Lifting the veil of value in truckload

Latest report in a multi-issue series covering
value creation in transportation and logistics
Lifting
the veil
of value
in truckload
The building blocks of successful truckload
operations are opaque to many people.
It doesn’t need to be that way.
BY MERGEGLOBAL VALUE CREATION INITIATIVE
Value Creation in Truckload
Get advanced copies of MergeGlobal reports by visiting www.americanshipper.com/TF2008
T
he first half of 2008 was a tough time for the U.S.
truckload industry.
On the demand side, the period of Jan. 1 through
June 30 saw the lowest year-on-year growth (in real terms) in
personal consumption — which accounts for about 70 percent
of the nation’s gross domestic product (GDP) — since the same
period in 1991. Although truck tonnage, a widely monitored
industry demand indicator reported by the American Trucking
Associations (ATA), was up 3.4 percent
year-on-year, the underlying demand fundamentals were weak and the index itself
benefited from easy comparisons relative
to a very soft first half of 2007.
On the supply side, the industry was
still feeling the effects of aggressive fleet
additions carried out in 2006, in advance
of the introduction of new engine emission
regulations at the start of 2007. Indeed,
2006 sales of Class 8 tractors, the backbone
of truckload operations, marked an all-time
record and were up 12 percent from a very
strong 2005.
The combination of soft demand and
plentiful supply contributed to keeping
industry-wide capacity utilization from
recovering faster during the first half
Figure 1
U.S. surface transportation
revenue by segment: 20071
Billions of US$
Total Market: $603 billion
$15 $43
6% Local
63%
49% Domestic
Local
40% Non-tractor private fleet
Regional
11% Non-tractor for hire
3% Dedicated for-hire tractor
27% OTR for-hire tractor
Truckload
51% International
19% Tractor private fleet
$36
9%
94% Inter-city
$508
28%
Long
haul
Intermodal 2
LTL
Ground
package
1
Revenue includes fuel and other surcharges. Width of boxes represents vertical
share of industry revenue. Height of boxes and percentage values represent segment
share of revenue within each vertical. Excludes bulk rail transportation.
2
Includes drayage.
Source: 2002 Vehicle Inventory and Use Survey, Securities and Exchange Commission filings,
MergeGlobal estimates.
of 2008 (we estimate that truckload capacity utilization bottomed during the
second and third quarters of 2007). This
inevitably resulted in depressed net rates
(which exclude fuel surcharges). Capacity
utilization for the U.S. truckload industry
hovered around 76 percent during the first
half of 2008, according to our research,
down from about 86 percent at the peak of
the cycle in mid-2005. Similarly, dry van
net rates averaged $1.44 per mile during
the period, according to Truckloadrate.
com, down from $1.47 a year earlier.
As if this wasn’t enough, the first half
of 2008 also saw the highest year-on-year
increase in first-half nationwide diesel
prices on record (the Energy Information
Administration provides full-year historical data back to 1995), an astonishing 48
percent. Higher diesel prices intensified
modal shift risk for the industry vis-à-vis
rail intermodal, particularly for (although
by no means limited to) loads traveling
800 miles or more. Developments in fuel
prices also put serious pressure on carriers’
costs, due to sudden upward swings in the
price per gallon of diesel that prevented
carrier fuel surcharge adjustments from
“catching up” with energy trends. The
average week-to-week growth in diesel
prices was 1.3 percent during the first
half, compared to a normal average in
recent history of about 0.5 percent for the
same period.
Not surprisingly, truckload profitability
deteriorated substantially for the first six
months of the year almost across the board.
Particularly hit were smaller carriers and
owner-operators, who lack the marketing,
IT and bulk-buying resources of most
The MergeGlobal Value Creation
Initiative comprises Brian Clancy,
David Hoppin, John Moses and
Jim Westphal, who are managing
directors of MergeGlobal, a specialist
firm that provides clients in the global
travel, transport and logistics industries with services ranging from financial advisory to strategic consulting.
This is the latest in a series of reports
in which MergeGlobal will team with
American Shipper for multi-issue
coverage throughout 2008.
AMERICAN SHIPPER:
NOVEMBER
2008 57
Value Creation in Truckload
large fleets. Truck bankruptcies for the
period reached 1,905 among fleets of at
least five tractors, more than double the
corresponding number in 2007. And that
doesn’t even include nominal owner-operators (essentially one-truck fleets), who
represent the most vulnerable and thus
most cyclical portion of the industry.
That’s not to say, however, that large
fleets were unscathed by the challenging
environment. The combined operating
ratio inclusive of fuel surcharge (defined
as operating expenses as a percentage of
operating revenue) of Celadon, Covenant,
J.B. Hunt Truckload, and Werner was 97.9
percent for the six-month period ended
June 30, up from 95.3 percent for the same
period in 2007.
Yet, in this tough environment of truck
failures and near-100 percent operating
ratios (ORs) there were two companies
that stood out and achieved OR levels
below 90 percent: Heartland Express and
Knight Transportation. This is nothing
new. Heartland’s OR for 2006 and 2007
was 78.4 percent and 81.3 percent, respectively, compared to 93.4 percent and 96.4
percent which the four large companies
mentioned above averaged for the same
years. Knight’s corresponding OR numbers were 82.0 percent and 85.6 percent.
Why are Heartland and Knight significantly more profitable than their
peers, both in good times (2006) and bad
(2008)? What are they doing that everyone
else isn’t? What sets them apart? More
generally, what drives profitability in
truckload?
The purpose of this article is twofold.
First, we will define and analyze the key
drivers of profitability in asset-based
truckload. To that end, we will present
empirical evidence and logic to support
our answers to the above questions. Second, it is our objective to present analysis
that is detailed enough to be meaningful
and actionable — as well as able to do
justice to the complexities of the truckload
industry (sometimes poorly understood
or underestimated by industry outsiders)
—yet pragmatic enough that it is accessible
to most readers.
It’s been our experience that literature on
truckload tends to be of two types, each at
one end of the analytical rigor spectrum.
On one end there are peer-reviewed,
Ph.D. thesis-caliber studies, typically
dealing with some aspect of resource optimization utilizing operations research
techniques, whose Greek-letter-driven
arguments, though relevant and evidently
necessary, are inaccessible to readers who
lack the technical training needed to understand the analytical language used.
58
AMERICAN SHIPPER:
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2008
Figure 2
Dry van market segmentation1
U.S. Class 8 tractor/trailer trucking loads above 125 miles: 2007
Millions of loads
Total loads
213
Flatbed 84
Reefer 66
Tank 34
Other 29
484
123
Loads using
equipment other
than dry van 2
31
Intra-market
dry van loads 3
Dry van loads
to/from
59 primary
markets 3
117
Dry van loads
between nonprimary markets 3
1
Dry van includes Basic Van, Drop Frame Van, Insulated Non-refrigerated Van, Beverage, and Curtainside.
2
Flatbed includes Flatbed, Low boy, Pole & Logging and Automobile Carrier; Tank
includes Dry Tank and Liquid Tank; Other includes Dump, Livestock, Open top, and
Other.
3
Markets are defined based on the 114 zones and 17 gateways included in the Freight
Analysis Framework of the U.S. Department of Transportation. From these zones and
gateways we have aggregated metropolitan areas (e.g., New York, Chicago) into 59
primary markets; 60 other (i.e., non-primary) markets are remainders in each state
and some gateways.
Source: U.S. Department of Transportation Freight Analysis Framework, 2002 Vehicle
Inventory and Use Survey, FTR Associates, MergeGlobal analysis and estimates.
On the other end are articles and commentary that address issues in truckload
from a high level. These are accessible
to many readers, but the points made are
general enough to limit their usefulness for
carriers and shippers alike. Within the latter
avenue of literature it isn’t uncommon for
readers to be presented with elusive terms
like “lane density” to describe success in
truckload, but such a concept can mean
different things to different people, to say
the least.
Our second objective, then, is to position
this article somewhere in between these two
extremes. To present evidence that digs a
bit deeper than, say, comparing operating
statistics across companies, but that also
remains intuitive throughout. In the famous
words attributed to Einstein, we intend to
present work that is “as simple as possible,
but no simpler.”
In summary, our view on the key profitability drivers in asset-based truckload
transportation is as follows:
• The key drivers of truckload profitability are not necessarily the obvious
ones. Oft-cited metrics, such as miles per
tractor per week, empty mile percentage
and fleet size explain surprisingly little
of the difference in profitability between
Heartland and Knight and a sample of
seven other large fleets we constructed
(mostly publicly traded companies).
• Other obvious profitability drivers, like net rate improvements and cost
controls, while clearly relevant, need to
be better understood. In other words, how
can a company in fact improve its access
to better rates, or how can it better position
itself in order to keep costs low?
• There are three key profitability
drivers in truckload: 1) serving lengths
of haul of 300 to 600 miles, 2) carefully
selecting favorable destination markets
(we shall explain what we mean by “favorable” shortly), and 3) aggressively
marketing the business in markets heavily imbalanced towards loads coming in
versus going out.
• While Heartland and Knight have
been particularly successful at implementing the above drivers, carriers can carefully
adjust portions of their operations to align
more closely with these drivers as part of
their efforts towards margin expansion;
additionally, these drivers can contribute to
guiding due diligence work in the context of
mergers and acquisitions in the industry.
As for the dim scenario outlined above
for the truckload industry during the first
half of 2008, going forward we expect the
following:
• After a second half slower than the
first and an even softer first half of 2009, we
Value Creation in Truckload
Figure 3
Dry van truckload revenue in primary U.S. markets: 20071
Dry van loads shown: 117 million
Seattle
Portland
Minneapolis
Grand
Rapids
Milwaukee
Salt
Lake City
Cleveland
Dayton
Denver
Indianapolis
Kansas City
Pittsburgh
Columbus
Boston
New
York
Philadelphia
Baltimore
Washington, D.C.
Richmond
Cincinnati
St. Louis
Louisville
San Jose
Albany
Detroit
Chicago
Sacramento
Rochester
Buffalo
Greensboro
Las Vegas
Tulsa
Oklahoma City
Los
Angeles
Memphis
Greenville
Spartanburg
Raleigh
Charlotte
Atlanta
Phoenix
Dallas
El Paso
San Diego
Virginia Beach
Nashville
Birmingham
Charleston
Tucson
Savannah
Austin
San Antonio
Jacksonville
Houston
New
Orleans
Laredo
Orlando
Tampa
Total revenue (US$)
Miami
$5 billion
$10 billion
$15 billion
Color legend: Market load imbalance
Heavily
inbound
imbalanced
Heavily
outbound
imbalanced
1
Revenue, and the underlying loads that drive it, includes private
fleets, dedicated carriers and over-the-road for-hire carriers.
Source: U.S. Department of Transportation Freight Analysis Framework, Truckloadrate.com, MergeGlobal estimates.
expect economic activity (as measured by
GDP) and personal consumption to recover
in the second half of 2009 and reach a peak
in 2010, before modestly decelerating in the
2011-12 timeframe. In the meantime, we
expect trucking activity (loosely measured
by ATA tonnage) to grow faster in 2009
than in 2008. Tonnage will also peak in
2010 and then slow down quickly relative
to the macro economy, to the point of being
nearly flat by the end of 2012, as trucking
would lead an expected overall slowdown
in U.S. GDP growth in 2013.
• We expect capacity utilization in the
truckload industry to improve at a much
faster rate in 2009 and 2010 than in 2008
due to the combination of a recovery in
demand, and an expected more disciplined
approach to capacity additions by truckload
carriers.
Industry definition
Truckload transportation is typically
defined as the movement of consignments
(simply referred to as loads) that are (usually) 10,000 pounds or more in weight, in
a single piece of equipment (most likely a
53-foot trailer hauled by a three-axle tractor), directly from origin to destination.
This definition, while correct, refers mainly
60
AMERICAN SHIPPER:
NOVEMBER
2008
to a particular “flavor” of truckload: the
tractor-trailer portion of the market.
As shown in Figure 1, the truckload
market, which is a segment of the U.S.
surface transportation industry, comprises
all surface transportation that is not rail
intermodal (the movement of containers
and trailers where a portion of the journey
is on rail), less-than-truckload or LTL (the
movement of consignments from different
shippers, usually less than 10,000 pounds in
weight, in common equipment) or ground
package (the movement of small packages,
usually less than 150 pounds in weight, in
specialized equipment, from tractor-trailers to walk-in straight trucks, all the way
down to bicycles).
Truckload, as defined above, is by far
the largest segment of the U.S. surface
transportation industry, accounting for
85 percent of industry revenue. It is thus a
critical element of the everyday functioning
of the U.S. economy. Intermodal transportation tends to be at the top of people’s minds
due to its exposure to international trade,
its recent past of fast growth, and its fuel
efficiency characteristics. But the more
“humble” truckload is actually some 34
times bigger than intermodal, as measured
by revenue.
As mentioned earlier, there are several
flavors of truckload, with different underlying drivers and different behaviors (e.g.,
volatility) throughout the economic cycle.
About 60 percent of truckload revenue is
captured by the private fleet segment, which
comprises all production and commercial
companies hauling their own freight using
their own equipment (although occasionally
they might transport others’ loads in order
to improve asset utilization). A substantial
portion of private fleet operations are local
in nature (intra-city or intra-metropolitan
areas). It is estimated that private fleet
lengths of haul average fewer than 125
miles.
The remaining 40 percent of the market
comprises carriers hauling freight on their
customers’ behalf, known as the for-hire
segment. Within the for-hire segment, 73
percent of revenue is generated utilizing
tractor-trailers of some kind, most typically
the three-axle tractor and 53-foot trailer
combination previously described.
A small portion of the for-hire tractortrailer truckload segment (about 3 percent)
is represented by dedicated operations,
where shippers hire carriers on the basis of
equipment rather than discrete loads. Since
the shipper pays for the use of carrier-oper-
Value Creation in Truckload
The segmentation of the U.S. surface
transportation industry presented in Figure
1 is both shipment size- and mode-based.
It divides transportation activity according
to both the size of the discrete underlying
loads hauled and the type of transportation
system, which we call mode, being used
— the latter defined not only in terms of
equipment (i.e., rail versus truck of some
kind), but on the basis of shipment management as well (e.g., OTR versus dedicated). Broadly speaking, shipment size
can vary from full 53-foot trailerloads, to
less-than-trailerloads (e.g., a few pallets),
to one or more small packages. Surface
transportation modes, as we define them,
include dedicated trucking (either private
fleet or outsourced), OTR trucking, rail
intermodal, LTL trucking, multi-stop
OTR trucking (an economic substitute to
LTL that is under serious pressure from
higher-than-historical diesel prices), and
small package ground transport.
Shipment size is a critical determinant
of distribution costs in the United States,
which amount to about 10 percent of GDP
according to the 19th annual State of Logistics Report. It is part of the fundamental
three questions logistics managers ask
themselves as they make replenishment
decisions:
• What product (i.e., commodity
type)?
• How much (i.e., shipment size)?
• When (i.e., transport mode)?
AMERICAN SHIPPER:
CLDN
CVTI
HTLD
JBT
KNX
PTSI
USXPS
USAK
WERN
Knight Transportation,
Heartland Express
outperform rest
of truckload industry
2007 sample of U.S. truckload companies
Operating ratios 1
91%
94% 94%
EBIT per mile 2
102%
98% 98% 98%
35¢
27¢
13¢
82%
-2¢
CVTI
PTSI
3¢ 2¢
USAK
JBT
3¢
USXPS 3
WERN
KNX
HTLD 3
CVTI
PTSI
USAK
USXPS 3
JBT
CLDN
WERN
CLDN
9¢ 9¢
78%
KNX
Celadon Group
Covenant Transportation
Heartland Express
J.B. Hunt Truckload
Knight Transportation
P.A.M. Transportation Services
U.S. Xpress Enterprises
USA Truck, Inc.
Werner Enterprises
1
Net of fuel surcharge. Calculated as operating expenses minus fuel surcharge,
divided by net revenue.
2
EBIT = Earnings before interest and taxes.
Sources: MergeGlobal, Company earnings releases, and Commercial Carrier Journal.
Shippers right-size
the supply chain
62
Figure 4
HTLD
ated equipment for a pre-specified period
of time (usually in a contractual manner)
regardless of whether the equipment is full
or empty, parked or moving, dedicated operations shift the load factor risk from the
carrier to the shipper. Many dedicated operations resemble private fleet operations,
where shippers decide to either partially
or entirely outsource the transportation
segment of their value chains.
The vast majority of for-hire tractortrailer truckload activity is defined as
over-the-road (OTR), where the shipper
hires a carrier to move a load from point
A to point B (that is, on a one-way basis,
where A and B typically are different cities
or metropolitan areas), thus taking on the
load factor risk exclusively for that load.
This is the segment that most people have
in mind when thinking about “truckload”
or “trucking,” and it will be the main focus
of the rest of this article. It is, we estimate,
a $140 billion market, about 30 percent
bigger than dedicated, intermodal, LTL
and ground package put together.
NOVEMBER
2008
The ultimate goal of this shipper-specific
decision-making process is to minimize the
sum of transport-related costs and inventory-related costs in the supply chain. The
pooling of these costs at the commodity
level is known as total distribution cost, or
TDC. There’s usually a tradeoff between
transport costs and inventory costs in
supply chains, because higher speed and
reliability in transport reduces inventory
costs on the one hand but increases transport
costs on the other. Every shipper, explicitly
or implicitly, employs some form of TDC
analysis to allocate shipments across modes
of transport in such a way that the TDC
incurred is as low as possible.
Commodity type plays a key role in TDCdriven decision making. It defines demand
volumes per unit of time, the variability and
seasonality associated with those volumes,
and the unit value of the goods handled.
Importantly, it also tends to define where
in the supply chain the decision takes place:
whether it is a plant sourcing raw materials
from a supplier, a distribution center placing orders at a manufacturing plant, or a
retail store replenishing inventories from
a DC. This is an important distinction because order size variability for many retail
products tends to increase as one moves up
the echelons or links in the supply chain,
from retail stores to raw material suppliers
(a phenomenon known as the “bullwhip”
effect). The optimal shipment size and mode
selection are thus specific to a shipper and
consignee, commodity type, and supply
chain link type.
For example, commodities for which
demand levels and unit values justify
steady truckload-sized shipments, and
whose demand patterns are smooth and
highly predictable, have a high propensity for dedicated truckload use (with the
majority of it, as Figure 1 shows, being
in-sourced).
In contrast, truckload shipments that
are less frequent, relatively more variable,
and less predictable tend to be serviced by
core carrier OTR (where shippers choose
to tender most shipments to a short list of
“preferred” carriers) or spot market OTR.
Less-than-trailerload sized shipments can
either be routed in a truckload operation
that performs multiple stops or handed
over to an LTL carrier.
Finally, the breakpoint between LTL
and small package ground transportation
is generally determined by the shipment’s
physical characteristics and cost-to-serve.
Shipments that have one or more individual
pieces that weigh more than 150 pounds
are routed via LTL, because small package
carriers’ material handling equipment cannot support heavy pieces. In other words,
small package carriers are able to handle
multi-piece LTL shipments within their
networks provided each individual piece
can navigate their sorting systems.
Focus on dry van loads
Having laid out a segment-level industry
definition, we can now properly state that, as
suggested in the introduction, our objective
is to understand the key value drivers of
Value Creation in Truckload
64
AMERICAN SHIPPER:
NOVEMBER
2008
CLDN
CVTI
HTLD
JBT
KNX
PTSI
USXPS
USAK
WERN
Figure 5
Truckload profitability
is not determined
by fleet size
2007 sample of U.S. truckload companies
22%
20%
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
-2%
-4%
y = -0.0016x + 0.0792
R2 = 0.0023
HTLD
KNX
CLDN
WERN
JBT
USAK
USXPS 3
PTSI
Celadon Group
Covenant Transportation
Heartland Express
J.B. Hunt Truckload
Knight Transportation
P.A.M. Transportation Services
U.S. Xpress Enterprises
USA Truck, Inc.
Werner Enterprises
Operating costs vs. fleet size
$1.45
Operating cost per mile
EBIT margin
EBIT margin vs. fleet size
JBT
PTSI
$1.40
USXPS 3
CVTI
$1.35
WERN
$1.30
KNX
CLDN
$1.25
USAK
CVTI
y = 0.0094x + 1.2906
R2= 0.0948
HTLD 3
$1.20
0
1
2
3
4
5
6
7
8
9
0
Fleet size (Thousands of tractors)
1
2
3
4
5
6
7
8
9
Fleet size (Thousands of tractors)
Figure 6
Higher equipment utilization does not
guarantee higher truckload profitability
2007 sample of U.S. truckload companies
EBIT margin vs. miles per tractor
22%
20%
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
-2%
-4%
90
EBIT margin vs. deadhead percentage
HTLD 3
KNX
y = -0.0045x + 0.5743
R2 = 0.263
WERN
JBT
CLDN
USXPS 3
USAK
PTSI
CVTI
95
100
105
110
115
120
125
Annual miles per tractor (thousands)
EBIT margin
EBIT margin
for-hire, inter-city (i.e., non-local), one-way
(i.e., OTR) truckload operations.
To that end, and in order to both simplify
the analysis and standardize markets, we
decided to focus on dry van loads to/from
59 primary U.S. metropolitan areas that
are at least 125 miles apart.
The length of haul part of our market
definition allows us to look at the portion
of the market most typically served by
for-hire OTR operators, rather than private
fleets or dedicated operators (which tend
to concentrate on routes that are shorter
than 125 miles). As for our equipment type
focus, we are interested in providing insight
into the most commoditized portion of the
truckload market, the dry van segment,
where barriers to entry are lowest and value
drivers are thus more nuanced.
In 2007, there were 117 million dry van
Class 8 tractor-trailer loads with lengths
of haul above 125 miles to/from our 59
primary markets (Figure 2). This represents 43 percent of all such loads over all
markets, and about a quarter of total Class
8 tractor-trailer loads traveling more than
125 miles that the U.S. economy generated
in that year (some 484 million, according
to FTR Associates).
Our 117 million dry van load sample
includes 10,088 unique origin-destination (OD) pairs and 120,532 unique
origin-destination-commodity (ODC)
combinations. The top five commodities
in the sample (machinery, plastics/rubber,
electronics, miscellaneous manufactured
products, and newsprint/paper) account
for 47 percent of all loads. The weighted
average length of haul over all loads in
the sample is 630 miles, with a slightly
U-shaped distribution among the 125 to
300-mile, 300 to 600-mile, and 600-plusmile length of haul brackets (39 percent,
26 percent, and 35 percent of all sample
loads, respectively). On average, each OD
pair in the sample generated 46 loads per
shipping day.
Dry van markets in the United States differ markedly in terms of size (as measured
by revenue) and load imbalance (defined as
outbound loads divided by inbound loads,
where the closest the ratio is to 1 the more
balanced the market is (Figure 3).
The largest markets are the Los Angeles
Basin, across Texas (Laredo-HoustonDallas), the upper Midwest (ChicagoDetroit-Cleveland), the New York area,
and Atlanta. Miami and the Bay Area in
Northern California are also large (and
heavily inbound imbalanced) markets.
There are more inbound imbalanced than
outbound imbalanced markets in the continental U.S. The three largest markets,
though relatively similar in size, have
22% y = 1.5421x - 0.1032
HTLD 3
20% R2 = 0.1706
18%
KNX
16%
14%
12%
WERN
10%
CLDN
8%
6%
JBT
4%
PTSI
2%
USAK
USXPS 3
0%
-2%
CVTI
-4%
6% 7% 8% 9% 10% 11% 12% 13% 14%
Deadhead miles as % of total
1
Net of fuel surcharge. Calculated as operating expenses minus fuel surcharge,
divided by net revenue.
2
EBIT = Earnings before interest and taxes.
3
Estimated.
Sources: MergeGlobal, Company earnings releases, and Commercial Carrier Journal.
very different balance characteristics: Los
Angeles is balanced, Chicago is outbound
imbalanced, and New York is inbound
imbalanced.
A number of balanced and outbound
imbalanced markets benefit from international gateways (border crossings or
maritime ports) and/or inland railheads,
which function as load-generating engines
within their geographical demarcations.
These include Los Angeles; Portland, Ore.;
Chicago; Memphis, Tenn.; St. Louis; Boston; and Laredo, Texas, among others.
This isn’t always the case, however. Im-
portant gateway markets, like New York,
Miami, Philadelphia, Seattle, Houston
and the California Bay Area (San Francisco/Oakland/San Jose) are all inbound
imbalanced. Similarly, the Atlanta, Dallas,
and Kansas City markets are inbound imbalanced despite being prominent inland
rail intermodal destinations. Clearly, the
reason for the imbalance is that all of
these markets are major population centers
with strong production and consumption
footprints. The fact that Los Angeles and
Chicago, being such heavily populated
areas, are nevertheless balanced and out-
Value Creation in Truckload
Figure 7
Key drivers of truckload profitability aren’t necessarily obvious ones
1
Lengths of haul
between 300
and 600 miles
2
Destination
market
selection
Front haul rate
Average rate
per mile
Back haul rate
Revenue
Deadhead
Loaded miles
3
EBIT
Total miles
per tractor
Aggressive
marketing
in inbound
inbalanced
markets
Operating
costs
Cost per mile
Source: MergeGlobal analysis.
The drivers that aren’t
66
AMERICAN SHIPPER:
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2008
Truckload trips between 1–1.5 days
tend to be the most profitable
US$ per load
$3,500
Average revenue
Total cost
Variable cost (Mileage driven)
Fixed cost (Hourly driven)
$3,000
$2,500
$2,000
weet spot
“s
”
We believe some closely tracked and
often-reported operating metrics that
conventional wisdom would have as
obvious candidates for key value drivers
in truckload have in fact little to do with
contributing to superior profitability. One
of them is fleet size.
Simply put, there are no economies of
fleet size in truckload. The left-hand panel
of Figure 5 shows that, outside of Knight
and Heartland, EBIT (earnings before
interest and tax) margins in 2007 were
relatively similar (and all below the two
leaders’ margins) for our sample companies regardless of fleet size. If anything,
as shown in the right-hand panel of Figure
5, fleet size seems to even be positively
Figure 8
n
The U.S. dry van, for-hire, OTR truckload market has a key characteristic from
a profitability perspective: it is clearly
dominated by two players — Heartland
Express and Knight Transportation. In
particular, as shown in Figure 4, these two
relatively similar companies (both have medium-sized fleets, are regionally oriented
and utilize a decentralized operating model)
have significantly better profitability characteristics than the largest, iconic players
(Werner, J.B. Hunt Truckload, U.S. Xpress),
long-haul players (Covenant, Celadon), and
relatively smaller, less known players (USA
Truck, P.A.M. Transportation).
In the following sections we will define
what factors contribute the most to truckload profitability and, when possible and
relevant, will provide examples of what
Heartland and/or Knight are doing with
regards to each of those key factors.
mentioned, that fleet size is positively
correlated with operating costs per mile),
the R-squared associated with each line (a
measure of the explanatory power of fleet
size with respect to margins and costs per
mile, respectively) is so low that the correlations are not statistically different from
zero. However, this is precisely our key
point: fleet size has no discernible impact
on either EBIT margins or operating costs
per mile. In truckload, you can be big or
small, but that says little about how profitable you might be.
Asset-based truckload operations, such
$1,500
Marg
i
Two sides of an industry
correlated with operating costs per mile.
Thus, we can safely conclude that fleet size
is not among the key profitability drivers
in truckload.
The scatter plots in Figure 5 and, most
importantly, the regression lines they
produce (which minimize the distance
between them and each observed value
plotted) suggest that fleet size is at least
uncorrelated with profitability. While the
regression lines do have a slope, negative
on the left (suggesting that fleet size is
negatively correlated with EBIT margin)
and positive on the right (suggesting, as
Cumulative revenue and costs
bound imbalanced markets, respectively,
is an indication of their importance as
freight hubs.
$1,000
$500
$0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1,180
1,455
1,745
Elapsed days
80
345
660
925
Length of haul (miles)
Sources: Truckloadrate.com, MergeGlobal analysis and estimates.
Value Creation in Truckload
Key drivers of truckload
profitability
In our view, the key drivers of truckload
profitability are not necessarily the obvious
ones (fleet size, miles per tractor, empty
mile percentage). And while it might be
obvious that rate per mile is among the key
value drivers in truckload (as pricing is
critical in businesses of any sort), the way
to access favorable rates is seldom clearly
conveyed by either industry analysts or even
practitioners. It might also be obvious that
minimizing costs is important for margin
expansion but, how exactly can carriers
better position themselves to lower their
operating costs?
We believe there are three key profitability drivers in truckload:
• Serving lengths of haul of 300 to
600 miles.
• Carefully selecting favorable destination markets.
• Aggressively marketing the business in markets that are heavily inbound
imbalanced in terms of loads coming in
68
AMERICAN SHIPPER:
NOVEMBER
2008
Figure 9
Controlling for length of haul, market
directional imbalance is a primary driver
of truckload rates1
Outbound
Market load imbalance
Load imbalance and deviation from stage-length adjusted length of haul
for primary U.S. markets
Inbound
as those conducted by Knight, Heartland
and the rest of our sample truckload companies, are, as the name indicates, asset intensive. It is therefore reasonable to assume
that asset utilization is probably among the
key determinants of profitability. If companies maximize miles per tractor while
at the same time minimizing empty miles
driven, the argument would go, they would
then maximize the amount of revenue miles
they get out of their assets — thus critically
contributing to better profits and returns.
The problem with that argument is that it is
not borne out by evidence (Figure 6).
Knight and Heartland trucks, for example, run significantly fewer miles than
those of Covenant or Werner. What’s more,
Heartland and Knight are among the companies in our sample with the highest empty
mile percentage (typically referred to as
deadhead percentage), surpassed only by
Werner and (narrowly) by U.S. Xpress.
Naturally, this is not to say that companies that park their trucks most of the year
would suddenly start seeing their profit
margins inexorably rising. There certainly
is a minimum level of utilization firms must
get out of their equipment, which is expensive, in order to be in business sustainably
(our 10-company sample suggests such a
level is somewhere around 90,000 to 95,000
miles per tractor per year, with about a 12
percent to 13 percent deadhead). But we do
mean that once minimum-utilization levels
are attained, winning in truckload is not
necessarily about maximizing miles per
tractor and/or minimizing empty miles.
0.8
Undersupplied
0.7
Salt
Lake
City
0.6
Cleveland
0.5
Kansas
Vineland
0.4
Memphis
City
Chicago
0.3
Tulsa
Charlotte
Laredo
0.2
San
St. Louis
Antonio
0.1
Portland
Greensboro
Grand Rapids
0
Los Angeles
Pittsburgh
Detroit
-0.1 Boston
Cincinnati
Dallas
-0.2
Phoenix
Atlanta
-0.3
Baltimore
Philadelphia
New
-0.4
Birmingham
Minneapolis
York
Houston
Columbus
-0.5
Indianapolis
-0.6
Miami Raleigh
2007 outbound loads 3
Jacksonville
-0.7
11 million
-0.8
San Jose
6.6 million
2.2 million
-0.9
Seattle
-0.10 Oversupplied
-0.11
-0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45
Deviation from average stage-length adjusted outbound rate per mile (US$) 2
1
Average rates for all equipment types (dry van, reefer and flatbed) during June 2008,
net of fuel surcharge.
2
For every market, the average stage-length adjusted outbound rate per mile is the rate
that is justified by the weighted average outbound length of haul to all destination markets.
Thus, the deviation from that rate is the portion of the observed (i.e., actual, as reported
by Truckloadrate.com) weighted average rate that is not explained by length of haul.
3
Includes all equipment types.
Sources: U.S. Department of Transportation Freight Analysis Framework, Truckloadrate.com,
MergeGlobal analysis and estimates.
versus going out (what we call “black hole”
markets).
As illustrated in Figure 7, each of these
three key drivers directly impacts one or
more of the discrete elements that define
truckload EBIT:
• Front-haul and back-haul rates.
• Deadhead percentage.
• Total miles per tractor.
• Operating cost per mile.
What follows will shed light on how
exactly these relationships play out.
Driver No. 1: The right
length of haul
Consistently serving loads with lengths
of haul of 300 to 600 miles (or loads that
take about one to one-and-a-half elapsed
days to be completed) allows a firm to
expand its operating margin by positioning itself in the area of the length of haul/
elapsed time spectrum where the distance
between load revenue and load costs tends
to be the largest (what we call the truckload
margin “sweet spot”). That is, as suggested
in Figure 8, serving the 300 to 600 mile
market has a margin-improving effect
on both rates and cost per mile. A full 26
percent of our sample dry van loads falls
within this length of haul band.
How does that attractive revenue-cost
differential at 300-600 miles come about?
Let’s look at the revenue side first. By staying within this length of haul band, companies improve the likelihood of attaining
attractive (i.e., higher than average) rates
per mile relative to longer haul operations
by virtue of allowing their equipment to
take more loads per period of time. Why
not then serve even shorter hauls? Because
of utilization risk. Very short hauls typically take place within metropolitan areas
that tend to be congested. Traffic and other
delays reduce asset productivity, possibly
below the minimum level described above.
Therefore, it is more attractive for a truckload operator to serve shorter hauls (about
250 miles or less) on a dedicated basis,
where load factor and utilization risk are
mitigated by the contractual nature of those
services. Indeed, 300-600 mile routes tend
Value Creation in Truckload
Driver No. 2: The right markets
In 2007, Heartland and Knight commanded the highest net rates per loaded mile
among our sample of trucking companies.
What enabled them to outperform their
peers in accessing attractive rates? Here’s
our hypothesis.
Like any other reasonably competitive
70
AMERICAN SHIPPER:
NOVEMBER
2008
Figure 10
Outbound imbalanced shapes are more
profitable than inbound imbalanced shapes
Dry van triangle itinerary profit margin vs. outbound/inbound load balance 1
40%
20%
Itinerary profit margin
to be run between suppliers and manufacturing plants, and between plants and DCs,
where there is less shipper propensity to
employ dedicated fleets relative to DCto-retail store routes. Moreover, serving
the 300-600 mile market allows trucking
companies not to aggressively compete
(rate-wise) with intermodal marketing
companies or so-called bimodal operators
for longer haul loads.
On the cost side, first is the issue of driver
retention. The regional nature of the “sweet
spot” length of haul lowers driver turnover
by allowing drivers to be home more often
or, at a bare minimum, by virtually ensuring
that a driver will permanently be within a
day’s drive from his or her loved ones. It
also allows drivers to operate mostly in
familiar territories. Lower driver turnover
is one of the most significant enablers of
lower cost per mile. Heartland’s turnover is
40 percent lower than the industry average,
while its operating cost per mile, as shown
in Figure 5, was about tied for lowest in 2007
among our sample companies. (With USA
Truck, the difference in margins between
the two is explained by USA Truck’s much
lower average revenue per loaded mile
excluding fuel surcharge, which in 2007
was the second lowest of our sample, with
Heartland the highest.)
Other cost-control advantages of regional
truckload operations include:
• More frequent and more consistent
(i.e., in-house conducted) maintenance
work performed on equipment (which
prevents service breakdowns and lowers
insurance premiums).
• Improved purchasing power with
regional suppliers (of everything from
parts to fuel).
• Relatively less complex dispatching
due to more repetitive load patterns.
Among the 59 primary markets in our
dry van sample (Figure 3), Washington;
Columbus, Ohio; Baltimore; Cincinnati;
and Nashville, Tenn., are the top five in
terms of most unique OD pairs in the 300600 mile length of haul range. Cleveland,
Chicago, Dallas, Houston and Los Angeles
(in that order) are the top five in outbound
loads generated for the same length of
haul range (and together account for a 30
percent share of all our sample 300-600
mile outbound loads).
0%
-20%
y = 1.1909x -1.0831
R² = 0.6472
-40%
-60%
60%
65%
70%
75%
80%
85%
90%
95%
100%
Itinerary outbound/inbound load balance
1
Each point in the chart represents one triangle (i.e., A to B, B to C, and C back to A)
itinerary linking 3 primary markets with at least 10,000 dry van loads per year on each
leg. The profit for each triangle itinerary is calculated by subtracting total trucking costs
over the three legs from the sum of revenue on each leg, weighted by the leg-specific
probability of obtaining a load. This probability depends on the outbound/inbound load
balance at each node. If there are more inbound loads than outbound loads at one node,
the probability to obtain a load for the next leg is assumed to be outbound loads divided
by inbound loads. Otherwise, the probability is assumed to be 100%. The X-axis shows
the average probability of obtaining a load in all three nodes, weighted by revenue on
the “next leg.” The Y-axis shows profit margin for each full triangle itinerary.
Sources: U.S. Department of Transportation Freight Analysis Framework, Truckloadrate.com,
MergeGlobal analysis and estimates.
market, pricing in truckload should be
defined, all else equal, by the interaction
between supply and demand. That is, holding everything else constant, truckload
rates should be higher where demand
outstrips supply and lower where the opposite is true. Figure 9 provides evidence
that in fact that is the case in the primary
U.S. trucking markets.
In particular, Figure 9 shows that,
controlling for length of haul, outbound
truckload rates are higher in outbound
imbalanced markets (where demand is
higher than supply), and lower in inbound
imbalanced markets (where supply is higher
than demand). What is more, we’ve found
that the more imbalanced a market is, the
higher the divergence between the market’s
average outbound rate and its average
stage-length adjusted outbound rate. This
market rate deviation from stage-length
adjusted rates per mile is best understood
as the portion of the average market rate
not explained by the market’s length of
haul profile associated with the outbound
loads it generates.
This simply means that, as one would
expect, the more demand outstrips supply
in a given market, the higher rates tend to
be. The key implication of this analysis is
that truckload carriers that are judicious
about which destination markets to serve
can choose to serve outbound imbalanced
(i.e., supply constrained) markets where
rates are likely to be much more attractive than those associated with inbound
imbalanced markets. This is why carefully
selecting favorable destination markets is
a key driver of truckload profitability and
why Figure 7 presented it as impacting
front haul rates.
Figure 10 provides further evidence that
carefully selecting destination markets
translates into higher average rates for
truckload carriers. A key distinction between Figures 9 and 10, however, is that the
latter goes one step further by showing full
triangle itineraries that result from linking
three markets in succession (which is more
realistic relative to how truckload companies
actually operate), rather than simply comparing possible destination markets. It also
goes further in that, rather than only using
rates to compare markets, it calculates and
compares full itinerary profitability.
Each point in Figure 10 represents a
triangle itinerary (where a truck may carry
a load from A to B, then another from B
to C, and finally a third one from C back
to A) linking three primary U.S. markets
with at least 10,000 dry van loads per year
on each leg. The profit for each triangle
itinerary is calculated by subtracting total
trucking costs over the three legs (which
Value Creation in Truckload
Figure 11
Marketing efforts can produce meaningful
differences in profitability
“Bad” Itinerary
Annual Miles
95,833
Revenue
$161,000
EBIT
$9,300
Operating ratio
94%
Deadhead
11.1%
Outbound
imbalanced
market.
RPM 1: $1.75, LoH 2: 600
:
ad
dhe
Dea
2
0, LoH
: $1.4
RPM
3
1
50
: 500
Inbound imbalanced
market yields only 6
outbound loads for every
10 loads into the market.
Elapsed time:
2.5 days
“Good” Itinerary
Annual miles
96,667
Revenue
$193,667
EBIT
$22,667
Operating ratio
86%
Deadhead
12.9%
Outbound
imbalanced
market.
ad 3
: 75
RPM 1: $2.10, LoH 2: 300
0, LoH 2
: 280
Inbound imbalanced market addressed
through increased marketing efforts.
While market has 8 outbound loads
for every 10 inbound, marketing
improves company’s results to 9
outbound for every 10 inbound.
dhe
RPM 1
: $1.9
Elapsed time:
1.5 days
Dea
take into account fully loaded operating
costs per mile and introduce leg-specific
cost drivers, such as traffic congestion)
from the sum of revenue on each leg,
weighted by the leg-specific probability of
obtaining a load. This probability depends
on the outbound/inbound load balance
at each node. If there are more inbound
loads than outbound loads at one node,
the probability of obtaining a load for the
next leg is assumed to be outbound loads
divided by inbound loads. Otherwise, the
probability is assumed to be 100 percent.
The figure’s X-axis shows the average
probability of obtaining a load in all three
nodes, weighted by revenue on the “next
leg.” The Y-axis shows profit margin for
each full triangle itinerary.
What we’ve found is that, among all
possible triangle itineraries that we were
able to construct within all primary markets (as defined in Figures 2 and 3) with
at least 10,000 dry van loads per year on
each leg, the most profitable ones are the
most outbound-imbalanced (or under-supplied) triangles. This is further proof that
carefully selecting destination markets
results not only in better rates, but improved
overall profitability. The implication is that
companies should seek to string together
outbound imbalanced markets when forming the “power shapes” that underlie their
dispatching operations.
1
2
RPM= Rate per mile (US$).
LoH= Length of haul (miles).
3
Distance in miles.
Source: MergeGlobal analysis.
Load tradeoffs
Figures 9 and 10 show that outbound
imbalanced markets are attractive because
they sustain higher average rates, and
power shapes (triangles, rectangles, etc.)
that string together outbound imbalanced
markets are more profitable than those
where one or more nodes in the shape are
inbound imbalanced markets that drive
down the shape’s overall balance.
However, it is clear from Figure 3 that
there are more inbound imbalanced than
outbound imbalanced markets in our
59 primary market sample. This means
companies won’t be able to consistently
move loads from one outbound imbalanced
market to another.
There are two things that companies can
do when serving an inbound imbalanced
market:
• Choose to serve an outbound imbalanced market that would put the truck back
into the flow of an outbound imbalanced
shape (bypassing the opportunity of getting
a load in the starting market).
• Conduct aggressive marketing — by
virtue of a strong sales force — in inbound
imbalanced markets in order to maximize
both the likelihood of getting a load out of
those markets and the attractiveness of the
72
AMERICAN SHIPPER:
NOVEMBER
2008
rate associated with those loads.
This section will provide commentary
on the first of these two alternatives.
The reason why we extended two arrows from Destination Market Selection in
Figure 7 to deadhead and miles per tractor
(two of the most widely used indicators of
asset utilization in the truckload industry)
is precisely because of the first point:
sometimes, and especially when a truck
finds itself in an inbound imbalanced (i.e.,
unattractive) market, it is better to leave
a market empty in order to get to a more
favorable market, even if this worsens the
company’s deadhead percentage and miles
per tractor indicators.
In other words, the blind pursuit of
high asset utilization in truckload has
consequences, which in many cases means
running loads where the company passes up
favorable markets that would either provide
a better rate or would take a truck to a place
where it can rejoin the flow of an outbound
imbalanced power shape. We believe it is no
coincidence that in 2007 Knight’s deadhead
percentage was higher than virtually all
other companies in our sample that publicly
reported it (only Werner’s was higher).
Similarly, Knight’s 2007 miles per tractor
was the second-lowest of the sample. We
believe these two indicators are a key part
of Knight’s superior profitability, rather
than a hindrance to it.
Driver No. 3: The right marketing
No matter how hard dispatchers work to
string together outbound imbalanced markets, trucks will eventually end up in inbound
imbalanced markets (e.g., New York) where
too many trucks chase too few outbound
loads and therefore pricing, for the fortunate
few that get a load, is depressed.
Under those circumstances, it is typically worthwhile for a trucking company
to invest in a strong, aggressive local sales
force that can:
• Significantly increase the likelihood
of getting outbound loads.
• Improve the rates associated with
outbound loads.
• Improve the likelihood of getting
outbound loads destined to favorable
markets.
Specifically, sales force investments
should go well beyond increasing the number
of sales agents in a market, and focus on
developing shipper industry specialization
and operations expertise in order to take
load share away from competitors.
As it turns out, this is exactly what
Value Creation in Truckload
Figure 12
Outbound revenue within 300-600-mile length of haul
vs. market imbalance in primary U.S. markets1
Seattle
Portland
Minneapolis
Milwaukee
Chicago
Denver
Sacramento
San Jose
Las Vegas
Albany
Boston
Cleveland
New
York
Pittsburgh
Indianapolis
Philadelphia
Columbus
Kansas City
Cincinnati
Baltimore
St. Louis
Washington, D.C.
Louisville
Richmond
Greensboro
Virginia Beach
Tulsa
Nashville
Oklahoma City
Raleigh
Greenville
Memphis
Charlotte
Spartanburg
Dayton
Atlanta
Phoenix
Dallas
San Diego
Rochester
Buffalo
Detroit
Salt
Lake City
Los
Angeles
Grand
Rapids
Birmingham
Charleston
Tucson
El Paso
Savannah
Austin
Jacksonville
Houston
San Antonio
New
Orleans
Laredo
Total revenue (US$)
Orlando
Tampa
Miami
$5 billion
$10 billion
$15 billion
Color legend: Market load imbalance
Heavily
inbound
imbalanced
Heavily
outbound
imbalanced
1
Dry van loads only. Load imbalance calculated for all lengths
of haul.
Source: U.S. Department of Transportation Freight Analysis Framework, Truckloadrate.com, MergeGlobal estimates.
Heartland has done in markets traditionally
recognized as “black holes” due to their being
heavily inbound imbalanced, such as Miami.
From a marketing standpoint, Heartland is
uniquely positioned in markets that other
trucking companies might be too quick to
dismiss as ones with poor return on investment. The key point is that small changes in
outbound load conversions and better rate
negotiations in inbound imbalanced markets
have a big impact in profitability, as Figure
11 exemplifies on a conceptual basis.
Finding attractive markets
The most attractive markets as defined
in this article (outbound imbalanced, with
high demand for loads in the 300-600 mile
“sweet spot”) are mostly located in the upper Midwest and the mid Atlantic (Figure
12). The majority of attractive markets are
located east of the Mississippi, coinciding
with U.S. population density patterns and
with early market development impacts of
the Interstate Highway System.
Areas like Texas, the U.S. Northeast
and the California Bay Area, all clearly
relevant from a general economic and
trucking activity standpoint, are important
markets in their own right for the 300-600
74
AMERICAN SHIPPER:
NOVEMBER
2008
mile load range, but are heavily inbound
imbalanced. Truckload carriers who often
find themselves in those markets should
assess the effectiveness of their sales force
(preferably on a local basis) and consider
strengthening it.
Miami, which is an important market
from a total loads standpoint (i.e., considering all lengths of haul), is relatively not
as strong when looking specifically at the
amount of outbound loads within 300-600
miles it generates per year (in contrast with
Jacksonville, for example, which is not that
big a market relative to other key markets
nationwide, but most of the outbound loads
it generates are within the length-of-haul
sweet spot). The implication is that sales
efforts should be particularly scrutinized
for a market like Miami, which is not only
heavily inbound imbalanced but also biased
towards out-of-the-sweet-spot loads.
Implications for growth
strategies
The three key value drivers outlined
above can serve as an effective tool for
companies developing and/or implementing growth strategies. In the context of an
acquisition, for example, it is important
to assess how well positioned a target is
in terms of generating profitable loads in
combination with the acquirer’s operations. For example, subscale or inefficient
workforce teams (at the market level)
from either side of the transaction can be
combined into a more capable, unified
workforce where geographic coverage is
enlarged or deepened and internal best
practices are shared.
Furthermore, acquirers can look at target
customer lists or, more to the point, the
load patterns of those customers, to assess
whether the load profile of a target would
complete previously inaccessible power
shapes, or complement shapes already run
in everyday operations.
Finally, acquirers need to assess the
length of haul profile of a target and
determine whether it would improve or
deteriorate the length of haul profile of the
combined entity relative to the favorable
band introduced earlier.
What’s ahead?
Figures 13 and 14 present historical and
forecast annual data on
• U.S. economic activity.
Value Creation in Truckload
Figure 13
U.S. economic activity and truck tonnage
trends: 2002-2012
Percent change from year before
GDP and personal
consumption growth
Truck tonnage.
Industry capacity utilization.
Industry pricing.
For 2008, we expect tonnage to close the
year a bit slower than the way it opened it,
but it will still end up with year-on-year
growth of about 3.1 percent. The reason
for the second-half reduction in tonnage
growth is an expected further slowdown in
consumer spending, due to the combined
effects of the credit crunch, depressed
housing prices, growing unemployment, a
weaker dollar than in the recent past, and
an overall lack of consumer confidence.
We expect consumer spending to remain
depressed through the first half of 2009.
The consumption outlook will begin to turn
around during the second half of 2009, as
credit availability improves and as disposable income previously devoted to expensive
gas at the pump is temporarily diverted to
non-oil consumer goods, so long as the global
economic downturn keeps oil prices below
recent highs. As consumer confidence is
gradually restored, we expect tonnage to
grow faster in the second half of 2009 relative
to the first, driven by shipper restocking of
shelves in anticipation of a more generalized
economic recovery by late 2009/early 2010.
Indeed, we project faster tonnage growth in
2009 than in 2008 (Figure 13).
We expect personal consumption and
overall GDP to peak in 2010, before cooling down somewhat in 2011-12. In the
meantime, we expect tonnage growth to
also peak in 2010 and then quickly slow
down (relative to the macro economy)
to the point of being nearly flat by the
end of 2012, as trucking would lead our
expected overall slowdown of the U.S.
economy in 2013.
4.5%
4.0%
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0%
Truck tonnage growth
•
•
•
7.0%
6.0%
5.0%
4.0%
3.0%
2.0%
1.0%
0.0%
-1.0%
-2.0%
Forecast
Real personal
consumption
Real GDP
2002
2003
2004
2005
2006
2002
2003
2004
2005
2006
Capacity utilization for the trucking industry bottomed in 2007, which coincided
with the lowest net rate growth (actually, a
decline) of the past several years (Figure
14). We project 2008 capacity utilization
to improve relative to 2007, aided more by
the fast rate at which capacity is leaving (or
not coming into) the industry (due to truck
2004
2005
2006
2007
2008
2009
2010
2011
Truckload rates growth
Truckload capacity utilization
90%
87%
84%
81%
78%
75%
72%
69%
66%
63%
60%
Forecast
2003
2009
2010
2011
2012
2007
2008
2009
2010
2011
2012
Sources: American Trucking Associations, Bureau of Economic Analysis, MergeGlobal
estimates.
U.S. Class 8 capacity utilization
and truckload pricing: 2002-2012
2002
2008
Forecast
Figure 14
8%
7%
6%
5%
4%
3%
2%
1%
0%
-1%
2007
2012
Class 8 capacity utilization (right axis)
Truckload revenue per loaded mile, excluding fuel surcharge, year-on-year growth (left axis)
Sources: American Trucking Associations, Bureau of Economic Analysis, Ward’s Auto,
Vehicle Inventory and Use Survey, U.S. Commerce Department, Truckloadrate.com,
Securities and Exchange Commission Filings, MergeGlobal analysis and estimates.
failures, the lowest Class 8 sales rate in decades, fleet reductions by large companies,
and strong truck exports in 2007 that have
continued, albeit at a lower rate, in 2008)
than by an uptick in demand.
We expect capacity utilization to improve
at a much faster rate in 2009 and 2010
than in 2008 because of the combination
of recovery in demand, and a more disciplined approach to capacity additions
by truckload carriers, for several reasons
(a resolve to recover their cost of capital
by 2010, a much more stringent access to
credit, and an expected relatively muted
2009-10 pre-buy season, among others).
Utilization rates will then start to slightly
ease up during 2011-12 as demand growth
(in terms of tonnage) decelerates on the one
hand and either new or existing players try
to capture market share through capacity
additions on the other.
As for pricing, as it has been the case
in the past, we expect it to continue to
move closely with utilization rates. More
immediately, we expect truckload net
rates to be about flat in 2008 relative to
2007 and to start growing in earnest by
the second half of 2009. Thereafter, we
project rate growth to continue through
2012, at an average annual rate of about
4 percent.
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