Fbp1107 Primary Distribution Benchmarking Survey

Primary Distribution Benchmarking

Survey 2009

Benchm

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Acknowledgements

The following companies took part in the benchmarking

survey outlined in this guide and are thanked for their

kind participation:

Aspray Transport Ltd.

Bandvulc Tyres Ltd.

Fridays Ltd.

Gist Ltd.

Global Manufacturing Supplies Ltd.

Howdens Joinery Co.

Knights of Old Ltd.

Norfolkline Ltd.

PD Logistics Ltd.

Pilkington UK Ltd.

Roadways Container Logistics Ltd.

Robert Wiseman Dairies plc.

Tesco plc.

i

Foreword

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

document contains.

iii

Contents

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

v

1 Background

Every successful organisation needs to manage its

assets effectively and can benefit from benchmarking its

performance against that of similar operators, especially

those deemed to be ‘best-in-class’ in their sector.

The Department for Transport, through its Freight Best

Practice programme, has supported a series of

benchmarking surveys that have developed a range of

key performance indicators (KPIs) in a variety of

industry sectors.

This particular KPI benchmarking survey covers the

primary distribution sector.

KPIs used in external benchmarking are essential tools

for the freight industry to understand and then improve

its performance. They provide a consistent basis for

measuring transport efficiency across different fleets,

comparing like with like.

This guide aims to:

Show companies how their own performance

compares with that of others

Measure performance across a range of KPIs

Identify recommendations to improve efficiency

Operators in the sector, whether survey participants or

not, can use this benchmarking guide to identify real

opportunities to maximise transport efficiency, reducing

both their running costs and environmental impact.

1.1 Measuring Performance in YourOwn Business

If you want to make well-informed tactical and strategic

decisions about your operation, you need to be able to

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.

The starting point for any performance improvement

programme should be to understand the current

performance of your operation. This means collecting

data on key aspects of your operation and turning this

into specific measurements that can help you identify

areas for improvement. Examples of such

measurements include how much fuel each vehicle

uses, how many miles your vehicles run empty and the

number of late deliveries you make. Those measures

most critical to your operation will be your firm’s KPIs.

They may, of course, be supported by other, less critical

measures.

A KPI on its own will not tell you much. Individual

measurements and raw data need to be turned into

information that can help you to make decisions. This

means setting a target and measuring and monitoring

KPIs over a period of time to see how your operation

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

showing progress in performance.

1.2 What Should the Key PerformanceIndicators Be?

There are many different KPIs that can be used to

measure performance in a freight transport operation

and it can be difficult to know which ones might be right

for you. This section is intended to explain the

characteristics of some useful KPIs that can be applied

in various types of operations. However, there are a

number of things you should consider beforehand in

order to decide which ones are actually right for you. A

KPI should be relevant to your particular operation and

it should also be SMART – Specific, Measurable,

Achievable, Realistic and Timed.

1

Already published are KPI survey guides for

the following sectors:

Key Performance Indicators for

Non-food Retail Distribution

Key Performance Indicators for the

Food Supply Chain


Pallet Sector


Next-day Parcel Delivery Sector


Builders’ Merchants Sector

All of these publications are available FREE

of charge from the Freight Best Practice

programme website

www.freightbestpractice.org.uk and from

the Hotline 0845 877 0 877.

2

Specific

KPIs should be specific, simple to use and easy to

understand. Complicated statistics and formulae can

lead to confusion about what is actually being measured

in the first place. If KPIs are specific and simple, they

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.

Measurable

KPIs can show changes in performance over time. For

this to happen, it is essential to compare like-with-like

data. It is easy, for instance, to fall into the trap of

comparing two drivers on different routes for time

utilisation or miles per gallon (MPG) – but if one route is

more demanding than the other, it could be misleading.

Similarly, comparing drivers of vehicles of substantially

different age or vehicle type can also be deceptive.

There are ways you can resolve these problems, such

as rotating drivers on different vehicles and different

routes and then monitoring both driver and vehicle

performance, to identify consistently high and poor

performers.

Achievable

Any targets set must be achievable. It may seem

beneficial to set high targets in the hope that this will

lead to greater improvements in performance, but

remember that people often become disillusioned if they

continually fall short of their targets. Regularly reviewing

performance towards targets and then resetting the

targets to encourage smaller, incremental (but

cumulative) improvements may work much better in the

long run.

Realistic

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

consistent. It is important that the data required to

produce a particular KPI can be collected easily and on

a regular basis, as comparison over time forms the

basis of benchmarking and then improving

performance.

Timed

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

needs of a particular business. Some information may

have to be collected on a daily basis, such as staff

absence levels in the warehouse, daily delivery drops or

nightly trunking volumes. If certain measures are not

recorded and presented to the agreed timescales, the

risk of changes in performance going unnoticed rises.

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

strategy, plan and make decisions, while at an

operational level they can also clearly show up any

areas that need improvement or a change in approach.

An individual KPI can tell you how well you are

performing at an operational level. However, when

looked at in combination with other measurements,

KPIs can also help build a picture of how well you are

performing in terms of revenue, profitability and overall

fleet efficiency, or in relation to customer service.

Figure 1 shows a basic, step-by-step process for

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

organisation.

The eight KPIs used by the companies that participated

in the primary distribution benchmarking survey are

detailed in Section 2.2 of this guide.

See the Freight Best Practice Guides

Performance Management for Efficient

Road Freight Operations

This guide explains the process of

measuring performance effectively. It

includes advice on how information is best

collected and interpreted to allow informed

decision making in order to achieve

operational efficiency improvements.

3

Set and Review Targets

Select KPIs

Reporting & Feedback

Data Collection

Yes

Yes

No

No

Review/Evaluation(Including Benchmarking)

Identify Strategy for Performance Improvement

Take ActionImplement Strategy

ResultsTargets

met?

Targetstoo high?

Performance Management

Checklist:

�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,

Realistic and Timed? (SMART)

Have you set targets for these KPIs?

Do you know how well your operation is

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?

Do you have a good system in place for

analysing and reporting your KPIs?

Do you use information technology systems

to help you?

Have you considered actions that can be

taken to improve your operation’s

performance and meet new, higher targets in

the future?

Figure 1 The Process of Selecting and Measuring KPIs

4

1.4 External Benchmarking

The basic process of measuring operational

performance internally is extremely useful, but to fully

understand how your operation compares with that of

your peers, you must benchmark against the

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.

The benchmarking survey described in this guide was

designed to highlight the performance of some of the

best-in-class operators in the primary distribution sector,

enabling you to compare the relative efficiency of your

own fleet and operation and identify measures you can

take to improve your performance.

5

2 The Primary DistributionBenchmarking Survey

2.1 The Nature of the PrimaryDistribution Sector

There are a number of definitions of primary distribution,

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

Transport operators often define primary distribution as

the final delivery of product to their customers or the

distribution of products to distribution centres and

processing sites. However, the term is entirely

dependent on the transport operator’s perspective and

its end customers. For example, the delivery of finished

products to a Regional Distribution Centre (RDC) will be

regarded as primary distribution by the manufacturer of

those products, but for the RDC, delivery of those same

finished products from the RDC to their end customers

will be the primary distribution element.

Primary distribution commonly involves the bulk

movement of goods – often a single commodity – often

over long distances and tends to be carried out by

larger sized heavy goods vehicles (HGVs).

There are many product-dependent variations in the

types of vehicles typically used for primary journeys,

from curtainsiders and box-bodied HGVs for palletised

goods to bulk tankers for liquids and skeletal trailers for

container movements.

2.2 The KPIs

In any benchmarking survey, it is essential to use the

most appropriate set of KPIs and that everybody in the

survey can accurately measure them.

The five core KPIs used in previous external

benchmarking surveys – namely vehicle fill, empty

running, time utilisation, deviation from schedule and

fuel consumption – were all considered alongside other

measures for this survey, but not all of them were

deemed to be relevant. The eight KPIs detailed in Table

1 were deemed most relevant to primary distribution

operators in this survey.

1

Department for Transport, ‘Delivering a Sustainable Transport System: The Logistics Perspective’, December 2008.

Table 1 The KPIs Measured during the Survey

KPI Description

Miles Per Gallon (MPG)Total distance run per vehicle divided by the total fuel consumed per

vehicle to calculate miles travelled per gallon.

Number of Incidents

The average number of incidents that take place per vehicle pro-rata’d per

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.

Worked HoursNumber of hours each vehicle is in operation as a percentage of its

theoretical maximum (24/7 operation).

Vehicle Fill

Average total gross weight of each vehicle when fully loaded as a

percentage of its theoretical maximum (maximum gross weight of the

vehicle as taxed).

Deliveries On Time In Full (OTIF)Total number of deliveries made on time and in full compared to the total

number of deliveries overall.

Delivery DamageTotal number of deliveries where damage to products occurred compared

to total number of deliveries made.

Delivery ComplaintsTotal number of delivery complaints that were not damage or OTIF-related

compared to total number of deliveries made.

6

2.3 How the Data was Collected

The primary distribution benchmarking survey was

based on a 48hr period in February 2009. Survey

participants had two options in terms of providing data

for their operation.

Option 1:

Six of the transport operators involved in the survey

used a benchmarking spreadsheet to enter their

operational data, inputting relevant KPI measurements

manually for each vehicle involved.

Option 2:

The other seven transport operators used the recently

launched On Line Benchmarking (OLB) tool. OLB is an

external operational performance comparison tool

offered through Freight Best Practice.

The eight KPIs used in this survey were deliberately

chosen to offer consistency with the KPIs included in

the On Line Benchmarking system. This was to

encourage participants to provide their data using OLB

and to allow data not entered this way to be transferred

simply into the OLB system for data aggregation and

reporting purposes.

The next section of this guide introduces the types of

transport operators and vehicles covered by this survey.

2.4 Survey Participants

Thirteen transport operators (as detailed in the

Acknowledgements page on page i) using a total of 794

vehicles in primary distribution operations participated in

this external benchmarking survey.

The following information provides a general overview

of the types of transport operators involved, illustrating

from an aggregated and anonymised perspective where

they were located and what types of primary distribution

vehicles were covered.

Geographical Spread

The geographical spread of the operators involved is

shown in Figure 2.

In terms of geographical spread, 30% of the surveyed

vehicles were located in the North West and 60% in a

corridor running diagonally from the South East to the

North West.

The Department for Transport On Line

Benchmarking (OLB) system provides an

internet-based resource that can be used

by transport operators to externally and

anonymously benchmark the performance

of their vehicles. Vehicle types and

operating characteristics can be selected

quickly and easily to compare

performance data from your fleet against

other operators nationally.

OLB can be accessed from the website

www.freightbestpractice.org.uk/benchmarking

Figure 2 Geographical Spread of Vehicles in the Survey

7

Primary Distribution Transport Sub-sectors

As previously stated, primary distribution covers a large

and diverse range of different road transport operations.

The spread of transport operators involved in this

survey is detailed in Table 2.

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

and containers.

Vehicle Types

The vehicle types involved in this survey, their gross

vehicle weights (GVW) and their route types are all

detailed in Figure 3.

‘Motorway’ refers to vehicles mainly used on motorway

journeys (e.g. trunking work).

‘Single’ refers to vehicles mainly used on single/dual

carriageways (e.g. A roads).

‘Urban’ refers to vehicles mainly used in built-up urban

areas (e.g. inner town/city).

As the chart below shows, the most common vehicles

involved in the survey were rigids of 18-26 tonnes GVW.

Rigids in this survey were particularly heavily employed

in ‘urban’ and ‘single’ transport operations, while artics

were more commonly employed for ‘motorway’ work.

Sub-sectorNumber of

Companies

Number

of

Vehicles

% of

Vehicles

Construction 2 22 3%

Containers 1 60 8%

Engineering 1 1 0%

Food/Drink 5 586 74%

General haulage 1 13 2%

Manufacturing 1 5 0%

Non-food retail 1 87 11%

Parcels 1 20 2%

Total 13 794

Table 2 Breakdown of Primary Distribution Sub-sectors

Involved

Figure 3 Vehicle Operational Diversity

Operation Type Percentage

Motorway 29%

Single 3%

Urban 68%

Table 3 Vehicle Route Types

8

The body types of vehicles involved in the survey also

varied, as shown below in Table 4.

For articulated vehicles, curtainsider and refrigerated

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.

As shown in Table 5, the largest number of vehicles in

the survey belonged to ‘own account’ operators.

Figure 4 illustrates the transmission types for all

vehicles in this survey. Most vehicles (69%) had manual

transmission, meaning that the driver had direct control

over gear selection and a more direct effect, therefore,

on fuel consumption performance.

Figure 5 illustrates that the majority of the vehicles in

this survey had a Euro III emission engine type (62%). A

small number of operators (6%) were unsure as to their

vehicles’ emissions standards.

Body Type Number %

AR

TIC

Box 3 1%

Container 60 7%

Curtainsider 138 17%

Flatbed 16 2%

Refrigerated 104 13%

Total 321 40%

RIG

ID

Box 7 1%

Cranemounted 1 0%

Curtainsider 23 3%

Refrigerated 442 56%

Total 473 60%

Table 4 Vehicle Body Types

Nature of Fleet

Description

No. of

Vehicles%

Hire and Reward 111 14%

Own Account 683 86%

Table 5 Nature of Fleet Analysis

Figure 4 Transmission Type of Vehicles

Figure 5 Emissions Standards of Vehicles

9

3 Survey Results

This survey’s 13 participants provided data using the

methods detailed in Section 2.3 of this guide.

Collating data from all the survey participants in order to

report on an aggregated basis proved difficult because

of a consistent issue of accuracy. The data was

checked and cleansed where inaccuracies or

inconsistencies were identified.

One of Freight Best Practice’s over-arching messages

has always been “if you can’t measure it, you can’t

manage it”. The word ‘accurately’ may now have to be

added to this message!

Each KPI is summarised, where possible, in three

different ways:

By gross vehicle weight (GVW)

By operational type

By primary distribution sub-sector

In some cases, KPIs have also been analysed by

geographical region.

3.1 Miles per Gallon (MPG)

MPG analysis is perhaps the most common key

performance indicator (KPI) used by transport operators

to determine their operational efficiency, as it is widely

understood and easy to calculate.

Odometer readings and fuel consumption were

recorded for each vehicle from all fleets during the

survey period.

From the survey’s total pool of 794 vehicles, 450

vehicles provided accurate MPG data. The remaining

344 vehicles were excluded as data recording

inaccuracies meant their MPG scores were not valid.

The 450 vehicles were segregated into their respective

GVWs as presented in Table 6.

Due to the diverse nature of primary distribution, a

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.

Gross Vehicle

Weight (GVW)

Total No. assessed

for MPG

RIG

IDS

3.5-7.5 tonnes GVW 30

7.5-18 tonnes GVW 40

18-26 tonnes GVW 106

26-32 tonnes GVW 6

AR

TIC

S 33-40 tonnes GVW 32

40-44 tonnes GVW 236

Grand Total 450

Table 6 Gross Vehicle Weights

Figure 6 Average MPG by GVW

10

The MPG analysis in Figure 6 shows that generally,

larger, heavier vehicles have a lower MPG, with the

exception of the 26-32 tonne rigids and the 40-44 tonne

artics.

This is no surprise since MPG performance generally

deteriorates as gross vehicle weight increases.

However, this is not to suggest that these vehicles have

lower overall efficiency since other aspects may also

affect MPG, such as type of route.

Artic vehicles, as shown earlier in Figure 3, tended to be

involved in motorway operations, which returned a

reasonable MPG for the size of vehicle, possibly related

to the need for fewer gear changes and less fluctuation

in speed. The 33-40 tonne artics, however, were mostly

involved in urban operations in this survey, which would

help to explain why these vehicles show a lower MPG

than larger artics.

The 26-32 tonne rigid vehicles in this survey were

involved in motorway operations, which would help to

explain their higher MPG figure compared with the 18-

26 tonne rigid vehicle category, which was mostly

involved in urban operations.

The overall average recorded during the survey across

all vehicle types was 8.54 MPG.

Figure 7 shows MPG performance by type of route run.

As might be expected, the MPG performance of

vehicles running on the motorway correlates with the

MPG performance of 40-44 tonne artics, as these

vehicles tend to operate on motorways.

Single carriageway running showed the best average

MPG. This may be because smaller vehicles are more

likely to be found on this type of road.

Figure 8 indicates that the engineering sub-sector had

the worst fuel consumption performance of the

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

cranes.

The best performing was the parcels sub-sector, whose

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.

Figure 9 confirms the average MPG per region, with all

vehicle weight types for each region aggregated

together.

8.65

13.02

7.79

8.54

0 5 10 15

Motorway

Single

Urban

Average

MPG

Rout

e ty

pe

Figure 7 Average MPG by Route Type

11

It can be seen that the North West region provided the

highest MPG performance in this survey, with the East

Midlands region providing the worst return. This could

be purely the result of different terrains affecting fuel

performance – for example, the number of hills in the

various areas. However, a lower MPG may also be

related to the type of operation undertaken in each

region.

For example, a higher concentration of vehicles

involved in urban type primary distribution would result

in lower average speeds, with more stop-start traffic

conditions and repeated gear changes.

This could certainly help to explain the MPG

performance for the London area, which came out as

the second worst region.

Figure 8 Average MPG by Sub-sector

Geographical Region

Figure 9 Average MPG by Region

12

3.2 Number of Incidents per 100,000Kilometres

This KPI refers to the total number of incidents per

vehicle per 100,000 kilometres (KMs), averaged across

a fleet.

Participants were told to include any event where

damage to vehicles, property or people occurred. This

allowed for all types of incidents to be considered.

The KPI calculation reflects the total number of

incidents recorded during the survey period in relation to

distance travelled to arrive at the number of incidents

per 100,000 KMs.

This indicator provides an overview of safety

performance per vehicle for an operator to benchmark

against, whether they operate with a single vehicle or

multiple vehicles.

For the 48hr survey timeframe, only two incidents were

reported across all of the participants and both were

from the same operator. This operator recorded total

distance travelled across all vehicles of 121,897 KMs,

resulting in a KPI of 1.64 incidents per 100,000 KMs for

that particular fleet.

Taking all 794 vehicles from all 13 transport operators

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

100,000 KMs.

3.3 Empty Distance Run

The survey required each operator to record the

distance travelled empty per vehicle. Empty running

was defined as when the vehicle was carrying no cargo

and for the purposes of the survey, ‘cargo’ was taken to

include empty packaging and the necessary re-

positioning of other equipment, such as an empty

container.

The empty distance run KPI compared empty distance

travelled with total distance travelled per vehicle. The

results are presented in Figures 10, 11 & 12 and include

data by vehicle type, route type and sub-sector

respectively.

Figure 10 shows that large articulated vehicles were

subject to the most empty running.

This could be due to them being commonly used for

motorway routes, where empty running might be

associated with the return journey after a long distance

delivery.

Smaller vehicle types in the survey experienced very

little empty running, something which can in part be

explained by these vehicles frequently being involved in

the carriage of empty packaging back to their base

depots.

Figure 10 Average Empty Running by GVW

13

Figure 11 shows that motorway routes involved a

significantly higher rate of empty running than either

single or urban routes, something which could be

explained due to vehicles returning empty from long-

haul deliveries where there was less requirement to

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.

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,

however this could not be confirmed after the survey,

therefore they are included in the graph for

completeness.

The average empty distance run per vehicle during the

survey equated to 13%.

Figure 11 Average Empty Running by Type of Route

Figure 12 Average Empty Running by Sub-sector

14

3.4 Vehicle Fill

An important measure for all operators, and in particular

primary distribution operators, is how well vehicles are

being filled when compared to the maximum theoretical

load.

There are two options for transport operators looking to

record vehicle fill – fill by weight or fill by volume. For

the purposes of this KPI, vehicle fill by weight was used.

Each survey participant was required to provide an

average vehicle utilisation figure during the survey

timeframe. This was then calculated against the

maximum possible weight each vehicle could legally

carry to determine average vehicle fill as a percentage.

Vehicle fill by weight was recorded at the beginning of

each vehicle journey. No account was taken of changing

levels of vehicle fill in the course of multi-drop

operations.

Figure 13 shows that the fill of vehicles varied

considerably between different vehicle weights with the

best utilised vehicles being the smallest.

Artics exhibited high levels of fill, which was to be

expected as these tend to complete longer distance,

motorway work. Smaller vehicles, meanwhile, tend to

reach their weight limit quickly, helping to raise their

level of average vehicle fill.

There is some fluctuation in the average vehicle fill for

other vehicle groups, with 7.5-18 tonne rigids coming

out as the least well utilised.

Figure 14 provides a breakdown of average vehicle fill

by type of route and shows that vehicles on urban and

motorway routes were the highest filled during the

survey. Vehicles on single carriageway routes were the

worst performing, either because vehicle load space

tended to be filled before maximum weight limits were

reached or because vehicle fill was sacrificed in order to

achieve specific customer delivery requirements.

Figure 13 Average Vehicle Fill by GVW

15

Further details on the balance between vehicle fill and

delivery performance are provided in section 3.6 of this

guide.

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.

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

transport requirements.

Figure 14 Average Vehicle Fill by Type of Route

Figure 15 Average Vehicle Fill by Sub-sector

16

3.5 Vehicle Time Utilisation

Another good KPI for measuring vehicle utilisation is the

amount of time that a vehicle spends actually out on the

road.

A tractor/trailer combination is an expensive asset so it

is important to keep the wheels turning and get

maximum productivity out of the vehicle.

Of course, vehicle time utilisation is heavily dependent

on the type of transport operation involved – for

example in terms of the number of shifts run, any

particular customer requirements, and any operational

restrictions that might impact on vehicles, like night-time

delivery curfews.

This indicator looked at the proportion of time each

vehicle spent out on the road during the 48 hours in the

survey timeframe.

Figure 16 shows large artics leading the field with a time

utilisation KPI of over 50%, indicating that these

vehicles worked either overnight or on multiple shifts,

with their results equating to more than 12 hours per

day out on the road. This would be consistent with their

routes being primarily motorway based and would help

ensure that the higher costs of such vehicles were

covered by a higher-than-average level of utilisation.

Rigid vehicles worked, on average, between six and 10

hours per day, according to the time utilisation

percentages. This would be compatible with single shift

operation, which is unsurprising given their primarily

urban or single-carriageway running, where more

delivery restrictions may be in place based on customer

requirements or regulations.

Figure 16 Average Time Utilisation by GVW

17

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 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

utilisation.

Figure 17 Average Time Utilisation by Type of Route

Figure 18 Average Time Utilisation by Sub-sector

18

3.6 On Time In Full Deliveries (OTIF)

Getting it right first time is the simple message related to

this performance indicator. If a transport operator can

deliver an order first time, on time and in full, this helps

to achieve optimum delivery efficiency. If they cannot,

additional costs will be incurred.

The results for this KPI are based on 774 vehicles as

some of the survey participants did not record data for

this area.

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 demonstrates no correlation between GVW

and OTIF performance. Overall, the level of OTIF

performance was extremely high, with an average

across all 774 vehicles of 99.27%. In isolation, this KPI

would suggest a high level of efficiency in the primary

distribution sector, however, as detailed at this end of

this section, other KPIs should be considered alongside

OTIF to determine an overall level of operator efficiency.

Figure 19 Average OTIF Deliveries by GVW

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

performance level.

19

Figure 20 Average OTIF Deliveries by Route Type

Figure 21 Average OTIF Deliveries by Sub-sector

20

3.7 Damaged Deliveries

Quality is just as important as quantity when it comes to

deliveries, with damaged goods impacting on customer

satisfaction levels.

Definitions of a damaged delivery vary, but in this

survey damaged deliveries were defined as those

declared as damaged by the customer.

Where a delivery is declared as damaged by the

customer, it often results in a need to re-manufacture

and re-deliver the product, invariably leading to

increased costs, increased freight requirements and

reduced efficiencies.

This KPI was calculated as the number of deliveries

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

two were declared as being damaged, the KPI would be

2%.

During the survey timeframe, all operators reported nil

damaged deliveries. This could be because no

damaged deliveries took place during the survey, or

because damaged deliveries did take place but were

not recorded by operators during the 48hr timescale of

the survey, or because complaints about damaged

deliveries made during the survey timeframe were not

received until much later.

3.8 Delivery Complaints

This KPI was based on the number of complaints

received by operators as a percentage of total

deliveries.

Delivery complaints were defined in this survey as

complaints from customers relating to deliveries other

than those relating to non-OTIF deliveries and

damages.

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%.

The types of vehicles with the highest number of

complaints were 18-26 tonne rigids, at 0.82%. Rigids of

3.5-7.5 tonnes and 26-32 tonnes received no delivery

complaints during the survey timeframe.

Urban route deliveries produced the highest number of

complaints during the survey, with the other two route

types receiving far fewer.

Vehicle Type GVW %

Rig

id

3.5-7.5 tonnes GVW 0.00%

7.5-18 tonnes GVW 0.20%

18-26 tonnes GVW 0.82%


Art

ic



Table 7 Delivery Complaints by GVW

Route Type %

Motorway 0.34%

Single 0.31%

Urban 0.62%

Table 8 Delivery Complaints by Type of Route

Within the survey period, the general haulage

sub-sector had the highest number of delivery

complaints.

It may be that, as with delivery damages, reports of

complaints did not reach the operators during the

survey timeframe.

3.9 Interventions

Operators were asked to indicate whether their vehicles

had any fuel saving interventions fitted. The

interventions are listed in Table 10, including the

proportion of vehicles which had them fitted.

Table 10 confirms that the most common type of

intervention used by primary distribution operators was

some form of aerodynamics.

21

Sub-sector %

Construction 0.00%

Containers 0.00%

Engineering 0.00%

Food/Drink 0.59%

General Haulage 7.46%

Manufacturing 0.00%

Non-Food Retail 0.00%

Parcels 0.00%

Table 9 Delivery Complaints by Sub-sector

Intervention Description

Total

number

of

vehicles

Rigid Artics

3.5 - 7.5

Tonnes

GVW

7.5 - 18

Tonnes

GVW

18 - 26

Tonnes

GVW

26 - 32

Tonnes

GVW

33 - 40

Tonnes

GVW

40 - 44

Tonnes

GVW

794 42 101 323 7 85 236

AerodynamicsIncludes cab, roof and

body fairing

756

(95%)

42

(100%)

80

(79%)

315

(98%)

0

(0%)

83

(98%)

236

(100%)

Drivers

Includes driver training

and driver motivation /

incentive schemes

667

(84%)

41

(98%)

78

(77%)

319

(99%)

7

(100%)

83

(98%)

139

(59%)

Operation

Measures to increase

vehicle fill and reduce

empty running

567

(71%)

41

(98%)

76

(75%)

313

(97%)

0

(0%)

72

(85%)

65

(28%)

Other

Includes anti-idling

schemes and the use of

approved engine

lubricants and synthetic

oils

701

(88%)

41

(98%)

94

(93%)

314

(97%)

0

(0%)

73

(86%)

179

(76%)

Telematics

Includes vehicle routing

and scheduling systems

and satellite navigation

systems

28

(4%)

0

(0%)

0

(0%)

0

(0%)

0

(0%)

1

(1%)

27

(11%)

Tyres

Includes regular wheel

alignment checks, fuel

efficient tyres, tyre

pressure management

and regular re-grooving

644

(81%)

41

(98%)

76

(75%)

313

(97%)

0

(0%)

76

(89%)

138

(58%)

Table 10 Summary of Interventions Used by Surveyed Vehicles

The least popular type of intervention was telematics

which, considering this category includes computerised

vehicle routing and scheduling systems and sat-nav

systems, is perhaps a little surprising in light of the

general increase in the popularity of such systems in

recent years.

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.

As can be seen in Table 10, there were very few

vehicles with no aerodynamic equipment fitted. The artic

33-40 tonne category did, however, include vehicles

both with and without aerodynamic interventions.

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.

A 9% improvement in MPG translates into a saving of

almost 5,748 litres of fuel over 100,000 miles, equating

to around 15,117 kg of carbon dioxide (CO2).

22

The Fuel Efficiency Trials Guide provides

a generic 12-step process for operators to

consider in implementing fuel efficiency

interventions within a vehicle fleet.

Figure 22 Variance in MPG for Vehicles in the Artic 33-40 tonne GVW category

4 Summary

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.

A number of positive recommendations can be made as

a result of this survey on ways in which primary

distribution transport operators can improve their

operational performance, save fuel and ultimately save

money. They include:

Ensuring accurate data collection, for example

MPG performance, for the purposes of

subsequent decision making

Ensuring vehicle fill matches the size of the

vehicle as consistently as possible

Reducing empty running, for example, through

greater use of back-loads

Keeping damage levels during deliveries to a

minimum

Exploring the use of relevant fuel saving

interventions

Establishing regular benchmarking activities

These points are explored further in the rest of this

section.

4.1 Accurate data collection

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

an accurate picture of fuel consumption per vehicle.

Accurate MPG analysis is vital to determine how much

fuel is being consumed by each vehicle and to allow

operators to target less fuel efficient vehicles for

improvement.

To achieve accurate MPG figures, the following

elements need to be measured accurately:

Vehicle odometer readings

Vehicle fuel consumption figures

There may be existing data sources in your company

that already provide this information, including:

Fuel cards

Driver job sheets

Vehicle tachographs

Telematics systems

4.2 Vehicle Fill

It is important to find the right balance between some

KPIs, for example between vehicle fill and on time in full

(OTIF) deliveries.

A transport operator needs to be careful not to

jeopardise vehicle fill for the sake of a better OTIF

rating.

Lower vehicle fill may have a positive effect on vehicle

MPG performance, but total fuel spend and number of

journeys are both likely to substantially increase as a

result of lower fill levels.

Vehicle fill levels can also be affected by the size of

vehicle specified. An operator specifying a vehicle that

is too large for their typical loads, whether by weight or

by volume, is always likely to achieve a low vehicle fill

level.

4.3 Empty Running

Empty running is an issue throughout the freight

industry, as this survey underlines.

Genuine empty running (where vehicles are running

with no cargo, including any empty packaging returns)

can have a significant impact on a transport operator’s

efficiency.

Empty running may prove difficult for a transport

operator to solve alone, but collaboration can prove the

key to dealing with it, for example by providing more

back-loads on a regular basis.

Finding regular back-loads across an entire vehicle fleet

can prove difficult for a single transport operator but the

recent growth of pallet networks and the introduction of

haulage exchanges offer operators greater opportunities

than ever for working together to consistently fill

vehicles and increase operational efficiency.

4.4 Damage

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

an issue.

23

It is important however, that this performance is

maintained. To this end, operators should:

Ensure the correct loading of all items, with lighter

items placed on top of heavier ones

Feed any cargo packaging issues back up the

supply chain – products may be shipped in

packaging not specified by yourself, causing your

delivery performance to be affected

Use stretch-wrap or shrink-wrap to secure pallet

stacks

Use cargo support straps or bars to secure loads

in the vehicle

Avoid excessive braking or acceleration during

driving (something which will also have fuel

consumption benefits)

4.5 Fuel Saving Interventions

There are many fuel saving interventions available for

transport operators to consider.

The data collated in this survey generally confirms that

when interventions have been used, which are relevant

to the vehicle type and type of operation, there are

significant fuel savings.

4.6 Regular Benchmarking

This survey has highlighted the importance of carefully

collecting the necessary data to analyse current

operational performance and plan for further

improvements.

If you can’t measure it accurately, you can’t manage it

accurately!

The survey has also made clear that operational

performance indicators should not be considered in

isolation. A suitable range of KPIs should be adopted

that fit the business model of the transport operator and

offer a comprehensive overview of overall operational

performance.

Once the right internal KPIs have been established, you

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-

class rating.

24

The Fuel Ready Reckoner, can help you

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

www.freightbestpractice.org.uk

The Department for Transport On Line

Benchmarking (OLB) system provides an

internet-based resource that can be used

by transport operators to externally and

anonymously benchmark the performance

of their vehicles. Vehicle types and

operating characteristics can be selected

quickly and easily to compare

performance data from your fleet against

other operators nationally.

OLB can be accessed from the website

www.freightbestpractice.org.uk/benchmarking

The Primary Distribution Transport Efficiency Road Map

shown below is an action plan of measures that can be

considered by anybody in the sector looking to improve

their efficiency.

It identifies the measures that can be ‘owned’ and

initiated by those managers responsible for transport

within the business, for example, fuel management and

load preparation.

It also shows wider strategic measures, such as

customer-related initiatives, which would require the

close involvement of other parts of the business, for

example the sales, marketing and procurement

departments.

The measures in the action plan are set out under six

key categories:

Saving Fuel

Equipment and Systems

Developing Skills

Performance Management

Fleet Performance

Supply Chain Reconfiguration

Specific actions are identified for each category,

together with signposts identifying relevant guidance

and support material from the Freight Best Practice

programme.

25

5 The Primary Distribution Transport Efficiency ‘Road Map’

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Supply Chain / Logistics Dire

Sector Initiative

Fuel Efficiency Intervention Trials

This guide describes a 12-step standardised process

for transport operators to use when considering the

trial for a fuel efficiency intervention – an important

starting point in understanding the operational

efficiency savings that could be possible for your fleet.

Sound Advice! The Fuel Efficient Truck Drivers’

CD

Featuring professional truck drivers and fuel experts

this 25 minute audio CD explores safe and fuel

efficient driving techniques and the benefits to you,

your company and the environment.

Telematics for Efficient Road Freight Operations

This guide provides information on the basic

ingredients of telematics systems, highlights how to

use this technology, the information obtained from it

and how to select the right system for your needs.

TOP provides practical ‘every day’ support material to

help operators implement best practice in the

workplace and acts in direct support of tasks essential

to running a successful fuel management programme.

Equipment & SYSTEMS

Performance MANAGEMENT

There are over 25 case studies showing how

companies have implemented best practice and the

savings achieved. Check out the following selection of

Scottish case studies:

• Tesco Sets the Pace on Low Carbon and Efficiency

• Engine Idling – Costs You Money and Gets You

Nowhere!

• Power to Your People - Motivation Breeds Success

Case STUDIES

Transport Operators’ Pack - TOPDeveloping SKILLS

Saving FUELPerformance Management for Efficient Road

Freight Operations

This guide explains the process of measuring

performance effectively. It includes advice on how

information is best collected and interpreted to allow

informed decision making in order to achieve

operational efficiency improvements.

Performance MANAGEMENT

Freight Best Practice publications, including those listed below, can be obtained

FREE of charge by calling the Hotline on 0845 877 0 877 or by downloading

them from the website www.freightbestpractice.org.uk

November 2009.

Printed in the UK on paper containing at least 75% recycled fibre.

FBP1107 © Queens Printer and Controller of HMSO 2009.

Fbp1107 Primary Distribution Benchmarking Survey

Business

externally

total distance

achieveoperational

usedby transport

total distance

highest number

key performance

transport