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www.emeraldinsight.com/0960-0035.htm
The potential for reducing empty
running by trucks: a retrospective
analysis
Alan C. McKinnon and Yongli Ge
Reducing empty
running by
trucks
391
Logistics Research Centre, Heriot-Watt University, Edinburgh, UK
Abstract
Purpose – The aim is to examine the recent trend in empty running by trucks in the UK and
assesses the potential for a further reduction in empty running in the food supply chain using a
new technique.
Design/methodology/approach – Data from the UK Government’s main road freight survey and
other studies are used to investigate the causes of the decline in empty running. Previous attempts to
quantify opportunities for backloading are reviewed. The 2002 KPI Survey in the UK food supply
chain created a large multi-fleet database of over 20,000 trips, which permitted retrospective analysis
of backloading opportunities. A method was devised to screen these opportunities against four
selection criteria and assess the overall potential for cutting empty truck-kms.
Findings – Suitable backloads were found for only 2.4 per cent of the empty journey legs,
representing 2 per cent of empty truck-kms. The analysis highlights the operational constraints on
backloading in a sector characterised by short average trip length, tight scheduling and variable use of
refrigeration.
Research limitations/implications – The analysis provides a more accurate and realistic
assessment of backloading potential than previous studies, though is still deficient in several respects.
The main shortcomings relate to the sampling method and structure of the Transport KPI Survey.
The analytical framework requires further development to refine backload search areas, incorporate
commercial data and permit sensitivity analysis.
Originality/value – The paper shows how retrospective analysis of road deliveries made over
a short period (48 hour) can identify opportunities for backloading at a sectoral level. It combines
government statistics and original survey data to provide both a macro- and micro-level perspective on
the empty running problem.
Keywords Supply chain management, Freight forwarding, Food products, Transport management,
United Kingdom
Paper type Research paper
Introduction
A fundamental difference between passenger and freight transport is that people
generally return to their starting point, whereas almost all freight consignments move
in one direction, from point of production to point of consumption. This creates a major
logistical challenge; that of finding backloads for returning vehicles. The efficiency of
any transport operation is critically dependent on the degree to which vehicle capacity
can be utilised in both directions. In the absence of a backload the vehicle must return
This study was supported by a grant from the UK Department for Transport as part of it Future
Integrated Transport research programme. The authors are grateful to the members of the
Industrial Advisory Group and to all the companies that participated in the 2002 Transport KPI
Survey.
International Journal of Physical
Distribution & Logistics Management
Vol. 36 No. 5, 2006
pp. 391-410
q Emerald Group Publishing Limited
0960-0035
DOI 10.1108/09600030610676268
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392
empty, usually at the carrier’s expense. This empty running not only represents
a wasted resource in economic terms; it is increasingly being regarded as an
environmental liability. It is hardly surprising, therefore, that most sustainable
distribution strategies, at both government (Department of the Environment,
Transport and the Regions, 1999) and company levels (Holman, 1996; ECR Europe,
2000), prioritise the reduction of empty running.
In the UK the proportion of kilometres run empty by trucks with gross weights over
3.5 ton or more has been steadily declining for over 30 years, yielding large economic
and environmental benefits. According to the government’s main survey of road
freight movement, the Continuing Survey of Road Goods Transport (CSRGT) it
declined from 33.7 per cent in 1973 to 26.5 per cent in 2003 (Department for Transport,
2004) (Figure 1). Other things being equal, if the percentage of empty running had
remained at its 1973 level, road haulage costs in 2003 would have been £1.3 bn higher
and an extra 1.1 million of ton of CO2 would have been emitted into the atmosphere by
trucks[1]. It is difficult to predict how long this favourable trend will continue and at
what level it will ultimately stabilise.
The research reported in this paper aimed to assess the potential for reducing
empty running by trucks in the British grocery supply chain. A new analytical
technique was developed to measure opportunities for backloading across a large
sample of trucks whose operations had been closely monitored over a 48-hour period.
It was possible retrospectively to reconstruct the patterns of vehicle and freight
movement over this period and to test the feasibility of load matches against different
sets of operational criteria. The main part of the paper outlines this analytical
procedure and summarises the results. This is preceded by two sections, which
put the research into context. The first updates an earlier analysis of the causes of the
decline in empty running in the UK and examines the factors constraining the
backloading of trucks. The second section reviews previous attempts to assess
the potential for cutting empty running and examines their various shortcomings.
The paper concludes with a discussion of the limitations of the new analytical
technique and ways in which they might be overcome.
36
% of truck-kms
34
30
28
26
24
22
Source: Department for Transport, 2004
'03
'01
99
97
95
93
91
89
87
85
83
81
79
77
75
73
20
19
Figure 1.
Proportion of truck-kms
run empty in the UK:
1973-2003
(vehicles . 3.5 tonnes
gross weight)
32
The main objectives of the paper are, therefore, to:
.
identify the main causes of the recent decline in the empty running of trucks in
the UK;
.
examine the constraints on backloading in the road freight sector;
.
review previous methods of assessing the potential for reducing empty running;
.
outline a new method of assessing this potential; and
.
present the results of an application of this method to an extensive survey of
trucking operations in the UK grocery supply chain.
Reasons for the decline in empty running
An attempt was made to explain the decline in empty running in the UK up to the
mid-1990s (McKinnon, 1996). This identified several possible factors and used
available statistics to assess their likely impact on the level of empty running. A study
of empty running in Norway, involving interviews with road transport companies,
identified a similar range of relevant factors (Lea, 1998). In the course of the present
study, the UK analysis was updated. The following factors were considered.
Outsourcing of road haulage operations
The decline in empty running has coincided with the long-term shift from in-house to
third-party transport. It is possible, therefore, that there may be a causal link between
these two trends. One might reasonably hypothesize that third-party haulage
companies would achieve higher levels of backloading than own account operators.
Although the latter have been free to “carry for others” since the deregulation of the
British road haulage industry in 1970 and many exercise this freedom, it is often felt
that they have a more limited knowledge of the haulage market and hence greater
difficulty in finding return loads. If this were true, the widespread outsourcing of the
transport function over the past 25 years would have contributed to the decline in
empty running. Time-series data from the CSRGT, however, indicate that the initial
premise is flawed (Department for Transport, 2004). The average levels of empty
running for the two groups of truck operator are very similar and have remained so
over the past 20 years (Figure 2). The growth in transport outsourcing is, therefore,
unlikely to have had much impact on the overall level of empty running.
Geographical imbalance in traffic flow
Much empty running is the result of geographical imbalances in traffic flows. An
inter-regional traffic imbalance (IRTI) index was devised to measure the extent to
which inter-regional road freight flows in the UK were unbalanced.
jI i 2 Oi j
IRTI ¼ i¼1
P
Ii
P
i¼1
where I and O denote, respectively, the inward and outward flows of road freight into
and out of region i.
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393
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60%
1983
1993
2003
50%
40%
30%
394
20%
10%
0%
Figure 2.
Empty running and
outsourcing trends
% empty
kms
% total kms
% empty
kms
Own account
% total kms
Third-party
Source: Department for Transport (2004)
Using CSRGT data, index values of 0.149 and 0.156 were calculated for, respectively,
1983 and 2003 (Central Statistical Office, 1984; Department for Transport, 2004). This
suggests that the degree of IRTI has shown a slight increase over the past 20 years.
Over the same period, the proportion of road freight moving inter-regionally increased
from 21.8 to 29.7 per cent, accentuating the effects of the growing IRTI. These changes
in the geographical pattern of road freight movement have not been analysed in detail
though they are likely to have been the result of three inter-related processes: wider
sourcing of commodities, centralisation of production and inventory and greater
regional specialisation (McKinnon and Woodburn, 1996).
Other things being equal, these trends in the pattern of freight flow will have made
it more difficult for companies to find backloads. It should be noted, however, that the
IRTI index is solely weight-based and takes no account of the cubic volume of freight
moving in each direction. The main government survey of road freight flows, upon
which this analysis is based, does not collect volumetric data.
Average length of haul
The longer the journey, the greater will be the incentive for hauliers to find a return
load. Beilock and Kilmer (1986) found a close direct correlation in the US agricultural
sector between the length of haul and the probability of a truck being loaded. Previous
regression analysis in the UK established the following relationship between trip
length and the probability of a vehicle running empty (McKinnon, 1996):
P e ¼ 0:448 2 0:0008t
ðr 2 ¼ 0:72Þ
where Pe is the probability of a journey leg being run empty and t the leg length.
Applying this formula to the increase in the average length of haul for road freight in the
UK between 1973 and 2003 (from 54 to 92 km) suggests that the lengthening of hauls was
responsible for around 40 per cent of the decline in empty running over this period.
Cost of road transport
For much of the period between 1973 and 1995, truck operating costs per kilometre
declined in real terms (Cooper et al., 1998). The decline in empty running cannot, therefore,
be explained simply as a market response to increasing resource costs. This may have
become a more significant factor over the past decade, however. The Government’s
Corporate Services Price Index indicates that road freight rates in the UK rose by a third in
real terms between 1995 and 2002 (Department for Transport, 2003a). This was partly a
consequence of the country’s fuel duty escalator policy, which saw diesel fuel taxes rise by
5-6 per cent per annum in real terms between 1994 and 1999. This gave both hauliers and
shippers an added incentive to minimise empty mileage.
Reducing empty
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395
Trip structure
On multiple drop rounds only the last leg of the journey is likely to be empty and this
generally represents a small proportion of the total distance travelled. (In the case of
multiple collection rounds it is usually the first leg that is empty.) It is for this reason that
the average proportion of truck-kms run empty generally declines as the average number
of stops (for collection and/or delivery) increases. Earlier research revealed that on trips
with five or more stops empty running averaged 18.3 per cent of truck-kms, as opposed to
34.3 per cent for trips with fewer than five stops (McKinnon, 1996). The same study found
that the proportion of trips with five or more stops had been increasing. CSRGT data for
the period 1993-2003 suggests that the proportion of trips in this category and the average
number of stops per trip continued, rather erratically, to follow a long-term upward trend.
This change in trip structure is likely to have had the effect of depressing the level of empty
running, though it is not possible to quantify the magnitude of this effect (Figure 3).
Reverse logistics
In many supply chains, there has been a strengthening return flow of packaging waste,
handling equipment and product (Christensen, 2002). This is mainly the result of the
following developments:
.
government directives requiring more recycling of packaging waste;
.
increased use of reusable handling equipment;
.
retrieval of end-of-life products and components for refurbishment and
remanufacturing (increasingly in accordance with EU directives);
.
growth in the return of products for servicing and repair;
.
growth of home shopping (typically 25-30 per cent of merchandise is returned); and
.
return/redistribution of “liability inventory”(i.e. of slow moving products) from
shops.
These trends are creating additional opportunities for backloading (Anderson et al.,
1999). Companies are being strongly encouraged to integrate return flows into existing
logistics networks (Cranfield School of Management et al., 2004) and backhauling
represents an obvious means of achieving this. The economic benefits of backhauling
“returns” have been modelled (Buellens et al., 1998), but no attempts have yet been made
to quantify the overall extent to which reverse logistics is reducing empty running.
Use of load matching services
In addition to traditional load matching agencies, which have relied on telephone and
fax communications, new online freight exchanges have emerged over the past seven
to eight years, providing web-enabled tendering, online auctions and bulletin boards
for road haulage services (Lewis, 2002). This is making it easier to match loads with
16.0
3.70
15.5
3.60
15.0
3.50
14.5
3.40
14.0
3.30
13.5
3.20
13.0
% of trips
average number of stops
396
% of trips with 5 or more stops
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3.10
average no. of stops
Figure 3.
Changes in trip structure
12.5
3.00
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Source: unpublished data from CSRGT (Department for Transport)
available vehicle capacity on both a short- and medium-term basis. Freight exchange
web sites contain case studies of clients that have achieved significant reductions in
empty running (e.g. www.freight-traders.co.uk and www.teleroute.co.uk). To date,
however, there has been no systematic assessment of their net effect on empty running.
Adoption of new management initiatives
These are well exemplified by the British grocery distribution sector. Large
supermarket chains have been employing several practices to improve backloading
(Anon, 1998):
.
Supplier collection. Where a returning shop delivery vehicle is routed via a
supplier’s premises to collect a load and deliver it the retailer’s distribution
centre.
.
Onward delivery. After delivering a consignment at the retailer’s distribution
centre, a supplier’s vehicle collects a load bound for a supermarket on or near its
return route.
.
Cross-shipment. Where retailers concentrate inventory of particular products in
particular locations and inter-haul them between distribution centres for
cross-docking prior to store delivery.
.
Factory gate pricing. Where the retailer assumes responsibility for the
organisation and cost of collecting supplies from a factory. This effectively
integrates primary distribution (from factory to DC) with secondary distribution
(from DC to shop) giving the retailer greater opportunity to maximise
backloading opportunities (Finegan, 2002; Aujla et al., 2003).
Data on backhauling provided by eight large UK grocery distributors indicated that in
2003 they backloaded 11 per cent of the 2.6 million cases they handled, while return
trips carrying a backload accounted for 13 per cent of all journeys (Aujla et al., 2004).
In summary, the reduction in empty running appears to be the result mainly of
factors from Cost of road transport to Use of load matching services, some of which are
likely to continue for the foreseeable future and prolong the downward trend in empty
running. Several other developments, however, may counteract their effects. They are
discussed in the next section.
Factors constraining the backloading of trucks
It is generally accepted that it will not be possible to eliminate empty running.
The backloading of trucks is subject to numerous constraints:
Reducing empty
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Priority given to the outbound delivery service
One of the main inhibiting factors is managers’ reluctance to risk jeopardising the
quality of outbound distribution to customers. Backloading increases the risk that
the vehicle will not be re-positioned in time to collect its next outbound load. In a
survey of company attitudes to backloading this was identified as the main constraint
on backloading (McKinnon, 1996). This depends, however, on the probability of a
backhaul being delayed and the manager’s perception of this probability. This is
addressed under the next heading.
Unreliability of collection and delivery operations
The risk of backloading operations being delayed (and thus disrupting later outbound
trips) can be quite high. The level of risk partly depends on the nature of the
backloading operation. Figure 4 shows these operations into four general categories
each of which has a different risk profile. The bi-lateral form of backloading is
generally the least risky operation as it does not require any extra journey legs, collects
the return consignment from the delivery point (with which the consignor has a direct
commercial relationship) and off-loads the goods at the vehicle base (over which the
consignor has direct control). More complex backhaul networks, comprising additional
journey legs and the loading/off-loading of products at separate nodes, are exposed to
greater risk of delay.
The frequency and duration of delays have been surveyed for various nodes in the
UK grocery supply chain (McKinnon and Ge, 2004). It was estimated in 2002 that
base
delivery point
collection point
delivery and collection point
prime move
loaded
backhaul
loaded
repositioning
empty
bi-lateral
Possible Source of Delay
Re-loading at original delivery point
Off-loading at vehicle base
Repositioning journey leg(s)
Loading at separate collection point
Off-loading at separate delivery point
triangular
rectangular
multiple backload
Figure 4.
Backhaul configurations
and the risk of delay
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roughly 29 per cent of journey legs were subject to a delay and that these delays
averaged 45 minute. Just under a third of these delays were caused mainly by traffic
congestion on the road network. Most of the delays occurred at the reception bays of
factories, distribution centres and shops. The survey data permitted an analysis of the
probability of delays occurring at different points in the supply chain and their average
length. Journey legs starting at one of the three main collection points, i.e. factories,
distribution centres and primary consolidation centres had, respectively, a 42, 36 and
30 per cent probability of being delayed, with these delays averaging 35-45 minute.
In operational terms, backloading is generally only feasible where there is sufficient
slack in the schedule and where the manager has confidence in that schedule. In recent
years, worsening traffic congestion has been reducing delivery reliability (Freight
Transport Association, 2005) while “backdoor” congestion at many distribution
centres has been increasing the average length and variability of off-loading times.
Efforts are, nevertheless, being made to tighten delivery schedules through the
proliferation of booking-times at industrial and commercial premises and introduction
of heavier penalties for failing to arrive within narrow time windows. These trends
may discourage further growth in backloading. Their negative effects are likely to be
partly offset, however, by the growth of telematics. By creating real-time visibility of
the transport operation and improving communication between driver and base,
telematics can make it easier for transport managers to organise backhauls within
increasingly congested networks (Department for Transport, 2003a, b).
Inadequate knowledge of available loads
Many possible matches of backhaul capacity with suitable loads are missed because of
a lack of transparency in the road haulage market. Vehicle operators simply do not
know what loads are available for possible backloading. It is partly for this reason that
roughly half the return loads carried by road in the UK are generated internally from
within the same company. A further 28 per cent represent a mixture of own company
and external traffic, while only 23 per cent of backloads are provided by other
companies (Lex Transfleet, 2002). Moreover, according to the same survey, information
about 61 per cent of the return loads obtained from other companies comes from “word
of mouth”. Reliance on such informal and haphazard communication, inevitably limits
the potential market for backhaul traffic. As explained above, however, the growth of
online load matching services is helping to improve this situation.
Lack of co-ordination between purchasing and logistics departments
It is likely that more backloading opportunities would be realised if the physical
movement of products was discussed as part of the trade negotiation between companies.
Many purchasing managers have traditionally taken the view that responsibility for
inbound delivery is best left with the supplier. As discussed earlier, the growth of factory
gate pricing is, in some sectors, transforming the corporate view of inbound transport. It is
effectively transferring responsibility for transport to the purchasing company and should
result in improved co-ordination of inbound and outbound deliveries.
Incompatibility of vehicles and products
Often the vehicle available to collect a backload is unsuited to the potential backload.
For companies with specialised vehicles and/or products with specific handling
characteristics, this can impose a tight constraint on backloading opportunities.
The growth of palletisation and increasing standardisation of handling equipment
(ECR Europe, 1997) has, nevertheless, been easing this constraint.
Reducing empty
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Resource constraints
It has been predicted that the combined effect of a shortage of truck drivers and the
application of the EU Working Time Directive to mobile workers in 2005 will reduce
delivery flexibility over the next few years (Clarke and Smeeton, 2003). This may
have a negative effect on the backloading of trucks.
399
Estimating the potential for reducing empty running
Three methods have so far been used to estimate this potential.
Opinion survey
In a survey undertaken by Browne and Allen (1997) a panel of 46 experts predicted that
empty running in the UK would fall from 29 per cent of truck-kms in the base year
(1995) to 25 per cent by 2000 and 24 per cent by 2005. A comparison of these forecasts
with the actual trend shown in Figure 1 shows that the panel over-estimated the rate of
decrease. Such subjective assessments, even when pooled within a panel, can only be
expected to give a broad indication of the direction and magnitude of the empty
running trend.
Macro-level traffic modelling
This approach has its origins in highway engineering and is based on the assumption
that the accuracy of freight traffic models can be improved if empty trips are
separately modelled. This is demonstrated by Holguin-Veras and Thorson (2003), who
developed a “trip chain model” based on the probability of successive trips in a
delivery round being empty. This probability was expressed as a function of the
volume of freight moving on particular inter-zonal links. When applied to trip data
collected by a roadside survey of trucks in Guatemala City, the model yielded much
more accurate estimates of traffic flows than an analysis that did not explicitly take
account of empty running. If adequately calibrated with commodity and vehicle flow
data, such a model could be used to estimate possible reductions in empty running
within various scenarios.
Micro-level load matching
If sufficiently detailed, survey data can be used retrospectively to match loads with
vehicles at a much more disaggregated level. Instead of using zonal commodity flow
data, one can link the specific origins and destinations of particular loads and vehicles.
This approach was first adopted by Cundill and Hull (1979) and used to estimate the
potential for cutting empty running by trucks in the UK in 1978. More recently Kelleher
et al. (2003, p. 270), working in the field of intermodal transport, have shown that it is
possible to cut the empty movement of containers by using a combination of constraint
satisfaction problem (CSP) modelling and genetic algorithms to create more efficient
patterns of “triangulated routes”. The Cundill and Hull research is the more relevant in
the present context, however, and will be reviewed in more detail.
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Cundill and Hull used road freight data collected in the course of urban freight studies
in Hull and Swindon to undertake an analysis of possible “destination and origin
matching” on journeys to and from these two English towns. They excluded trips
shorter than 50 km and confined their attention to standard “platform and box-bodied”
vehicles which could handle a broad range of commodities. Using industry standard
data on vehicle operating costs and haulage rates they were able to determine the
profitability of diverting a vehicle from its direct return route to collect an available load.
The search area for profitable loads was bounded by an ellipse drawn around this return
route and allowed for the possibility of a vehicle initially travelling beyond the
destination point to obtain a backload.
Cundill and Hull acknowledged that their model took no account of the scheduling
of deliveries (“urgency”), the handling characteristics of the products (“fragility”) and
the possible “incompatibility” of vehicles and loads. As they had no data on these
constraints, they arbitrarily scaled down their estimate of “profitable matches” by
75 per cent to make it more realistic. As this was not based on any empirical evidence,
it seriously undermined the credibility of their estimate of potential savings in empty
running. This deflated estimate was, nevertheless, grossed up to a UK level to indicate
the general potential for reducing empty running. It indicated that an extra 660,000
profitable load matches could be achieved annually, each one saving 235 vehicle-kms.
The resulting saving of 155 million empty vehicle-kms per annum was equivalent to
3.5 per cent of total empty running in 1978. This would have brought the empty
running level down from 33 to 32 per cent of truck-kms – much less than the reduction
in average empty running that has actually been achieved since then (Figure 1).
This study had three major limitations. First, in the absence of information about the
scheduling of trips and handling characteristics of the loads the researchers had to
over-simplify the load matching procedure and scale down the calculated benefits by an
arbitrary amount. Second, the analysis was confined to lorry journeys to and from two
towns. It was not known how representative freight movements to and from these towns
were of the general pattern of road freight transport across the UK, particularly in terms
of directional imbalances and average trip length. Third, the modelling of road haulage
rates was based on national average figures and took no account of the fact that
backhaul rates are typically below average and sometimes barely cover marginal costs.
The method used in the present study to assess the scope for reducing empty
running also involves “micro-level load matching” but overcomes the first two
limitations. Unlike the Cundill and Hull analysis, however, it makes no attempt to
calculate the profitability of picking up backloads.
Road freight survey
The data used in this analysis came from the 2002 KPI survey of road freight
movements in the UK food supply chain (McKinnon and Ge, 2004). This was one of a
series of surveys commissioned by the UK Government to assess road transport
efficiency and enable companies to benchmark their performance. It took the form of a
“synchronised audit” of fleets over the same 48-hour period with standardised data
collected on a consistent basis. The start and end times, origins and destinations
and loading of all trips completed within this period were monitored. A total of
27 companies participated in the survey, providing data on the operations of fleets
based at 53 different locations across the UK.
As participation in the survey required companies to make a significant resource
commitment, the initiative had to be intensively marketed. Various promotional
activities were organised by a major trade association and the government to publicise
the survey. The sample of companies and fleets was not, therefore, the result of random
selection, but instead relied mainly on self-selection. Care must, therefore, be exercised in
interpreting the aggregate results, as these may not be representative of the UK grocery
supply chain as a whole. Comparison of key survey parameters with corresponding
values from the CSRGT, however, suggested that the survey was fairly representative of
the general movement of groceries by road in the UK (McKinnon et al., 2003).
As the focus of the study was the load carrying unit, the activities of rigid vehicles and
trailers were surveyed, but not the tractor units of articulated trucks. The 3,634 vehicles
included in the survey travelled a total of approximately 1.45 million km over the two-day
period and delivered the equivalent of just under a quarter of a million pallet-loads[2].
A total of 24,443 journey legs were monitored, although only 12,364 (50.6 per cent) had
sufficient locational data to permit their inclusion in the backloading analysis.
Discussions with participating companies revealed that most opportunities for
backloading exist among trips with fewer than five legs. Fleets making multiple drop
deliveries with five or more legs, and composed largely of rigid vehicles, were,
therefore, excluded from the sample. This left a total of 29 fleets whose vehicles were
responsible for 8,995 of the journey legs surveyed over the 48 periods. About 2,272 of
these legs were run empty.
The 29 fleets were divided into three sectors, mainly in relation to the level in the
supply chain at which deliveries were made:
(1) Primary distribution between factories and regional distribution centres (RDCs)
(all articulated vehicles).
(2) Secondary distribution from RDCs to supermarkets (mainly articulated vehicles).
(3) Mixed distribution to large and small retail and catering outlets (both
articulated and rigid vehicles).
Reducing empty
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401
Table I shows the distribution of journey legs across these three sectors as well as the
average leg length. There was a preponderance of legs at the secondary distribution
level. At this level, many shop delivery vehicles were backloaded with empty roll
cages. This was classed as “running with returns” rather than empty running, on the
grounds that it represented an essential stage in the distribution process and limited
the opportunity to pick up a backload. On the other hand, a vehicle carrying only its
usual complement of wooden pallets, as typically occurred in primary distribution, was
considered to be empty as it could be backloaded with product.
Sector
Number of journey legs
Percentage
of empty
legs
Total Empty
Primary
815
Secondary 6,953
Mixed
1,227
Total
8,995
263
1,779
230
2,272
12
78
10
100
Distance travelled (km)
Percentage
Average leg length (km)
of
empty
All legs Empty Loaded
Total Empty
94,424 19,876
57,5013 121,519
66,838 13,426
736,275 154,821
21
21
20
21
116
83
54
82
76
68
58
68
135
88
54
86
Table I.
Summary statistics for
the sample of journey
legs analysed
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Specification of the model
Data generated by the KPI survey were used to match empty journey legs with loads
travelling between similar origins and destinations. This load matching was undertaken
at four levels of screening. In an effort to keep the analysis realistic, an industrial advisory
group[3] was consulted on the choice of parameter values for this screening process:
402
Location
Only empty legs in excess of 100 km were included in the analysis, double the distance
threshold used by Cundill and Hull (1979) which was considered to be too low. A large
proportion of the journey legs run by fleets in the mixed category fell below this distance
threshold. Also, the distance between a potential backload’s origin and destination (i.e. the
length of the loaded backhaul) had to be over one-third of the length of the empty leg.
Where empty legs are very short, operators generally have little economic incentive to find
a return load. A maximum radius of 50 km was used to define the circular area around the
origin and destination of the empty leg within which a search was made for suitable loads.
This limited the extent to which a vehicle could deviate from the direct return route.
Vehicle compatibility
In the grocery sector, one of the main vehicle differentiators is refrigeration. At this second
level of screening, a distinction was made between ambient-temperature, chilled and
frozen loads. They could only be assigned to vehicles offering the appropriate level of
temperature control. As it can take several hours for a refrigerated vehicle operating at
ambient temperature to reach the 1-48 centigrade required for chilled product or 218/218
centigrade required by frozen food, backloads of these products were only assigned to
vehicles that had operated within a similar temperature range on the previous journey leg.
Some of the vehicles in the survey were compartmentalised and capable of carrying
products at different temperatures. Within the model, it was possible for such vehicles to
collect mixed loads of ambient, chilled and frozen product, so long as there was adequate
capacity for each type of product in the various compartments.
Vehicle capacity
One of the major advantages of using transport KPI data in the modelling of backloading
opportunities was that it measures freight flows and vehicle capacity in terms of both
weight and pallet numbers, the latter serving as a surrogate measure of freight volume. At
this level of screening an assumption was made that a backload would only be collected by
an empty vehicle if it had enough capacity to carry it in full. No allowance was made for the
collection of part-orders. Where a vehicle was partitioned into different temperature
compartments, the maximum capacity of each compartment (expressed in pallet-loads)
was defined by the actual number of pallets carried at that temperature range on the
preceding journey leg. This meant that if the vehicle was under-utilised on the outbound
leg, the backhaul capacity would be under-estimated. This procedure was sub-optimal but
unavoidable as no data had been collected on the total capacity available in the different
temperature compartments on individual trips.
Time schedule
The KPI survey collected data on the scheduled start and end times of trips. It could,
therefore, be established when the empty vehicle should have arrived at its destination.
To permit backloading it was necessary to build extra flexibility into the schedule.
It was assumed, therefore, that the scheduled return time could be relaxed by up to
2 hour. Industry standard times were used for loading and unloading operations.
These different levels of screening were applied cumulatively in the order specified.
Analytical method
An interactive query interface was created using a geographical information system
package (SAS/GIS) to allow the user to identify backloading opportunities at the
level of a company, zone or individual trip (Figure 5). Trip data from the KPI survey
were stored in an access database. Locational information, recorded as post-codes, were
geo-coded into longitude and latitude co-ordinates using Code-Point software. Data
required for analysis were imported into the GIS framework using coding instructions
written in the SAS programming language. Within this framework, spatial data were
integrated with attribute data relating to the vehicles, loads and trips. Algorithms were
developed to interrogate interactively the KPI survey database and assess the potential
for backloading at the four levels of screening. Using the GIS software it was possible
to construct maps showing the pattern of vehicle movement and, if necessary,
manually alter the matching of individual loads.
No attempt was made to link the GIS query interface to a road network database.
Distances were, therefore, calculated on a straight-line basis, though modified in two ways:
(1) The straight line (“crow-fly”) distances were converted into road network distances
using a standard “wiggle” factor of 1.2 for the UK road network (Cooper, 1983).
(2) Allowance was made for major geographical barriers, such as rivers, estuaries
and mountains. Journeys between 12 zones affected by these barriers were
routed via eleven “way points” to ensure feasible routing. Most of these way
points were bridges over rivers or estuaries.
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Access
Database
Spatial Data
Conversion to
Longitude
/Latitude
National
Code-Point
Grid
Reference
Excel
Macro
GIS
Postcode data
• Vehicle Capacity
• Load on legs
• Nature of goods
• Trip Schedule, etc.
Attribute Data
Parameters
S
A
S
Sector Level
STAT
Company Level
Rules:
• Transit times
• Loading/Unloading Rate
• etc
Identify empty legs
Define Search Area)
Macro
Screening constraints: location, compatibility, vehicle capacity,
time schedule
Figure 5.
Analytical framework
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Results
Location criterion
Out of a total sample of 8,995 journey legs monitored, 573 (6.4 per cent) were
empty and had a length of 100 km or more. Load matches meeting the locational
criteria were found for 249 of these legs. Some of these potential backloads were
matched to more than one empty leg, however, while some empty legs were
associated with more that one load. Once this duplication was eliminated a total of
181 unique matches was established between empty leg and available load, 31.6
per cent of the total.
Approximately two-thirds of the backloading opportunities arose within sectors. As
expected, the load matches were concentrated in primary and secondary distribution
where journeys are long enough to justify backloading.
Vehicle compatibility criterion
Of the 181 backloads emerging from the locational screening, 48 required
temperature-control but had been assigned to vehicles without the necessary
refrigeration capability. Eliminating these load matches reduced the pool to 133
(23.2 per cent of the original sample of empty legs over 100 km).
Vehicle capacity criterion
Of these 133, 72 backloads would exceed the carrying capacity of the allocated vehicles
(or the appropriately refrigerated compartments within these vehicles) and would,
therefore, fail the vehicle capacity test. This cut the number of potential matches to 61
(10.6 per cent of the total).
Time schedule criterion
In the case of three-quarters of the remaining 61 backhauls, the vehicle would not
arrive at its next collection point within two hours of the scheduled arrival time and
thus would not satisfy the time requirement. Of the original sample of 573 empty
journey legs only 14 (2.4 per cent) would be assigned a backload which met all four
criteria.
Potential savings
The following formula was used to estimate the potential reduction in empty running
(R) resulting from a successful load match:
R ¼ Bx þ By 2 D
where Bx is the length of the empty leg assigned a backload (on trip X), By the length of
the empty leg on the trip previously carrying that load (trip Y) and D the distance that
the vehicle on trip X must deviate from the direct return route to collect that load
(Figure 6).
The average values for Bx, By and D were, respectively, 140, 128 and 48 km.
This yielded an average saving in empty running per load match of 220 km.
Across the 14 matches meeting the four criteria a total of 3,024 empty vehicle-kms
would have been saved out of a total of 139,534 vehicle-kms run empty by the
sample vehicles over the 48-hour period. This represents only 2 per cent of the
empty running.
By
Ox
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Ox
Oy
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Bx
D
Figure 6.
Elimination of two empty
backhauls
Trip Y
Trip X
The low potential for additional backloading can be attributed to several factors:
.
Current level of return loading. Many of the available backloading opportunities
were already being exploited. The average per cent of empty running across the
sample fleets was roughly 5 per cent below the average for all road freight
movement in the UK. Many of the return journeys from shop to distribution
centre carried roll-cages as part of a closed-loop delivery system for handling
equipment. Also, several of the companies represented in the sample already
operated “supplier collection” programmes.
.
Relatively short average length of the empty journey legs sampled. The average
length of these legs was 68 km and only a quarter of them exceeded the threshold
distance of 100 km.
.
Limited duration of the trips. Roughly two-thirds of the journey legs had a
driving time of 2 hour or less (Figure 7). The average driving time was only 102
minute. The distribution of most groceries is characterised by high
time-sensitivity, regular shuttling of vehicles between factories, distribution
centres and shops and rapid vehicle turnaround. To be realistic, therefore, the
model offered limited flexibility for rescheduling trips.
.
Size and composition of the sample. The probability of finding a return load is
partly a function of the sample size. Other things being equal, the larger the
sample of vehicles and greater the density of trips within an area, the higher this
probability will be. Although almost 9,000 journey legs were surveyed over the
48-hour period, this would have represented only a small proportion of the total
25%
% of legs
20%
15%
10%
5%
0%
30
0-
0
-6
31
0
-9
61
0
12
91
0
15
21
1
1
minutes
0
18
51
0
1
0
24
21
81
2
11
40
>2
Figure 7.
Delivery times for the
sample of journey legs
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.
amount of freight movement within the grocery distribution system.
Furthermore, a sample composed of vehicles engaged in long distance
trunking (at the primary distribution level) would have been likely to generate
more backloading opportunities. As explained earlier, the sample contained a
preponderance of journey legs at the secondary distribution level of the supply
chain. Roughly 79 per cent of all the empty running occurred at this level, where
the average length of empty legs, at 68 km, was relatively low.
Vehicle compatibility constraints. Temperature-control requirements impose tight
constraints on backloading in the grocery sector. Across the sample, there was a
fairly even division of loads and vehicles between refrigerated and non-refrigerated.
At the secondary distribution level, where most of the deliveries were concentrated,
significant use is made of multi-temperature vehicles capable of carrying ambient,
chilled and/or frozen products in separate compartments. On 9.6 per cent of the
journey legs surveyed products were carried at more than one temperature. The
analysis revealed numerous instances of a vehicle’s overall carrying capacity
exceeding the total weight/volume of an available backload, but individual
compartments being unable to accommodate components of that load requiring a
particular degree of temperature control. Calculating the capacity of these
compartments on the basis of their loading on the previous journey leg, as explained
earlier, is also likely to have resulted in some under-estimation of the backloading
capability. This contributed to the large reduction in the number of potential
backloads that occurred at the capacity screening stage.
Critique
The analysis of backloading potential using transport KPI survey data overcame two
of the problems encountered by Cundill and Hull (1979). First, the availability of data
on delivery schedules and load characteristics permitted more complex and realistic
modelling of load-matching opportunities and removed the need to deflate estimates by
an arbitrary amount. Second, the KPI survey data related to a much more
geographically dispersed pattern of flow. It, therefore, provided a sounder basis for
generalising about opportunities for backloading at a national level. Another
advantage was its coverage of a much broader range of vehicle sizes and types than
was achieved in the Cundill and Hull’s study.
The analysis, nevertheless, had several shortcomings, most of them related to the
use of transport KPI survey data.
The sample was neither randomly generated nor well stratified by sub-sector.
The size of the useable sample was also constrained by the availability of accurate
post-code data for trip origins and destinations. These sampling deficiencies can be
partly attributed to the fact that the KPI survey was designed primarily to allow
companies to benchmark their performance rather than to conduct a retrospective
analysis of possible efficiency gains. It also demanded a high level of company
commitment, particularly to compile standardised data for the trip audit. Future KPI
surveys are likely to rely more heavily on vehicle positioning data captured by
telematics systems, reducing the need for manual data entry and permitting easier
interfacing with customer data files. By facilitating data collection, telematics should
increase sample size and diversity and improve the accuracy of trip origin and
destination data.
In the case of articulated vehicles, only the activities of trailers were recorded.
No reference was made to tractor units. When assessing potential load matches,
however, it is important to know how tractors as well as trailers are scheduled. The
time available to pick up a backload is often influenced more strongly by the utilisation
of the tractor than that of the trailer. The tractor, after all, is the more expensive asset
and, combined with the driver, the unit with by far the highest time-related costs.
Companies in the sample operated an average of 2.1 trailers for every tractor unit,
allowing them to drop trailers at factories or distribution centres for loading and
unloading. The screening of backloading opportunities with respect to scheduling
constraints would be much more realistic if allowance were made for this decoupling of
tractors and trailers.
The degree of realism would also be enhanced if companies provided information
about the degree of delivery flexibility. In the absence of this information, an estimate
had to be made of the average amount of “slack” in delivery schedules, based on
consultation with the industrial advisory group. It was not known to what extent the
scheduling of particular trips might be constrained by, for example, drivers’ hours
restrictions or cross-docking procedures. Future transport KPI surveys could overcome
this problem by incorporating an additional question about the latest possible arrival
time into the trip audit.
The search for backloads within circular search areas around delivery points was
simplistic. Mentzer (1986) advocates the application of a “conical backhaul principle”
with the search for return loads confined to a cone-shaped area, narrowing with
distance from the pick-up point. This resembles the elliptical zones employed by
Cundill and Hull (1979). Mentzer also proposed the subdivision of the search area into
zones of varying priority for backloading. This refinement will be incorporated into
future versions of the SAS/GIS model.
No allowance was made for the backloading of part-orders where a vehicle lacked
the capacity to carry the full consignment. This was due to uncertainty about how
the remainder of the order would be transported and the consequences for vehicle
utilisation on other trips. It is likely that confining backhauling to full orders will have
resulted in some under-estimation of backloading opportunities.
The analysis had no commercial dimension, reflecting the fact that the transport
KPI surveys do not enquire about transport costs and rates. It was not possible,
therefore, to assess the profitability of picking up return loads on particular routes.
According to the project’s industrial advisers, however, the distance parameters used
in the analysis would be likely to suppress uneconomic backhauls. Nor was it possible
to examine the effect of the level of backloading on the market for road transport
services in the grocery sector. This will clearly affect average operating costs, freight
rates and general competitive conditions within the sector. For a discussion of the
economic implications of backloading, readers should consult Beilock and Kilmer
(1986).
Conclusion
The proportion of truck-kms run empty in the UK has declined by an average of 0.2 per
cent per annum over the past 30 years. It appears to have been associated with general
freight trends (increase in the average length of haul and in the proportion of
multi-drop deliveries), the growth of reverse logistics, the development of new load
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matching services and the adoption of various backloading initiatives. There is no real
prospect of empty running being completely eliminated. As a result of geographical
imbalances in traffic flow, a lack of transparency in the road freight market, short haul
lengths, scheduling constraints and the incompatibility of vehicles and loads, there will
remain a substantial amount of empty running. It is difficult to estimate, however, at
what level the downward trend in empty running will level off.
This paper has reviewed previous attempts to estimate the potential for
reducing empty running. The creation of a large, multi-company data-base of truck
movements at different levels in the UK grocery supply chain permitted the
development of a new method of estimation involving spatial modelling and the
screening of possible backloads against four operational criteria. This analysis
suggests that, across the 29 vehicle fleets sampled, there was very limited
potential for reducing the distance that the trucks ran empty. It highlights the
effects of operational constraints on backloading, particularly, where the average
length of haul is short, the scheduling is tight and a large proportion of freight
requires refrigeration.
Deliveries in many other sectors are likely to be less constrained. Wider
generalisation about the remaining potential for backloading will have to await
similar analysis in other sectors. Comparable KPI surveys have been undertaken in
the automotive and non-food retailing sectors, and the UK Government is planning
others in the construction, chemicals and parcels sectors. There is a danger,
however, that if this type of backloading analysis is applied on a sector-specific
basis, opportunities for cross-sectoral backloading will be under-estimated. Future
research should, therefore, examine ways of integrating sectoral transport KPI
data-bases.
The research reported in this paper could also be developed in several other ways.
The various parameters used in the analysis, such as the distance threshold, the shape
and dimensions of the backload search area and the amount of slack in the delivery
schedule, could be varied individually and in differing combinations to assess their
impact on the final results. Logistics managers in the participating companies could be
interviewed to add a behavioural dimension to the analytical modelling. Finally, the
estimated reductions in empty running could be translated into savings in operating
cost, fuel consumption and emissions to demonstrate, at both micro- and macro-levels,
the economic and environmental benefits of increased backloading.
Notes
1. It is possible that this decline in empty running has been achieved at the expense of a
reduction in the average load factor on laden vehicles. Vehicles previously running empty,
for example, may now be carrying very small loads, depressing average vehicle utilisation
across the national truck fleet. Analysis of vehicle lading statistics suggests otherwise,
however. Average load factors have remained reasonably stable during most of the period
over which empty running has declined (Department for Transport, 2004).
2. Pallets of differing dimensions, roll cages, trays and trolleys were converted into an
equivalent number of “industry ” pallets (1,200 by 1,000 mm) using standard ratios.
3. This group comprised senior managers from two large logistics service providers and a
major food manufacturer, the chief executive of a distribution trade association and senior
executives from vehicle routing software and telematics companies.
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Corresponding author
Alan C. McKinnon can be contacted at: A.C.McKinnon@hw.ac.uk
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