Fbp1107 Primary Distribution Benchmarking SurveyDocument Transcript
Primary Distribution Benchmarking
The following companies took part in the benchmarking
survey outlined in this guide and are thanked for their
Aspray Transport Ltd.
Bandvulc Tyres Ltd.
Global Manufacturing Supplies Ltd.
Howdens Joinery Co.
Knights of Old Ltd.
PD Logistics Ltd.
Pilkington UK Ltd.
Roadways Container Logistics Ltd.
Robert Wiseman Dairies plc.
Freight Best Practice is funded by the Department for
Transport and managed by AECOM to promote
operational efficiency and reduce environmental impact
within freight operations.
Freight Best Practice offers FREE essential information
for the freight industry, covering topics such as saving
fuel, developing skills, equipment and systems,
operational efficiency and performance management.
All FREE materials are available to download from
www.freightbestpractice.org.uk or can be ordered
through the Hotline on 0845 877 0 877.
Additional free copies of the guide can be obtained by
calling the Freight Best Practice Hotline on
0845 877 0 877. It can also be downloaded from the
programme’s website www.freightbestpractice.org.uk
Disclaimer: While the Department for Transport (DfT) has made every
effort to ensure the information in this document is accurate, DfT does
not guarantee the accuracy, completeness or usefulness of that
information; and it cannot accept liability for any loss or damages of
any kind resulting from reliance on the information or guidance this
1 Background 1
1.1 Measuring Performance in Your Own Business 1
1.2 What Should the Key Performance Indicators Be? 1
1.3 Which KPIs are Right for Me? 2
1.4 External Benchmarking 4
2 The Primary Distribution Benchmarking Survey 5
2.1 The Nature of the Primary Distribution Sector 5
2.2 The KPIs 5
2.3 How the Data was Collected 6
2.4 Survey Participants 6
3 Survey Results 9
3.1 Miles per Gallon (MPG) 9
3.2 Number of Incidents per 100,000 Kilometres 12
3.3 Empty Distance Run 12
3.4 Vehicle Fill 14
3.5 Vehicle Time Utilisation 16
3.6 On Time In Full Deliveries (OTIF) 18
3.7 Damaged Deliveries 20
3.8 Delivery Complaints 20
3.9 Interventions 21
4 Summary 23
4.1 Accurate Data Collection 23
4.2 Vehicle Fill 23
4.3 Empty Running 23
4.4 Damage 23
4.5 Fuel Saving Interventions 24
4.6 Regular Benchmarking 24
5 The Primary Distribution Transport Efficiency ‘Road Map’ 25
Operators in the sector, whether survey participants or
1 Background not, can use this benchmarking guide to identify real
Every successful organisation needs to manage its opportunities to maximise transport efficiency, reducing
assets effectively and can benefit from benchmarking its both their running costs and environmental impact.
performance against that of similar operators, especially
those deemed to be ‘best-in-class’ in their sector. 1.1 Measuring Performance in Your
The Department for Transport, through its Freight Best Own Business
Practice programme, has supported a series of
If you want to make well-informed tactical and strategic
benchmarking surveys that have developed a range of
decisions about your operation, you need to be able to
key performance indicators (KPIs) in a variety of
accurately measure the performance of the resources
you use to deliver your services. Only then can you
identify areas for improvement and assess how effective
any operational changes have been.
Already published are KPI survey guides for
the following sectors: The starting point for any performance improvement
Key Performance Indicators for programme should be to understand the current
Non-food Retail Distribution performance of your operation. This means collecting
data on key aspects of your operation and turning this
Key Performance Indicators for the into specific measurements that can help you identify
Food Supply Chain areas for improvement. Examples of such
Key Performance Indicators for the measurements include how much fuel each vehicle
Pallet Sector uses, how many miles your vehicles run empty and the
number of late deliveries you make. Those measures
Key Performance Indicators for the most critical to your operation will be your firm’s KPIs.
Next-day Parcel Delivery Sector They may, of course, be supported by other, less critical
Key Performance Indicators for the measures.
Builders’ Merchants Sector
A KPI on its own will not tell you much. Individual
All of these publications are available FREE measurements and raw data need to be turned into
of charge from the Freight Best Practice information that can help you to make decisions. This
programme website means setting a target and measuring and monitoring
www.freightbestpractice.org.uk and from KPIs over a period of time to see how your operation
the Hotline 0845 877 0 877. performs against target. Weekly, monthly and annual
reports allow you to identify trends, monitor progress
and see which areas need the greatest improvement.
Producing graphs or charts will often be the best way of
This particular KPI benchmarking survey covers the showing progress in performance.
primary distribution sector.
KPIs used in external benchmarking are essential tools 1.2 What Should the Key Performance
for the freight industry to understand and then improve Indicators Be?
its performance. They provide a consistent basis for
measuring transport efficiency across different fleets, There are many different KPIs that can be used to
comparing like with like. measure performance in a freight transport operation
and it can be difficult to know which ones might be right
This guide aims to: for you. This section is intended to explain the
Show companies how their own performance characteristics of some useful KPIs that can be applied
compares with that of others in various types of operations. However, there are a
number of things you should consider beforehand in
Measure performance across a range of KPIs order to decide which ones are actually right for you. A
Identify recommendations to improve efficiency KPI should be relevant to your particular operation and
it should also be SMART – Specific, Measurable,
Achievable, Realistic and Timed.
Specific needs of a particular business. Some information may
have to be collected on a daily basis, such as staff
KPIs should be specific, simple to use and easy to absence levels in the warehouse, daily delivery drops or
understand. Complicated statistics and formulae can nightly trunking volumes. If certain measures are not
lead to confusion about what is actually being measured recorded and presented to the agreed timescales, the
in the first place. If KPIs are specific and simple, they risk of changes in performance going unnoticed rises.
can be easily communicated across the business and
there is no need for staff to have an in-depth knowledge
of the area being measured. 1.3 Which KPIs are Right for Me?
The size, type and management structure of a company
are all likely to influence the range of KPIs you might
use. KPIs can be used to help managers develop
KPIs can show changes in performance over time. For
strategy, plan and make decisions, while at an
this to happen, it is essential to compare like-with-like
operational level they can also clearly show up any
data. It is easy, for instance, to fall into the trap of
areas that need improvement or a change in approach.
comparing two drivers on different routes for time
utilisation or miles per gallon (MPG) – but if one route is
An individual KPI can tell you how well you are
more demanding than the other, it could be misleading.
performing at an operational level. However, when
Similarly, comparing drivers of vehicles of substantially
looked at in combination with other measurements,
different age or vehicle type can also be deceptive.
KPIs can also help build a picture of how well you are
performing in terms of revenue, profitability and overall
There are ways you can resolve these problems, such
fleet efficiency, or in relation to customer service.
as rotating drivers on different vehicles and different
routes and then monitoring both driver and vehicle
Figure 1 shows a basic, step-by-step process for
performance, to identify consistently high and poor
measuring your performance. The checklist that goes
with it shows some important questions you can ask to
help set up a performance measurement system in your
Any targets set must be achievable. It may seem
beneficial to set high targets in the hope that this will
See the Freight Best Practice Guides
lead to greater improvements in performance, but
remember that people often become disillusioned if they Performance Management for Efficient
continually fall short of their targets. Regularly reviewing Road Freight Operations
performance towards targets and then resetting the This guide explains the process of
targets to encourage smaller, incremental (but measuring performance effectively. It
cumulative) improvements may work much better in the includes advice on how information is best
long run. collected and interpreted to allow informed
decision making in order to achieve
Realistic operational efficiency improvements.
Remember that important decisions will be taken as a
result of the data collected and presented so the data
collection method needs to be realistic, reliable and The eight KPIs used by the companies that participated
consistent. It is important that the data required to in the primary distribution benchmarking survey are
produce a particular KPI can be collected easily and on detailed in Section 2.2 of this guide.
a regular basis, as comparison over time forms the
basis of benchmarking and then improving
Frequency of monitoring is an important consideration.
Weekly or monthly monitoring is recommended for
many KPIs but this can depend on the measure and the
Figure 1 The Process of Selecting and Measuring KPIs
Performance Management or
Have you reviewed your existing KPIs or
looked at those that might be appropriate for
your type of operation?
Are they Specific, Measurable, Achievable,
Set and Review Targets Realistic and Timed? (SMART)
Have you set targets for these KPIs?
Do you know how well your operation is
Data Collection performing against your targets?
Do you need to raise or lower them?
Have you considered external benchmarking
to compare your operation’s performance with
that of others?
Have you reviewed or set up a data collection
system to give you the information you need?
Reporting & Feedback
Do you have a good system in place for
analysing and reporting your KPIs?
Results Yes Do you use information technology systems
Targets to help you?
Have you considered actions that can be
No taken to improve your operation’s
performance and meet new, higher targets in
Identify Strategy for
1.4 External Benchmarking The benchmarking survey described in this guide was
designed to highlight the performance of some of the
The basic process of measuring operational best-in-class operators in the primary distribution sector,
performance internally is extremely useful, but to fully enabling you to compare the relative efficiency of your
understand how your operation compares with that of own fleet and operation and identify measures you can
your peers, you must benchmark against the take to improve your performance.
best-in-class performers in your sector.
This process of external benchmarking will enable you
to understand the characteristics displayed by the
best-in-class performers across a range of KPIs. In
other words, understanding exactly why some operators
perform better than others in certain KPIs will help you
to decide the best measures to implement in your own
operation to improve efficiency.
Primary distribution commonly involves the bulk
2 The Primary Distribution movement of goods – often a single commodity – often
Benchmarking Survey over long distances and tends to be carried out by
larger sized heavy goods vehicles (HGVs).
2.1 The Nature of the Primary There are many product-dependent variations in the
Distribution Sector types of vehicles typically used for primary journeys,
from curtainsiders and box-bodied HGVs for palletised
There are a number of definitions of primary distribution,
goods to bulk tankers for liquids and skeletal trailers for
with perhaps the most accurate being from the
Department for Transport, which describes it as “the
transport of goods from the point of production or port to
the wholesaler, primary consolidation or import centre”1 2.2 The KPIs
Transport operators often define primary distribution as In any benchmarking survey, it is essential to use the
the final delivery of product to their customers or the most appropriate set of KPIs and that everybody in the
distribution of products to distribution centres and survey can accurately measure them.
processing sites. However, the term is entirely
dependent on the transport operator’s perspective and The five core KPIs used in previous external
its end customers. For example, the delivery of finished benchmarking surveys – namely vehicle fill, empty
products to a Regional Distribution Centre (RDC) will be running, time utilisation, deviation from schedule and
regarded as primary distribution by the manufacturer of fuel consumption – were all considered alongside other
those products, but for the RDC, delivery of those same measures for this survey, but not all of them were
finished products from the RDC to their end customers deemed to be relevant. The eight KPIs detailed in Table
will be the primary distribution element. 1 were deemed most relevant to primary distribution
operators in this survey.
Table 1 The KPIs Measured during the Survey
Total distance run per vehicle divided by the total fuel consumed per
Miles Per Gallon (MPG)
vehicle to calculate miles travelled per gallon.
The average number of incidents that take place per vehicle pro-rata’d per
Number of Incidents 100,000 km travelled with an incident being defined as “damage to
vehicle, property or people”.
Empty Distance Run Distance run empty per vehicle as a percentage of total distance run.
Number of hours each vehicle is in operation as a percentage of its
theoretical maximum (24/7 operation).
Average total gross weight of each vehicle when fully loaded as a
Vehicle Fill percentage of its theoretical maximum (maximum gross weight of the
vehicle as taxed).
Total number of deliveries made on time and in full compared to the total
Deliveries On Time In Full (OTIF)
number of deliveries overall.
Total number of deliveries where damage to products occurred compared
to total number of deliveries made.
Total number of delivery complaints that were not damage or OTIF-related
compared to total number of deliveries made.
Department for Transport, ‘Delivering a Sustainable Transport System: The Logistics Perspective’, December 2008.
2.3 How the Data was Collected The eight KPIs used in this survey were deliberately
chosen to offer consistency with the KPIs included in
The primary distribution benchmarking survey was the On Line Benchmarking system. This was to
based on a 48hr period in February 2009. Survey encourage participants to provide their data using OLB
participants had two options in terms of providing data and to allow data not entered this way to be transferred
for their operation. simply into the OLB system for data aggregation and
The next section of this guide introduces the types of
Six of the transport operators involved in the survey transport operators and vehicles covered by this survey.
used a benchmarking spreadsheet to enter their
operational data, inputting relevant KPI measurements
manually for each vehicle involved.
2.4 Survey Participants
Thirteen transport operators (as detailed in the
Option 2: Acknowledgements page on page i) using a total of 794
vehicles in primary distribution operations participated in
The other seven transport operators used the recently
this external benchmarking survey.
launched On Line Benchmarking (OLB) tool. OLB is an
external operational performance comparison tool The following information provides a general overview
offered through Freight Best Practice. of the types of transport operators involved, illustrating
from an aggregated and anonymised perspective where
they were located and what types of primary distribution
The Department for Transport On Line vehicles were covered.
Benchmarking (OLB) system provides an
internet-based resource that can be used Geographical Spread
by transport operators to externally and
anonymously benchmark the performance The geographical spread of the operators involved is
of their vehicles. Vehicle types and shown in Figure 2.
operating characteristics can be selected
quickly and easily to compare In terms of geographical spread, 30% of the surveyed
performance data from your fleet against vehicles were located in the North West and 60% in a
other operators nationally. corridor running diagonally from the South East to the
OLB can be accessed from the website
Figure 2 Geographical Spread of Vehicles in the Survey
Primary Distribution Transport Sub-sectors Vehicle Types
As previously stated, primary distribution covers a large The vehicle types involved in this survey, their gross
and diverse range of different road transport operations. vehicle weights (GVW) and their route types are all
The spread of transport operators involved in this detailed in Figure 3.
survey is detailed in Table 2.
‘Motorway’ refers to vehicles mainly used on motorway
Table 2 Breakdown of Primary Distribution Sub-sectors journeys (e.g. trunking work).
‘Single’ refers to vehicles mainly used on single/dual
Number carriageways (e.g. A roads).
Number of % of
Vehicles ‘Urban’ refers to vehicles mainly used in built-up urban
areas (e.g. inner town/city).
Construction 2 22 3%
Containers 1 60 8% As the chart below shows, the most common vehicles
involved in the survey were rigids of 18-26 tonnes GVW.
Engineering 1 1 0%
Rigids in this survey were particularly heavily employed
Food/Drink 5 586 74% in ‘urban’ and ‘single’ transport operations, while artics
were more commonly employed for ‘motorway’ work.
General haulage 1 13 2%
Manufacturing 1 5 0% Table 3 Vehicle Route Types
Non-food retail 1 87 11% Operation Type Percentage
Parcels 1 20 2% Motorway 29%
Total 13 794 Single 3%
Food and drink is the largest primary distribution
sub-sector covered by this benchmarking survey in
terms of vehicle numbers, followed by non-food retail
Figure 3 Vehicle Operational Diversity
The body types of vehicles involved in the survey also Figure 4 Transmission Type of Vehicles
varied, as shown below in Table 4.
Table 4 Vehicle Body Types
Body Type Number %
Box 3 1%
Container 60 7%
Curtainsider 138 17%
Flatbed 16 2%
Refrigerated 104 13%
Total 321 40%
Box 7 1%
Cranemounted 1 0%
Curtainsider 23 3% Figure 4 illustrates the transmission types for all
vehicles in this survey. Most vehicles (69%) had manual
Refrigerated 442 56%
transmission, meaning that the driver had direct control
Total 473 60% over gear selection and a more direct effect, therefore,
on fuel consumption performance.
For articulated vehicles, curtainsider and refrigerated
Figure 5 Emissions Standards of Vehicles
trailers were the most prevalent, making up 17% and
13% of the survey sample respectively. For rigid
vehicles, refrigerated bodies were the most popular,
making up 56% of the survey sample. Refrigerated
bodies and trailers therefore accounted for a total of
69% of all the vehicles surveyed.
Table 5 Nature of Fleet Analysis
Nature of Fleet No. of
Hire and Reward 111 14%
Own Account 683 86%
As shown in Table 5, the largest number of vehicles in
Figure 5 illustrates that the majority of the vehicles in
the survey belonged to ‘own account’ operators.
this survey had a Euro III emission engine type (62%). A
small number of operators (6%) were unsure as to their
vehicles’ emissions standards.
Odometer readings and fuel consumption were
3 Survey Results recorded for each vehicle from all fleets during the
This survey’s 13 participants provided data using the survey period.
methods detailed in Section 2.3 of this guide.
From the survey’s total pool of 794 vehicles, 450
Collating data from all the survey participants in order to vehicles provided accurate MPG data. The remaining
report on an aggregated basis proved difficult because 344 vehicles were excluded as data recording
of a consistent issue of accuracy. The data was inaccuracies meant their MPG scores were not valid.
checked and cleansed where inaccuracies or
The 450 vehicles were segregated into their respective
inconsistencies were identified.
GVWs as presented in Table 6.
One of Freight Best Practice’s over-arching messages Table 6 Gross Vehicle Weights
has always been “if you can’t measure it, you can’t
manage it”. The word ‘accurately’ may now have to be Gross Vehicle Total No. assessed
added to this message! Weight (GVW) for MPG
Each KPI is summarised, where possible, in three 3.5-7.5 tonnes GVW 30
7.5-18 tonnes GVW 40
By gross vehicle weight (GVW)
By operational type 18-26 tonnes GVW 106
By primary distribution sub-sector
26-32 tonnes GVW 6
In some cases, KPIs have also been analysed by
geographical region. 33-40 tonnes GVW 32
3.1 Miles per Gallon (MPG) 40-44 tonnes GVW 236
MPG analysis is perhaps the most common key Grand Total 450
performance indicator (KPI) used by transport operators
to determine their operational efficiency, as it is widely Due to the diverse nature of primary distribution, a
understood and easy to calculate. simple MPG figure may be misleading if vehicle types
are not considered. The average MPG performance
achieved for different vehicle types in the survey is
shown in Figure 6.
Figure 6 Average MPG by GVW
The MPG analysis in Figure 6 shows that generally, The 26-32 tonne rigid vehicles in this survey were
larger, heavier vehicles have a lower MPG, with the involved in motorway operations, which would help to
exception of the 26-32 tonne rigids and the 40-44 tonne explain their higher MPG figure compared with the 18-
artics. 26 tonne rigid vehicle category, which was mostly
involved in urban operations.
This is no surprise since MPG performance generally
deteriorates as gross vehicle weight increases. The overall average recorded during the survey across
However, this is not to suggest that these vehicles have all vehicle types was 8.54 MPG.
lower overall efficiency since other aspects may also
affect MPG, such as type of route. Figure 7 shows MPG performance by type of route run.
As might be expected, the MPG performance of
Artic vehicles, as shown earlier in Figure 3, tended to be vehicles running on the motorway correlates with the
involved in motorway operations, which returned a MPG performance of 40-44 tonne artics, as these
reasonable MPG for the size of vehicle, possibly related vehicles tend to operate on motorways.
to the need for fewer gear changes and less fluctuation
in speed. The 33-40 tonne artics, however, were mostly Single carriageway running showed the best average
involved in urban operations in this survey, which would MPG. This may be because smaller vehicles are more
help to explain why these vehicles show a lower MPG likely to be found on this type of road.
than larger artics.
Figure 8 indicates that the engineering sub-sector had
the worst fuel consumption performance of the
Figure 7 Average MPG by Route Type
sub-sectors surveyed (an average of 4.91 MPG). This is
perhaps because such vehicles often tend to be left
idling to run on-vehicle plant or machinery, such as
Average 8.54 cranes.
The best performing was the parcels sub-sector, whose
Urban 7.79 MPG performance was 78% better than the next best
performing sub-sector, non-food retail. This could be
explained by the types of vehicles involved in parcels
distribution, as a greater number of smaller and lighter
loaded vehicles in this sector would help generate a
better overall average MPG performance.
Motorway 8.65 Figure 9 confirms the average MPG per region, with all
vehicle weight types for each region aggregated
0 5 10 15
Figure 8 Average MPG by Sub-sector
It can be seen that the North West region provided the For example, a higher concentration of vehicles
highest MPG performance in this survey, with the East involved in urban type primary distribution would result
Midlands region providing the worst return. This could in lower average speeds, with more stop-start traffic
be purely the result of different terrains affecting fuel conditions and repeated gear changes.
performance – for example, the number of hills in the
various areas. However, a lower MPG may also be This could certainly help to explain the MPG
related to the type of operation undertaken in each performance for the London area, which came out as
region. the second worst region.
Figure 9 Average MPG by Region
3.2 Number of Incidents per 100,000 3.3 Empty Distance Run
The survey required each operator to record the
This KPI refers to the total number of incidents per distance travelled empty per vehicle. Empty running
vehicle per 100,000 kilometres (KMs), averaged across was defined as when the vehicle was carrying no cargo
a fleet. and for the purposes of the survey, ‘cargo’ was taken to
include empty packaging and the necessary re-
Participants were told to include any event where positioning of other equipment, such as an empty
damage to vehicles, property or people occurred. This container.
allowed for all types of incidents to be considered.
The empty distance run KPI compared empty distance
The KPI calculation reflects the total number of travelled with total distance travelled per vehicle. The
incidents recorded during the survey period in relation to results are presented in Figures 10, 11 & 12 and include
distance travelled to arrive at the number of incidents data by vehicle type, route type and sub-sector
per 100,000 KMs. respectively.
This indicator provides an overview of safety Figure 10 shows that large articulated vehicles were
performance per vehicle for an operator to benchmark subject to the most empty running.
against, whether they operate with a single vehicle or
multiple vehicles. This could be due to them being commonly used for
motorway routes, where empty running might be
For the 48hr survey timeframe, only two incidents were associated with the return journey after a long distance
reported across all of the participants and both were delivery.
from the same operator. This operator recorded total
distance travelled across all vehicles of 121,897 KMs, Smaller vehicle types in the survey experienced very
resulting in a KPI of 1.64 incidents per 100,000 KMs for little empty running, something which can in part be
that particular fleet. explained by these vehicles frequently being involved in
the carriage of empty packaging back to their base
Taking all 794 vehicles from all 13 transport operators depots.
into account, the total distance travelled by all vehicles
was over 507,000 KMs, against which these two
incidents resulted in an overall KPI of 0.39 incidents per
Figure 10 Average Empty Running by GVW
Figure 11 Average Empty Running by Type of Route A container vehicle can be re-routed after a delivery to
fill an empty container with a back-load on its return
journey to the container depot. But the 47% empty
running rate thrown up in this survey suggests that most
containers delivered to customers were returned empty.
However, as Table 2 demonstrates earlier in this guide,
the sample sizes for these sub-sectors are relatively
small and therefore this data may not be truly
representative of the sub-sectors as a whole.
Empty running appears to be less of an issue in other
sub-sectors, possibly as a result of implemented
operational changes and initiatives. General Haulage
has an empty running figure of 26% which compares
accurately to a commonly referred to industry average
figure of 25%.
It is entirely possible that the transport operators
involved in this survey in the Manufacturing and Parcels
sub-sectors may not have recorded empty running,
Figure 11 shows that motorway routes involved a however this could not be confirmed after the survey,
significantly higher rate of empty running than either therefore they are included in the graph for
single or urban routes, something which could be completeness.
explained due to vehicles returning empty from long-
The average empty distance run per vehicle during the
haul deliveries where there was less requirement to
survey equated to 13%.
carry empty packaging back to the base depot.
Figure 12 illustrates that vehicles involved in the
construction, containers and engineering sub-sectors
incurred substantially greater levels of empty running
than those in other sub-sectors.
Figure 12 Average Empty Running by Sub-sector
3.4 Vehicle Fill Figure 13 shows that the fill of vehicles varied
considerably between different vehicle weights with the
An important measure for all operators, and in particular best utilised vehicles being the smallest.
primary distribution operators, is how well vehicles are
being filled when compared to the maximum theoretical Artics exhibited high levels of fill, which was to be
load. expected as these tend to complete longer distance,
motorway work. Smaller vehicles, meanwhile, tend to
There are two options for transport operators looking to reach their weight limit quickly, helping to raise their
record vehicle fill – fill by weight or fill by volume. For level of average vehicle fill.
the purposes of this KPI, vehicle fill by weight was used.
There is some fluctuation in the average vehicle fill for
Each survey participant was required to provide an other vehicle groups, with 7.5-18 tonne rigids coming
average vehicle utilisation figure during the survey out as the least well utilised.
timeframe. This was then calculated against the
maximum possible weight each vehicle could legally Figure 14 provides a breakdown of average vehicle fill
carry to determine average vehicle fill as a percentage. by type of route and shows that vehicles on urban and
motorway routes were the highest filled during the
Vehicle fill by weight was recorded at the beginning of survey. Vehicles on single carriageway routes were the
each vehicle journey. No account was taken of changing worst performing, either because vehicle load space
levels of vehicle fill in the course of multi-drop tended to be filled before maximum weight limits were
operations. reached or because vehicle fill was sacrificed in order to
achieve specific customer delivery requirements.
Figure 13 Average Vehicle Fill by GVW
Figure 14 Average Vehicle Fill by Type of Route The parcels sub-sector also experienced a poor rate of
fill which could be related to the light weight of parcels
or vehicles being sent out irrespective of fill, as
customers in this sector tend to require collection or
delivery within a certain time limit.
The manufacturing sub-sector achieved the best level of
vehicle fill. This may have been due to operators in this
sector only sending deliveries out once their vehicle fill
had been maximised, thanks to having direct control
over both the goods produced and their related
Further details on the balance between vehicle fill and
delivery performance are provided in section 3.6 of this
Figure 15 highlights average vehicle fill by sub-sector
and shows that the engineering sub-sector had the
lowest rate. This may have been due to load size and
shape restrictions preventing a high level of utilisation.
Figure 15 Average Vehicle Fill by Sub-sector
3.5 Vehicle Time Utilisation Figure 16 shows large artics leading the field with a time
utilisation KPI of over 50%, indicating that these
Another good KPI for measuring vehicle utilisation is the vehicles worked either overnight or on multiple shifts,
amount of time that a vehicle spends actually out on the with their results equating to more than 12 hours per
road. day out on the road. This would be consistent with their
routes being primarily motorway based and would help
A tractor/trailer combination is an expensive asset so it ensure that the higher costs of such vehicles were
is important to keep the wheels turning and get covered by a higher-than-average level of utilisation.
maximum productivity out of the vehicle.
Rigid vehicles worked, on average, between six and 10
Of course, vehicle time utilisation is heavily dependent hours per day, according to the time utilisation
on the type of transport operation involved – for percentages. This would be compatible with single shift
example in terms of the number of shifts run, any operation, which is unsurprising given their primarily
particular customer requirements, and any operational urban or single-carriageway running, where more
restrictions that might impact on vehicles, like night-time delivery restrictions may be in place based on customer
delivery curfews. requirements or regulations.
This indicator looked at the proportion of time each
vehicle spent out on the road during the 48 hours in the
Figure 16 Average Time Utilisation by GVW
Figure 17 Average Time Utilisation by Type of Route Figure 18 shows that the construction and engineering
sub-sectors had the lowest level of utilisation in terms of
hours worked. The economic downturn at the time of
the survey may explain this. The non-food retail
sub-sector proved to have the highest level of time
Figure 17 shows that, unsurprisingly, vehicle utilisation
in terms of hours worked was greatest in motorway
running, where longer trips are often involved.
The time utilisation for single and urban running
vehicles suggests that these are not used as intensively.
Single and urban delivery points tend to be open only
during the day, for one thing, restricting operators to
single shift operation.
Figure 18 Average Time Utilisation by Sub-sector
3.6 On Time In Full Deliveries (OTIF) Figure 19 demonstrates no correlation between GVW
and OTIF performance. Overall, the level of OTIF
Getting it right first time is the simple message related to performance was extremely high, with an average
this performance indicator. If a transport operator can across all 774 vehicles of 99.27%. In isolation, this KPI
deliver an order first time, on time and in full, this helps would suggest a high level of efficiency in the primary
to achieve optimum delivery efficiency. If they cannot, distribution sector, however, as detailed at this end of
additional costs will be incurred. this section, other KPIs should be considered alongside
OTIF to determine an overall level of operator efficiency.
The results for this KPI are based on 774 vehicles as
some of the survey participants did not record data for
The OTIF KPI is calculated per vehicle as the
percentage of completed deliveries made on time and in
full within the survey’s 48 hour timeframe. For example,
if a vehicle completed 100 deliveries during the period
covered by the survey of which 90 were recorded as
OTIF, the KPI measurement would be 90%.
Figure 19 Average OTIF Deliveries by GVW
Figure 20 Average OTIF Deliveries by Route Type Motorway and urban routes provided the highest levels
of OTIF delivery, indicating that operators in this survey
experienced few external delays on such routes or built
contingencies into their delivery schedules.
Figure 21 illustrates average OTIF delivery performance
by sub-sector. The parcels sub-sector did not record
OTIF data for this survey and is therefore not shown.
General haulage shows below average performance.
Specific reasons for this were not captured in this
survey. 7 out of 8 sectors achieved a 99% or better
Figure 21 Average OTIF Deliveries by Sub-sector
3.7 Damaged Deliveries Table 7 Delivery Complaints by GVW
Quality is just as important as quantity when it comes to
deliveries, with damaged goods impacting on customer Vehicle Type GVW %
3.5-7.5 tonnes GVW 0.00%
Definitions of a damaged delivery vary, but in this
survey damaged deliveries were defined as those
declared as damaged by the customer. 7.5-18 tonnes GVW 0.20%
Where a delivery is declared as damaged by the
customer, it often results in a need to re-manufacture 18-26 tonnes GVW 0.82%
and re-deliver the product, invariably leading to
increased costs, increased freight requirements and
26-32 tonnes GVW 0.00%
This KPI was calculated as the number of deliveries
33-40 tonnes GVW 0.46%
declared as being damaged by the operator’s customer
compared to the total number of deliveries made. For
example, if a vehicle completed 100 deliveries of which 40-44 tonnes GVW 0.36%
two were declared as being damaged, the KPI would be
The types of vehicles with the highest number of
During the survey timeframe, all operators reported nil complaints were 18-26 tonne rigids, at 0.82%. Rigids of
damaged deliveries. This could be because no 3.5-7.5 tonnes and 26-32 tonnes received no delivery
damaged deliveries took place during the survey, or complaints during the survey timeframe.
because damaged deliveries did take place but were
not recorded by operators during the 48hr timescale of Urban route deliveries produced the highest number of
the survey, or because complaints about damaged complaints during the survey, with the other two route
deliveries made during the survey timeframe were not types receiving far fewer.
received until much later.
Table 8 Delivery Complaints by Type of Route
3.8 Delivery Complaints Route Type %
This KPI was based on the number of complaints
received by operators as a percentage of total
deliveries. Single 0.31%
Delivery complaints were defined in this survey as
complaints from customers relating to deliveries other
than those relating to non-OTIF deliveries and
Examples of delivery complaints would be where the
delivery had been made too early, which can obviously
impact upon the operation of a customer (particularly if
working to just-in-time schedules), or where there were
issues relating to the conduct of the driver or operation
of the vehicle.
If a particular vehicle completed 100 deliveries, one of
which led to a complaint due to being delivered 24hrs
early, the KPI measurement would be 1%.
Table 9 Delivery Complaints by Sub-sector Within the survey period, the general haulage
sub-sector had the highest number of delivery
It may be that, as with delivery damages, reports of
complaints did not reach the operators during the
Engineering 0.00% 3.9 Interventions
Food/Drink 0.59% Operators were asked to indicate whether their vehicles
had any fuel saving interventions fitted. The
General Haulage 7.46% interventions are listed in Table 10, including the
proportion of vehicles which had them fitted.
Table 10 confirms that the most common type of
Non-Food Retail 0.00% intervention used by primary distribution operators was
some form of aerodynamics.
Table 10 Summary of Interventions Used by Surveyed Vehicles
number 3.5 - 7.5 7.5 - 18 18 - 26 26 - 32 33 - 40 40 - 44
Intervention Description of Tonnes Tonnes Tonnes Tonnes Tonnes Tonnes
vehicles GVW GVW GVW GVW GVW GVW
794 42 101 323 7 85 236
Includes cab, roof and 756 42 80 315 0 83 236
body fairing (95%) (100%) (79%) (98%) (0%) (98%) (100%)
Includes driver training
667 41 78 319 7 83 139
Drivers and driver motivation /
(84%) (98%) (77%) (99%) (100%) (98%) (59%)
Measures to increase
567 41 76 313 0 72 65
Operation vehicle fill and reduce
(71%) (98%) (75%) (97%) (0%) (85%) (28%)
schemes and the use of
701 41 94 314 0 73 179
Other approved engine
(88%) (98%) (93%) (97%) (0%) (86%) (76%)
lubricants and synthetic
Includes vehicle routing
and scheduling systems 28 0 0 0 0 1 27
and satellite navigation (4%) (0%) (0%) (0%) (0%) (1%) (11%)
Includes regular wheel
alignment checks, fuel
644 41 76 313 0 76 138
Tyres efficient tyres, tyre
(81%) (98%) (75%) (97%) (0%) (89%) (58%)
and regular re-grooving
The least popular type of intervention was telematics As can be seen in Table 10, there were very few
which, considering this category includes computerised vehicles with no aerodynamic equipment fitted. The artic
vehicle routing and scheduling systems and sat-nav 33-40 tonne category did, however, include vehicles
systems, is perhaps a little surprising in light of the both with and without aerodynamic interventions.
general increase in the popularity of such systems in
recent years. Figure 22 shows that there was an improvement of 9%
in average MPG for vehicles in this category fitted with
aerodynamic interventions on motorway routes.
The Fuel Efficiency Trials Guide provides
a generic 12-step process for operators to A 9% improvement in MPG translates into a saving of
consider in implementing fuel efficiency almost 5,748 litres of fuel over 100,000 miles, equating
interventions within a vehicle fleet. to around 15,117 kg of carbon dioxide (CO2).
The Effects of Aerodynamic Interventions
A common way of assessing the benefits of fuel saving
interventions is by measuring MPG performance. This
section of the guide looks at the effects of aerodynamic
interventions, as the most common type of fuel
efficiency intervention reported in the survey, on the
MPG performance of the vehicles involved.
Only the 450 vehicles with valid MPG measurements as
described in Section 3.1 were included in this analysis.
Figure 22 Variance in MPG for Vehicles in the Artic 33-40 tonne GVW category
4 Summary Vehicle tachographs
This survey of the primary distribution sector confirms
that operational performance can vary greatly between
operators, due to the dynamic make-up of the sector. 4.2 Vehicle Fill
A number of positive recommendations can be made as It is important to find the right balance between some
a result of this survey on ways in which primary KPIs, for example between vehicle fill and on time in full
distribution transport operators can improve their (OTIF) deliveries.
operational performance, save fuel and ultimately save
money. They include: A transport operator needs to be careful not to
jeopardise vehicle fill for the sake of a better OTIF
Ensuring accurate data collection, for example
MPG performance, for the purposes of
subsequent decision making Lower vehicle fill may have a positive effect on vehicle
Ensuring vehicle fill matches the size of the MPG performance, but total fuel spend and number of
vehicle as consistently as possible journeys are both likely to substantially increase as a
result of lower fill levels.
Reducing empty running, for example, through
greater use of back-loads Vehicle fill levels can also be affected by the size of
vehicle specified. An operator specifying a vehicle that
Keeping damage levels during deliveries to a
is too large for their typical loads, whether by weight or
by volume, is always likely to achieve a low vehicle fill
Exploring the use of relevant fuel saving level.
Establishing regular benchmarking activities 4.3 Empty Running
These points are explored further in the rest of this Empty running is an issue throughout the freight
section. industry, as this survey underlines.
Genuine empty running (where vehicles are running
4.1 Accurate data collection with no cargo, including any empty packaging returns)
can have a significant impact on a transport operator’s
The fact that valid fuel consumption figures were
recorded for only 450 vehicles out of the 794 involved in
this survey suggests that many operators do not have Empty running may prove difficult for a transport
an accurate picture of fuel consumption per vehicle. operator to solve alone, but collaboration can prove the
key to dealing with it, for example by providing more
Accurate MPG analysis is vital to determine how much
back-loads on a regular basis.
fuel is being consumed by each vehicle and to allow
operators to target less fuel efficient vehicles for Finding regular back-loads across an entire vehicle fleet
improvement. can prove difficult for a single transport operator but the
recent growth of pallet networks and the introduction of
To achieve accurate MPG figures, the following
haulage exchanges offer operators greater opportunities
elements need to be measured accurately:
than ever for working together to consistently fill
Vehicle odometer readings vehicles and increase operational efficiency.
Vehicle fuel consumption figures
There may be existing data sources in your company
that already provide this information, including: This survey suggests that the level of damages among
primary distribution transport operators was negligible
and that damage to goods during delivery is not really
Driver job sheets an issue.
It is important however, that this performance is 4.6 Regular Benchmarking
maintained. To this end, operators should:
This survey has highlighted the importance of carefully
Ensure the correct loading of all items, with lighter
collecting the necessary data to analyse current
items placed on top of heavier ones
operational performance and plan for further
Feed any cargo packaging issues back up the improvements.
supply chain – products may be shipped in
packaging not specified by yourself, causing your If you can’t measure it accurately, you can’t manage it
delivery performance to be affected accurately!
Use stretch-wrap or shrink-wrap to secure pallet The survey has also made clear that operational
stacks performance indicators should not be considered in
isolation. A suitable range of KPIs should be adopted
Use cargo support straps or bars to secure loads
that fit the business model of the transport operator and
in the vehicle
offer a comprehensive overview of overall operational
Avoid excessive braking or acceleration during performance.
driving (something which will also have fuel
The Department for Transport On Line
Benchmarking (OLB) system provides an
4.5 Fuel Saving Interventions internet-based resource that can be used
by transport operators to externally and
There are many fuel saving interventions available for
anonymously benchmark the performance
transport operators to consider.
of their vehicles. Vehicle types and
The data collated in this survey generally confirms that operating characteristics can be selected
when interventions have been used, which are relevant quickly and easily to compare
to the vehicle type and type of operation, there are performance data from your fleet against
significant fuel savings. other operators nationally.
OLB can be accessed from the website
The Fuel Ready Reckoner, can help you www.freightbestpractice.org.uk/benchmarking
determine types of interventions available. It
is a FREE web-based tool that can be
accessed by logging on to the Freight Best
Practice website at Once the right internal KPIs have been established, you
www.freightbestpractice.org.uk can compare your current performance not just against
your own previous performance but also against other
operators in your sector.
Measuring performance against other operators can
add real value in terms of understanding how efficient
your operation is and provides further indicators as to
what other initiatives could be adopted to improve your
efficiency, with the ultimate aim of achieving a best-in-
5 The Primary Distribution Transport Efficiency ‘Road Map’
The Primary Distribution Transport Efficiency Road Map The measures in the action plan are set out under six
shown below is an action plan of measures that can be key categories:
considered by anybody in the sector looking to improve
Equipment and Systems
It identifies the measures that can be ‘owned’ and
initiated by those managers responsible for transport Developing Skills
within the business, for example, fuel management and Performance Management
It also shows wider strategic measures, such as
Supply Chain Reconfiguration
customer-related initiatives, which would require the
close involvement of other parts of the business, for Specific actions are identified for each category,
example the sales, marketing and procurement together with signposts identifying relevant guidance
departments. and support material from the Freight Best Practice
Efficiency Measures for General Consideration
General Specific Action Ownership or Initiator
Appoint Fuel Champion.
Audit current fuel management processes.
Fuel Management Implement effective fuel management programme
Saving Fuel Fuel Management Stock Control
Scheduling Audit order processing routing scheduling process.
Computerised Vehicle Review potential benefits of computerised routing and
Routing and scheduling.
Scheduling (CVRS) for Optimise journey planning.
Equipment Telematics Consider benefits of in-cab telematics and
Telematics for Efficient communications’ systems to enhance expected arrival
Road Operations times, traffic avoidance and performance monitoring.
Systems Review vehicle specification and potential benefits of
the introduction of vehicles with increased:
Vehicle Specification Payload
Truck Specification for Load bed
Best Operational Cubic capacity
Efficiency Consider fitment of auxiliary equipment to reduce
loading/ unloading time.
Conduct training review audit.
Developing SAFED for HGVs: A Implement driver development programme.
Guide to Safe and Fuel Consider implementation of driver league tables and
Efficient Driving for rewards scheme.
Performance Establish relevant key performance indicators and
Performance Fleet Performance measure performance:
Management Tool Set targets
Management incorporating CO2 Benchmark across depots
Emissions Calculator External Benchmarking
On Line Benchmarking
Increase pre-loading of vehicles with next day’s loads
to ensure maximum productive delivery time.
Load Preparation If operations are short distance, preparation by yard
staff of second trip loads whilst vehicle is on first trip.
If operations are short distance, encourage customers
to accept pre-8am deliveries, allowing second run
loading during morning peak traffic congestion and
Delivery Windows post-peak second run.
Sales staff to ‘lead’ customers to available delivery
Supply Chain / Logistics Dir
Fleet times. Increase pre-loading of vehicles with next day’s
loads to ensure maximum productive delivery time.
Pricing Mechanism Consider rate discounts for off-peak deliveries.
Consider delivery of less than full loads on nominated
Zoning days by geographical area. Consider rate discounts for
Establish standardised customer service levels and
Inter-Depot serve customer from most appropriate depot for the
Co-operation delivery site.
Supplier Direct Investigate benefits of supplier direct delivery.
Deliveries Establish economic thresholds.
Back-loading Review opportunities for reducing empty running by
Make Back-loading back-loading. For example, collect parcels for delivery to
Supply Work for You depot on return trip.
Review industry-wide opportunities for trip reduction
Reconfiguration Consolidation Centres through the use of consolidation centres.
Unitisation of Investigate market opportunities for further unitisation
Customer Orders of deliveries and consolidation of orders.