessary to interpret and respond properly to the detailed
questions about logistics. Use of a general business directory
for the sampling frame was another factor. As much as 20 to 30
percent of the information in such directories may be
outdated. In addition, rules for inclusion in general business
directories can be very broad, whereas the rules for
eligibility for the survey were quite restrictive.
8
Consequently, many of the randomly selected firms were
ineligible for the survey because they generally shipped in
less-than-truckload (LTL) quantities. A third factor was the
Commodity Flow Survey (CFS) being conducted at the same time
by the U.S. Bureau of the Census. Many of the contacted firms
were heavily involved in providing shipment data for the CFS
and, consequently, were unable to take the time required to
respond to this survey.
Phase Two
Because the survey was conducted by the Center for
Logistics Research at The Pennsylvania State University, a
directory of alumni with degrees in business logistics was
readily available. This sampling frame was therefore used in
the second data collection effort. Concentrating on business
logistics alumni offered the following potential advantages:
. Penn State's large business logistics network allowed
access to a wide number and variety of firms.
. Contacting individuals with advanced logistics training should greatly improve comprehension of the survey questionnaire.
. Loyalty to one's alma mater should increase the
willingness to respond.
. The information in the alumni directory was of
higher quality than that in many standard lists and
directories.
As in the first data collection effort, alumni at
prospective firms were contacted by telephone and questioned
about their company's current shipment patterns and
willingness to participate in the research. Eligible companies
then received the survey questionnaire along with an
introductory letter. Follow-up phone calls were made two to
three weeks after the initial contact to answer questions and
solicit cooperation.
The results of the second data collection activity were
as follows:
. 271 companies were contacted and screened for
eligibility.
. 187 companies were sent a questionnaire.
. 52 companies completed the survey satisfactorily,
resulting in a response rate of 27.8 percent.
9
Phase Three
The third data collection effort involved mailing the
survey questionnaire to approximately 500 companies chosen
from the 1993 edition of the Council of Logistics Management
Membership Roster. In this case no effort was made to screen
the companies for eligibility except to ensure that no
companies contacted during either of the previous data
collection efforts were included in the mailing. Follow-up
calls, however, were made two to three weeks after the initial
mailing.
Only nine of the 500 companies responded satisfactorily,
a response rate of only 1.8 percent.
Sample Size and Characteristics
The final shipper survey sample included 72 companies,
and the resulting dataset contained 297 traffic lane
observations. Table 1 shows a breakdown of these observations
by type of product. Shipments of food and kindred products
(SIC 20) and shipments of chemicals and allied products (SIC
28) together accounted for nearly half (47.5 percent) of the
total number of lane observations. Paper and allied products
(SIC 26) along with stone, clay, and glass products (SIC 32)
contributed another 18 percent of the observations. Product
values ranged from $0.0045 per lb ($0.01 per kg) to $75.00 per
lb ($165.35 per kg) with an average of $4.14 per lb ($9.13 per
kg). Table 2 shows the distribution of transport modes and
types of equipment among the observations. In over 80 percent
of the cases, trucks were the principal mode used in the
traffic lane. About one out of every six observations involved
rail transport, either trailer-on-flatcar (TOFC) or boxcar.
Inventory carrying costs ranged from 2.0 percent of
inventory value to 140 percent. The average inventory carrying
cost was 18.1 percent of inventory value. Of the 72 companies
responding to the survey, 13 were unable to provide any
information on their inventory carrying costs. Some companies
stated that their current product costs and interest rates
were so low that inventory carrying costs were not considered
in making daily transportation decisions.
Fixed order costs varied between $2.20 and $320.00 per
shipment. The average was $44.53. Only 53 of the 72 companies
in the sample were able to specify their fixed order costs.
10
Click HERE for graphic.
11
Click HERE for graphic.
12
FREIGHT TRANSPORTATION ANALYZER
The Freight Transportation Analyzer (FTA) is a personal
computer-based program that calculates not only the freight
costs of various transportation options but the total logistics
cost as well, including the cost of carrying inventories at the
origin and destination. It essentially implements a
deterministic economic order quantity (EOQ) model adapted to
incorporate transportation costs.
The FTA program takes as input data about the product, the
transportation alternatives, related costs at the origin and
destination, and the inventory management system at the
destination. Product data include unit weight, annual demand for
the product at the destination, and product value at the origin.
Transportation alternatives are described in terms of maximum
quantity per shipment, fixed charges per shipment, and average
transit time. Cost data include fixed order costs per shipment
andinventory carrying costs in transit and at the destination.
Input data on the inventory management system at the destination
include the administrative lead time, the inventory review
period, and the frequency at which demand forecasts are updated.
In addition to these data, the FTA also accepts input for many
other variables including the accuracy of a firm's demand
forecasts, transit time variability, in-transit damage factors,
and production scheduling. These latter variables, however,
were not considered important to the objectives of this study.
The output of the FTA program is a detailed breakdown of a
shipper's total annual logistics cost associated with each
transportation alternative for a given product and traffic lane.
In this study the detailed costs were summarized into three
components:
. Annual freight cost.
. Annual fixed order cost.
. Annual inventory carrying cost.
The FTA was run on each product-lane observation for which
complete information was provided. Because some companies could
not provide data on their inventory carrying cost or their fixed
order cost, not all 297 observations could be used. For each
complete product-lane observation, the FTA calculated the
freight, order, and inventory carrying costs for the shipper's
current mode of transport as well as for each of the following
two types of longer combination vehicle:
. Rocky Mountain double, which is a tractor pulling a 48-ft
(14.6-m) trailer followed by a 28-ft (8.5-m) trailer.
. Turnpike double, which is a tractor pulling two 48-ft
(14.6-m) trailers.
13
For product-lane observations involving shipments that
typically weigh-out, two FTA cases were generated, one for
each of the two gross vehicle weight (GVW) scenarios defined
in table 3. This table shows the GVW limit and typical payload
for each type of LCV in each scenario. The difference between
the GVW limit and typical payload is greater than the tare
weight of the LCV because it is generally not possible to
completely fill the cubic capacity of both trailers. Under the
low weight capacity scenario, the current GVW limit of 80,000
lb (36,288 kg) is retained for both types of LCV. Because of
the extra tare weight of a 48-ft (14.6-m) trailer over a 28-ft
(8.5-m) trailer, the typical payload for a turnpike double
under this scenario is less than the typical payload for a
Rocky Mountain double. The GVW limits are higher under the
high weight capacity scenario and are set to take advantage of
the additional cubic capacity offered by each type of LCV. The
higher GVW limits, however, are not in proportion to the
additional cubic capacity. They are affected by the Federal
Bridge Formula, which is designed to protect bridges,
especially those with relatively long spans, from the
additional weight. In both GVW limit scenarios, the base case
was the shipper's current mode of transport under existing GVW
limits.
Table 3. Weight capacity of longer combination vehicles under
two gross vehicle weight (GVW) limit scenarios.
Click HERE for graphic.
metric conversion: l lb = 0.4536 kg
For lane observations involving shipments that typically
cube-out, the number of FTA runs made for each observation
depended on the current payload weight. Only one FTA run was
necessary if the current payload was less than 38,000 lb
(17,237 kg). Because cubic capacity rather than GVW is the
limiting factor for these shipments, the results of the FTA
program would have been the same for each GVW scenario.
Therefore, only one FTA run was necessary for each of these
lane observations, and its output was used for both GVW
scenarios. The cubic capacity ratios shown in table 4 were
used to determine shipment size and, therefore, the number of
shipments required to accommodate the annual lane volume under
each alternative mode of truck transport. Most of the
observations of shipments that typically cube-out had current
payloads of less than 38,000 lb (17,237 kg). For the few
observations of shipments that typically cube-out and weigh
38,000 lb (17,237 kg) or more, two FTA runs were made. One run
used the GVW limits and typical payloads for the low weight
capacity scenario. The other run was based on either the GVW
limits and typical
14
payloads for the high weight capacity scenario or the cubic
capacity ratios shown in table 4, depending on whether GVW or
cubic capacity was the limiting factor.
Table 4. Cubic capacity of longer combination vehicles
compared to single trailers of various sizes.
Click HERE for graphic.
Metric conversion: 1 ft3 = 0.028 m3
The payload and cubic capacity guidelines shown in
tables 3 and 4 were compiled from numerous sources, including
the Western Highway Institute, the American Trucking
Associations, interviews with shippers and carriers, and
previous studies of longer combination vehicles.
A number of companies in the shipper survey reported a
range of product values rather than a single value. For these
product-lane observations, one or two FTA runs were made based
on the high product value and another one or two runs were
made using the low product value. The number of FTA cases
generated for each product value depended on the weight of the
payload and whether the shipment typically cubed-out or
weighed-out, as described above.
If a company reported using more than one mode or
trailer size in a lane, the one chosen for the FTA run was the
mode or trailer size involved in transporting the bulk of the
annual lane volume.
An important input to the FTA is the carrier's rate or
freight charge for each alternative mode. Shippers provided
this information in the survey for their current mode. For
each of the LCV alternatives, two LCV rate assumptions were
considered for shipments currently made by truck. The first
assumption was that shippers would pay the same rate for each
type of LCV that they were currently paying for single trailer
transport. This
15
assumption is not unrealistic. In a recent survey of motor
carriers who operate LCVs, SO percent of the respondents
stated that LCV operation had lowered their typical rates, and
46 percent said there was no significant difference.(5) The
FTA was therefore run on the basis of this first assumption.
The second assumption was that LCV rates would be higher,
reflecting the higher costs per mile of operating LCVs. This
assumption was implemented by a derived set of factors which
were applied to the annual freight costs calculated by the FTA
under the first LCV rate assumption. Table 5 indicates these
factors and how they were derived. The operating costs per
loaded mile were taken directly from a previous study.(6)
These costs were based on the assumption that 15 percent of a
vehicle's mileage is empty haul. The gross vehicle weights
shown in the table were the ones used in the previous study.
Although they differ slightly from the gross vehicle weights
assumed for the high and low weight capacity scenarios, the
costs that vary with weight do not change rapidly enough to
alter the derived set of factors significantly.
Table 5. LCV rate adjustment factors based on a comparison of
operating costs per loaded mile.
Click HERE for graphic.
metric conversion: 1 ft = 0.3 )48 m; 1 mi = 1.609 km; 1 lb =
0.4536 kg
The second LCV rate assumption affected only the
computation of the total annual freight cost for each FTA run.
Calculation of annual order and inventory carrying costs by
the FTA does not depend on the LCV rate. Consequently, there
was no need to rerun the FTA for the second LCV rate
16
revised annual freight costs were then added to the order and
inventory carrying costs from the FTA runs to produce total
logistics costs under the second LCV rate assumption.
The LCV rate assumptions described above applied only to
product-lane observations where the current mode was truck.
For lanes in which the product was currently shipped by rail
boxcar or rail-truck intermodal, an LCV rate of $1.30 per mi
($0.81 per km) was used to run the FTA. Under the assumption
that LCV operators will charge the same rate as currently
charged for single trailers, this rate turned out to be perhaps
a little too high. Nearly all of the rail and intermodal
observations involved either medium lane or long lane distances.
The average single trailer freight charges paid by shippers in
the survey were $1.24 per mi ($0.77 per km) for medium lane
shipments and $1.14 per mi ($0.71per km) for long lane
shipments. On the other hand, under the assumption that LCV
operators will charge higher rates for LCV service because of
the higher operating costs involved, the assumed LCV rate of
$1.30 per mi ($0.81 per km) was perhaps too low for some
situations. After applying the rate adjustment factors in
table 5 to the current single trailer rates charged shippers
in the survey, the results showed that the average rate for
Rocky Mountain doubles for medium lane distances would be
$1.36 per mi ($0.85 per km) under the low weight capacity
scenario and $1.42 per mi ($0.88 per km) under the high weight
capacity scenario. For long lane shipments the respective
rates would be $1.25 per mi ($0.78 per km) and $1.31 per mi
($0.81 per km). The average rates for turnpike doubles were
determined to be $1.48 per mi ($0.92 per km) and $1.61 per mi
($1.00 per km) for medium lane shipments under the low and
high weight capacity scenarios, respectively, and $1.36 per mi
($0.85 per km) and $1.48 per mi ($0.92 per km) for long lane
shipments under the two GVW scenarios. The originally assumed
LCV rate of $1.30 per mi ($0.81 per km), therefore, appeared
to be a reasonable compromise. Because of the small number of
rail and intermodal shippers in the sample, no effort was made
to rerun the FTA for different LCV rates for these cases.
Table 6. Breakdown of FTA observations by current principal
mode.
Click HERE for graphic.
Altogether some 347 runs of the FTA were made. The
output was organized into two datasets, one for each GVW
scenario. Each dataset contained 228 cases or observations.
17
Table 6 shows the breakdown of these cases by current
principal mode. Each observation consisted of the annual
freight cost, order cost, inventory carrying cost, and total
logistics cost for the current mode, Rocky Mountain double
LCV, and turnpike double LCV as determined by the FTA.
18
3. EFFECTS OF LCV USAGE ON THE LOGISTICS COSTS
OF TRUCK SHIPPERS
INTRODUCTION
This chapter presents the results of the FTA runs for
shipments currently handled by tractor and single trailer. It
begins by addressing the overall effect of LCV use on the
total logistics cost of current motor carrier users, showing
how the cost consequences of LCV use can vary by type of
LCV,GVW limits, and LCV rates relative to single trailer
rates. It then looks at several individual factors and
combinations of factors that could influence how much of an
effect LCV use would have on a truck shipper's total logistics
cost. Based on these findings, the chapter then considers how
many shippers might switch from single trailer truck hauls to
some type of LCV.
OVERALL EFFECTS
The FTA results indicated that, in a large majority of
cases, companies would reduce their total logistics cost by
switching from single trailer truck transport to some type of
LCV. The extent to which a company's total logistics cost for
a given product in a given lane would change as a result of
switching depended on the type of LCV,GVW limits, and LCV
rates relative to single trailer rates. Table 7 summarizes the
FTA results with respect to these three parameters.
Two of the more noteworthy findings in table 7 are the
large average and median percent reductions in total logistics
cost associated with LCV usage and the wide range in the
percent change in costs that can result from switching to
LCVs. For example, under the high weight capacity scenario
with LCV rates the same as current single trailer truck rates,
the FTA calculated that use of Rocky Mountain doubles would
lead to cost savings of at least 33 percent in half of the
product-lane observations. Under the same situation, use of
turnpike doubles would lead to cost reductions of at least 42
percent in half of the cases. For the vast majority of
product-lane observations, switching to some type of LCV would
reduce total annual logistics cost. The average reduction in
total logistics cost for Rocky Mountain doubles ranged from 17
percent to 27 percent, depending on GVW limits and LCV rates
relative to single trailer truck rates. For turnpike doubles
the range in average cost reduction was between 13 percent and
32 percent. In some cases cost savings as high as 45 to 52
percent were predicted by the FTA for Rocky Mountain doubles
and 48 to 59 percent for turnpike doubles. At the other
extreme, significant cost increases were also possible. The
FTA results revealed cases where switching to Rocky Mountain
doubles would increase the shipper's total annual logistics
cost by as much as 42 percent and switching to turnpike
doubles would increase it by as much as 73 percent.
19
Table 7. Overall effect of LCVs on the total logistics cost of
single trailer truck shippers by type of LCV,GVW limits, and
relative LCV rates.
Click HERE for graphic.
Notes: Negative percentages imply an increase in total
logistics cost. Percentages in the rightmost column are
based on a sample size of 176 cases.
Higher GVW limits tended to increase the economic benefits of
LCVs. The average and the median percent cost reduction
resulting from the use of LCVs was generally higher under the
high weight capacity scenario than under the low weight capacity
scenario. The importance of higher GVW limits was especially
noticeable for turnpike doubles. In going from an 80,000-lb
(36,288-kg) to a 131,000-lb (59,422-kg) GVW limit, the average
percent cost reduction for turnpike doubles increased between 7
and 9 percentage points, depending on the relative LCV freight
rate. The increase in the median percent cost reduction was
even larger at around 20 percentage points. For Rocky Mountain
doubles, on the other hand, the effect of going from an
80,000-lb (36,288-kg) GVW limit to a 115,000-lb (52,164-kg)
limit was an increase in the average percent cost reduction of
only about 2~/: to 4 percentage points.
Higher LCV rates had a noticeable but not tremendously large
dampening effect on the generally favorable economic
consequences of LCV use. Compared to the situation in which the
LCV rate in each case was the same as the shipper's current
single trailer truck rate, higher LCV rates based on higher
operating costs per mile tended to reduce the average
20
percent cost reduction by around 5 to 12 percentage points,
depending on LCV type and GVW limits. Again the effect was
stronger for turnpike doubles than for Rocky Mountain doubles.
Higher LCV rates also increased the number of cases where a
shipper would experience an increase in total annual logistics
cost by switching to LCVs, but only to a fairly small extent.
The relative attractiveness of the alternative truck
configurations greatly depended on GVW limits. Table 8 shows the
distribution of lane observations by type of truck configuration
resulting in the lowest total logistics cost. When GVW limits
were kept at their current level of 80,000 lb (36,288 kg), Rocky
Mountain doubles held a slight edge over turnpike doubles. At
current GVW limits, turnpike doubles are virtually worthless for
truckload shipments that typically weigh-out in single trailers.
The FTA results indicated, however, that for low-density
products that normally cube-out in single trailer truckload
shipments, turnpike doubles could be advantageous even under
current weight limits. Turnpike doubles were by far the most
cost-effective configuration under the high weight capacity
scenario where shipments that normally weigh-out in single
trailers can take advantage of the additional cubic capacity
offered by twin 48-ft (14.6-m) trailers.
Table 8. Truck configuration resulting in the lowest total
logistics cost by GVW limits and relative LCV rates.
Click HERE for graphic.
Higher LCV rates relative to current single trailer rates also
influenced which configuration was the most economical in terms
of total logistics cost. Compared to GVW limits, however, the
effect was relatively modest. In general it was to increase the
attractiveness of single trailers and Rocky Mountain doubles at
the expense of turnpike doubles' particularly under the high
weight capacity scenario.
21
INFLUENCE OF VARIOUS COST, PRODUCT, AND LANE VARIABLES
The FTA results presented in the previous section showed
that use of LCVs would lower the total logistics cost of the
surveyed shippers in the vast majority of cases. The results,
however, also showed tremendous variation in the degree to
which the total logistics cost would change. In an effort to
explain this wide variation, a number of variables related to
costs and to product and traffic lane characteristics were
examined individually and in combination with each other to
determine what effect they had on the percent change in total
logistics cost.
Ratio of Freight to Inventory Costs
Switching from single trailer truck transport to an LCV
entails trading off higher inventory carrying costs against
lower shipping costs. The degree to which using LCVs raises or
reduces a firm's total logistics cost could depend on the
comparative magnitude of the firm's freight and inventory
carrying costs using single trailers. If the inventory
carrying cost of a commodity is much smaller than the cost of
shipping it in single trailer truckload quantities in a given
traffic lane, then switching to an LCV will probably not
increase the inventory carrying cost more than it decreases
the freight cost. The net result would be a reduction in total
logistics cost. On the other hand, if the inventory carrying
cost is already about the same as or greater than the cost of
shipping, then switching from single trailers to LCVs would
likely increase the inventory carrying cost more than it
reduces the freight cost, resulting in an increase in the
total logistics cost.
Figures 1 and 2 support the above hypotheses. Each
figure is a plot of the percent reduction in total logistics
cost as a result of switching to LCVs versus the ratio of
freight cost to inventory carrying cost for single trailer
transport. Figure 1 shows the relationship under the low
weight capacity scenario while figure 2 corresponds to the
high weight capacity scenario. Both figures are based on the
assumption that LCV rates will be higher than single trailer
rates; however, the patterns displayed in these plots were the
same under the assumption that LCV rates would not differ from
current single trailer truckload rates.
In the FTA results, the percent change in total
logistics cost was highly sensitive to changes in the
freight-to-inventory cost ratio for ratios below approximately
4.0. LCV usage always resulted in a lower total logistics cost
when the freight cost for single trailer shipments was at
least twice as large as the inventory carrying cost. The ratio
at which LCVs went from reducing to increasing the total
logistics cost was generally between 1.5 and 1.7. In most
cases where the ratio was less than 1.0, meaning that the
inventory carrying cost was already greater than the single
trailer freight cost, use of LCVs resulted in a higher total
logistics cost. The more the ratio dropped below 1.0 (that is,
the more the inventory carrying cost increased over the
freight cost), the greater the negative impact of LCV usage on
the total logistics cost.
22
Click HERE for graphic.
23
Click HERE for graphic.
24
As figures 1 and 2 indicate, freight-to-inventory cost
ratios in the FTA datasets were generally quite high. The
median ratio was 10.28, and in 81.25 percent of the cases, the
current single trailer freight cost was at least twice as
large as the inventory carrying cost. There were only 12 cases
(6.8 percent of the total) in which the inventory carrying
cost exceeded the single trailer freight cost.
Figures 1 and 2 suggest an obvious way by which a firm
could determine whether or not and to what extent it might
benefit from using LCVs. The procedure is to determine the
firm's inventory carrying cost and compare it to the cost of
shipping via single trailer truckloads in the traffic lane. If
the shipping cost is greater than the inventory carrying cost
by a factor of two or more, then use of LCVs would most likely
be justified from the standpoint of lowering the firm's total
logistics cost. Of course, market-related factors as well as
others besides logistics costs may influence whether or not a
firm will consider using longer combination vehicles.
Product Value
Given that LCVs are likely to be inappropriate in
situations where the inventory carrying cost is nearly the
same as or larger than the transport cost, it would seem that
anything which increases the cost of storing a particular
product would tend to reduce the attractiveness of LCVs. Many
factors determine a firm's cost of holding inventory. They
include interest rates, insurance, property taxes, capital,
warehousing, depreciation, and obsolescence. Many of these
factors, in turn, are affected by product value. For example,
high-valued products often require special handling and
storage in climate controlled warehouses. Thus, product value
can greatly influence inventory carrying cost, although the
relationship may sometimes be subtle and indirect.
Nevertheless, the FTA results were used to test the hypothesis
that, as product value increases, the positive effect of LCVs
on total logistics cost decreases.
The product-lane observations in the FTA datasets
encompassed a wide range of product values, varying from a
minimum of $0.02 per lb ($0.04 per kg) to a maximum of $75.00
per lb ($165.35 per kg). Roughly one-third of the observations
had product values under $1.00 per lb ($2.20 per kg) while
another third had product values at or above $3.00 per lb
($6.61 per kg). Thus, the sample contained an adequate mix of
high- and low-valued commodities with which to test the above
hypothesis.
Table 9 shows the observed relationship between product
value and percent cost reduction associated with use of LCVs
for different LCV types, GVW limits, and relative LCV rates.
Except for turnpike doubles under the low weight capacity
scenario, the signs of the correlation coefficients generally
supported the hypothesis that higher product values are
associated with less favorable results from using LCVs.
However, the magnitudes of the coefficients were small,
indicating that the relationship is a fairly weak one. Product
value
25
by itself, therefore, did not help to explain much of the wide
variation in the impact of LCV use on total logistics cost.
Table 9. Correlation between product value and the percent
reduction in total logistics cost resulting from
LCV usage.
Click HERE for graphic.
Inventory carrying cost, expressed as a percentage of
inventory value, varied between 2 percent and 140 percent in
the FTA datasets. This variable was multiplied by product
value to form a composite variable which was then correlated
with the percent reduction in total logistics cost from using
LCVs. The results are shown in table 10. The magnitudes of the
correlation coefficients were once again very small,
indicating very little correlation between the composite
variable and the cost effects of LCV usage. Moreover, the
signs of the coefficients in this case were inconsistent. They
indicated a negative correlation for the high weight capacity
scenario and a positive correlation for the low weight
capacity scenario. As was the case for product value alone,
the result of multiplying product value by the inventory
carrying cost expressed as a percentage of inventory value was
not very helpful in explaining the FTA results.
Annual Lane Volume
As the demand for a product rises in a given traffic
lane and the volume shipped to meet the demand increases,
freight costs tend to account for a larger proportion of the
total logistics cost. Conversely, as lane volume decreases,
inventory costs tend to increase as a percentage of total
logistics cost.(4) Heavy annual lane volumes, therefore,
should favor the use of LCVs.
26
Table 10. Correlation between percent cost reduction from
using LCVs and the composite variable formed by
multiplying product value by the inventory carrying
cost expressed as a percentage of inventory value.
Click HERE for graphic.
Annual lane volumes for truck shipments in the FTA
datasets varied considerably. The smallest volume was 351 cwt
(15,921 kg), while the largest was 2,970,000 cwt (134,716,230
kg). Half of the observations involved annual lane volumes of
25,000 cwt (1,133,975 kg) or more.
The FTA results indicated that annual lane volume does
influence the effect of LCVs on total logistics cost. Table 11
shows the percent reduction in total logistics cost from using
LCVs at three levels of annual lane volume. Below annual
volumes of 5,000 cwt (226,795 kg), the effectiveness of LCVs
dropped sharply. In fact, the majority of cases in which LCV
usage increased the total logistics cost involved annual lane
volumes of less than 5,000 cwt (226,795 kg). The average
effect of LCVs at the lowest volume level ranged from a 3.39
percent increase in total logistics cost for turnpike doubles
operating under high GVW limits and higher rates relative to
single trailer rates to a 9.62 percent decrease in total
logistics cost for Rocky Mountain doubles operating under low
GVW limits and rates equivalent to existing single trailer
rates. At volumes above 5,000 cwt (226,795 kg), on the other
hand, LCVs decreased total logistics cost by an average of 16
percent to 42 percent, depending on LCV type, GVW limits, and
relative LCV rates. Under the low weight capacity scenario,
the average impact of LCVs did not change much at volumes in
the middle and upper ranges. Under the high weight capacity
scenario, however, the average percent reduction in total
logistics cost was about 7 percentage points higher for Rocky
Mountain doubles and 9 to 10 percentage points higher for
turnpike doubles at volumes over 25,000 cwt (1,133,975 kg)
compared to volumes between 5,000 and 25,000 cwt (226,795 to
1,133,975 kg).
27
Table 11. Average percent reduction in total logistics cost
from use of LCVs for different levels of annual
lane volume.
Click HERE for graphic.
Metric conversion: 1 cwt = 45.359 kg
While the FTA results showed a logical overall
relationship between annual lane volume and the
cost-effectiveness of LCVs, the relationship was not a simple
linear one. Moreover, the ability of annual lane volume to
predict the actual amount of change in total logistics cost
resulting from the use of LCVs was extremely weak. Table 12
shows the correlation between annual lane volume and percent
cost reduction. In general, the correlation coefficients were
very low. These coefficients along with the results shown in
table 11 suggest that annual lane volume is a good indicator
of the direction in which total logistics cost will change as
a result of using LCVs but is a poor indicator of how much of
a change will occur.
The correlation coefficients in table 12 were much
higher for the high weight capacity scenario than for the low
weight capacity scenario. In addition, the coefficients for
turnpike doubles operating under low GVW limits indicated a
negative correlation between annual lane volume and percent
cost reduction. These results further illustrate the relative
inefficiencies of operating LCVs in general and turnpike
doubles in particular under current GVW restrictions. Much of
the additional cargo space cannot be utilized. As a result, as
the volume in the traffic lane increases, more LCV shipments
are required than would be necessary under higher GVW limits.
Consequently, freight costs also rise, in some cases more
rapidly than they would if single trailers were employed.
28
Table 12. Correlation between annual lane volume and the
percent reduction in total logistics cost
resulting from LCV usage.
Click HERE for graphic.
Lane Distance
Generally, the farther a product is shipped, the lower
the freight cost is per mile or per hundredweight. However,
the total freight cost tends to contribute to a greater share
of the total logistics cost as lane distance increases.
Conversely, as shipping distances decrease, inventory carrying
costs tend to account for a higher proportion of the total
logistics cost.(4) LCVs, therefore, should become more
cost-effective as shipping distances increase.
Table 13 shows the average percent reduction in total
logistics cost from LCV usage for short, medium, and long
lanes in the FTA datasets. Substantial cost savings from LCV
use were possible in each lane distance category. However, at
distances above 1,000 mi (1,609 km), LCVs always lead to a
reduction in the total logistics cost when operating under
high GVW limits. Only turnpike doubles running under low GVW
limits at freight rates higher than current single trailer
rates in some cases caused the total logistics cost to go up
at distances above 1,000 mi (1,609 km). In the short and
medium distance lanes, however, there was virtually no
correlation between shipping distance and percent change in
total logistics cost. Use of LCVs for short and medium
distances produced very large cost savings in some cases and
very large cost increases in others.
Annual Lane Ton-Mileage
Annual lane ton-mileage is a composite measure of annual
lane volume and lane distance. In the FTA datasets, values
ranged from 5,000 (7,295 metric ton-km) to
29
67,288,000 (98,173,192 metric ton-km). The average was
4,207,000 (6,138,013 metric ton-km) and the median was 736,000
(1,073,824 metric ton-km). Based on the observed effects of
annual lane volume and lane distance, higher values of annual
lane ton-mileage should increase the likelihood of higher cost
savings from the use of LCVs.
Table 13. Average percent reduction in total logistics cost
from use of LCVs for different traffic lane
distances.
Click HERE for graphic.
metric conversion 1 mi = 1.609 km
The average percent reduction in total logistics cost
resulting from the use of LCVs is shown in table 14 for three
levels of annual lane ton-mileage. The average impact of LCVs
increased markedly in going from one level to the next. The
jump in the average cost reduction percentages was especially
acute between the low and middle annual lane ton-mileage
categories. Most instances of LCVs causing the total logistics
cost to rise occurred in the under 250,000 annual ton-mi
(364,750 metric ton-km) category. Above 1,000,000 annual
ton-mi (1,459,000 metric ton-km), use of LCVs always resulted
in a decrease in total logistics cost except for turnpike
doubles operating under low GVW limits at LCV rates higher
than current single trailer rates. In the FTA datasets, nearly
44 percent of the cases involved annual flows greater than
1,000,000 ton-mi (1,459,000 metric ton-km), and nearly
three-fourths of the observations involved annual flows of
250,000 ton-mi (364,750 metric ton-km) or more.
30
Table 14. Average percent reduction in total logistics cost
from use of LCVs for different levels of annual
lane ton-mileage.
Click HERE for graphic.
Metric conversion: 1 ton-mi = 1.459 metric ton-km
The correlation between annual lane ton-mileage and the
percent reduction in total logistics cost resulting from the
use of LCVs is shown in table 15. Again the magnitudes of the
coefficients were rather low, although they were somewhat
better than the correlation coefficients found for product
value. Unlike the case for annual lane volume, the correlation
between annual lane ton-mileage and percent cost reduction was
consistently positive. In addition, the coefficients were
higher for the high weight capacity scenario than for the low
weight capacity scenario, particularly for turnpike doubles.
This again points out the lower efficiency of LCVs operating
under low GVW limits. As was the case for annual lane volume
and lane distance, annual lane ton-mileage appeared to be
better at indicating the likelihood of cost savings from LCV
usage than it was at predicting how much savings could be
expected.
Product Value and Annual Lane Volume
Neither product value nor annual lane volume showed much
correlation with the percent change in total logistics cost
resulting from use of LCVs, although the latter variable was a
good indicator of whether or not LCVs would be beneficial to a
shipper. These two variables were stratified and
cross-tabulated to analyze their combined effect. Three
categories of product value were defined:
31
Table 15. Correlation between annual lane ton-mileage and the
percent reduction in total logistics cost resulting from LCV
usage.
Click HERE for graphic.
. Low - less than $1.00 per lb ($2.20 per kg).
. Medium - between $1.00 per lb ($2.20 per kg) and $2.99 per
lb ($6.59 per kg).
. High - $3.00 per lb ($6.61per kg) or more.
Approximately one-third of the 176 observations in each FTA
dataset fell in each of the above categories. Annual lane
volume was also separated into three categories, defined as
follows:
. Low - less than 15,000 cwt (680,385 kg).
. Medium - between 15,000 cwt (680,385 kg) and 49,999 cwt
(2,267,905 kg).
. High - 50,000 cwt (2,267,950 kg) or more.
Again, approximately one-third of the FTA cases fell in each
of these categories. The cross-tabulation of product value and
annual lane volume therefore yielded nine combinations or
cells of these two variables with each cell having roughly the
same number of observations.
The results of the cross-tabulation analysis are
summarized in tables 16 through 19. Tables 16 and 17 cover
Rocky Mountain doubles under the low and high weight capacity
scenarios, respectively. Tables 18 and 19 cover turnpike
doubles.
32
Table 16. Combined effect of annual lane volume and product
value on total logistics cost using Rocky Mountain
doubles under existing GVW limits.
Click HERE for Graphic
33
Table 17. Combined effect of annual lane volume and product
value on total logistics cost using Rocky Mountain
doubles under higher GVW limits.
Click HERE for Graphic
34
Table 18. Combined effect of annual lane volume and product
value on total logistics cost using turnpike doubles
under existing GVW limits.
Click HERE for Graphic
35
Table 19. Combined effect of annual lane volume and product
value on total logistics cost using turnpike
doubles under higher GVW limits.
Click HERE for Graphic
36
The effect of product value was noticeable mainly for annual
lane volumes under 15,000 cwt (680,385 kg). At these low
volumes, the average and median percent reduction in total
logistics cost from using LCVs dropped considerably as product
value increased. In addition, the percentage of cases in which
LCV use resulted in an increase in the total logistics cost rose
significantly with each successively higher level of product
value when the annual lane volume was low. On the other hand,
even when the annual lane volume was low, the average percent
cost reduction was above the overall average when the product
value was low, as a comparison with table 7 showed.
Consequently, the likelihood that LCVs would actually increase
the total logistics cost appeared to be a consequence of the
combined effect of low lane volumes coupled with medium or high
product values.
For annual lane volumes in the medium and high range, the
effect of product value was not nearly as detectable. At these
volume levels, the average and median cost reduction percentages
were generally quite high regardless of the level of product
value. Under the high weight capacity scenario, however, the
average percent cost reduction for medium and high annual lane volumes
was always lowest for product values in the highest category.
Thus, high product value did appear to reduce the cost-
effectiveness of LCVs at medium and high annual lane volumes,
but generally not to the point where LCVs became
counterproductive.
Product Value and Annual Lane Ton-Mileage
The same categories of product value defined above were also
cross-tabulated with annual lane ton-mileage. The following
three levels of annual lane ton-mileage were used, based on a
plot of this variable against percent cost reduction as well as
the distribution of annual lane ton-mileage values in the FTA
datasets:
. Low - 350,000 ton-mi (510,650 metric ton-km) or less.
. Medium - Over 350,000 ton-mi (510,650 metric ton-km) but
less than or equal to 1,000,000 ton-mi (1,459,000 metric
ton-km).
. High - Over 1,000,000 ton-mi (1,459,000 metric ton-km).
The results of this cross-tabulation, summarized in tables 20
through 23, were similar to those obtained in the analysis of
product value and annual lane volume. When the annual lane
ton-mileage was low, the cost-effectiveness of LCVs was reduced
considerably as product value increased. The combination of low
ton-mileage and either medium- or high-valued products accounted
for the majority of cases in which LCV usage resulted in an
increase rather than a decrease in the total logistics cost.
When annual lane ton-mileage was low but product value was also
low, the average percent cost reduction was at or above the
overall average. Above 350,000 annual ton-mi (510,650 metric
ton-km), the effect of product value was not evident except
under the high weight capacity scenario. At medium
37
and high levels of annual lane ton-mileage, high product
values had a noticeable dampening effect on the
cost-effectiveness of LCVs operating under higher GVW limits.
However, the effect was not strong enough to make LCVs
counter-productive. In fact, the cost reduction percentages
were still quite large and in some cases were still well above
the overall average.
INTRAMODAL DIVERSION TO LCVs
The FTA identified the truck configuration which had the
lowest total logistics cost for each product-lane observation.
As the results presented in the previous sections have shown,
in most cases the lowest cost configuration was some type of
LCV. Whether shippers in these cases would actually switch to
LCVs is a question which cannot be answered directly from the
responses to the shipper survey or from the FTA output. The
answer undoubtedly depends on many complex factors, and each
shipper may weight each factor differently. Nevertheless,
total logistics cost is presumably one of the more important
considerations .
The simplest approach to estimating an intramodal LCV
diversion rate would be to assume that a shipper will use
whichever trailer configuration produces the lowest total
logistics cost. Most shippers, however, would probably not
switch to an LCV configuration unless the resultant cost
savings exceeded some fixed percentage of the firm's current
total logistics cost. What that percentage might be is
difficult to determine. Therefore, the decision was made to
estimate a range of diversion rates based on different cost
savings thresholds.
Table 24 indicates the sensitivity of intramodal LCV
diversion rates to several factors: GVW limits, minimum
percent reduction in total logistics cost needed for diversion
to occur, and LCV freight rates relative to current single
trailer rates. The table shows the percentages of FTA cases in
which diversion was assumed to occur as well as the
corresponding percentages of annual lane ton-mileage involved.
The following are some general observations from table 24:
. LCV diversion rates under the high weight capacity
scenario tended to be larger than those under the low weight
capacity scenario, although for minimum required cost
savings below 20 percent, the differences were fairly small.
. Not surprisingly, as the minimum percent cost savings
required to induce diversion increased, the diversion rate
dropped considerably, especially under the low GVW scenario.
38
Table 20. Combined effect of annual lane ton-mileage and
product value on total logistics cost using Rocky
Mountain doubles under existing GVW limits.
Click HERE for graphic.
39
Table 21. Combined effect of annual lane ton-mileage and
product value on total logistics cost using Rocky
Mountain doubles under higher GVW limits.
Click HERE for graphic.
40
Table 22. Combined effect of annual lane ton-mileage and
product value on total logistics cost using
turnpike doubles under existing GVW limits.
Click HERE for graphic.
41
Table 23. Combined effect of annual lane ton-mileage and
product value on total logistics cost using
turnpike doubles under higher GVW limits.
Click HERE for graphic.
42
Table 24. Percent of FTA cases and ton-mileage assumed to
divert to LCVs under different GVW limits, cost
savings thresholds, and relative LCV freight
rates.
Click HERE for graphic.
43
. LCV diversion rates were generally lower when LCV freight
harges were higher than current single trailer freight
charges. The differences between the diversion rates for
the two LCV rate levels were relatively small when the
minimum required cost savings was less than 10 percent.
For cost savings of 10 percent and higher, however,the
differences became increasingly larger.
. Regardless of the GVW scenario or the level of LCV
freight charges, the estimated LCV diversion rates in
table 24 were considerably high. Even with a minimum
required cost savings of 15 percent, the estimated
diversion rates were between 70 percent and 81 percent of
the cases in the FTA datasets. This corresponded to 82.5
percent to 96.9 percent of the total annual ton-mileage
for all of the survey observations.
44
4.EFFECTS OF LCV USAGE ON THE LOGISTICS COSTS OF
RAIL AND INTERMODAL SHIPPERS
INTRODUCTION
This chapter presents the FTA results for those
product-lane observations in which the current primary mode of
transportation was either rail boxcar or truck-rail
intermodal. The FTA datasets contained only 24 rail and 27
intermodal observations. Although these samples were too small
for any multivariate analysis, some simple tabulations of the
FTA results were made in order to gain some insight into the
possible impact of LCVs on the total logistics cost of rail
and intermodal shippers.
RAIL BOXCAR SHIPPERS
Table 25 summarizes the overall effect of LCV usage on
the total logistics cost of the rail boxcar shippers. If LCVs
were required to operate under 80,000-lb (36,288-kg) GVW
limits, two-thirds of the rail shippers in the sample would
incur a higher total logistics cost in the traffic lane as a
result of switching to Rocky Mountain doubles. The average
cost increase would be 36 percent, and in half of the cases,
the cost increase would be 14 percent or more. The
consequences of switching to turnpike doubles under existing
GVW limits would be even more severe. The cost increase would
be at least 26 percent in half the cases and would average
nearly 49 percent. On the other hand, the FTA predicted a much
different outcome for the high weight capacity scenario. If
Rocky Mountain doubles were allowed to operate at gross
vehicle weights up to 115,000 lb (52,164 kg), most of the rail
boxcar shippers would experience a decrease in total logistics
cost as a result of switching to the LCV mode. Although the
average effect would be a 3.5 percent increase in the total
logistics cost, in half of the cases, the total logistics cost
would decrease by at least 13.7 percent. For turnpike doubles
operating at gross vehicle weights up to 131,000 lb (59,422
kg), the FTA results were even more favorable for LCVs. In
roughly 7 out of every 10 cases, switching from rail boxcars
to turnpike doubles resulted in a lower total logistics cost.
The average cost reduction was around 10.5 percent, and in
half of the cases, the reduction was 25 percent or more.
Table 26 indicates that rail boxcars would continue to
be the most cost-effective means of transport for current rail
users under the low GVW scenario, while turnpike doubles would
be the cost-effective mode in most cases under the high weight
capacity scenario.
Under both GVW scenarios, the variation in the FTA
results was extremely large. As table 25 shows, for some rail
shippers, switching to LCVs could result in cost increases of
well over 200 percent under the low GVW limit scenario and
over 100 percent in the high
45
GVW limit situation. At the other extreme, switching to LCVs
could reduce some rail shippers' total logistics cost by well
over 50 percent.
Table 25. Overall effect of LCVs on the total logistics cost
of rail boxcar shippers.
Click HERE for graphic.
Notes: Negative percentages imply an increase in total
logistics cost. Percentages in the rightmost column
are based on a sample size of 24 cases. The FTA used
an LCV freight rate of $1.30 per mi ($0.81 per km).
Table 26. Transportation mode with the lowest total
logistics cost for rail boxcar shippers.
Click HERE for graphic.
Note:Percentages are based on a sample size of 27 cases.
To understand better why the overall effects of LCVs on
rail boxcar shippers was so wide ranging, the shippers'
current rail freight charge per mile was plotted against the
percent reduction in total logistics cost. Figures 3 and 4
display the results for Rocky Mountain doubles under the low
and high weight capacity scenarios, respectively. The
corresponding charts for turnpike doubles are shown in figures
5 and 6.
46
Click HERE for graphic.
47
Click HERE for graphic.
48
Click HERE for graphic.
49
Click HERE for graphic.
50
Each figure shows a distinct relationship between the
rail freight charge and the percent change in total logistics
cost resulting from a switch to LCVs. The correlation between
the two variables, shown in table 27, was fairly strong. The
relationship, however, was clearly nonlinear. In fact, below a
rail charge of roughly $2.50 per mi ($1.55 per km), each
additional small decrease in the rail rate tended to result in
an increasingly larger percent increase in total logistics
cost from switching to an LCV. In other words, below a rail
rate of around $2.50 per mi ($1.55 per km), the relative
effect on total logistics cost of switching from rail boxcars
to LCVs was much more sensitive to changes in the rail freight
rate, implying much higher cross-elasticities in that range.
This suggests that if a rail carrier's current freight charge
was at or below this threshold rate, the carrier might have to
reduce its rate by only a very small amount in order to offset
any logistics cost benefits of LCVs and to prevent its traffic
from diverting.
Table 27. Correlation between rail freight charge per mile
and percent reduction in total logistics cost
from switching to LCVs.
Click HERE for graphic.
The rail freight rate at which there would be no
difference in the total logistics costs of rail boxcars and
LCVs varied by type of LCV and GVW limits. For Rocky Mountain
doubles, this rate was between $2.50 and $3.50 per mi ($1.55
to $2.18 per km) under the low weight capacity scenario and
between $2.00 and $2.50 per mi ($1.24 to $1.55 per km) under
the high weight capacity scenario. For turnpike doubles, the
breakeven rail rate was between $2.50 and $4.50 per mi ($1.55
to $2.80 per km) under existing GVW limits and around $2.00
per mi ($1.24 per km) under higher allowable GVW limits.
INTERMODAL SHIPPERS
The overall effects of switching from TOFC/COFC
intermodal transportation to LCVs on the total logistics cost
of intermodal shippers are summarized in table 28. As was the
case for rail boxcar shipments, LCVs were generally less
cost-effective compared to intermodal service when operating
under restrictive GVW limits than when operating under much
higher limits. In a slight majority of cases, use of Rocky
Mountain doubles under the
51
low weight capacity scenario entailed an increase in the total
logistics cost. The average cost increase was 4.2 percent. For
turnpike doubles operating under existing GVW limits, nearly
half of the cases showed an increase in the total logistics
cost and half showed a decrease. On the average, however, the
effect of switching to turnpike doubles was about a S percent
increase in the total logistics cost. Under the high weight
capacity scenario with its more liberal GVW restrictions, both
types of LCV had much more favorable impacts. Use of Rocky
Mountain doubles reduced the total logistics cost in
two-thirds of the cases. The average cost reduction was 6.6
percent and in half of the cases the reduction was at least
11.4 percent. Use of turnpike doubles at higher GVW limits
reduced the total logistics cost in 8 out of every 9 cases.
The average cost reduction was over 16 percent and in half of
the cases the reduction was over 26 percent. Table 29
indicates that none of the three modes was predominant under
the low weight capacity scenario, while turnpike doubles were
the most cost-effective mode in nearly 9 out of 10 cases under
the high weight capacity scenario.
The variation in the effect of LCVs on the total
logistics cost of intermodal shippers was not nearly as wide
ranging as was the case for rail boxcar users. In fact, the
range in the cost reduction percentages was about the same as
that for single trailer truckload shippers. In an attempt to
explain the variation, intermodal freight rates were plotted
against percent reduction in total logistics cost. The results
are displayed in figures 7 through 10 for each combination of
LCV type and GVW scenario.
Table 28. Overall effect of LCVs on the total logistics cost
of intermodal shippers.
Click HERE for graphic.
Notes: Negative percentages imply an increase on total
logistics cost. Percentages in the rightmost column are
based on a sample size of 27 cases. The FTA used an LCV
freight rate of $1.30 per mi ($0.81 per km).
52
Table 29. Transportation mode with the lowest total logistics
cost for intermodal shippers.
Click HERE for graphic.
Note: Percentages are based on a sample size of 27 cases.
The relationship between intermodal freight rate and LCV
percent cost reduction was not nearly as well defined. Table
30 shows the correlation between the two variables. Except for
Rocky Mountain doubles under the low weight capacity scenario,
the correlation coefficients were not as large as those shown
in table 27 between rail freight rate and LCV percent cost
reduction. Nevertheless, some relationship was discernible.
Under the low weight capacity scenario, switching to LCVs
tended to increase the total logistics cost when the
intermodal freight rate was below average and to decrease the
total logistics cost when the intermodal freight rate was
above average. With higher GVW limits, the FTA results showed
that most intermodal shippers would have a lower total
logistics cost using LCVs. Those few shippers who would have a
higher total logistics cost were currently paying below
average intermodal freight rates. Given the fairly narrow
range of intermodal freight rates in the FTA datasets, the
effect of Rocky Mountain doubles was quite sensitive to
changes in intermodal freight rate under both GVW scenarios.
The effect of turnpike doubles was likewise highly sensitive
to changes in the intermodal freight rate, but only under the
low weight capacity scenario.
Table 30. Correlation between intermodal freight charge per
mile and percent reduction in total logistics cost
using LCVs.
Click HERE for graphic.
53
Click HERE for graphic.
54
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55
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56
Click HERE for graphic.
57
5. ESTIMATION OF NATIONWIDE LCV USAGE
INTRODUCTION
The second major objective of this study was to apply
the results of the FTA program to the difficult problem of
estimating the nationwide use of LCVs. The original intent was
to predict both intramodal as well as intermodal diversion.
However, the number of rail boxcar and truck-rail intermodal
traffic lane observations in the shipper survey dataset was
too small to develop a credible model of rail-to-LCV or
intermodal-to-LCV diversion. Consequently, the effort was
restricted to estimating the extent of diversion from single
trailer truckload shipments to LCVs.
This chapter begins by presenting a simple intramodal
diversion model based on the results of the FTA. It then
describes how this model was applied to data from the 1987
Truck Inventory and Use Survey (TIUS) to estimate the
nationwide diversion of single trailer truckload shipments to
LCVs. The resulting projections are then presented and
compared with projections from previous research.
INTRAMODAL DIVERSION MODEL
The FTA results indicated that annual lane ton-mileage
was an important factor in determining whether or not a
truckload shipper would benefit from LCVs. Product value was
also a factor, but its effect was mainly visible at only very
low annual lane volumes. Therefore, a simple classification
model based on annual lane ton-mileage was developed using the
shipper survey data and FTA results.
. The model incorporated the following four factors:
. Annual lane ton-mileage.
. Gross vehicle weight limits.
. Minimum percent cost savings required before truckload
shippers will be induced to switch from single trailers to
LCVs.
. LCV freight rates relative to current rates for single
trailer truckload shipments.
After careful study of plots of annual lane ton-mileage versus
LCV percent cost reduction, the following four levels of
annual lane ton-mileage were chosen for the model:
. Less than 50,000 (72,950 metric ton-km).
59
. 50,000 or more but less than 250,000 (364,750 metric
ton-km).
. 250,000 or more but less than 1,000,000 (1,459,000).
1,000,000 or more.
The gross vehicle weight limit factor was represented by the
high and low weight capacity scenarios. Because the minimum
cost savings necessary to induce diversion to LCVs could not
be deduced from the shipper survey and probably varies among
shippers anyway, the model included levels of 0, 5, 10, 15,
20, and 25 percent. Finally, the relative LCV freight rate
factor was represented by the following two levels:
. Same - shippers will pay the same rate for LCV service
that they currently pay for single trailer truckload
transport.
. Higher - shippers will pay a somewhat higher rate for
LCV service, reflecting the generally higher operating
cost per mile of LCVs.
The model was essentially a four-dimensional matrix
whose cells were formed by the various combinations of the
above four factors. The contents of each cell specified the
percentage of annual ton-mileage estimated to divert to LCVs
under the particular combination of factors defining the cell.
These percentages were taken from the results of the FTA as it
was applied to traffic lane data from the shipper survey.
The model is presented in table 31. The percent of
ton-miles diverting increases as the level of annual
ton-mileage increases. The model assumes that no diversion
will take place for annual flows below 50,000 ton-mi (72,950
metric ton-km) in a traffic lane. Flows in that range were
judged to be too small to justify use of LCVs even though the
FTA results indicated that, in a few such cases, LCVs would
reduce the total logistics cost. A company which ships
48,000-lb (21,773-kg) payloads in a 200-mi (322-km) traffic
lane 10 times a year has an annual freight flow of 48,000
ton-mi (70,050 metric ton-km) in the lane. That is a rate of
less than one truckload shipment per month. The assumption was
made that a company would not consolidate such a small number
of annual shipments by switching to LCVs.
ESTIMATION PROCEDURE
Correct application of the intramodal diversion model
requires nationwide statistics on individual shipper or
company traffic flows or a representative sample of company
shipments. Unfortunately, such data were not available when
this study was conducted. As a convenient but less than ideal
substitute, individual truck data from the 1987 Truck
Inventory and Use Survey (TIUS) were used.
60
Table 31. Percent of annual traffic lane ton-mileage that would
divert to LCVs.
Click HERE for graphic.
61
The TIUS is conducted every five years by the U.S. Bureau of
the Census. It is based on a stratified random sample of
private and commercial trucks registered or licensed in each
State during the survey year. The sample is divided into the
following five strata:
. Stratum 1 - pickup.
. Stratum 2 - panel truck, van, utility vehicle, jeep, and
station wagon on truck chassis.
. Stratum 3 - small single-unit truck with gross vehicle
weight rating (GVWR) less than 26,000 lb (11,794 kg).
. Stratum 4 - large single-unit truck with GVWR greater
than or equal to 26,000 lb (11,794 kg).
. Stratum 5 - truck tractor.(7)
Only stratum 5 was needed to estimate nationwide LCV usage.
This stratum contained 34,619 individual truck records.
TIUS sample data are normally used to generate national
estimates of the number of trucks and truck miles for various
truck characteristics such as truck type and axle arrangement,
major use, range of operation, operating weight, overall
length, type of operation and jurisdiction, kind of carrier,
and products carried. By making some assumptions, it is also
possible to derive estimates of ton-mileage from TIUS sample
data.
The TIUS provides a wide variety of data on truck
activity, but it does have a few limitations, particularly
with regard to the use to which it was put in this study. A
major drawback is the fact that TIUS records represent
individual truck activity rather than individual shipper
activity. Another limitation is the absence of data on
individual shipments. Thus, it is not possible to determine
origin-destination or traffic lane flows directly from the
TIUS data. A third weakness is the fact that reported annual
mileage in many cases is based on the truck owner's own rough
estimates rather than on actual odometer readings.(8)
Despite these problems, the TIUS dataset does contain
several data items that appeared to be especially useful for
estimating nationwide LCV usage. Table 32 lists the pertinent
fields or data items that were selected from the TIUS records
on the public use tape.
The remainder of this section describes the procedure
that was followed to derive estimates of intramodal diversion
to LCVs nationwide.
62
Table 32.Definitions of relevant data items selected from 1987
TIUS public use records.
Click HERE for graphic.
63
In the first step, TIUS stratum 5 records meeting the
following criteria were selected:
. Vehicle type (VEHTYP) is truck tractor pulling trailer(s).
. Body type (BODTYP) is basic enclosed dry cargo van.
. Single trailer configuration with at least two axles on the
trailer (AXLRE codes 14, 16, 18, 19, 20, and 21).
Records in TIUS stratum 5 include tankers, grain hoppers, dump
trucks, refrigerated vans, wreckers, beverage trucks, straight
trucks, as well as dry vans. Because the shipper survey data
included only truck shipments made in enclosed dry cargo
trailers, only those TIUS records involving truck tractors
pulling such trailers were considered. Single-axle semi-
trailers were excluded because they are generally small
trailers, usually 28-ft (8.5-m) in length. The assumption was
made that any shipper who currently uses small trailers is
more likely to switch to a larger sized single trailer before
switching to an LCV. The TIUS dataset also contains records
for tractors pulling two and even three trailers. These
records were also excluded because the shipper survey data did
not include any lane observations involving double and triple
trailer configurations.
In the second step, each selected vehicle's
weight-limited payload was computed using the following
formula:
Weight-limited payload (lb) = MAXWGT - EMPWGT (1)
In the third step, each selected vehicle's cube-limited
payload weight was computed as two-thirds of the
weight-limited payload weight. Analysis of the shipper survey
data revealed that the average payload of cube-limited
shipments was two-thirds the average payload of weight-limited
shipments (the actual factor was 0.6615).
The fourth step involved estimating the vehicle's annual
weight-limited ton-mileage for trips over 200 mi (322 km) from
its home base. The following equation was used:
Annual weight-limited ton-miles=ANNMIL x ( PLONG / 100 )
x (PCARWT / 100 )
x ( weight-limited payload . 2,000 ) (2)
The vehicle's annual cube-limited ton-mileage for trips
over 200 mi (322 km) from its home base was estimated in step
5 from the following formula:
Annual cube-limited ton-miles = ANNMIL x ( PLONG . 100 )
x (PCARSZ . 100 )
x ( cube-limited payload . 2,000 ) (3)
64
In step 6, the vehicle's total annual weight-limited and
cube-limited ton-mileage were each allocated to each of the
following commodity groups according to the percent of the
vehicle's annual mileage accounted for while carrying the
particular commodity:
. Processed foods and tobacco products (SIC 20 & 21) -PRFOOD.
. Textiles and apparels (SIC 22 & 23) - TEXTIL.
. Lumber and fabricated wood products (SIC 24) - LUMBER.
. Furniture and/or hardware (SIC 25) - FURN.
. Paper and paper products (SIC 26) - PAPER.
. Chemicals and/or drugs (SIC 28) - CHEM.
. Petroleum and petroleum products (SIC 29) - PETROL.
. Plastics and/or rubber products (SIC 30) - PLASTK.
. Building materials, such as gravel, sand, concrete, flat
glass, etc. (SIC 32) - BLDGMA
. Glass products (SIC 32) - GLASS.
. Fabricated metal products (SIC 34) - FABMTL.
. Machinery, including electrical or nonelectrical and
electronic (SIC 35 & 36) - MACHINE.
. Miscellaneous products of manufacturing (SIC 38 & 39) -
MSCMFG.
Each of these commodity groups was represented by at least
four observations in the shipper survey data. The commodity
groups that were not included consisted of the following: live
animals, fresh farm products, unrefined mining products, logs
and forest products, primary metal products, transportation
equipment, refuse, industrial water, and hazardous wastes.
Another TIUS commodity group excluded from consideration was
mixed cargo, which includes small packages. This group was
assumed to represent all less-than-truckload (LTL) shipments.
The intramodal diversion model was applied in the
seventh step. The ton-mileage diversion percentages shown in
table 31 were used to estimate how much of each vehicle's
commodity-specific annual weight-limited and cube-limited
ton-mileage would be diverted to LCVs under the different GVW
scenarios, LCV freight rate assumptions, and minimum
65
percent cost savings thresholds. The results for each vehicle
were then multiplied by the vehicle's expansion factor
(EXPFAC) to obtain nationwide estimates for the entire truck
population.
In the eighth and final step, the estimates of
nationwide diverted ton-mileage were converted to
vehicle-miles. This was accomplished by dividing the
weight-limited tonmileage estimates by the average
weight-limited payload and the cube-limited ton-mileage
estimates by the average cube-limited payload. From the TIUS
data the average weightlimited payload was determined to be
47,756 lb (21,681 kg), which was rounded up to 48,000 lb
(21,773 kg), and the average cube-limited payload was found to
be 31,837 lb (14.441 kg). which was rounded up to 32,000 lb
(14,515 kg).
An important assumption in the procedure described above
was that, when a truck is neither cube-limited nor
weight-limited, there is no incentive to utilize LCVs. Thus,
the two key TIUS data fields were PCARSZ, the percent of
annual mileage the vehicle carried payloads that filled its
maximum cargo size, and PCARWT, the percent of annual mileage
the vehicle carried payloads that weighed the maximum cargo
weight. The existence of these two data items was the main
reason for choosing to use the TIUS dataset despite its
serious limitations. Close inspection of the TIUS data,
however, revealed that these two percentages were often
overlapping. For example, a truck owner may have reported a
cube-limited percentage of 75 and a weight-limited percentage
of 60. Thus, for 75 percent of its annual mileage the truck
was running cube-limited and for 60 percent of its annual
mileage it was running weight-limited. Clearly there must have
been some overlapping mileage in which the vehicle was
operating both cube-limited and weight-limited. There was no
easy resolution of this problem except to compute and report
diverted cube-limited and weight-limited ton-mileage
separately. Each of these estimates by itself understates the
total LCV ton-mileage, while the sum of the two overstates it.
ESTIMATED INTRAMODAL DIVERSION
Tables 33 through 36 present the results of the
intramodal diversion estimation procedure described in the
previous section. The high estimates in these tables are the
amount of diversion that would occur if shippers switched to
LCVs for any amount of reduction in total logistics cost. The
low estimates are the amount of diversion that would occur if
shippers switched to LCVs only if the resulting reduction in
total logistics cost was at least 25 percent. All the
estimates shown in the tables are for 1987. To obtain
estimates of diverted vehicle-miles for 1992, the numbers in
the tables can be multiplied by a factor representing the
growth in truck travel between those two years. Published
highway statistics show that the total number of rural
truck-miles of travel by tractor-trailer combinations rose
from 55,978 million (90,069 million truck-km) in 1987 to
63,984 million (102,950 million truck-km) in 1992.~9 ' ' This
was an increase of 14 percent. Hence, an appropriate growth
factor to apply to the numbers in tables 33 through 36 would
be 1.14 to obtain 1992 estimates.
66
Table 33. Estimated truck vehicle-miles (in millions)
diverting to LCVs under existing GVW limits
with LCV freight rates same as current single
trailer truckload rates.
Click HERE for graphic.
67
Table 34. Estimated truck vehicle-miles (in millions)
diverting to LCVs under existing GVW limits with
LCV freight rates higher than current single
trailer truckload rates.
Click HERE for graphic.
68
Table 35. Estimated truck vehicle-miles (in millions)
diverting to LCVs under higher GVW limits with LCV
freight rates same as current single trailer
truckload rates.
Click HERE for graphic.
69
Table 36. Estimated truck vehicle-miles (in millions)
diverting to LCVs under higher GVW limits with LCV
rates higher than current single trailer truckload
rates.
Click HERE for graphic.
70
The percentages appearing at the bottom of tables 33 to
36 were based on an estimated total truck-mileage of 4,063.2
million (6,537.7 million truck-km) in 1987. This total was
derived from the TIUS records selected in the diversion
estimation procedure. It represents the number of miles
operated by truck tractors pulling single enclosed dry van
semi-trailers with at least two axles while hauling one of the
13 selected commodity groups on trips at least 200 mi (322 km)
long. It accounted for about 22 percent of the 18,667.4
million long-range truck-mi (30,035.8 million truck-km) made
in 1987 for the 13 selected commodity groups for all body
types and combinations of tractor-trailers."" In turn, the
total long-range truck-mileage for the 13 commodity groups
comprised about 49 percent of the 38,383 million long-range
truck-mi (61,758.2 million truck-km) made in 1987 for all
truck-tractors hauling any kind of commodity, including mixed
cargo, in any kind of body type and configuration of
trailers.(11)
The estimates of nationwide intramodal diversion to LCVs
ranged from 0.5 billion to 1.5 billion truck-mi (0.8 billion
to 2.4 billion truck-km) for weight-limited freight and from
0.75 billion to 2.1 billion truck-mi (1.2 billion to 3.4
billion truck-km) for cube-limited freight. As mentioned
earlier in this chapter, there was an undetermined amount of
overlap between the weight-limited and cube-limited truck
mileage so that adding the two estimates would likely
overstate the amount of diversion.
The broad range of estimated nationwide intramodal
diversion indicates the significant extent to which such
factors as GVW limits, LCV freight rates relative to single
trailer truckload rates, and minimum required cost savings can
affect the amount of diversion that might occur. For example,
going from existing GVW limits to higher limits more suited to
LCVs could divert an additional 288.8 million truck-mi (464.7
million truck-km) if LCV freight charges were the same as
single trailer truckload rates. The significance of how much
reduction in total logistics cost would be required to induce
shippers to use LCVs is reflected in the differences between
the high and low estimates, which ranged from 329.7 million
truck-mi (530.5 million truck-km) to 1,285.9 million truck-mi
(2,069.0 million truck-km), depending on the GVW limit and LCV
freight rate scenario. Whether or not the estimates are
realistic is impossible to determine, but they do show a great
deal of sensitivity to factors which are difficult to
determine.
COMPARISON WITH OTHER ESTIMATES
One way to gauge the reasonableness of the above
estimates is to compare them with estimates of intramodal
diversion from other studies. Such comparisons are burdened
with their own inherent difficulties because of differences in
types of LCVs considered, assumptions about LCV operating
costs and productivity, assumed GVW limits, estimation
methodology, assumptions about shipper and carrier acceptance
of LCVs, sources and quality of data, and so on. In the end,
all projections of nationwide LCV use are purely speculative.
Consequently, the only comparison made was with estimates
developed in a recent Transportation Research Board (TRB)
study.(3)
71
The TRB study analyzed four prototype truck-trailer
configurations with lower axle weights but higher than
currently allowed gross weights in accordance with a proposal
advanced by former Federal Highway Administrator Francis C.
Turner. The four Turner prototypes considered in the study
consisted of a seven-axle tractor-semitrailer combination,
nine- and eleven-axle double trailer configurations, and a
nine-axle B-train double trailer combination. These
prototypes were analyzed in terms of their effects on
pavement wear, bridge costs, safety, traffic operations,
freight costs, productivity, and use.
The market for the double-trailer prototypes was
divided into a number of sectors defined by trailer body
type, private versus for-hire carrier, and local versus
intercity travel. The various body types included dry vans,
reefers, flatbeds, dry bulk trailers, and tankers. Dry vans
were further segmented into less-than-truckload and truckload
shipments and the latter were further divided into
low-density and high-density commodity groups. A market
potential of low, moderate, or high was assigned to each of
these sectors, based on a review of past projections of LCV
usage, estimates of the costs of the Turner prototypes,
interviews with carriers, and current use of multiple trailer
configurations. Each market rating was then quantified as
follows:
. A low market potential meant a 10 percent potential shift
of traffic to Turner prototypes.
. A moderate market potential corresponded to a potential 33
percent shift.
. A high market potential entailed a potential 67 percent
shift.
These diversion rates were then applied to current
vehicle-miles of travel by 5-or-more axle
tractor-semitrailers, S-axle truck-trailers, and 5- and
6-axle double trailers in each market sector. The total
number of vehicle-miles of travel for all combination trucks
was taken from the 1987 Highway Statistics publication. This
total was distributed among the various market segments based
on data from the 1982 TIUS, adjusted to account for the
increased use of twin trailer combinations between 1982 and
1987.
Of the many market sectors identified in the TRB study,
only the ones involving intercity truckload shipments in dry
vans closely matched the market sectors considered in the
current study. The TRB study assessed the market potential of
each of these sectors as follows:
. The private carrier, intercity, dry van, low-density
truckload market potential was rated as low to moderate.
. The for-hire carrier, intercity, dry van, low-density
truckload market potential was rated as low.
72
. The private carrier, intercity, dry van, high-density
truckload market potential was rated as moderate.
. The for-hire carrier, intercity, dry van, high-density
truckload market potential was rated as moderate.
The TRB study's estimates of traffic diversion to
Turner prototypes in each of the intercity dry van truckload
market sectors are shown in table 37. For comparison with
tables 33 through 36, low density freight may be regarded as
cube-limited and high density freight as weight-limited. In
comparing the TRB study's results with the estimates made in
the current study, it is more correct to use only tables 35
and 36 which cover the high weight capacity scenario.
Table 37. Estimated billions of truck miles diverting to
Turner prototypes in the intercity dry van
truckload market sectors.(3)
Click HERE for graphic.
Metric conversion: 1 truck-mi = 1.609 truck-km
The TRB study estimated between 0.7 and 1.4 billion
truck-mi (1.1 to 2.3 billion truck-km) would divert to Turner
prototypes in the low density intercity truckload freight
sectors. This overlapped the bottom of the range of 1.2 to
2.1 billion truck-mi (1.9 to 3.4 billion truck-km) of
diverted cube-limited freight estimated by the current study
under the high weight capacity scenario. On the other hand,
the TRB study's estimate of 4.5 billion truck-mi (7.2 billion
truck-km) of high density freight diverting to Turner
prototypes was three times higher than the highest estimate
of diverted weight-limited truck mileage in the current
study.
One reason for the large difference in the estimates of
diverted weight-limited freight was the fact that the TRB
study's estimates were based on a total truck mileage of 20.4
billion (32.8 billion truck-km) compared to 4.1 billion
truck-mi (6.6 billion truck-km) in the current study. Unlike
the current study, the TRB study considered not only
diversion from single trailer truckload shipments but twin
trailer shipments as well. The TRB study also did
73
not exclude any commodity groups. Consequently, a better
measure of comparison between the two studies is the estimated
market shares.
The TRB study estimated that 11 percent to 21 percent of
the total truck-miles of intercity low density dry van
truckload traffic would divert to LCVs, compared to an
estimated range of 31 to 51 percent in the current study. The
much lower market share in the TRB study reflects that study's
assessment of the market potential of the low density dry van
truckload sectors as low to moderate. For the high density
market sectors, whose market potential was rated as moderate,
the estimated market share was 33 percent, compared to a range
of 23 percent to 36 percent estimated by the current study.
Thus, a major difference between the results of the two
studies was due to the TRB study's low assessment of the LCV
market potential of low density or cube-limited truckload
freight traffic.
74
6. CONCLUSIONS
The underlying purpose of this study was to examine the
issue of LCV usage from the standpoint of the individual firm
or shipper rather than from the viewpoint of the motor carrier
or truck operator. This approach was quite different from that
of previous studies, which have often focused on the fixed and
variable costs of operating LCVs and the potential gains in
productivity which these truck configurations may offer to
the carrier. Based on these expected gains in carrier
productivity, previous studies have predicted significant
savings in freight costs for shippers. Freight costs, however,
comprise only a portion of the shipper's total logistics cost
which also includes the cost of carrying inventory. If by
switching from single trailer truckloads to LCVs, a shipper's
inventory carrying costs increase more than any savings in
transportation costs, then any productivity gains available to
the carrier will be lost to the shipper. The basic intent of
the current study was to determine the effects of LCVs on the
shipper's total logistics cost and how these effects might influence
the demand for LCV transport.
A major finding of this study was that, given sufficient
flows of a company's product in a lane, LCVs would generally
have a positive impact on the total logistics cost of firms
that currently ship in single trailer truckload quantities.
The actual amount that might be saved was difficult to predict,
but on the average, a reduction in total logistics cost between
13 percent and 32 percent could be expected, depending on the
type of LCV used, GVW limits, and the difference between LCV and
single trailer truckload freight charges. The higher the flow
and the longer the lane, the greater the likelihood that LCVs
would benefit the truckload shipper. In general, use of LCVs at
annual lane volumes below 5,000 cwt (226,795 kg) appears likely
to increase a firm’s total logistics cost, while at annual lane
volumes over 25,000 cwt (1,133,975 kg), LCVs are likely to
produce cost savings averaging between 16 Percent and 42
percent.
The influence of product value on the cost-effectiveness of
LCVs was surprisingly small. The correlation between product
value and the percent change in total logistics cost caused by
use of LCVs was exceedingly low. The effect of product value was
most apparent when the flow of a company's product in a lane
was below 15,000 cwt (680,385 kg) or 350,000 ton-mi (510,650
metric ton-km). In such cases, higher product values
significantly increased the likelihood that LCVs would greatly
elevate the company's total logistics cost. The main effect of
product value at moderate and large annual lane volumes was to
slightly reduce the average percentage of reduction in total
logistics cost resulting from the use of LCVs.
Predicting whether or not the use of LCVs for a given product
in a given lane will lower a company's total logistics cost
appears to be considerably easier than predicting the actual
magnitude of the change. Annual lane volume, lane distance, and
ane ton-mileage appear to be good indicators of whether or not
LCVs will be beneficial. However, none of these variables was
highly correlated with differences in total logistics cost
between
75
LCVs and single trailers. Perhaps an even better indicator is
the ratio of annual freight costs to annual inventory carrying
costs using single trailers in a lane. If the freight costs are
two or more times greater than the inventory carrying costs,
switching from single trailers to LCVs will in all likelihood
greatly reduce the total logistics cost. On the other hand, if
the inventory carrying costs are nearly the same as or greater
than the freight costs, then the chances are good that
switching from single trailers to LCVs will increase the
total logistics cost.
The effect of LCVs on the total logistics cost of rail
carload shippers was highly sensitive to the rail freight rate
per mile. While diversion from rail cars to Rocky Mountain
doubles does not appear likely, the results of the analysis
indicate that turnpike doubles when operating under higher than
existing GVW limits could reduce the total logistics cost
enough to induce some shippers to switch from rail to LCVs.
Because of the small number of rail observations in the shipper
survey data, it was not possible to estimate the amount of
diversion that might occur.
Turnpike doubles could also provide some serious competition
to rail-truck intermodal services if existing GVW limits were
to be raised. The analysis predicted a lower total logistics
cost for most of the intermodal shippers in the survey sample
as a result of switching to turnpike doubles.
Given the finding that LCVs are likely to reduce truckload
shippers' total logistics cost in most cases where traffic lane
volumes are moderate to heavy, the more difficult question still
to be answered is what effect will this have on the demand for
LCV freight services. Will shippers switch from single trailers
to LCVs in order to reduce their logistics costs? If so, how
much of a cost savings is required before a firm will make the
necessary adjustments in its production scheduling and
inventory management system to accommodate the higher shipment
quantities entailed by the use of LCVs? To what extent is the
demand for LCVs influenced by the potential savings in total
logistics cost? These are important questions which could not
be answered directly in this study. However, it was shown
that the level of cost savings necessary to induce diversion
from single trailers to LCVs has a tremendous impact on
projections of LCV demand.
With regard to the question about the relative importance
of logistics cost reduction to the demand for LCVs, a few
comments can be made. First of all, it became clear while
screening companies for eligibility to participate in the
shipper survey that many firms do not consider logistics
costs in general or inventory carrying costs in
particular when making modal choice decisions. In some cases,
particularly for small and even some medium-sized firms, it is
because the company did not have a logistics management system
sophisticated enough to determine what its inventory and other
logistics costs were. In other cases, the companies' product
costs and interest rates were so low that they no longer
considered inventory carrying costs in making daily
transportation decisions. Secondly, thereare other ways for
a company to reduce its total logistics cost than by
switching from single trailers to LCVs. A primary example of
this is thecurrent trend toward using the Just-in-Time concept
76
which trades off inventory and transportation costs by
maintaining small inventories, shipping in smaller quantities,
and using faster modes of transportation. Therefore, even though
this research has shown that LCVs are likely to have a positive
effect on shippers' total logistics cost, the trend toward
smaller shipment sizes and more rapid on-time deliveries would
seem to reduce the demand for LCVs from the standpoint of
many shippers.
77
REFERENCES
1. Providing Access for Large Trucks, Special Report 223,
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79
ORNL-6840
INTERNAL DISTRIRUTION
1-5. M. S. Bronzini 17. D. E. Reichle
6. J. B. Cannon 18. R. B. Shelton
7. S. M. Chin 19. F. Southworth
8. D. L. Greene 20. Central Research Library
9. S. G. Hildebrand 21. Document Reference Section
10. P. S. Hu 22-23. Laboratory Records
11. M. A. Kuliasha 24. Laboratory Records - RC
12-16. D. P. Middendorf 25. ORNL Patent Office
EXTERNAL DISTRIBUTION
26. Dr. Douglas R. Bohi, Director, Energy and Natural
Resources Division, Resources for the Future, 1616 P
Street NW, Washington, DC 20036.
27. Dr. Thomas E. Drabek, Professor, Department of
Sociology, University of Denver, Denver, CO 80208-0209.
28-47. Arthur C. Jacoby, U.S. Department of Transportation,
Federal Highway Administration, Office of Policy
Development, Transportation Studies Division, Industry
and Economic Analysis Branch (HPP-11), Room 3324, 400
Seventh Street, SW, Washington, DC 20590
48. Mr. Calvin D. MacCracken, President, Calmac
Manufacturing Corporation, 101 West Sheffield Avenue,
Englewood, New Jersey 07631.
49. Ms. Jacqueline B. Shrago, Vice-Chancellor, Information
Technologies, Tennessee Board of Regnets, 1415
Murfreesboro Road, Suite 350, Nashville, TN 37217
50. Mr. George F. Sowers, Senior Vice President, Law
Companies Group, Inc., 114 Townpark Drive, Suite 250,
Kennesaw, GA 30144 5599.
51. Dr. C. Michael Walton, Paul D. and Betty Robertson Meed
Centennial, Professor and Chairman, Department of Civil
Engineering, College of Engineering, The University of
Texas at Austin, Cockrell Hall, Suite 4.2, Austin, TX
78712.
52. Office of Assistant Manager for Energy Research and
Development, DOE-ORO, P.O. Box 2001, Oak Ridge,
Tennessee 37831-8600.
53-54. Office of Scientific and Technical Information, U.S.
Department of Energy, P.O. Box 62, Oak Ridge, Tennessee
37831.
