WM3: Data management and data collection techniques

for sustainable distribution

Data needs and data review for Green Logistics research

Report

Jacques Leonardi, Michael Browne

Julian Allen, Tom Cherrett, Sharon Cullinane, Julia Edwards, Richard Eglese, Alan McKinnon, Maja Piecyk, Andrew Potter, Tony Whiteing, Allan Woodburn

Department for Transport Studies

5 May 2009

London

Green Logistics WM3: Data management and data collection techniques for sustainable distribution

Content

1. Introduction ..................................................................................................................1

2. Method of this Work Module 3 study.........................................................................2

2.1 Reviewed published literature of the 'Green Logistics' consortium members .......3

3. For whom? the actors needs different data................................................................4

3.1 Data for policy makers ...........................................................................................4

3.2 Data for businesses .................................................................................................6

3.3 Data for research.....................................................................................................6

4. Main data sources for Green Logistics research .......................................................8

4.1 Data collection methods: research papers and reviews ..........................................9

4.2 National UK transport, energy and CO2 statistics ................................................10

4.3 Freight, energy, CO2, environment and sustainability: surveys, reports and datasets .............................................................................................................................11

4.4 ITS - Intelligent Transport Systems .....................................................................12

4.5 Urban freight and reverse logistics.......................................................................13

4.6 Supply chains, energy and freight transport efficiency ........................................15

4.7 European countries and international transport statistics .....................................17

4.8 Conversion factors for energy and CO2................................................................18

5. Data, indicators and units: collection among consortium partners.......................19

6. Intermediate conclusion.............................................................................................23

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

1. Introduction There is a need for an improved data quality, and a constant improvement in data management, not only from the 'applied' point of view of transport policy makers, shippers, logisticians and carriers, but also from the 'fundamental' point of view of research and technology development. Due to its diversity and the fundamental approaches chosen, most of the general problems and difficulties related to data quality and data management in freight transport principally apply for the Green Logistics (GL) project.

For the Green Logistics (GL) project, "A consistent, systematic and comprehensive approach to data collection across the different work modules in the overall work programme is important due to the following issues regarding information on freight and distribution:

1) Data is often incomplete and inconsistent

2) Differences often occur in the units of measurement used

3) Data is often held by many different organisations

4) There is a shortage of data necessary for freight" (GL homepage 2008)

Here some examples to illustrate these four types of difficulties:

1) Looking at the international freight transport, for example, shows that it is difficult to separate the high sea container hinterland transport from and to ports, and the maritime transport of the same container, because the shipment is not the basic unit of the statistics survey, it is the vehicle. Ship and truck move the same container, and the tonnage transported is the same, but the tonnage counts for both trips. There are many examples where the data is either incomplete or inconsistent and there is a need for clarification here.

2) If we consider the carbon footprint problem and its implications for freight transport, it appears that electricity use induce different carbon footprints, depending on the grid, time of the year, and country. Therefore, the transport of import products is not leading to the same carbon footprint per kilometre or per tonne, and there is a great need for harmonisation of transport emissions factors for freight. Like for emissions, standard units are rather clear for UK, but Defra published again 2008 a new set of factors, and a handbook, but these valuable factors are not valid outside of UK. Other units problems needs to be further investigated and workable, transparent solutions will be proposed.

3) Recent studies show that transport CO2 and fuel use data were collected by different organisations, that were applying different survey methods, and leading to big differences in the numbers. Solutions to this problem will be suggested.

4) Looking at original data collection efforts in UK or in Europe, there is little survey work, besides the central statistics looking at a set of about 10-20 indicators, but then, these additional surveys were collecting much more detailed information with up to 1000 indicators. Despite many suggestions in the past, in order to diversify the sources of information and improve the quality of the data collection efforts, most of the freight data available is rather limited in variety, scope, time and space.

The objectives of the Workmodule 3 are therefore:

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

• Consider the opportunity to harmonise the data, indicators, survey design and survey methods for internal use in the green logistics consortium

• Review the available literature and statistics for

o units

o sources

o existing datasets

• Final objective of this work module is to propose some harmonisation solutions for data, units and main sources for future research on green logistics.

The purpose of this intermediate study is to review a first set of harmonisation solutions with the help of a literature collection among GL consortium members, an identification of key problems and potentials, and a proposition to overcome some possible barriers and difficulties on green logistics data use.

2. Method of this Work Module 3 study In order to fulfil these objectives, this study

• works with literature and primary sources

• develops a structure for analysis

• collects among partners the approaches, survey design and survey methods used,

in order to:

• establish common points and differences in a comparative analysis, and finally

• deduce the solutions.

Figure 1: Data harmonisation process outline

data of consortium partner 1: • sources • main focus • main outputs • etc.

Analysis of common points & differences Suggestions for solution Focus of data collection in green logistics

data of partner 6

data of partner 2 data of partner 4

data of partner 3

data of partner 5

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

2.1 Reviewed published literature of the 'Green Logistics' consortium members 1 A.C. McKinnon and M.I. Piecyk (2008): Measurement of CO2 Emissions from Road Freight

Transport: A Review of UK Experience; Energy policy (submitted)

2 A. McKinnon, J. Edwards, M. Piecyk and A. Palmer (2008): Traffic congestion, reliability and logistical performance; LRN 2008*, 8-13

3 M. Piecyk and A. McKinnon- A survey of expert opinion on the environmental impact of road freight transport in the UK in 2020; LRN 2008, 329-334

4 J. Edwards, A. McKinnon and S. Cullinane (2008): Carbon Auditing Online versus Conventional Retail Supply Chains: Issues associated with Picking, Packing and Delivering a Robust Methodology; Euroma paper

5 J.Edwards, S.Cullinane and A.McKinnon (2008): Carbon auditing conventional and online book supply chains; LRN 2008, 341-346

6 S. Cullinane, J. Edwards and A. McKinnon (2008): Clicks vs. bricks on campus - Assessing the Environmental Impact of Online Food Shopping; LRN 2008, 358-363

7 Vasco Sanchez-Rodrigues, Andrew Potter, and Mohamed Naim (2007): Determine the Uncertainties Hindering Sustainability in the UK Freight Transport Sector; Proceedings of the 12th Logistics Research Network Conference, Hull, 5th to 7th September 2007

8 I. Harris, M. Naim, A. Palmer, A. Potter and C. Mumford (2008): Assessing the Impact of Cost Optimization Based on Infrastructure Modelling on CO2 Emissions; Grubbstrom, R.W. (Ed.). Proceedings of the 15th International Working Seminar on Production Economics, Vol. 3. Innsbruck, pp. 151-161.

9 Y. Wang, A. Potter and M. Naim (2008): The potential for a regional electronic logistics marketplace; LRN 2008, 259-264

10 F. McLeod, A. Hickford, S. Maynard, T. Cherrett and J. Allen (2008): Reverse Logistics Report - Developing Innovative and More Sustainable Approaches to Reverse Logistics for the Collection, Recycling and Disposal of Waste Products from Urban Centres; www.greenlogistics.org

11 R. Eglese, W. Maden and A. Slater (2006): A Road TimetableTM to aid vehicle routing and scheduling; Computers & Operations Research 33, 3508–3519

12 A. Woodburn (2008): An Investigation of Container Train Service Provision and Load Factors in Great Britain; UTSG January 2008 Southampton

13 A. Woodburn (2006): Appropriate indicators of rail freight activity and market share: A review of UK practice and recommendations for change; Transport Policy 14 (2007) 59–69

14 M. Browne, C. Rizet, J. Leonardi and J. Allen (2008): Analysing energy use in supply chains: the case of fruits, vegetables & furniture; LRN 2008, 395-402

15 J. Leonardi, C. Rizet, M. Browne, J. Allen, P.J. Perez-Martinez and R. Worth (2008): Improving energy efficiency in the road freight transport sector; LRN 2008, 133-139

(bibliography as of 10 Oct. 2008)

* LRN 2008: A. Lyons (Eds) Logistics Research Network 2008 - Conference proceedings, Univ of Liverpool

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

3. For whom? the actors needs different data The first problem appears to be the diversity of green logistics and sustainable freight transport solutions. Not only diversity of solutions, but also complexity of approaches, contradictory environmental north-south debates, ubiquity of the carbon and energy problems, multiplicity of actors and intervention levels, all adds to contribute to an overall intransparency of the situation. Therefore, there is a need for a clear structure, when analysing the data needs.

At the OECD/ITF global forum on globalisation, transport and environment, a consensus appears on the fact that sustainable transport, as the most pressing transport policy challenge, has to be developed by multiple solutions paths, even if the establishment of a global carbon trade market is regarded as the most efficient solution.

Since one of the purposes of the green logistics project is to provide adequate data and helpful data management solutions for decision makers, it seems that an orientation towards policy needs is providing a solid structure (chapter 3.1). But, this first structure is not enough, because there is a high diversity of business needs (chapter 3.2: data for businesses) and research approaches that also require a high amount of 'data for research' (chapter 3.2).

3.1 Data for policy makers Each of the diverse paths for sustainable transport policies have different data needs:

Economic and fiscal policies

Taxes, carbon trade (European carbon Trading System), verification and compliance etc., all the Kyoto related instruments and economic tools need accurate data on fuel use and transport performance. However, the instruments are under development and the freight transport sector is included only in national reporting on total emissions under the Kyoto process. First attempts to include aviation and maritime emissions in the Kyoto reporting system are under way. The needs for data in this field is high. Most of the countries of the world have established special statistics divisions and are reporting to the Climate Convention Secretariat (UNFCCC). Those data include freight emissions at a very aggregated level. The discussion on how to include road freight transport into this reporting system and into a freight transport sector emission reduction commitment is ongoing.

For this type of policy, data sources are well known and generally widely available. More details on economic and policy data are in:

• chapter 4.1 sources for methodologies and data management

• chapter 4.2 energy

• chapter 4.5 international

• chapter 4.6 national.

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

Laws and regulations

New laws and regulations relevant for freight transport and logistics were developed and applied in recent years. Many discussions are ongoing in order to create and prepare decisions for new rules, in accordance to market needs, standards, or international agreements.

These new rules, laws or regulation principles are either

• promoted and implemented (London congestion charge)

• already implemented butstill the subject of some discussion (Euro-Standard for emissions)

• just decided but not implemented (e.g. inclusion of the aviation sector in the EU Emission Trading Scheme)

• politically controversial (Low emission zone, market rules for biofuels etc.)

• or under development (accountability rules and allocation for carbon market caps for transport under Kyoto protocol etc.).

Many opportunities are still open for international, national or local legislation.

For regulatory policy, the quality of data sources is fundamental for the compliance system, but far less central for the part of the work dealing with the development of new rules and regulations. Following sources are relevant:

• Chapter 4.2 energy and CO2

• Chapter 4.3 urban

• Chapter 4.5 international

• Chapter 4.6 national

Local policy

The local authorities needs both economic instruments and regulations, mainly at a practical level, and they also need spatial disaggregated data that applies to their territorial area of influence.

At the city level, all types of Green Logistics data are relevant (Chapters 4.1 to 4.8), except international data that might be less important (Chapter 4.5).

Planning and infrastructure management

Actors like intermodal terminals managers, new rail and port infrastructure developers, congestion and traffic management experts, planning, transport and land use manager needs to have instant access to accurate and up to date data in their daily work. During decision making processes, long term data and prospective trends are used.

Following sources are mostly needed

• Chapter 4.2 energy and CO2

• Chapter 4.3 urban

• Chapter 4.6 national

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

3.2 Data for businesses Business data needs are almost exclusively focussed on costs and operational efficiency. Transport performance indicators and fuel use indicators are also widely used (Chapter 4.2), as are data from telematics systems.

One field that develops increasing interest is the carbon footprint analysis (Chapter 4.4).

Two main problems for business data is confidentiality and the overwhelming quantity of relevant information and datasets, due to the use of ITC. Both problems determine the need for data management solutions.

For businesses in general, all types of green logistics data sources are potentially relevant (Chapters 4.1 to 4.8). Since most of the companies produces their own datasets, they are interested in exchange and benchmarking.

However, in the field of 'green logistics', benchmarking studies comparing cost and benefits of different solution are quickly facing the dilemma of confidentiality.

Nonetheless, some resources are available to provide benchmarking data. Sources include:

• Academic publications

• The FreightBestPractice programme funded by the DfT

• Reports produced by trade associations such as the Institute of Grocery Distribution, Efficient Consumer Response, Freight Transport Association etc.

• Research and directories by other organisations such as eyefortransport

• The SCOR Model – this now includes a return element and is therefore of relevance to reverse logistics. However, it is not clear if environmental performance measures are included.

One of the challenges for businesses using such data is degree of comparability with the practitioner’s own situation. While broad surveys do enable the company to position themselves, case studies can often be dismissed as not being relevant because of differences in the logistics network to which they are applied.

Obtaining more comparative business studies, including comparable, transparent datasets, is one of the most pressing need for the 'green logistics' field, as it could help to identify and spread beneficial solutions more easily.

It is important to focus on the transferability of these datasets to maintain their usefulness for industry.

3.3 Data for research Data is needed for answering research questions

Within the ‘green logistics’ community many questions have to be answered with the help of original data, that needs to be produced in the course of the research. This leads to the problem that each study aiming at obtaining a quantitative result may have to produce different datasets, and this increases the problem of diversity.

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

Do we have a 'green logistics' research community consensus about data management?

Looking at the different publications and sources, there is no contradiction or major opposition between scientists in the way they are dealing with data. It is obvious that differences are to be found in the questions and the methods, but this is not leading - as far as can be observed in the publications mentioned chapter 2.1 - to any fundamental contradiction. Some debate between 'models oriented' and 'experiments oriented' research, or between 'market' and 'regulatory' approaches, might have been suggesting in the past, that some fundamental problems could be thought likely to result in significant problems in data use and applications. Within the Green Logistics project, the different approaches explored so far do not appear to result in major problems.

On the contrary, it seems that the consensus on the main objectives of 'green logistics' research is leading to a recognition of common principles for 'good quality' research methods, such as surveys methods, models, experiments, evaluation etc., and to a core set of key topics, such as fuel use, distance, mode choice, vehicle type, load factor, time windows, delivery patterns, telematics use etc.

Data for models

Models are essential, and quantitative work needs data. Transport model studies were developed since many years, using a very high amount of existing data, like for example governmental statistics or specific surveys, but also leading to very high amount of new available data. All types of sources are in principle relevant for models (Chapters 4.1 - 4.8)

Data for consultancy

Policy makers need consultancy for their decision making processes on transport and environmental fields. All types of sources could become relevant for a policy oriented study (Chapters 4.1 - 4.8)

Data for evaluations

Tests and experimentations needs also to be independently evaluated. Relevant here are following sources:

• Chapter 4.1 data management and methods

• Chapter 4.2 energy and CO2

• Chapter 4.3 ITS

• Chapter 4.5 international

• Chapter 4.6 national

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

4. Main data sources for Green Logistics research The central question of this step is:

• What do we use as the main data sources within the Green Logistics project?

The identified following sources are providing quantitative data, guidance on data management, advice on data collection and surveys, and are all related to green logistics. They are used by the partners in past and present studies. Some of the partners developed original data, documented in the following structured list of data sources. New data management techniques and methods were also developed or commented in previous studies.

The main data sources identified are now on the homepage of the 'greenlogistics.org' site, under WM3 data.

The following structure was developed:

4.1 Data collection methods: research papers and reviews

4.2 National UK transport, energy and CO2 statistics

4.3 Freight, energy, CO2, and sustainability

4.4 ITS - Intelligent Transport Systems

4.5 Urban freight and reverse logistics

4.6 Supply chains, energy and freight transport efficiency

4.7 European countries and international transport statistics

4.8 Conversion factors for energy and CO2

These 'main data sources for Green Logistics research' will be updated on a regular basis.

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

4.1 Data collection methods: research papers and reviews • J. Allen and M. Browne (2008) Review of survey techniques used in urban freight studies • BESTUFS 2008: BESTUFS Best Practice in data collection, modelling approaches and

application fields for urban commercial transport models • A. S. Fowkes and D. H. Johnson (2008), What impact will the economic and social

climate have on the growth of rail freight, Presentation at the Adam Smith Institute and Marketforce’s 4th Annual Conference, Brussels, November 2008.

• I. Harris, M. Naim and C. Mumford (2007) Global Supply Chains: Developing Skills, Capabilities and Networks - A review of infrastructure modelling for Green Logistics; Logistics Research Network - 2007 Conference

• ISCTSC Working group on urban freight o Routhier, Patier (2008) How to Improve the Capture of Urban Goods Movement Data?

Resource paper o recommendations of the workshop (Meyburg, Rooda 2008)

• ISCTSC Working group on long distance freight o McKinnon, Leonardi (2008) The Collection of Long Distance Road Freight Data in

Europe; Resource paper o recommendations of the workshop (Kochelmann et al 2008)

• D. H. Johnson, A. S. Fowkes, A. E. Whiteing, H. Maurer and S. Shen (2008), Emissions Modelling with a Simple Transport Model. Paper presented at ETC, Leeuwenhorst, Netherlands, October 2008.

• H. Lu, T. Fowkes, M. Wardman (2008) Amending the Incentive for Strategic Bias in Stated Preference Studies: Case Study in Users' Valuation of Rolling Stock, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2049, 128-135

• Sanchez-Rodrigues V., Piecyk M., McKinnon A., Potter A., Naim M. and Edwards J. (2008), “Assessing the application of the focus groups method in logistics”, Proceedings of the British Academy of Management Conference, Harrogate, 9th-11th September.

• V. Sanchez Rodrigues, D. Stantchev, A. Potter, M. Naim, A. Whiteing (2008) Establishing a transport operation focused uncertainty model for the supply chain ; International Journal of Physical Distribution & Logistics Management, Vol 38, 5, 388-411

• S. Shen, A. S. Fowkes, D. H. Johnson and A. E. Whiteing (2009), Forecasting Great Britain Road Freight Demand with Univariate Time Series Models. UTSG Conference, January 2009, London.

• Triantafyllou M., Cherrett T. (2009) ‘Developing business establishment surveys to understand Reverse logistics processes within a multi-retailer shopping environment’. Transportation Research Board Annual Conference, Paper 09-2483, To be presented as part of the Travel Survey Methods Committee (ABJ40) special session on Freight Survey Methodologies.

• Uncertainties related to freight transport costs and modelling (A literature review) (2007) GL Literature review reports

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

4.2 National UK transport, energy and CO2 statistics

• BERR - Department for Businesses Enterprise & Regulatory Reform ( 2008): Energy consumption in the United Kingdom: transport data tables. 2008 updateExcel spreadsheet

• Civil Aviation Authority (2008): UK Airport Statistics

• DfT - Department for Transport (2008): Statistics (data, tables and publications)

• DfT - Department for Transport (2008): Road freight statistics (CSRGT)

• DfT - Department for Transport (2008): Road Statistics 2007: Traffic, Speeds and Congestion

• DfT - Department for Transport (2008): Railways; National Rail Travel Survey (NRTS)

• DfT - Department for Transport (2008): UK Maritime Statistics

• Office for National Statistics (2004): News Release. Greenhouse gas emissions from transport industries.

• Office for National Statistics (2004): Greenhouse gas emissions from transport report

• Office for National Statistics (2008): News Release. Greenhouse gas emissions fall in 2006 - Environmental Accounts Spring 2008.

• Office for National Statistics (2008): Virtual bookshelf

• Office of Rail Regulation (2008): Rail statistics

• Welsh Assembly Government (2008): Welsh Transport Statistics

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

4.3 Freight, energy, CO2, environment and sustainability: surveys, reports and datasets

Heriot-Watt University Logistics Research Centre papers, reports and data collections

• Analysis of Transport Efficiency in UK Supply Chains;

• Analysis of CO2 from Freight Transport in the UK;

• Key Performance Indicators of Distribution in the Automotive Industry;

• Longer and Heavier Vehicle Project;

• A.C. McKinnon and M.I. Piecyk (2008): Measurement of CO2 Emissions from Road Freight Transport: A Review of UK Experience; Energy policy

• A.C. McKinnon (2008) The Potential of Economic Incentives to Reduce CO2 Emissions from Goods Transport ; Paper prepared for the first International Transport Forum on Transport and Energy: the Challenge of Climate Change, Leipzig, 28-30 May 2008

• McKinnon: Synchronised Auditing of Truck Utilisation and Energy Efficiency - A Review of the British Government's Transport KPI Programme

Faber Maunsell 2008: Freight best practices programme http://www.freightbestpractice.org.uk/

• Key Performance Indicators for the Pallet Sector;

• KPIs for Food and Drink Supply Chains;

• KPIs for Non-food Retail Distribution ;

• KPIs for the Builders Merchant Sector;

• KPIs for the Food Supply Chain;

• KPIs for the Next-day Parcel Delivery Sector;

EU-projects

• Heriot-Watt LRC et al. (2002): SULOGTRA - Analysis of the Effects on Transport of Trends in Logistics and Supply Chain Management

• COST 355 WG1 Freight and Energy, • vehicle and fleet approach ; • supply chain approach ; • transport modeling approach ; • last mile approach ; • Final report (2008)

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

4.4 ITS - Intelligent Transport Systems

Lancaster University Management School

• R. Eglese, W. Maden, A. Slater (2006): A Road TimetableTM to aid vehicle routing and scheduling; Computers & Operations Research 33, 3508-3519

• Maden W, Eglese R, Black D (2009) Vehicle Routing and Scheduling with Time Varying Data, JORS Special Issue on Transportation, Logistics and the Environment (forthcoming)

• A. Sbihi and R.W. Eglese (2007) The Relationship between Vehicle Routing and Scheduling and Green Logistics - A Literature Survey

• A. Sbihi and R. W. Eglese (2007) Combinatorial optimization and Green Logistics; 4OR: A Quarterly Journal of Operations Research, Volume 5, Number 2, Pages 99-116, ISSN 1619-4500

Cardiff University IMRC

• I. Harris, M. Naim, A. Palmer, A. Potter, C. Mumford (2008): Assessing the Impact of Cost Optimization Based on Infrastructure Modelling on CO2 Emissions; LRN 2008;

• ITeLS project: Integrating Transport and e-Commerce in Logistics Supply Chains

• McCLOSM project: Mass-customized Collaborative Logistics for Sustainable Manufacture

• Y. Wang, A. Potter and M. Naim (2008): The potential for a regional electronic logistics marketplace; LRN 2008;

• Wang, Y., Potter, A. and Naim, M.M. (2008), “An evaluation of Electronic Logistics Marketplaces within supply chains”, in Dwivedi, A. and Butcher, T. (Eds.) “Supply Chain Management and Knowledge Management - Integrating Critical Perspectives in Theory and Practice”, Palgrave Macmillan.

• Working Papers - Logistics & Operations Management

Faber Maunsell 2008: Freight best practices programme http://www.freightbestpractice.org.uk/

• IT systems at Marshalls pave the way for operational efficiency

• Operational Efficiency Brings Savings for Yearsley

• The Benefits of Central Supply Chain Management: Corus and TDG

• Wayfinding Research: Using satellite navigation to improve efficiency in the road freight industry

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

4.5 Urban freight and reverse logistics

University of Westminster (UoW) Transport Studies Group (TSG) selected publications, reports and datasets on urban logistics

• J. Allen, M. Browne, T. Cherrett, F. McLeod (2008): Review of UK Urban Freight Studies, University of Westminster, Transport Studies Group, London

• TfL - Transport for London (2008): London Freight Data Report

• BESTUFS - Best Urban Freight Solutions; European project; http://www.bestufs.net/

o BESTUFS II (2008): BESTUFS Best Practice in data collection, modelling approaches and application fields for urban commercial transport models;

o BESTUFS II (2008): Good practice guide on urban freight transport;

o BESTUFS I (2000): Best urban freight solutions handbook; Statistical data, data acquisition and data analysis regarding urban freight transport;

Recent UoW TSG articles and papers

• Browne, Michael and Woodburn, Allan G. and Allen, Julian (2007) Evaluating the potential for urban consolidation centres. European Transport / Trasporti Europei, 35 . pp. 46-63. ISSN 1825-3997

• Browne, Michael and Allen, Julian (2007) Logistics and the waste sector: a London case study. In: Logistics Research Network Annual Conference 2007, 05 - 07 Sep 2007, Hull.

• Browne, Michael and Allen, Julian and Attlassy, Mahmoud (2007) Comparing freight transport strategies and measures in London and Paris. International Journal of Logistics, 10 (3). pp. 205-219. ISSN 1367-5567

• Browne, Michael and Allen, Julian and Anderson, Stephen (2005) Low emission zones: the likely effects on the freight transport sector. International Journal of Logistics, 8 (4). pp. 269-281. ISSN 1367-5567

• Browne, Michael and Allen, Julian and Anderson, Stephen and Woodburn, Allan G. (2006) Night-time delivery restrictions: a review. In: Taniguchi, Eiichi, (ed.) Recent Advances in City Logistics: Proceedings of the 4th International Conference on City Logistics. Elsevier, Oxford, UK. ISBN 0080447996

• Anderson, Stephen and Allen, Julian and Browne, Michael (2005) Urban logistics: how can it meet policy makers' sustainability objectives? Journal of Transport Geography, 13 (1). pp. 71-81. ISSN 0966-6923

• Browne, Michael and Piotrowska, Marzena and Allen, Julian and Woodburn, Allan G. (2008) The potential use of urban consolidation centres in the hotel industry in London. In: Taniguchi E., Thompson R.G. (Eds) Innovations in City Logistics; 5th International Conference on City Logistics, 11 - 13 Jul 2007, Crete, Greece. ISBN: 978-1-60456-725-0 (prepress)

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

University of Southampton Transportation Research Group publications, reports and datasets on reverse logistics

• Cherrett T. J., Hickford A. J., Maynard S., (2007). "The Potential for Local Bring-sites to Reduce Householder Recycling Mileage." Transportation Research Record, 2011

• Cherrett T. J., Maynard S., (2006). "Transport Impacts of Household Waste Recycling Centres." Proceedings of the Institution of Civil Engineers, Waste and Resource Management, 159(1), 13-21

• McLeod F., Hickford A., Maynard S., Cherrett T. and Allen J. (2008): Developing Innovative and More Sustainable Approaches to Reverse Logistics for the Collection, Recycling and Disposal of Waste Products from Urban Centres

• Fraser N McLeod and Tom J Cherrett (2007) Maximising Efficiency in Domestic Waste Collection through Improved Fleet Management; Logistics Research Network - 2007 Conference

• Hickford A. J., Cherrett T. J., Maynard S., (accepted for publication in 2009) "The Transport Implications of Bring-Site Recycling." Proceedings of the Institution of Civil Engineers, Waste and Resource Management

• Hickford, A., Cherrett, T (2008) The characteristics of returns systems employed by businesses on Winchester High Street - Initial findings from the High Street Business Managers Survey. Working Report, Transportation Research Group, University of Southampton. December (16 pages).

• Maynard S., Cherrett T. J., Waterson B. J., (2009) "Monitoring Household Waste Recycling Centres Performance using Mean Bin Weight Analyses." International Journal of Integrated Waste Management, Science and Technology, Volume 29, Issue 2, 614-620

• Maynard S., Cherrett T. J., Waterson B. J., (accepted for publication in 2009) "Gauging HWRC Performance from Vehicle Weigh Ticket Data." Proceedings of the Institution of Civil Engineers, Waste and Resource Management

• Maynard, S., Cherrett, T (2008) The characteristics of retail waste logistics on Winchester High Street - Initial findings from the High Street Business Managers Survey. Working Report, Transportation Research Group, University of Southampton. December (40 pages).

• McLeod F. N., Cherrett T. J., (2008). "Quantifying the transport impacts of domestic waste collection strategies." Waste Management, 28(11), 2271-2278

• McLeod, F., Cherrett, T (2008) The characteristics of core goods and service vehicle activity in Winchester High Street - Initial findings from the High Street Business Managers Survey. Working Report, Transportation Research Group, University of Southampton. December (41 pages)

• Triantafyllou, M., Cherrett, T (2009) Developing business establishment surveys to understand Reverse logistics processes within a multi-retailer shopping environment. Transportation Research Board Annual Conference, Paper 09-2483, To be presented as part of the Travel Survey Methods Committee (ABJ40) special session on Freight Survey Methodologies.

• Triantafyllou, M., Cherrett, T (2009) Understanding the returns mechanisms of a dedicated shopping centre. University Transport Studies Group conference. London.

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

4.6 Supply chains, energy and freight transport efficiency • M. Browne et al (2008): Analysing energy use in supply chains: the case of fruits,

vegetables; LRN 2008

• M. Browne et al. (2005) Life Cycle Assessment in the Supply Chain: A Review and Case Study, Transport Reviews, Vol. 25, No. 6, 761-782

• S. Cullinane, J. Edwards and A. McKinnon (2008): Clicks vs. bricks on campus LRN 2008

• J. Edwards, A. McKinnon and S. Cullinane (2008): Carbon Auditing Online versus Conventional Retail Supply Chains: Issues associated with Picking, Packing and Delivering a Robust Methodology; Euroma paper

• J.Edwards, S.Cullinane and A.McKinnon (2008): Carbon auditing conventional and online book supply chains; LRN 2008

• ITeLS project: Integrating Transport and e-Commerce in Logistics Supply Chains

• McKinnon et al (2004): Analysis of Transport Efficiency in UK Supply Chains

• McLeod F. N., Cherrett T. J., Song L, (2006). "Transport impacts of local collection/delivery points." International Journal of Logistics Research and Applications, September, 9(3), 307-317

• Rizet, B. Keïta (2005): Chaînes logistiques et consommation d'énergie: cas du yaourt et du jean.

• V. Sanchez-Rodrigues, A. Potter, and M. Naim (2007): Determine the Uncertainties Hindering Sustainability in the UK Freight Transport ; Proceedings of the 12th Logistics Research Network Conference, Hull, 5th to 7th September 2007

• Song L, Cherrett T. J., McLeod F. N., (accepted for presentation 2009) "Solving the last mile problem - modelling the transport impacts of collection/delivery points." Proceedings of the 88th Annual Meeting of the Transportation Research Board

Faber Maunsell 2008: Freight best practices programme http://www.freightbestpractice.org.uk/

• Consolidate and Save

• Focus on Double Decks

• Heathrow Airport Retail Consolidation Centre

• Jaguar Sprints Forward

• London Construction Consolidation Centre

• Operational Efficiency Brings Savings for Yearsley

• Reducing the environmental impact of distribution: Transco National Logistics

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

Other organizations

• Efficient Consumer Response Europe – includes a project on Sustainable Transport that has a number of very short case studies.

• Eyefortransport – Green Transportation & Logistics Report published in December each year. Includes a directory of providers of ‘Green Logistics’ services.

• Institute of Grocery Distribution – various supply chain reports plus supply chain analysis (Some parts open to subscribers only).

• Road Haulage Association - Carbon Footprint Explained - how to reduce emissions and save money

• Supply Chain Council – for more information on the SCOR Model.

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

4.7 European countries and international transport statistics

European Union and data from European countries

• EEA- European Environmental Agency 2008: Transport - Indicators. Statistics on Freight transport energy, pollutants and CO2 ; data until 2006

• Eurostat 2008: Transport.

• ITF Leipzig Forum 2008, Workshop 'Reducing CO2 Emissions in Goods Transport'

• MEEDATT - Ministère de l'Écologie, de l'Energie, du Développement durable et de l'Aménagement du territoire - Economie et statistique (2008) Transport - Données d' ensemble

• OECD/ITF - Organisation of Economic Cooperation and Development / International Transport Forum (2008): statistics and data on Europe of 27 and other countries including USA, Canada, Australia, Japan and South Korea

• OECD/ITF (2008): Global Forum on Sustainable Development: Transport and Environment in a Globalising World - Guadalajara, Mexico

• VTT (2008): LIPASTO Calculation system for traffic emissions and energy consumption; Finland system on traffic and freight emission data collection

United Nations and sub-organizations

• UNCTAD United Nations Conference on Trade and Development (2008): Handbook of statistics 2008

• WTO - World Trade Organization (2008): International trade statistics

International maritime transport

• UNCTAD (2008): Review of Maritime Transport 2008

International aviation

• ICAO - International Civil Aviation Organisation (2008): Environmental Report 2007

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

4.8 Conversion factors for energy and CO2

The documents below provide data essential for all other sectors.

• DEFRA (2008) Guidelines for Company Reporting on Greenhouse Gas Emissions Annexes updated July 2008 Annex 1 - Fuel Conversion Factors, Defra

• BERR - Department for Businesses Enterprise & Regulatory Reform (2008) Energy statistics: explanatory note

• Ademe (2007) Bilan carbone entreprises et collectivites. Guide des facteurs d'emissions

• IEA - International Energy Agency (2008) Key World Energy Statistics 2008

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

5. Data, indicators and units: collection among consortium partners

Data and Indicators were extracted from the 2 main publications of the partners (list in Chapter 2.1). In many cases, own quantitative data were presented as result of the investigation. Authors are presenting original data values on these following indicators.

Table 1: Structured list of data, indicators and units, represented in partners publications (10 Oct 2008)

1) KPI Unit/ definitions Vehicle fill degree of loading against actual capacity by weight by volume or

by unit loads carried. Empty running in absolute terms the relocation of empty vehicles, but including

legs where returns and packaging were carried. Time utilisation measured by seven categories of use, including being loaded or

running on the road Deviations from schedule

covering any delay deemed to be significant, with causes such as congestion en route or waiting at delivery point

Fuel consumption actual fuel used, correlated to factors such as loading and airflow management equipment.

2) Transport and logistics Units Basic data Tonnage tonnes Vehicle-km km Tonne-kilometres tkm Mean load (tkm/km) t Distance travelled, length of haul km/delivery Detailed data Distance Km/HGV km Km/car km Vehicle miles involving a drop or delivery miles Changes in the number of vehicle kilometres % Load Tonnes distributed t Tonnage by road t

m3, nb of pallets, cages, parcels Volume of delivery/consignment of trip Delivered tonnage daily t Total weight t Carrying capacity, payload t % of km run empty % Vehicle load factor % by weight or by volume Deliveries characteritics Goods delivered per delivery point No of pallets, cages, parcels

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

No(No = number) Handling factor (nb of load/unload per shipment) No Number of vehicle trips/ deliveries/ collections No Number of stops No Number of deliveries No Number of delivery rounds No Number of deliveries per round No Vehicle trips made loaded No Number of deliveries daily No Vehicle numbers No Number of lorries /year No Number of HGVs/hour

Changes in the number of vehicle trips % Changes in the number of vehicles % Time

No Vehicle hours Loading/unloading time hours, hours/day, % of trip time Travel time hours, hours/day, % of trip time Delay time hours, % or total trip time Time window for delivery hours, minutes Changes in parking time and frequency % Changes in travel time % Average speed of vehicles kmph Road freight per head of population t Modes/Fleet Artics No

No Rigid lorry Van No Car No Motorcycle No Bicycle No Pedestrian No Shipping No Rail No 3) Supply chain, consumer trip and carbon footprint

Units

Type of product, goods Type of production/ storage / transport / shop Tonnage of production/ storage / transport / shop tonnes Energy used per tonne of product MJ/t Consumer buying trip load kg Consumer buying trip distance km % of shopping trips made by car % % of trips where shopping is the single purpose % Energy used per kg of product goe/kg CO2 emission per kg product gCO2eq/kg

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

4)Economy Units Business types Food retail Nb Clothing retail Nb Other retail Nb Restaurant Nb Public House Nb Hotel Nb Banks Nb Other Services Nb Warehousing Nb Manufacturing Nb Personal Services Nb Business characteristics Number of employees Nb Hire and reward Nb or % Own account Nb or % Costs Total logistics costs £ Logistics costs per item/kg £/kg Changes in logistics costs %/a Transport costs £ Changes in transport costs % Vehicle operating costs £ Changes in operating costs % Costs of fuel £/l; £/gal. Costs of one tonne CO2 £/t or €/t or $/t 5) Policy Units Restriction zones Scale Clean vehicles Emission class Coordinated transport: Consolidation etc. T.B.A., Vehicles per day Congestion mitigation Vehicle per hours, total hours Charging £, Vehicles per day Information systems T.B.A. (to be agreed) Public private partnership T.B.A. Progress reports 6) Impacts: Pollutants Units SO2 kg CO2 t CO kg HC kg NOx kg PM ppm PM10 ppm

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

7) Energy and CO2 Units Total fuel use (litres or gallons) litre, gallon Changes in the total fuel use % Energy use GJ, kWh Fuel consumption efficiency mpg, l/100km Fuel intensity of transport l/tkm Total CO2 emissions kg CO2

CO2 intensity gCO2/tkm Changes in vehicle emissions % 8) Networks/traffic Units Road arcs Nb Road nodes Nb Distances km Travel time h / mn Time windows for delivery hh:mn - hh:mn

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

6. Intermediate conclusion Differences and possible inconsistency in the methods?

Of course, major difference are prevailing in the approaches and survey methods. One partner focuses on calculations and use existing datasets, another prefers direct data collection through interview and company questionnaires to produce its own 'primary' dataset, and the next study is mainly consisting in comparative data analysis. All are equally valid, depending on their objectives.

No major problems detected in existing approaches

Looking at the different publications and sources, there is no contradiction or major opposition between scientists in the way they are dealing with data. It is obvious that differences are to be found in the questions and the methods, but this is not leading - as far as can be observed in the publications mentioned chapter 2.1 - to any fundamental contradiction. Some debate between 'models oriented' and 'experiments oriented' research, or between 'market' and 'regulatory' approaches, might have been suggesting in the past, that some fundamental problems could be thought likely to result in significant problems in data use and applications. Within the Green Logistics project, the different approaches explored so far do not appear to result in major problems.

Common principles and key topics for data collection and management

On the contrary, it seems that the consensus on the main objectives of 'green logistics' research is leading to a recognition of common principles for 'good quality' research methods, such as surveys methods, models, experiments, evaluation etc., and to a core set of key topics, such as fuel use, distance, mode choice, vehicle type, load factor, time windows, delivery patterns, telematics use etc.

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Green Logistics WM3: Data management and data collection techniques for sustainable distribution

ANNEXE:

Reminder on the purpose of Green Logistics WM3

'Data management and data collection techniques for sustainable distribution'

Objective

This is a cross-project work module concerned with surveys and other data collection techniques required within the project. The objective of this module is to ensure that data collection techniques applied in the programme are compatible and closely coordinated. This will permit pooling of data across the consortium and minimise the data collection burden on companies participating in the surveys.

A consistent, systematic and comprehensive approach to data collection across the different work modules in the overall work programme is important due to the following issues regarding information on freight and distribution:

* Data is often incomplete and inconsistent

* Differences often occur in the units of measurement used

* Data is often held by many different organisations

* There is a shortage of data necessary for freight

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