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Tribal street cleaning benchmarking - detailed

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Capital Ambition and LEDNET
Street Services Productivity
Review: detailed analysis
March 2009
CONFIDENTIAL
Contents
Introduction
Overview of
Street
Cleaning in
London
A street cleaning
blueprint?
Emerging
findings
• How to use this
pack
• High-level
comparison
• Street cleaning
“diagnostics”
• Context
• Value for Money
overview
• Street cleaning
blueprints
• Environmental
factors influencing
street cleaning
performance
• Potential savings
• Objectives
• Methodology
Next steps
• For
participating
Boroughs
• For Capital
Ambition
Appendix
• Borough
profiles
• Best
practice
summaries
• Supporting
data
• Ease to serve and
VfM performance
2
CONFIDENTIAL
Introduction
How to use this pack
This pack has been designed to help local authorities to improve the value for money of
their street cleaning services
If you are a….
You can use this pack to….
Chief Executive
Ask evidence-based questions of your street
cleaning service, to ensure it is operating as
efficiently and effectively as possible
Director of Environment Services
Challenge your Head of Service, in a supportive
way, to think about how they can improve the
quality and cost-effectiveness of the service
Head of Street Cleaning and/or
Waste Management
Evaluate whether your operations are delivering
value for money, and if they can be improved
through using best practice approaches
Contract manager
Review whether you are getting the best value
from your contracted service, and whether
specifying or monitoring differently might
deliver better outcomes
3
CONFIDENTIAL
Context
Introduction
As one of the their most visible services, local authorities are under pressure to improve
the quality of street cleaning services within an environment of reduced resources
The street cleaning service
Visibility:
Pressure:
Action:
Street services is one
of the most visible
aspects of a Council’s
business, and has a
strong impact on
customer satisfaction
with the Council
Boroughs are under
increasing pressure to
improve quality of
street cleaning
services with reduced
resources
Exploring the drivers
of value for money
services helps
understand how
resources can be
deployed more
effectively
How can we do
more with less?
4
CONFIDENTIAL
Introduction
Objectives
Building a blueprint for value for money Street Cleaning services will help Boroughs
across London target activity to reduce costs and improve performance
The aims of this project were:
п‚·
To develop practical ideas to improve performance and efficiency in London boroughs’
street cleaning services
•
To show what a “good Value for Money” street cleaning service might look like
We have developed emerging Street Cleaning
Blueprints for different types of authorities which
use these findings and recommendations to show
what a value for money Street Cleaning service
might look like
5
CONFIDENTIAL
Methodology
Introduction
The process we went through was designed to ensure that we gathered a solid evidence
base of data
Project Management
Project Kick-off
Intelligence
gathering
Analysis
Validation
Final outputs and
presentation
Outcomes:
п‚· Understand the main differences in expenditure and
outputs in ten London boroughs .
п‚· Insight into the key factors driving the value for money
position of Street Services in London.
п‚· Discuss potential implications for other authorities in
London and further activity for Capital Ambition.
6
CONFIDENTIAL
Introduction
Methodology
Contrasting approaches were designed to test possible approaches to using comparison
between boroughs to highlight improvements.
Street cleaning review
Recycling review
Approach
In-depth, quantitative analysis
High-level, qualitative analysis
Time
• 22 days
• 8 days
• More in-depth review of
operations
• More specific findings and
recommendations for use by
authorities
• High-level overview provides a
useful snapshot of approaches
and ideas for improvement
• In-depth nature may be more
useful for Heads of Service than
Directors and Chief Executives
• High level so may work may be
required to explore issues in more
detail
Advantages
Limitations
7
CONFIDENTIAL
Framework
Introduction
We have used this framework to assess the drivers of cost and performance of street
cleaning service
Local Environment
п‚·
п‚·
п‚·
п‚·
п‚·
Number of Town Centres
Population density
Population deprivation
Km of road
Condition of roads
Context
Service Spend
People
п‚·
п‚·
п‚·
п‚·
Plant
Number of officers
Structure of service
Number of administrators
Spans of control
п‚·
п‚·
п‚·
п‚·
Number of vehicles
Types of vehicles
Downtime of vehicles
Manual / automatic split
Inputs &
Structure
Activities
п‚·
п‚·
п‚·
п‚·
Approaches to litter prevention
Extent of and approach to enforcement
Deployment of cleaning resources
Work with other services
Outputs
п‚· Tonnage of street
sweeping arisings
п‚· Fly tipping incidents
п‚· Fixed penalty notices
issued
Outcomes
п‚· Performance levels
п‚· Customer satisfaction
Outputs &
Outcomes
8
CONFIDENTIAL
Introduction
Notes on the data
Though the data used in this report has some caveats, it is, in our judgement, still
sufficiently robust to provide the basis for the key findings and recommendations
Data source
Notes
Public data: Best Value Performance
Indicators, Revenue Account
Return, National Statistics,
Department for Transport
•
Individual borough questionnaire
responses
•
•
•
Some differences and disputes in methodologies for
collecting and assessing returns (e.g. population sizes, road
lengths, BV 199a).
However, the public data is broadly accurate enough to
enable meaningful questions, implications and findings to
be drawn.
Some minor variances occur in definitions of the
questionnaire occur which does not always enable an exact
like for like comparison.
However, the data is more than comparable enough so as
not to make the findings from the questionnaire
benchmarking debateable.
9
CONFIDENTIAL
Contents
Introduction
Overview of
Street
Cleaning in
London
A street cleaning
blueprint?
Emerging
findings
• How to use this
pack
• High-level
comparison
• Street cleaning
“diagnostics”
• Context
• Value for Money
overview
• Street cleaning
blueprints
• Environmental
factors influencing
street cleaning
performance
• Potential savings
• Objectives
• Methodology
Next steps
• For
participating
Boroughs
• For Capital
Ambition
Appendix
• Borough
profiles
• Best
practice
summaries
• Supporting
data
• Ease to serve and
VfM performance
10
CONFIDENTIAL
Street cleaning in London
High-level comparison
The authorities involved in the review include 6 Outer London Boroughs and 4 Inner
London Boroughs
Harrow
Waltham
Forest
Brent
Redbridge
Tower
Hamlets
Southwark
Wandsworth
Lambeth
Bromley
Croydon
11
Source: London Councils website
CONFIDENTIAL
Street cleaning in London
High-level comparison
Wandsworth
Waltham
Forest
Tower
Hamlets
Southwark
Croydon
Redbridge
Bromley
Outer
Outer
Outer
Outer
Inner
Outer
Inner
Inner
Outer
Inner
No
No
No
Yes
No
Yes
Yes
No
Yes
No
Overall spend
07/08
ВЈ8.4m
ВЈ3.8m
ВЈ3.9m
ВЈ8.2m
ВЈ3.6m
ВЈ7.2m
ВЈ6.03m
ВЈ4.7m
ВЈ5.02m
07/08 spend per
km of road
ВЈ17.8k
ВЈ4.6k
ВЈ9.8k
ВЈ21.2k
ВЈ7.4k
ВЈ19.4k
ВЈ12.2k
ВЈ10.4k
ВЈ12.2k
Inner/ Outer
London
In-house?
Harrow
Brent
Lambeth
A wide range of boroughs were involved in the review. This pack contains analysis of
seven boroughs from the current phase, and also includes the three original boroughs
Please note: London Borough of Croydon was unable to provide
all necessary data within the project timescales. This authority
has been included in the analysis where possible, but there are
some areas where data is missing for this council.
12
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Street cleaning in London
High-level comparison
Brent
Bromley
Croydon
Harrow
Lambeth
Redbridge
Southwark
Tower
Hamlets
Waltham
Forest
Wandsworth
The remit of “Street Cleaning” varies between Boroughs. Some are limited to street
cleaning and fly tipping removal whereas others cover graffiti removal and enforcement.
Graffiti removal
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Fly posting
removal
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Cleaning of bins
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Cleaning of other
street furniture
No
No
No
No
Yes
No
No
Yes
No
No
Highways
Greensward grass cutting
No
No
No
Yes
No
No
Yes
No
No
No
Enforcement
No
No
Yes
Yes
No
Yes
Yes
No
No
No
Overall spend
ВЈ8.4m
ВЈ3.8m
ВЈ3.9m
ВЈ8.2m
ВЈ3.6m
ВЈ7.2m
ВЈ6.0m
ВЈ4.7m
ВЈ5.0m
Spend per km of
road
ВЈ17.8k
ВЈ4.6k
ВЈ9.8k
ВЈ21.2k
ВЈ7.4k
ВЈ19.4k
ВЈ12.2k
ВЈ10.4k
ВЈ12.2k
The limited scope of the project has prevented us from
isolating only the core street cleaning elements of activity
and expenditure, however we believe the data enables a
sufficiently “like for like” comparison to be made.
13
Source: RSe analysis of
questionnaire responses
CONFIDENTIAL
Street cleaning in London
Value for Money
There is a great deal of variance within London Street Cleaning outcomes and
expenditure.
ВЈ7,174
ВЈ7,762
ВЈ8,166
ВЈ8,681
Islington
Southwark
Croydon
ВЈ17,595
ВЈ6,654
Tower Hamlets
ВЈ11,681
ВЈ6,177
Kensington & Chelsea
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Westminster
ВЈ5,409
Hammersmith & Fulham
ВЈ10,486
ВЈ5,397
Bromley
Lambeth
ВЈ5,391
Haringey
ВЈ10,026
ВЈ5,369
Merton
Brent
ВЈ5,183
Wandsworth
Newham
ВЈ5,127
Enfield
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ВЈ8,746
ВЈ4,978
Havering
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
ВЈ10,019
ВЈ4,976
Barnet
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
Camden
ВЈ4,662
Lewisham
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Hackney
ВЈ4,525
Waltham Forest
Haringey 40
ВЈ4,514
Havering 38
Greenwich
Hounslow 37
Barking & Dagenham 38
ВЈ4,358
Hillingdon 35
Sutton
Ealing 35
Greenwich 35
ВЈ4,245
Harrow 34
Redbridge
Waltham Forest 33
ВЈ4,116
Brent 32
Kingston Upon Thames 32
Ealing
Bexley 28
Newham 32
ВЈ4,093
Islington 28
Bexley
Hackney 27
ВЈ4,086
Lambeth 25
Richmond Upon Thames 26
Harrow
Lewisham 24
Hammersmith & Fulham 25
ВЈ3,363
Tower Hamlets 22
Hounslow
Southwark 19
Wandsworth 22
ВЈ'000 (from left)
1 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 1 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 1 0 0
0 0 0 0 0 0
0 1 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 1
0 0 1 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
ВЈ3,304
Barnet 16
Camden 19
Rank of Street Cleaning & Litter Responsibilities
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Richmond Upon Thames
Redbridge 15
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
ВЈ2,720
Merton 14
Croydon 14
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Hillingdon
Bromley 13
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
ВЈ2,390
Sutton 8
Westminster 11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
Barking & Dagenham
Enfield 7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
Kingston Upon Thames
Performance is equally variable,
ranging from 2% to 40% of
streets with unacceptable levels
of litter and detritus.
Kensington & Chelsea 2
Rank of BV 199a (from top)
Sample London Borough spend
on Street Cleaning & Litter
Responsibilities varies from
ВЈ2.7m to ВЈ17.5m.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
14
1 2 3 4 5 6 7 8 9 # # # # # # # # # # # # # # # # # # # # # # #
Source: RSe analysis of 2007/08 RA return and 2006/07 BV 199a.
CONFIDENTIAL
Street cleaning in London
Value for Money
However, Value for Money analysis does not tell the whole story about cost and
performance of Street Cleaning services
Value for Money quadrant
ВЈ5,391
ВЈ5,397
ВЈ5,409
ВЈ6,177
ВЈ6,654
ВЈ7,174
ВЈ7,762
ВЈ8,166
ВЈ8,681
ВЈ8,746
Kensington & Chelsea
Tower Hamlets
Islington
Southwark
Croydon
Hackney
ВЈ17,595
ВЈ5,369
Hammersmith & Fulham
Westminster
ВЈ5,183
ВЈ11,681
ВЈ5,127
Bromley
Lambeth
ВЈ4,978
Merton
Haringey
ВЈ10,486
ВЈ4,976
Wandsworth
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Brent
ВЈ4,662
Enfield
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ВЈ10,026
ВЈ4,525
Havering
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
ВЈ10,019
ВЈ4,514
Barnet
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
Camden
ВЈ4,358
Lewisham
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Newham
ВЈ4,245
Waltham Forest
Havering 38
Haringey 40
ВЈ4,116
Hounslow 37
Greenwich
Hillingdon 35
Barking & Dagenham 38
ВЈ4,093
Ealing 35
Greenwich 35
ВЈ4,086
Brent 32
Harrow 34
Sutton
Bexley 28
Waltham Forest 33
ВЈ3,363
Islington 28
Kingston Upon Thames 32
Ealing
Hackney 27
Newham 32
Redbridge
Richmond Upon Thames 26
ВЈ'000 (from left)
1 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 1 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 1 0 0
0 0 0 0 0 0
0 1 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 1
0 0 1 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
ВЈ3,304
Lambeth 25
Rank of Street Cleaning & Litter Responsibilities
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
ВЈ2,720
Hammersmith & Fulham 25
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
Bexley
Lewisham 24
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ВЈ2,390
Barnet 16
Camden 19
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
Harrow
Croydon 14
Redbridge 15
Southwark 19
Wandsworth 22
Tower Hamlets 22
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
Hounslow
Merton 14
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
Hillingdon
Sutton 8
Bromley 13
Barking & Dagenham
Enfield 7
Westminster 11
Kingston Upon Thames
- Overall service
spend
Kensington & Chelsea 2
Rank of BV 199a (from top)
- BV199a
performance
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Richmond Upon Thames
Contributing
indicators:
1 2 3 4 5 6 7 8 9 # # # # # # # # # # # # # # # # # # # # # # #
Strengths:
Limitations:
- Provides a high level
comparison of spend and
performance
- Does not reflect the
different environments in
which Councils work
- Allows for straightforward
comparison across
London
- Provides a limited
assessment of service
performance
We have therefore developed a
way of considering how easy to
serve a Borough is, and how well
Councils are performing (both in
cost and performance terms)
within this context.
Ease to Serve Index
15
CONFIDENTIAL
Street cleaning in London
Environmental factors
There are several factors which are thought to shape the environment in which a street
cleaning service operates
Environmental factor
Impact on street cleaning
Used in index?
Density of the
population
Correlation between density and street cleaning
performance: Boroughs with a higher population density
appear to have cleaner streets, but spend more per km of
road to achieve this.
a
Residents from
other countries
Strong correlation between proportion of residents born outside the
UK and street cleaning performance: Boroughs with a larger
proportion of residents from other countries are more likely to have
lower street cleaning performance, possibly due to different
expectations about the roles of the Council and the citizen in
maintaining street cleanliness
a
Deprivation of the
population
Weak correlation between deprivation and street cleaning
performance: more deprived areas are only slightly less
likely to have clean streets
r
Condition of the
local roads
Almost no correlation between street condition and street
cleaning performance: Boroughs with roads in poor
condition appear no less likely to have clean streets, but it
is likely that their operations are designed to overcome this
issue
r
16
CONFIDENTIAL
Street cleaning in London
Ease to serve
We have calculated how easy to serve an area is and how well the Street Cleaning
service is performing
Value for Money quadrant
ВЈ6,177
ВЈ6,654
ВЈ7,174
ВЈ7,762
ВЈ8,166
ВЈ8,681
Southwark
Croydon
ВЈ17,595
ВЈ5,409
Islington
Westminster
ВЈ5,397
Tower Hamlets
ВЈ8,746
ВЈ5,391
Kensington & Chelsea
ВЈ11,681
ВЈ5,369
Hammersmith & Fulham
Lambeth
ВЈ5,183
Bromley
ВЈ10,486
ВЈ5,127
Haringey
ВЈ10,026
ВЈ4,978
Merton
Contributing indicators –
VfM performance:
RSe VfM performance vs ease to serve
Brent
1 2 3 4 5 6 7 8 9 # # # # # # # # # # # # # # # # # # # # # # #
- BV199a performance
Bromley
VfM performance
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Brent
ВЈ4,976
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Newham
ВЈ4,662
Enfield
Wandsworth
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
ВЈ10,019
ВЈ4,525
Havering
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
Camden
ВЈ4,514
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Hackney
ВЈ4,358
Haringey 40
Barnet
Havering 38
ВЈ4,245
Hounslow 37
Lewisham
Hillingdon 35
Waltham Forest
Greenwich 35
Barking & Dagenham 38
ВЈ4,116
Ealing 35
ВЈ4,093
Harrow 34
Greenwich
Waltham Forest 33
ВЈ4,086
Brent 32
Kingston Upon Thames 32
Sutton
Newham 32
Redbridge
Bexley 28
ВЈ3,363
Islington 28
Ealing
Hackney 27
ВЈ'000 (from left)
1 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 1 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 1 0 0
0 0 0 0 0 0
0 1 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 1
0 0 1 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
ВЈ3,304
Richmond Upon Thames 26
Rank of Street Cleaning & Litter Responsibilities
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
ВЈ2,720
Lambeth 25
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
Bexley
Lewisham 24
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Harrow
Tower Hamlets 22
Hammersmith & Fulham 25
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
ВЈ2,390
Barnet 16
Camden 19
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
Hounslow
Merton 14
Croydon 14
Redbridge 15
Southwark 19
Wandsworth 22
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
Hillingdon
Sutton 8
Bromley 13
Barking & Dagenham
Enfield 7
Westminster 11
Kingston Upon Thames
- Overall service
spend
Kensington & Chelsea 2
Rank of BV 199a (from top)
- BV199a
performance
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Richmond Upon Thames
Contributing
indicators:
Ease to serve index
Croydon
Harrow
Lambeth
Redbridge
Southwark
Tower Hamlets
Waltham Forest
Wandsworth
- BV89 (Customer
satisfaction)
Ease to serve
- Spend per km of road
Ease to serve:
- Spend per tonne of
arising
- Level of population density
- % of population born outside the UK
Strengths:
Limitations:
Strengths:
Limitations:
- Provides a high level
comparison of spend and
performance
- Does not reflect the
different environments in
which Councils work
- Provides context for each
service
- Allows for straightforward
comparison across
London
- Provides a limited
assessment of service
performance
- Cannot provide an
exhaustive insight into
performance and
environment
- Enables clear comparison
between authorities in
peer groups
- Highlights best practice
services within each ease
to serve category
- The environmental factors
have been selected
because of their
relationship to
performance
17
CONFIDENTIAL
Street cleaning in London
Ease to serve
Coupling this Ease to Serve index with VfM performance indicates that generally, the
easier to serve an area is, the higher the VfM performance of the service will be. Some
Boroughs are performing better than might be expected given their areas, whereas
others are not performing as well as might be expected.
RSe VfM performance vs ease to serve
High VfM
performance
Brent
Bromley
Croydon
Harrow
VfM
Performance
Lambeth
Redbridge
Southwark
Tower Hamlets
Waltham Forest
Wandsworth
Low VfM
performance
Easier to serve
Harder to serve
Ease to serve
18
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Street cleaning in London
Ease to serve
This is a starting point for the Ease to Serve Index - in order to ensure it is as useful as
possible, additional contributory indicators should be considered by London authorities
Ease to serve index
Contributing indicators –
VfM performance:
RSe VfM performance vs ease to serve
Brent
Bromley
VfM performance
- BV199a performance
Croydon
Harrow
Lambeth
Redbridge
Southwark
Tower Hamlets
Waltham Forest
Wandsworth
- BV89 (Customer
satisfaction)
Ease to serve
- Spend per km of road
Ease to serve:
- Spend per tonne of
arising
- Level of population density
- % of population born outside the UK
Strengths:
Limitations:
- Provides context for each
service
- Cannot provide an
exhaustive insight into
performance and
environment
- Enables clear comparison
between authorities in
peer groups
- Highlights best practice
services within each ease
to serve category
- The environmental factors
have been selected
because of their
relationship to
performance
The Ease to Serve indicators
were included due to their
correlation with street cleaning
performance (found in the
previous phase of the project).
The following slides detail some
of the other factors that could be
considered when trying to
improve the robustness of the
index.
19
CONFIDENTIAL
Street cleaning in London
Ease to serve
Value for Money performance does not currently take into account the level of service
each authority provides
Proposed inclusion: An indicator that reflects the frequency of the service provided
Why?
Authorities that have a thriving night-time
economy are required to provide a night-time
street cleaning service. This impacts on
service expenditure but is not assessed as
part of BV199a. As such, those authorities
will be shown to be expensive but gain no
VfM advantage for their extra service
provision.
Difficulties?
Isolating cost data as required is likely to prove
challenging, particularly for outsourced services.
Agreeing consistent definitions for frequency of
service is crucial should this be factored into the
index.
Next Steps:
How?
Isolating core, day-time spend from a
service’s total budget would reflect relative
expenditure more accurately.
Factoring required frequency of service into
the environmental factors would also go
some way to addressing the issue.
1.
Assess appetite for the indicator’s inclusion
2.
Identify the best way to reflect the issue
3.
Collect more detailed cost breakdowns and
frequency of service information
4.
Include indicator in Index
20
CONFIDENTIAL
Street cleaning in London
Ease to serve
Environmental categorisation does not currently take into account the size of an
authority’s day-time population
Proposed inclusion: An indicator that reflects the size of an authority’s day-time
population
Why?
An authority’s street cleaning workload is
likely to be affected by the size of its daytime population. Some London authorities
see a large day-time exodus out of the area,
whilst others cater for large numbers of
workers. The latter are likely to feel an
impact on the cleanliness of their streets.
How?
Daytime population figures can be taken
from the Neighbourhood Statistics section of
the ONS website. This could then be
correlated against street cleaning
performance in order to test its impact.
Difficulties?
This indicator should not replace population
density. However, there is a danger that the index
would be double-counting population levels if both
were included as the authorities with high levels of
population density are also likely to have a large
day-time population.
Next Steps:
1.
Correlate daytime population figures against
street cleaning performance
2.
If there is a strong relationship then
authorities should be ranked against this
indicator and include it as part of the Ease to
Serve index
21
CONFIDENTIAL
Street cleaning in London
Ease to serve
Environmental categorisation does not currently take into account the number of major
transport hubs located in each authority
Proposed inclusion: An indicator that reflects the number of major transport hubs
within an authority’s area
Why?
Major transport hubs have several
implications for a street cleaning service,
such as more people using the local streets
and potentially littering. Accordingly, those
authorities with a high number of major hubs
face difficulties those without do not.
How?
Locations of train/tube/bus stations are
readily available from a number of sources.
Tube station entry & exit information is
available from the TfL website & rail
information from the ORR website.
Difficulties?
Entry & exit information is not available for bus
stations.
Definitional problems are likely to arise as to what
constitutes a �major hub’ e.g. are all tube stations
included or just the busiest?
Next Steps:
1. Agree definition of �major hub’
2.
Correlate number of major hubs against street
cleaning performance
3.
Agree inclusion of indicator then authorities
should be ranked against it and it be included
as part of the Ease to Serve index
22
CONFIDENTIAL
Street cleaning in London
Ease to serve
Environmental categorisation does not currently take into account the variation in road
usage of each authority road network
Proposed inclusion: An indicator that reflects the variation in road usage within an
authority’s area
Why?
The usage of different roads affects the
street cleaning requirement of those roads.
Roads that have a primarily commercial use
are likely to require greater frequency of
cleaning and thus are at an environmental
disadvantage.
How?
Collecting a breakdown of road type/usage is
best done through individual authorities,
having agreed consistent definitions.
Difficulties?
Department for Transport road type definitions
does not provide enough detail to assess this
indicator.
Next Steps:
1.
Agree definition of road types
2.
Collate data from participating authorities
3.
Correlate road type breakdown against street
cleaning performance, rank authorities
accordingly and include in Ease to Serve index
23
CONFIDENTIAL
Street cleaning in London
Ease to serve
The Ease to Serve index currently ranks authorities against each indicator, aggregates
that rank and that score dictates an authority’s environmental ease to serve
Proposed inclusion: To weight indicators due to the strength of correlation with street
cleaning performance
Why?
The environmental factors have a varying
impact on street cleaning performance: some
are particularly important and some less so.
The Ease to Serve index should therefore
weight authorities’ rank against these
indicators according to the degree of impact
the factor has.
How?
Each factor could be correlated against
street cleaning performance in order to
assess the impact of the factor. Authorities
could then be ranked against the indicator
and have that rank weighted by the
correlation strength of the factor.
Difficulties?
By weighting the indicators against their
correlation to street cleaning performance, there is
a danger that the index is self-referential.
Next Steps:
1.
Agree the indicators to be included in the
index
2.
Correlate each indicator against street cleaning
performance to provide the weighting
3.
Rank authorities against each indicator and
rank their aggregate, weighted score in order
to finalise their position in the index
24
CONFIDENTIAL
Street cleaning in London
Data footnote
Although the performance data used relates to 2006/07, we are comfortable that it is still
useful for this comparative exercise.
•
We have carried out this analysis using
BV199a performance data from
2006/07, as 2007/08 data was not
available at the time of this review.
•
Many boroughs are likely to have
improved their street cleaning
performance between 2006/07 and
2008
•
The diagram on the right shows that
the majority of London Boroughs have
improved their BV199a performance
from 05/06 to 06/07. We would expect
this trend to continue for 2007/08 and
as such the relative VfM positions of the
sample authorities are unlikely to be
substantially different in 2008.
Number of authorities that have
improved performance
Number of authorities that have
maintained performance
Number of authorities that have
decreased performance
25
Source: RSe VfM analysis of 2005/06 and 2006/07 data
CONFIDENTIAL
Contents
Introduction
Overview of
Street
Cleaning in
London
A street cleaning
blueprint?
Emerging
findings
• How to use this
pack
• High-level
comparison
• Street cleaning
“diagnostics”
• Context
• Value for Money
overview
• Street cleaning
blueprints
• Environmental
factors influencing
street cleaning
performance
• Potential savings
• Objectives
• Methodology
Next steps
• For
participating
Boroughs
• For Capital
Ambition
Appendix
• Borough
profiles
• Best
practice
summaries
• Supporting
data
• Ease to serve and
VfM performance
26
CONFIDENTIAL
Emerging findings
Blueprint process
We have developed “diagnostics” and “blueprints” to help build up a picture of a “good
Value for Money” street cleaning service
Collect data
Carry out
analysis
Develop
diagnostics
Develop
blueprints
What does a good Value for Money
street cleaning service look like?
27
CONFIDENTIAL
Emerging findings
“Diagnostics”
We have developed “diagnostics” to provide an overview of the common characteristics
of street cleaning services operating in different environments in London
The “diagnostics”
slides show common
characteristics for
Expenditure, People,
Plant and Activities
across each ease to
serve-performance
category. These
slides can be found in
the detailed report.
Emerging findings
“Diagnostics”
A “hard to serve” and low performing Borough (e.g. Tower Hamlets) is likely to have the following
characteristics. Tower Hamlets appears to be spending a lot on managers and vehicles but is not
getting as much value (productivity) from them as higher performing authorities.
Expenditure
• No expenditure breakdown data was available for
this authority
• Tower Hamlets has an outsourced service
• Spend per km of road is average
Plant
• Tower Hamlets generates the fewest tonnes of
arisings per vehicle of any of the authorities (117
tonnes)
• Tower Hamlets has a higher than average
proportion of barrows (barrows account for 65%
of Tower Hamlets fleet – the average is 60%)
• They have approximately average numbers of
staff per mechanised vehicle (average is 4.3 staff
per vehicle)
• The number of vehicles per km of road is high
(0.23 for Tower Hamlets vs an average of 0.14)
• No cost per vehicle data was available for this
authority
People
• Spans of control are likely to be low (Tower Hamlets has
14 staff per manager vs an average of 20)
• Numbers of staff per km of road are in the middle range
(0.4 staff per km of road – equals average)
• Have most managers per km of road (Tower Hamlets
has 0.024 vs an average of 0.018)
• Frontline productivity is comparatively low – Tower
Hamlets collects 26 tonnes per FTE
Activities
You can use
these diagnostics
to see how
London Boroughs
are operating at
present
• Tonnage of arisings per km of road is lower in Tower
Hamlets than any other authority – 9 tonnes vs an
average of 24
• Number of litter bins per km of road is low (average is
2.9 vs 1.5 in Tower Hamlets)
• Number of recycling boxes/bags per km of road is lower
than average (average is 195 per km vs 143 in Tower
Hamlets)
23
Source: RSe analysis of 2007/08 RA return and questionnaire response
28
CONFIDENTIAL
Emerging findings
Street cleaning blueprints
The objectives of the two pieces of work were focused on producing outputs that would
enable authorities to benchmark and improve their own performance
What the
�Blueprints’ are:
• A suggestion for what a Value
for Money street cleaning
service would look like.
• A resource against which
authorities can benchmark
their own operations and
performance.
• A first iteration, based on the
diagnostics and available data.
What the �Blueprints’
are not:
• A definitive direction as to
how you must run and structure
your street cleaning service.
• Static. It is likely that time will
alter Value for Money best
practice and the blueprints will
evolve as more data becomes
available.
29
CONFIDENTIAL
Emerging findings
Street cleaning blueprints
A Value for Money service is likely to have the following expenditure characteristics, depending on the
environment in which it operates.
Expenditure
Easy to serve
Median to serve
Hard to serve
• The service should have a low
spend per km of road – targeting
c.£5k per km is realistic – whilst
only 50% of that budget needs be
spent on staff
• The street cleansing service should
not under-spend but can expect to
spend slightly below the average –
c.ВЈ12k per km of road should
suffice
• Authorities will be required to
spend a high amount per km of
road – c.£20k – in order to
properly fund the service
• Investing heavily in plant should
provide quality vehicles and
effective vehicle resilience
• The service should spend c.65% of
it’s budget on staff & 25% on plant
• A greater proportion of the spend
will be required for people than in
services in other environments –
c.70%, with c.10% required for
plant
30
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Emerging findings
Street cleaning blueprints
A Value for Money service is likely to have the following fleet characteristics, depending on the
environment in which it operates.
Plant
Easy to serve
Median to serve
Hard to serve
• Over half of the service’s fleet
should consist of mechanised
sweepers, with far less reliance on
barrows and tippers than services
in other environments
• A street cleaning service should
use c.15 vehicles per 100km of
road, reflecting a relatively high
reliance upon mechanical
sweepers – at least 30% of the
fleet should be made up of
mechanical sweepers
• The use of non-mechanical
sweepers need not be high – no
more than 25 barrows should be
required
• Authorities should not spend on
average more than c.ВЈ20k per
vehicle
• The service should not employ
more than 3 staff per vehicle
• The service will require a high
number of vehicles per km of road
– over 20 per 100km
• Accordingly, the spend per vehicle
is likely to be above ВЈ25k
• The number of vehicles per 100km
of road required is likely to be
below 10
• No more than 2 FTEs per vehicle
will be required in the service
• The fleet should contain c.2
mechanical sweepers per 100km
of road
•
No more than 50% of the fleet
should be made up of barrows,
whilst the staff to vehicle ratio
should result in c.6 staff per
vehicle
• On average, services should spend
no more than ВЈ20k per vehicle
31
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Emerging findings
Street cleaning blueprints
A Value for Money service is likely to have the following staff characteristics, depending on the
environment in which it operates.
People
Easy to serve
Median to serve
Hard to serve
• Spans of control will be low (below
12) as will the total number of
staff required – 1 FTE per 10 km
of road
• Spans of control should be set at
around 10-12 FTEs per manager
• Spans of control should be
relatively high – there should be
between 25 & 30 FTEs per
manager
• The numbers of managers
required will also be low – 0.1 per
10km of road
• Due to the heavier reliance on
mechanisation, it will be fair to
expect that the average tonnage
of arisings per frontline FTE will be
high
• The service should employ c.3
FTEs per 10km of road and c.0.21
managers per 10km of road
• With a relatively leanly staffed
service, the average tonnage of
arisings generated per FTE should
be high – over 75 tonnes per FTE
• Numbers of staff per km of road
should also be high – c.6 FTEs per
10km – as should numbers of
managers - 0.22 managers per
10km
• Having productive front-line staff is
key: services should look for their
staff on average to generate over
75 tonnes of arisings per FTE
32
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Emerging findings
Street cleaning blueprints
A Value for Money service is likely to engage in the following activities, depending on the environment
in which it operates.
Activities
Easy to serve
Median to serve
Hard to serve
• The tonnage of arisings a service
collects per km of road will be
below the London average
• Collecting a high tonnage of
arisings per km of road is
important – services should look to
outstrip the average and collect at
least 25 tonnes per km of road
• Ensuring a high number of litter
bins per km of road are in place is
key – surpassing the average of
2.9 per km should be a minimum
• Enforcement does not seem to be
as key for these authorities as it is
for those in hard to serve areas –
if it is adopted as a priority then it
is important to employ a high
number of officers issuing high
numbers of FPNs
• Collecting a high tonnage of
arisings is important for services
within this environment – above
average (24 tonnes per km of
road) should be a minimum
• The authority will not be required
to provide a high number of litter
bins per km of road – between 2
and 2.5 is likely to suffice – and
should not expect to need to
employ a high number of
enforcement officers nor issue
many FPNs
• Effective enforcement policies are
key. Services should employ a
significant number of enforcement
officers and aim to issue over
1,000 FPNs
• Alongside enforcement, services
should ensure sufficient numbers
of litter bins are provided – over
3.5 per km of road
33
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Emerging findings
Best practice
During the course of the project we have discussed a number a best practice innovations that
authorities currently undertake. This slide provides a snapshot of those whilst all the ideas can be
found in the appendix.
Expenditure
Wandsworth has devised flexible contract
terms in order to incentivise their contractors
to improve performance. For example, the
client team monitor complaints data and
target the contractor to reduce these. If the
contractor successfully reduces the number
of complaints the service receives then
Wandsworth rewards them financially.
Plant
Brent has increased its use of vehicles and
has seen a measurable improvement in
performance, particularly in industrial areas
where detritus was previously a significant
problem
People
Lambeth monitors its staff productivity by
requiring its staff to clean 550m2 per hour. This is
a reduction from the previous target distance
(800m2) and has led to a dramatic increase in
street cleanliness.
Activities
Southwark currently issue the second highest
number of Fixed Penalty Notices of any authority in
the country. They tied this focus on enforcement
with the installation of additional litter bins and
adopted a �no grey areas’ approach to FPNs. The
residents now know that if they drop litter it will
cost them and professional fly tippers have been
removed very quickly.
34
Source: RSe interviews
CONFIDENTIAL
Emerging findings
Street cleaning blueprints
Many authorities consider outsourcing street cleaning to improve value for money. We
have found no evidence to suggest that outsourcing will deliver a better VfM service
Do in-house or
outsourced services
provide better value for
money street cleaning
services?
• RSe’s VfM by environment ranking shows outsourced services to be both
high (Bromley, Wandsworth), median (Croydon, Lambeth, Brent) and low
performing (Tower Hamlets) and a similar pattern emerging for in-house
services, with Southwark being high performing, Redbridge and Waltham
Forest being median performing and Harrow low performing
• In-house services collect far more tonnes of arisings per km of road (29)
than outsourced services (19) whilst at the same time spending less per km
of road (ВЈ11.7k) than outsourced services (13.7k)
• Both in-house and outsourced services are likely to have c.0.34 FTEs per
km of road, however, outsourced services have smaller spans of control (16
FTEs to managers) than in-house services (23) and lower spend per FTE
(£25k per FTE in in-house services vs £24k in outsourced services – a
saving of c.ВЈ150k per authority)
• Tonnage of arisings per FTE does not vary much between the two types of
service (c.63 tonnes per FTE) but tonnage of arisings per vehicle does (324
tonnes per vehicle in in-house services vs 210 in outsourced services)
• Spend per vehicle shows no variation at all between the two types of
service
35
CONFIDENTIAL
Emerging findings
Potential savings
The findings from this review can be used to help assess the potential scope for
efficiency savings in Street Cleaning services
•
Realising efficiency savings is a key priority for the majority of local
authorities - Boroughs can help identify areas for cost reduction by
comparing their services against the blueprints in this pack
•
We can also identify – at a high level – the Boroughs where there might be
scope for reducing costs by comparing expenditure against average
expenditure
•
The following slide shows the potential value of savings that could be
achieved if Boroughs spending above average for their “Ease to Serve”
category were to reduce their costs to the average level for that category
•
Authorities should note that this expenditure will not guarantee the highest
level of performance but represents an acceptable level of funding with
which to operate.
36
CONFIDENTIAL
Emerging findings
Potential savings
Analysis indicates that if Boroughs spending above average for their “Ease to Serve”
category reduced spend to the average level, approximately ВЈ2.8m could be saved
Current cost:
Average cost:
Potential saving:
Value of saving:
• How much is
currently spent by
the 10 London
Boroughs reviewed
on Street Cleaning
in total?
• What is the average
expenditure for each
Ease to Serve
category?
• How many
authorities currently
spend more than
the average for their
Ease to Serve
category?
• How much could be
saved across the 10
authorities if
expenditure was
brought down to the
average level?
Approximately
ВЈ51m p.a.
“Easy to serve”: £3.8m
“Median to serve”:
ВЈ4.3m
“Hard to serve”: £7.5m
4
•
•
•
•
authorities:
Brent
Lambeth
Waltham Forest
Wandsworth
Approximately
ВЈ2.8m could be
saved (equating
to ~10% of each
of these
authorities’ Street
Cleaning budget)
37
CONFIDENTIAL
Emerging findings
Potential savings
The table below shows the potential savings that could be achieved if each authority
reduced their expenditure in line with the average expenditure for their environment.
Ease to serve
“Easy to serve”
“Median to
serve”
“Hard to serve”
Authority
Current
spend per km
Average spend per
km of road across the
category
Bromley
ВЈ4,578
Croydon
Unknown
Harrow
ВЈ9,750
Waltham Forest
ВЈ10,444
Redbridge
ВЈ7,370
Wandsworth
ВЈ12,162
ВЈ2,319
Southwark
ВЈ19,423
ВЈ1,794
Lambeth
ВЈ21,247
Brent
ВЈ17,798
Tower Hamlets
ВЈ12,745
ВЈ4,578
Potential saving
per km of road
N/A
Unknown
-ВЈ93
ВЈ9,843
ВЈ17,629
ВЈ602
-ВЈ2,472
ВЈ4,361
ВЈ169
-ВЈ4,884
38
CONFIDENTIAL
Contents
Introduction
Overview of
Street
Cleaning in
London
A street cleaning
blueprint?
Emerging
findings
• How to use this
pack
• High-level
comparison
• Street cleaning
“diagnostics”
• Context
• Value for Money
overview
• Street cleaning
blueprints
• Environmental
factors influencing
street cleaning
performance
• Potential savings
• Objectives
• Methodology
Next steps
• For
participating
Boroughs
• For Capital
Ambition
Appendix
• Borough
profiles
• Best
practice
summaries
• Supporting
data
• Ease to serve and
VfM performance
39
CONFIDENTIAL
Next steps
Local authorities
We recommend that local authorities take the following steps to make use of the findings
from our report
• Identify whether your authority operates in a “hard”, “median” or
“easy to serve” environment
• Compare your expenditure, staff, plant and activities to the
“Blueprint” for that environment
• Identify the changes you could make to your service to improve your
VfM performance using the “blueprint”
Practical best-practice
innovations are detailed in the
report - these might stimulate
service improvement ideas
40
CONFIDENTIAL
Next steps
Capital Ambition and LEDNET
We recommend that Capital Ambition and LEDNET take the following steps to make use
of the findings from this report
• Disseminate this pack to Heads of Street Cleaning Services across
London
• Encourage its use as a benchmarking tool
• Consider collecting cost and activity data from all Boroughs to carry
out a pan-London benchmarking exercise
• Follow up with authorities in 6 months to find out how they have
used the findings
41
CONFIDENTIAL
Contact details
Ben Rowland
Ben.Rowland@tribalgroup.co.uk
020 7808 1122
Olivia Crill
Olivia.Crill@tribalgroup.co.uk
0207 808 1156
Andrew Middleton
Andrew.Middleton@tribalgroup.co.uk
0207 808 1146
42
CONFIDENTIAL
Contents
Introduction
Overview of
Street
Cleaning in
London
A street cleaning
blueprint?
Emerging
findings
• How to use this
pack
• High-level
comparison
• Street cleaning
“diagnostics”
• Context
• Value for Money
overview
• Street cleaning
blueprints
• Environmental
factors influencing
street cleaning
performance
• Potential savings
• Objectives
• Methodology
Next steps
• For
participating
Boroughs
• For Capital
Ambition
Appendix
• Borough
profiles
• Best
practice
summaries
• Supporting
data
• Ease to serve and
VfM performance
43
CONFIDENTIAL
Appendix
Borough
profiles
44
CONFIDENTIAL
Appendix
Borough profile: Brent
The boroughs involved each operate within a distinctive local environment
Brent
Population
271,400
Area
43km2
Population density
6,278 persons /km2
Km of road
472km
Annual tonnage of arisings
10,884
BV 199a score
32.0
BV 199b score
20
BV 199c score
3
BV 199d score
3
BV89 score
65
45
Source: Questionnaire response, ONS Census data, 2007/08 BV scores
CONFIDENTIAL
Appendix
Borough profile: Bromley
The boroughs involved each operate within a distinctive local environment
Bromley
Population
299,100
Area
150km2
Population density
1,994 persons /km2
Km of road
830km
Annual tonnage of arisings
10,500
BV 199a score
12.5
BV 199b score
4
BV 199c score
1
BV 199d score
3
BV89 score
67
46
Source: Questionnaire response, ONS Census data, 2007/08 BV scores
CONFIDENTIAL
Appendix
Borough profile: Croydon
The boroughs involved each operate within a distinctive local environment
Croydon
Population
337,000
Area
87km2
Population density
3,895 persons /km2
Km of road
km
Annual tonnage of arisings
BV 199a score
14.4
BV 199b score
2
BV 199c score
0
BV 199d score
2
BV89 score
61
47
Source: Questionnaire response, ONS Census data, 2007/08 BV scores
CONFIDENTIAL
Appendix
Borough profile: Harrow
The boroughs involved each operate within a distinctive local environment
Harrow
Population
214,600
Area
50km2
Population density
4292 persons /km2
Km of road
400km
Annual tonnage of arisings
4,000
BV 199a score
34
BV 199b score
8
BV 199c score
1
BV 199d score
3
BV89 score
56
48
Source: Questionnaire response, ONS Census data, 2007/08 BV scores
CONFIDENTIAL
Appendix
Borough profile: Lambeth
The boroughs involved each operate within a distinctive local environment
Lambeth
Population
272,000
Area
27km2
Population density
10,142 persons /km2
Km of road
385km
Annual tonnage of arisings
Unknown
BV 199a score
25.0
BV 199b score
6
BV 199c score
1
BV 199d score
3
BV89 score
67
49
Source: Questionnaire response, ONS Census data, 2007/08 BV scores
CONFIDENTIAL
Appendix
Borough profile: Redbridge
The boroughs involved each operate within a distinctive local environment
Redbridge
Population
251,900
Area
56km2
Population density
4,466 persons /km2
Km of road
482km
Annual tonnage of arisings
6,262
BV 199a score
14.8
BV 199b score
19
BV 199c score
2
BV 199d score
4
BV89 score
59
50
Source: Questionnaire response, ONS Census data, 2007/08 BV scores
CONFIDENTIAL
Appendix
Borough profile: Southwark
The boroughs involved each operate within a distinctive local environment
Southwark
Population
269,200
Area
29km2
Population density
9,331 persons /km2
Km of road
370km
Annual tonnage of arisings
27,611
BV 199a score
19.2
BV 199b score
3
BV 199c score
2
BV 199d score
1
BV89 score
70
51
Source: Questionnaire response, ONS Census data, 2007/08 BV scores
CONFIDENTIAL
Appendix
Borough profile: Tower Hamlets
The boroughs involved each operate within a distinctive local environment
Tower Hamlets
Population
212,800
Area
20km2
Population density
10,764 persons /km2
Km of road
496km
Annual tonnage of arisings
3,817
BV 199a score
22.4
BV 199b score
13
BV 199c score
6
BV 199d score
4
BV89 score
60
52
Source: Questionnaire response, ONS Census data, 2007/08 BV scores
CONFIDENTIAL
Appendix
Borough profile: Waltham Forest
The boroughs involved each operate within a distinctive local environment
Waltham Forest
Population
221,700
Area
39km2
Population density
5,685 persons /km2
Km of road
450km
Annual tonnage of arisings
6,000
BV 199a score
33
BV 199b score
16
BV 199c score
3
BV 199d score
3
BV89 score
61
53
Source: Questionnaire response, ONS Census data, 2007/08 BV scores
CONFIDENTIAL
Appendix
Borough profile: Wandsworth
The boroughs involved each operate within a distinctive local environment
Wandsworth
Population
279,000
Area
34km2
Population density
8,144 persons /km2
Km of road
413km
Annual tonnage of arisings
8,195
BV 199a score
22.0
BV 199b score
1
BV 199c score
6
BV 199d score
1
BV89 score
74
54
Source: Questionnaire response, ONS Census data, 2007/08 BV scores
CONFIDENTIAL
Appendix
Diagnostics
55
CONFIDENTIAL
Emerging findings
“Diagnostics”
A “hard to serve” and low performing Borough (e.g. Tower Hamlets) is likely to have the following
characteristics. Tower Hamlets appears to be spending a lot on managers and vehicles but is not
getting as much value (productivity) from them as higher performing authorities.
Expenditure
• No expenditure breakdown data was available for
this authority
• Tower Hamlets has an outsourced service
• Spend per km of road is average
Plant
• Tower Hamlets generates the fewest tonnes of
arisings per vehicle of any of the authorities (117
tonnes)
• Tower Hamlets has a higher than average
proportion of barrows (barrows account for 65%
of Tower Hamlets fleet – the average is 60%)
• They have approximately average numbers of
staff per mechanised vehicle (average is 4.3 staff
per vehicle)
• The number of vehicles per km of road is high
(0.23 for Tower Hamlets vs an average of 0.14)
• No cost per vehicle data was available for this
authority
People
• Spans of control are likely to be low (Tower Hamlets has
14 staff per manager vs an average of 20)
• Numbers of staff per km of road are in the middle range
(0.4 staff per km of road – equals average)
• Have most managers per km of road (Tower Hamlets
has 0.024 vs an average of 0.018)
• Frontline productivity is comparatively low – Tower
Hamlets collects 26 tonnes per FTE
Activities
• Tonnage of arisings per km of road is lower in Tower
Hamlets than any other authority – 9 tonnes vs an
average of 24
• Number of litter bins per km of road is low (average is
2.9 vs 1.5 in Tower Hamlets)
• Number of recycling boxes/bags per km of road is lower
than average (average is 195 per km vs 143 in Tower
Hamlets)
56
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Emerging findings
“Diagnostics”
A “hard to serve” and median performing Borough (e.g. Brent, Lambeth and Southwark) is likely to
have the following characteristics. Southwark and Lambeth spend more on frontline staff and vehicles
than on management, and is reflected in their productivity.
Expenditure
• Brent and Lambeth are outsourced services whilst
Southwark is in-house – all spend c.£8m p.a.
• Authorities in this peer group spend more per km
of road than any other group (c.ВЈ19k per km)
• ~70% of budget spent on staff and ~10% on
plant
Plant
• Authorities are likely to have 35 – 50 mechanical
vehicles at a cost of c.ВЈ21k per vehicle
• The split between mechanical vehicles and
barrows varies – 53% of Southwark’s fleet are
barrows compared to 71% of Brent’s and 74% of
Lambeth’s fleet
• These Boroughs have higher numbers of staff per
vehicle (Lambeth has 7 staff per vehicle,
Southwark 6 & Brent 4 vs an average of 4)
• Above average number of vehicles per km of road
– Brent, Lambeth (both 0.36) & Southwark (0.23)
all have above average (0.15) number of vehicles
People
• Southwark & Lambeth have two of the highest spans of
control, whilst Brent’s is below average
• Staff numbers per km of road are likely to be on (Brent 0.4)
or above (Lambeth 0.6; Southwark 0.7) average
• Have above average numbers of managers per km of road
– Brent has 0.03; Lambeth 0.023 and Southwark 0.022 vs
an average of 0.018
• Have vastly varied frontline FTE productivity – Brent staff
generate 60 tonnes of arisings per FTE compared to 119 in
Southwark
Activities
• Tonnage of arisings are likely to be higher in this group –
Southwark generate the most and Brent the third most
• The number of litter bins per km of road varies –
Southwark has above average numbers whilst Brent and
Lambeth have below average numbers of bins
• The number of FPNs issued varies greatly – Southwark
issue 1,796; Lambeth issue 226 and Brent issue none
despite Southwark and Brent employing similar numbers of
enforcement staff
57
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Emerging findings
“Diagnostics”
A “median to serve” and low performing Borough (e.g. Harrow) is likely to have the following
characteristics. Harrow appears to be investing less in its service than other authorities – its staff
numbers also appear lower than expected yet are not counterbalanced with mechanised vehicles.
Expenditure
• Harrow is an in-house service spending a very low
amount in overall terms (ВЈ3.9m) and per km of
road (ВЈ10k)
• 65% of Harrow’s budget is spent on staff and
25% on plant
Plant
• Harrow has the lowest number of vehicles (22),
but each vehicle proves very expensive c.ВЈ40k
• Harrow has the same number of staff per vehicle
as the average across the authorities studied – 4
staff per vehicle
• Harrow has very low numbers of vehicles per km
of road – 0.06 vs an average of 0.16
• 73% of Harrow’s vehicle fleet is made up of
Tipper/Collection vehicles, more than any other
authority
People
• Harrow’s spans of control is only slightly below the
average across the authorities – 18 staff per manager in
Harrow vs an average of 20
• Staff numbers per km of road in Harrow (0.2) are below
the average (0.4) and comparable to those in Redbridge
and Bromley
• The numbers of managers per km of road in Harrow
(0.013) are below average (0.018)
• Harrow’s frontline FTEs collect the second lowest tonnes
of arisings per FTE (45) of any of the authorities studied
Activities
• Harrow’s tonnage of arisings per km of road (10) is low
compared to the wider average (24)
• Harrow has a fairly average number of litter bins per km
of road – 2.5 compared to 2.9 across the authorities
• Harrow has considerably more missed recycling & refuse
collections per month than any other authority
58
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Emerging findings
“Diagnostics”
A “median to serve” and median performing Borough (e.g. Redbridge and Waltham Forest) is likely to
have the following characteristics. They use a relatively low proportion of mechanised vehicles and
staff numbers per km of road are also comparatively low.
Expenditure
• Both authorities run street cleaning in-house and
have below average overall spend and spend per
km of road
• Likely to spend c.80% of their budget on staff
costs and 15% on plant
Plant
• There is a wide variation in numbers of vehicles
used (30 in Waltham Forest & 81 in Redbridge)
and the number of vehicles per km of road (0.07
in Waltham Forest & 0.17 in Redbridge)
• Waltham Forest has more staff per vehicle (6)
than average (4) whilst Redbridge has less (3)
• Redbridge has a low spend per vehicle (c.£14k)
whereas Waltham Forest’s (c.£23k) is slightly
below the average (c.ВЈ24k)
• Around 20% of these authorities’ fleets are likely
to be made up of mechanical sweepers
People
• Spans of control vary across this group, with Redbridge
having 14 staff per manager and Waltham Forest having
32 – higher than any other authority
• Waltham Forest have an average number of staff per km
of road (0.4) whereas Redbridge have below average
(0.2) – both authorities have fewer managers per km of
road than the average
• The frontline staff in these types of authorities are likely
to generate c.55 tonnes of arisings per FTE
Activities
• The authorities are likely to generate below average
tonnage of arisings per km of road (Redbridge generate
10 tonnes and Waltham Forest 20 vs an average of 24)
• Both authorities have fewer litter bins per km of road
(Redbridge has 2.6; Waltham Forest 1.6) than the
average across the wider study (2.9)
• The authorities issued varying numbers of FPNs –
Waltham Forest issued 12 whilst Redbridge issued 381
59
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Emerging findings
“Diagnostics”
A “median to serve” and high performing Borough (e.g. Wandsworth) is likely to have the following
characteristics. Wandsworth makes more use of mechanical sweepers than other “median to serve”
Boroughs. They also invest heavily in strong management.
Expenditure
• Wandsworth contracts its street cleansing service
to Connaught, spending (c.ВЈ5m) slightly less than
the average across the authorities (c.ВЈ5.7m)
• 65% of Wandsworth’s budget is spent on staff and
25% on plant
Plant
• Wandsworth is broadly in line with the total
number of vehicles used on average (c.70) and
the number of vehicles per km of road (c.0.15)
• Despite this, other than Bromley, Wandsworth
has the fewest number of staff per vehicle of any
of the authorities (2 staff per vehicle)
• On average, Wandsworth’s vehicles cost them
less (c.ВЈ18k) than the wider average (c.ВЈ24k)
• Mechanical sweepers make up more of
Wandsworth’s fleet than is common across the
other authorities & they use fewer nonmechanical sweepers than any other authority
People
• Wandsworth has the lowest spans of control – the
service has just over seven staff per manager
• The number of staff Wandsworth employ per km of road
(0.3) is slightly below the average (0.4), whereas the
number of managers per km of road (0.036) is the
highest of any of the Boroughs
• Wandsworth’s tonnage of arisings per frontline FTE are
very high (132), second only to Bromley’s (148) and
closely comparable to Southwark’s (119)
Activities
• Wandsworth collects the second highest tonnage of
arisings per km of road – 32 tonnnes per km compared
to 75 in Southwark and an average of 24
• Similarly, they have the second highest number of litter
bins per km of road – 3.4 bins per km compared to 7.6
in Southwark and an average of2.9
• Wandsworth employs relatively few enforcement officers
(8) who issue a low number of FPNs (123)
60
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Emerging findings
“Diagnostics”
An “easy to serve” and high performing Borough (e.g. Bromley) is likely to have the following
characteristics. Bromley’s high use of mechanical sweepers is suited to its local area. It also invests
heavily in strong management.
Expenditure
• Bromley runs an in-house street cleansing service
which costs less than any of the services studied,
particularly by spend per km of road (c.ВЈ5k)
• 50% of Bromley’s spend is on staff, whilst 30% is
on plant
Plant
• Bromley has a below average number of vehicles
(43 vs 72) & the lowest number of vehicles per
km of road – 0.05
• Bromley has the fewest number of staff per
vehicle – 1.9 vs an average of 4.3
• Bromley seem to use the most expensive vehicles
– its average spend per vehicle is c.£28k vs an
average of ВЈ24k
• Bromley’s fleet is made up of a higher proportion
of mechanical sweepers than any of the other
Boroughs
People
• Bromley’s spans of control (9 staff per manager) are
lower than any other authority’s and more than half the
amount of the average (20)
• The number of staff Bromley employ per km of road
(0.1) & the number of managers they employ per km of
road (0.01) are fewer than any other authority
• The average tonnage of arising per frontline FTE is
accordingly higher (148) than any of the other
authorities and considerably above the average (78)
Activities
• The tonnage of arisings Bromley collects per km of road
is low – 13 tonnes per km vs an average of 24
• The number of litter bins per km of road in Bromley
(2.3) is slightly below the average (2.9)
• Bromley employ fewer enforcement officers (3) than any
other authority and issue a low number of FPNs (45)
61
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Appendix
Blueprints
62
CONFIDENTIAL
Emerging findings
Street cleaning blueprints
A Value for Money service in an “easy to serve” area is likely to have the following characteristics. It
is likely to spend a smaller proportion of its budget on staff, and will use more mechanised vehicles.
Expenditure
• The service should have a low spend per km of
road – targeting c.£5k per km is realistic – whilst
only 50% of that budget needs be spent on staff
• Investing heavily in plant should provide quality
vehicles and effective vehicle resilience
Plant
• Over half of the service’s fleet should consist of
mechanised sweepers, with far less reliance on
barrows and tippers than services in other
environments
• Accordingly, the spend per vehicle is likely to be
above ВЈ25k
• The number of vehicles per km of road required
is likely to be below 0.1
• No more than 2 FTEs per vehicle will be required
in the service
People
• Spans of control will be low (below 12) as will the total
number of staff required – 0.1 FTE per km of road
• The numbers of managers required will also be low –
0.01 per km of road
• Due to the heavier reliance on mechanisation, it will be
fair to expect that the average tonnage of arisings per
frontline FTE will be high
Activities
• The tonnage of arisings a service collects per km of road
will naturally be below the London average
• The authority will not be required to provide a high
number of litter bins per km of road – between 2 and 2.5
is likely to suffice – and should not expect to need to
employ a high number of enforcement officers nor issue
many FPNs
63
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Emerging findings
Street cleaning blueprints
A Value for Money service in a “median to serve” area is likely to have the following characteristics. It
is likely to invest more in staff but approximately a third of its fleet should still be comprised of
mechanised vehicles.
Expenditure
• The street cleansing service should not underspend but can expect to spend slightly below the
average – c.£12k per km of road should suffice
• The service should spend c.65% of it’s budget on
staff & 25% on plant
Plant
• A street cleaning service should use c.0.15
vehicles per km of road, reflecting a relatively
high reliance upon mechanical sweepers – at
least 30% of the fleet should be made up of
mechanical sweepers
• The use of non-mechanical sweepers need not be
high – no more than 25 barrows should be
required
• Authorities should not spend on average more
than c.ВЈ20k per vehicle
• The service should not employ more than 3 staff
per vehicle
People
• Spans of control should be set at around 10-12 FTEs per
manager
• The service should employ c.0.3 FTEs per km of road
and c.0.021 managers per km of road
• With a relatively leanly staffed service, the average
tonnage of arisings generated per FTE should be high –
over 75 tonnes per FTE
Activities
• Collecting a high tonnage of arisings per km of road is
important – services should look to outstrip the average
and collect at least 25 tonnes per km of road
• Ensuring a high number of litter bins per km of road are
in place is key – surpassing the average of 2.9 per km
should be a minimum
• Enforcement does not seem to be as key for these
authorities as it is for those in hard to serve areas – if it
is adopted as a priority then it is important to employ a
high number of officers issuing high numbers of FPNs
64
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Emerging findings
Street cleaning blueprints
A Value for Money service in a “hard to serve” area is likely to have the following characteristics. It is
likely to have much higher staff numbers and an effective use of enforcement resources.
Expenditure
• Authorities will be required to spend a high
amount per km of road – c.£20k – in order to
properly fund the service
• A greater proportion of the spend will be required
for people than in services in other environments
– c.70%, with c.10% required for plant
Plant
People
• Spans of control should be relatively high – there should
be between 25 & 30 FTEs per manager
• Numbers of staff per km of road should also be high –
c.0.6 FTEs per km – as should numbers of managers 0.022 managers per km
• Having productive front-line staff is key: services should
look for their staff on average to generate over 75
tonnes of arisings per FTE
Activities
• The service will require a high number of vehicles
per km of road – over 0.2 per km
• The fleet should contain c.0.02 mechanical
sweepers per km of road
• No more than 50% of the fleet should be made
up of barrows, whilst the staff to vehicle ratio
should result in c.6 staff per vehicle
• On average, services should spend no more than
ВЈ20k per vehicle
• Collecting a high tonnage of arisings is important for
services within this environment – above average (24
tonnes per km of road) should be a minimum
• Effective enforcement policies are key. Services should
employ a significant number of enforcement officers and
aim to issue over 1,000 FPNs
• Alongside enforcement, services should ensure sufficient
numbers of litter bins are provided – over 3.5 per km of
road
65
Source: RSe analysis of 2007/08 RA return and questionnaire response
CONFIDENTIAL
Appendix
Best
practice
summaries
66
CONFIDENTIAL
Appendix
Best practice summaries
Expenditure
• Wandsworth have devised flexible contract terms in order to incentivise their
contractors to improve performance. For example, the client team monitor
complaints data and target the contractor to reduce these. If the contractor
successfully reduces the number of complaints the service receives then
Wandsworth rewards them financially.
• In 2003 Southwark’s street cleaning service was brought in-house. The
department were told how much was available to spend and the service was
designed from scratch at that point. There is a great deal of flexibility in the way
the budget is split, allowing for and rewarding innovation on a yearly basis.
• Several authorities are very specific about how the contract should be delivered.
Brent requested a move away from litter picking to street sweeping; Lambeth
re-balanced their contract to provide a more continuous cleaning presence;
Wandsworth specifies how often each street in the Borough should be cleaned
and on which days
67
Source: RSe interviews
CONFIDENTIAL
Appendix
Best practice summaries
People
• Southwark had poor detritus performance relative to its litter score. As a result
they split the litter picking and detritus sweeping duties so that each FTE had a
dedicated duty and that each road was both litter picked and detritus swept. Not
only did this greatly improve the Borough’s detritus score, it also freed up
resource that could be re-invested in increasing the number of supervisors the
service employed.
• Lambeth monitors its staff productivity by requiring its staff to clean 550m2 per
hour. This is a reduction from the previous target distance (800m2) and has led to
a dramatic increase in street cleanliness
• Redbridge focuses heavily on staff development. For example, it has a “Golden
Broom” award to reward street cleaning staff for good performance
68
Source: RSe interviews
CONFIDENTIAL
Appendix
Best practice summaries
Plant
• One of the key stipulations in Wandsworth’s contract specification is that a new
fleet is formed at the start of the contract period, made up entirely of brand new
vehicles.
• Brent has increased its use of vehicles and has seen a measurable improvement in
performance, particularly in industrial areas where detritus was previously a
significant problem
69
Source: RSe interviews
CONFIDENTIAL
Appendix
Best practice summaries
Activities
• Wandsworth employs a large client team that exhaustively monitors contractor
performance
• Southwark currently issue the second highest number of Fixed Penalty Notices of
any authority in the country. They tied this focus on enforcement with the
installation of additional litter bins and adopted a �no grey areas’ approach to
FPNs. The residents now know that if they drop litter it will cost them and
professional fly tippers have been removed very quickly.
• Redbridge structures its service around its 7 area committee areas. A good
relationship is built up with each area committee, so that street cleaning activity is
tailored to specific needs in each area
70
Source: RSe interviews
CONFIDENTIAL
Appendix
Supporting
data
71
CONFIDENTIAL
Supporting data
Appendix
The following section contains supporting data to support the “diagnostics” and
blueprints in each of the following areas
Local Environment
п‚·
п‚·
п‚·
п‚·
п‚·
Number of Town Centres
Population density
Population deprivation
Km of road
Condition of roads
Context
Service Spend
People
п‚·
п‚·
п‚·
п‚·
Plant
Number of officers
Structure of service
Number of administrators
Spans of control
п‚·
п‚·
п‚·
п‚·
Number of vehicles
Types of vehicles
Downtime of vehicles
Manual / automatic split
Inputs &
Structure
Activities
п‚·
п‚·
п‚·
п‚·
Approaches to litter prevention
Extent of and approach to enforcement
Deployment of cleaning resources
Work with other services
Outputs
п‚· Tonnage of street
sweeping arisings
п‚· Fly tipping incidents
п‚· Fixed penalty notices
issued
Outcomes
п‚· Performance levels
п‚· Customer satisfaction
Outputs &
Outcomes
72
CONFIDENTIAL
Environment
A street cleaning blueprint?
There is a weak correlation between a borough’s deprivation index score and the
cleanliness of its streets. However the most deprived borough in London has an
equivalent level of cleanliness to the least deprived.
Correlation between 2007 Index of Deprivation score and street
cleanliness (BVPI 199a 2006/07)
Brent
BV199a - % of streets with
unacceptable levels of litter and
detritus
40
Bromley
35
Croydon
30
Harrow
25
Lambeth
Redbridge
20
Southwark
15
Tower Hamlets
10
Waltham Forest
Wandsworth
5
0
0
10
20
30
40
50
2007 Index of Deprivation Average Score
73
Source: 2007 Index of Deprivation average score (CLG); 2006/07 BVPI return; RSe analysis
CONFIDENTIAL
A street cleaning blueprint?
Environment
There is a weak correlation between population density and street cleanliness. Boroughs
with a higher population density appear to have cleaner streets, but spend more per km
of road to achieve this.
Correlation between population density and BV199a
% of streets with unacceptable levels of
litter and detritus
40
35
30
Brent
Bromley
Croydon
Harrow
Lambeth
Redbridge
Southwark
Tower Hamlets
Waltham Forest
Wandsworth
25
20
15
10
5
0
0
2000
4000
6000
8000
10000
12000
Population density (based on 2006 mid year estimate population)
74
Source: 2006/07 BVPI return; ONS; RSe analysis
CONFIDENTIAL
A street cleaning blueprint?
Environment
Unclassified road condition also has only a weak correlation to street cleanliness, with
significant variance between boroughs above and below the trend line. If two outlying
boroughs are removed, there is no correlation at all.
% of streets with unacceptable levels
of litter and detritus
Correlation between condition of unclassified roads (BVPI 224b) and street
cleanliness (BVPI 199a)
40
Brent
35
Bromley
30
Croydon
Harrow
25
Lambeth
20
Redbridge
15
Southwark
10
Tower Hamlets
Waltham
Forest
Wandsworth
5
0
0
5
10
15
BVPI 224b - % of the unclassified road network where structural
maintenance should be considered
20
75
Source: 2006/07 BVPI return; RSe analysis
CONFIDENTIAL
A street cleaning blueprint?
Environment
There is a strong correlation between proportion of residents not born in the UK and BV199a
performance, though Waltham Forest and Redbridge have similar proportions of non UK-born
residents but very different BV199a scores, indicating it is not a limiting factor
Correlation between residents who were born outside the UK and BV 199a
BV199a - % of streets with unacceptable
levels of litter and detritus
40.0%
35.0%
30.0%
Brent
Bromley
Croydon
Harrow
Lambeth
Redbridge
Southwark
Tower Hamlets
Waltham Forest
Wandsworth
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
0%
10%
20%
30%
40%
50%
% of residents who were born outside the UK
76
Source: ONS Census data, 2007/08 BV199a
CONFIDENTIAL
Supporting data
Appendix
Local Environment
п‚·
п‚·
п‚·
п‚·
п‚·
Number of Town Centres
Population density
Population deprivation
Km of road
Condition of roads
Context
Service Spend
People
п‚·
п‚·
п‚·
п‚·
Plant
Number of officers
Structure of service
Number of administrators
Spans of control
п‚·
п‚·
п‚·
п‚·
Number of vehicles
Types of vehicles
Downtime of vehicles
Manual / automatic split
Inputs &
Structure
Activities
п‚·
п‚·
п‚·
п‚·
Approaches to litter prevention
Extent of and approach to enforcement
Deployment of cleaning resources
Work with other services
Outputs
п‚· Tonnage of street
sweeping arisings
п‚· Fly tipping incidents
п‚· Fixed penalty notices
issued
Outcomes
п‚· Performance levels
п‚· Customer satisfaction
Outputs &
Outcomes
77
CONFIDENTIAL
Spend
A street cleaning blueprint?
Boroughs need to spend differing amounts per km of road depending upon how difficult
to serve their environment is. Within each peer group it is not the authorities spending
least per km of road that are delivering best value for money
Street cleaning spend per km of road
Spend per km of road (ВЈ)
25,000
20,000
15,000
10,000
5,000
0
0.0
3.3
6.6
9.9
Ease to serve
78
CONFIDENTIAL
Spend
A street cleaning blueprint?
Although staff is always the biggest area of spend, the proportion of budget spent on
staff and plant varies across the group
Street cleaning service budget split
Low
Lambeth
Southwark
% of budget
spent on staff
Wandsworth
Ease to
serve
% of budget
spent on
plant
% of budget
spent on
'other'
Harrow
Waltham Forest
Redbridge
High
Bromley
0%
20%
40%
60%
80%
100%
% of budget spent
79
CONFIDENTIAL
Supporting data
Appendix
Local Environment
п‚·
п‚·
п‚·
п‚·
п‚·
Number of Town Centres
Population density
Population deprivation
Km of road
Condition of roads
Context
Service Spend
People
п‚·
п‚·
п‚·
п‚·
Plant
Number of officers
Structure of service
Number of administrators
Spans of control
п‚·
п‚·
п‚·
п‚·
Number of vehicles
Types of vehicles
Downtime of vehicles
Manual / automatic split
Inputs &
Structure
Activities
п‚·
п‚·
п‚·
п‚·
Approaches to litter prevention
Extent of and approach to enforcement
Deployment of cleaning resources
Work with other services
Outputs
п‚· Tonnage of street
sweeping arisings
п‚· Fly tipping incidents
п‚· Fixed penalty notices
issued
Outcomes
п‚· Performance levels
п‚· Customer satisfaction
Outputs &
Outcomes
80
CONFIDENTIAL
People
A street cleaning blueprint?
As Boroughs become more difficult to serve, they are likely to need more staff and as
such, wider spans of control. Currently some large variations exist amongst peer groups
Street cleaning spans of control
35.0
Number of FTEs per manager
30.0
Bromley
Redbridge
Waltham Forest
Harrow
Wandsworth
Southwark
Lambeth
Brent
Tower Hamlets
25.0
20.0
15.0
10.0
5.0
0.0
0.0
3.3
6.6
9.9
Ease to serve
81
CONFIDENTIAL
People
A street cleaning blueprint?
Boroughs that are harder to serve require greater numbers of staff per km of road than
those Boroughs that are easier to serve…
Street cleaning staff per km of road
Number of staff per km of road
0.7
0.6
Bromley
Redbridge
Waltham Forest
Harrow
Wandsworth
Southwark
Lambeth
Brent
Tower Hamlets
0.5
0.4
0.3
0.2
0.1
0.0
0.0
3.3
6.6
9.9
Ease to serve
82
CONFIDENTIAL
People
A street cleaning blueprint?
…and they also require also require more managers per km of road
Street cleaning managers per km of road
Number of managers per km of road
0.040
0.035
Bromley
Redbridge
Waltham Forest
Harrow
Wandsworth
Southwark
Lambeth
Brent
Tower Hamlets
0.030
0.025
0.020
0.015
0.010
0.005
0.000
0.0
3.3
6.6
9.9
Ease to serve
83
CONFIDENTIAL
People
A street cleaning blueprint?
The top performing Boroughs, irrespective of the environment in which they serve,
collect more tonnes of arisings per frontline FTE, indicating greater productivity.
Average tonnes of arising per frontline FTE
Street cleaning frontline FTE productivity
160
140
Bromley
Redbridge
Waltham Forest
Harrow
Wandsworth
Southwark
Lambeth
Brent
Tower Hamlets
120
100
80
60
40
20
0
0.0
3.3
6.6
9.9
Ease to serve
84
CONFIDENTIAL
Supporting data
Appendix
Local Environment
п‚·
п‚·
п‚·
п‚·
п‚·
Number of Town Centres
Population density
Population deprivation
Km of road
Condition of roads
Context
Service Spend
People
п‚·
п‚·
п‚·
п‚·
Plant
Number of officers
Structure of service
Number of administrators
Spans of control
п‚·
п‚·
п‚·
п‚·
Number of vehicles
Types of vehicles
Downtime of vehicles
Manual / automatic split
Inputs &
Structure
Activities
п‚·
п‚·
п‚·
п‚·
Approaches to litter prevention
Extent of and approach to enforcement
Deployment of cleaning resources
Work with other services
Outputs
п‚· Tonnage of street
sweeping arisings
п‚· Fly tipping incidents
п‚· Fixed penalty notices
issued
Outcomes
п‚· Performance levels
п‚· Customer satisfaction
Outputs &
Outcomes
85
CONFIDENTIAL
Plant
A street cleaning blueprint?
As was the case with staff, Boroughs that are harder to serve require more vehicles per
km of road. It seems that some of the Boroughs we reviewed have too many vehicles
per km of road, whilst some have too few given their local environment
Street cleaning vehicles per km of road
Number of vehicles per km of road
0.40
0.35
Bromley
Redbridge
Waltham Forest
Harrow
Wandsworth
Southwark
Lambeth
Brent
Tower Hamlets
0.30
0.25
0.20
0.15
0.10
0.05
0.00
0.0
3.3
6.6
Ease to serve
9.9
86
CONFIDENTIAL
Plant
A street cleaning blueprint?
This graph again indicates the importance of local environment when considering the
make-up of your street cleaning service: the harder your area to serve, the greater
reliance you will need on staff
Ratio of street cleaning staff to vehicles
7
Number of staff per vehicle
6
Bromley
Redbridge
Waltham Forest
Harrow
Wandsworth
Southwark
Lambeth
Brent
Tower Hamlets
5
4
3
2
1
0
0.0
6.6
3.3
Ease to serve
9.9
87
CONFIDENTIAL
Plant
A street cleaning blueprint?
Whilst some variation in vehicle spend can be explained by understanding the different
types of vehicles authorities use, it is clear that some authorities are spending far more
on their vehicles than they need be
Cost of street cleaning vehicles
45000
40000
Spend per vehicle (ВЈ)
35000
Bromley
Redbridge
Waltham Forest
Harrow
Wandsworth
Southwark
Lambeth
Brent
Tower Hamlets
30000
25000
20000
15000
10000
5000
0
0.0
3.3
6.6
9.9
Ease to serve
88
CONFIDENTIAL
Plant
A street cleaning blueprint?
The top performing Boroughs of those studied all have smaller proportions of nonmechanical sweepers than those authorities performing less well
Breakdown of Fleet
Tower Hamlets
Brent
% mechanical
sweepers
Lambeth
Southwark
% nonmechanical
sweepers
Wandsworth
% other (inc.
Tippers)
Harrow
Waltham Forest
Redbridge
Bromley
0%
20%
40%
60%
80%
100%
% of Fleet
89
CONFIDENTIAL
Plant
A street cleaning blueprint?
There appears to be almost no correlation between the proportion of mechanised
vehicles used and BV199a performance. This suggests that the optimum level of
mechanisation is dependent on the local environment of the Borough.
Correlation between reliance on mechanised sweepers and BV199a
BV199a - % of streets with unacceptable
levels of litter and detritus
40
35
Brent
Bromley
Harrow
Lambeth
Redbridge
Southwark
Tower Hamlets
Waltham Forest
Wandsworth
30
25
20
15
10
5
0
0%
10%
20%
30%
40%
50%
60%
70%
Proportion of fleet that are mechanised sweepers
90
CONFIDENTIAL
Supporting data
Appendix
Local Environment
п‚·
п‚·
п‚·
п‚·
п‚·
Number of Town Centres
Population density
Population deprivation
Km of road
Condition of roads
Context
Service Spend
People
п‚·
п‚·
п‚·
п‚·
Plant
Number of officers
Structure of service
Number of administrators
Spans of control
п‚·
п‚·
п‚·
п‚·
Number of vehicles
Types of vehicles
Downtime of vehicles
Manual / automatic split
Inputs &
Structure
Activities
п‚·
п‚·
п‚·
п‚·
Approaches to litter prevention
Extent of and approach to enforcement
Deployment of cleaning resources
Work with other services
Outputs
п‚· Tonnage of street
sweeping arisings
п‚· Fly tipping incidents
п‚· Fixed penalty notices
issued
Outcomes
п‚· Performance levels
п‚· Customer satisfaction
Outputs &
Outcomes
91
CONFIDENTIAL
Activities
A street cleaning blueprint?
In each ease to serve category, the top-performing Borough is that which is collecting
the greatest tonnage of arisings per km of road…
Tonnage of arisings per km of road
Tonnage of arisings per km of road
80
70
Bromley
Redbridge
Waltham Forest
Harrow
Wandsworth
Southwark
Lambeth
Brent
Tower Hamlets
60
50
40
30
20
10
0
0.0
6.6
3.3
9.9
Ease to serve
92
CONFIDENTIAL
A street cleaning blueprint?
Activities
…and similarly is the authority that provide its residents with the highest number of litter
bins per km of road
Number of litter bins per km of road
Number of litter bins per km of road
8
7
Bromley
Redbridge
Waltham Forest
Harrow
Wandsworth
Southwark
Lambeth
Brent
Tower Hamlets
6
5
4
3
2
1
0
0.0
3.3
6.6
9.9
Ease to serve
93
CONFIDENTIAL
Activities
A street cleaning blueprint?
Effective enforcement is not necessarily the correct approach to waste minimisation in
every Borough, however, there are wide variations in the activity of enforcement teams
across the services considered
Relationship between the number of Enforcement officers employed and the
number of Fixed Penalty Notices issued
2000
1800
No. of FPNs issued
1600
Bromley
Redbridge
Wandsworth
Southwark
Lambeth
Brent
Tower Hamlets
1400
1200
1000
800
600
400
200
0
0
5
10
15
20
25
30
35
40
No. of Enforcement officers
94
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