Introduction to LIMCO, a research project about Logistics, Environment and Costs

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Introduction to LIMCO, a research project about Logistics, Environment and Costs

Inger Beate Hovi

LIMCO seminar, Oslo Science Park 11^th December 2019

Program for the seminar:

0815 Introduction to LIMCO, a Research Project About Logistics, Environment and Costs. Inger Beate Hovi, TØI, project manager

0830 How can Transparent Cost Functions give Efficient and Sustainable Transport and Logistics? Christian Mjøsund, TØI and Eirill Bø, Sitma AS

0900 Integration of Vehicle data with ERP for More Efficient Data Flows?

Stein Erik Grønland, Sitma AS

0915 Modeling Approaches to Address Urban Freight’s Challenges. Can vehicles big data be utilized? Professor Michael Browne, University of Gothenburg

0945 Is Zero Carbon Logistics Possible?

Professor Alan McKinnon, Kühne Logistics University in Hamburg

1015 Supply Chain Perspective on Competitive Strategies and Green Supply Chain Management Strategies. Professor Lauri Ojala, Turku School of Economics 1045 Questions and discussion

1100 End of seminar

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LIMCO – Logistics, environment and costs

Knowledge-building research project for industry

Research project with industry participation, financed 80 % by NRC and 20 % by users

Knowledge-building also for industry

Research partners:

User partners:

International experts

 Professor Michael Browne (University of Gothenburg),

 Professor Lauri Ojala (Turku School of Economics),

 Professor Alan McKinnon (Kühne Logistics University in Hamburg)

Project period: August 2018 - June 2021

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Uke 2

Overall idea:

Increase efficiency and reduce environmental impacts from trucks by utilizing data which a few years back did not exist

Unutilized potential in data from transport and enterprise resource planning for transport planning and optimization

Cooperation with transport and logistics firms for data availability and commercial relevance

Ongoing data capture from ~620 trucks

Developing transparent cost functions for transport and logistics

Test data from approximately 80 trucks for April 2019:

Uke 23 Uke 42

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Fleet Management System-data subset

Engine performance variables

Fuel consumption in liters per day and driver

Distance (km)

Coasting (% of dist)

Cruise control (% of dist)

Automatic gear (% of dist)

Over speed (% of dist)

Idle time

Torque

Driving time

Braking Score

Average consumption l / 10 km

CO2 emission average (kg / km)

Average speed (km / h)

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Cooperation with Norwegian Business school, gives results

Awarded prizes, best;

Master Theses

Bachelor Theses

In logistics for 2019

News article on TØI’s webpage

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Costs and environmental impact of transport

Transport cost model developed in the LIMCO project

Model customized to the participating firms’ individual transport schedules

Output variables are standardized in order to perform analyses on a dataset that includes data from all firms

Tool for firms to make cost-effective and environmentally friendly decisions

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Smart Statistics?

A task in the project is to investigate whether information from FMS, ERP and other professional systems can reduce reporting burdens and increase efficiency for producing smart statistics

Truck surveys are known to have a high reporting burden and low participation levels

Commodity flow surveys have been conducted twice, are expensive and lack information about mode of transportation

Seeks to answer the following research question:

How can new vehicle data provide a better basis for planning?

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More information about LIMCO

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Project web site: Norwegianand English Samferdselsartikkel om prosjektet Samferdselsartikkel om økonomisk kjøring

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How can Transparent Cost Functions give efficient and sustainable transport and logistics?

Christian S. Mjøsund, TØI and Eirill Bø, Norwegian Business School

Transparency issues within a transport buyer and provider relationship.

How can a transparent transport cost model increase efficiency and reduce uncertainty and risk in a transport buyer and seller relationship?

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Transparency

• We seek to investigate the hypothesis that increased transparency between the suppliers and purchasers of transport services is a

driver for improved logistical efficiency and ultimately lower overall costs.

• It is fairly easy to show that the “battle and truce” model of negotiation may not lead to optimal results for either party in the

transport context.

Transparent transport agreement

Full insight to the cost structure Full insight to all transport processes

* Fill rate?

* Hours per year?

* Right price?

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The study: economic modelling

• Fixed costs

• depreciation of capital equipment, cost of capital, insurance, administration, taxes

• Variable costs (per km driven)

• fuel, maintenance, tyre replacement

• Staff costs (per hour of operation)

• staff time for driving, loading, unloading:

depends on factors such as average speed and loading rates

Important questions regarding transport

What is the “right” price for the transport? Do we really know what we pay for?

What are the greatest cost-drivers?

What are the greatest influences on productivity?

Where is (is there?) incentive to increase the productivity through the agreement we have today?

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Principal – agency theory

Moral hazard Adverse selection

Hidden information problem Hidden operational problem

Pre contractual opportunism Post contractual opportunism

Contract signed

Agency theory; two types of contract

• A behavioural-based contract rewards the agent on the basis of what they do

• An outcome-based contract means the agent is rewarded on results.

RISK

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Price format and risk; different contract types

Risk Need for information

High Low

Low High

Transport buyer Transport provider

Important Important

Unimportant

T r a n s p o r t p r o v i d e r T

r a n s p o r t b u y e r

Collabarativ based contract Behavor based contract

Price per trip/kilometer

Outcome based contract Price per unit (ton)

What is cost based pricing?

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Why calculate the transport costs?

• Right bases for pricing

• Demand order and continues following up

• Visualize all the processes in the transport, both supplier and buyer

• Visualize cost drivers

• Provides an overall picture of the cost of the company`s deliver service

• Visualize productivity

Partnership?

Make demands on the transport company

• Demands on total driven distance

• Demands on coordination with others

• Demands on competence around transport planning that can improve the fill rate

• Demands on technology which will make it possible to follow up productivity and service-demand in transport

• Remove every possibility for “speculation”

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Side

Costs and environmental impact of transport

We are developing a transport cost model as a part of the LIMCO project

The model is customized to the participating firms’ individual transport arrangements and schedules

Output variables from the model are standardized in order to perform

analyses on a dataset that includes data from all firms

The model forms a basis for estimating costs and environmental impacts in different cases / scenarios

In order to answer the project research questions

A tool for firms to make cost-effective and environmentally friendly decisions

Decision Support Tool (DST)

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Transport cost models in the LIMCO project How?

Mapping all cost elements in the firm’s transport schedules / operations

Based on information reported from the firms on :

Transport routes and schedules

Times and frequencies

Amount and characteristics of goods

Fill rates

Loading- and unloading times etc.

Which vehicles are in operation on the different routes

Vehicle cost calculations

Two modules: Distribution and long- distance transport

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Side

The structure of the LIMCO cost model

Vehicle cost calculations Key figures:

Fixed yearly costs per vehicle Variable costs per km Staff costs per hour in operation

Route scheduling Key figures:

Number of km in total Number of km per route Hours worked per route Time spent on various activities

Calculation of costs and CO2 emissions from transport

Key figures:

Costs per route/item transported Emissions per route/item

transported

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Additional information:

Load capacity of vehicle Fuel consumption Level of idling

Additional information :

Which vehicles are used on the different routes?

Total number of kilometres per vehicle Fill rates and return volumes

Output from the cost model

An example of output from the model: Overview of costs and emissions for one of the Limco project firms’ transport arrangement

Key figures:

Destinations Fixed costs Variable costs Staff costs In total Fuel consumption (l)

C02 emission (kg)

Cost per round trip

Cost per delivery

Cost per pallet

Cost per km

CO2 per round trip

CO2 per pallet

Destination 1 557 447 515 712 955 654 2 028 813 32 651 86 852 1 989 1 989 44 14 85 2

Destination 2 823 011 973 056 1 151 144 2 947 212 58 157 154 697 3 619 3 619 83 12 190 4

Destination 3 596 257 706 332 888 906 2 191 496 42 258 112 407 3 512 3 512 71 12 180 4

Destination 4 473 822 485 479 590 325 1 549 626 27 764 73 852 4 967 4 967 84 13 237 4

Destination 5 901 039 1 806 026 2 280 269 4 987 334 105 040 279 407 2 056 2 056 50 13 115 3

In total 3 372 462 4 507 955 5 887 855 13 768 272 267 089 710 458 2 642 2 642 59 13 136 3 Key figures costs (NOK)

Total costs (NOK) Environmental impact Key figures emissions (kg)

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Side

Simulations

17 How large are cost and emission reductions when the time spent on all loading and unloading operations is reduced by 10 %?

Example illustrating how the model could be used as Decision Support Tool in transport operations

Key figures:

Destinations Fixed costs Variable costs Staff costs In total Fuel consumption (l)

C02 emission (kg)

Cost per round trip

Cost per delivery

Cost per pallet

Cost per km

CO2 per round trip

CO2 per pallet

Destination 1 0,0 % ‐0,3 % ‐5,9 % ‐3,0 % ‐0,4 % ‐0,4 % ‐3,0 % ‐3,0 % ‐3,0 % ‐3,0 % ‐0,4 % ‐0,4 %

Destination 2 0,0 % ‐0,1 % ‐4,5 % ‐1,8 % ‐0,2 % ‐0,2 % ‐1,8 % ‐1,8 % ‐1,8 % ‐1,8 % ‐0,2 % ‐0,2 %

Destination 3 0,0 % ‐0,1 % ‐4,9 % ‐2,1 % ‐0,2 % ‐0,2 % ‐2,1 % ‐2,1 % ‐2,1 % ‐2,1 % ‐0,2 % ‐0,2 %

Destination 4 0,0 % ‐0,1 % ‐4,3 % ‐1,7 % ‐0,2 % ‐0,2 % ‐1,7 % ‐1,7 % ‐1,7 % ‐1,7 % ‐0,2 % ‐0,2 %

Destination 5 0,0 % ‐0,2 % ‐5,8 % ‐2,8 % ‐0,3 % ‐0,3 % ‐2,8 % ‐2,8 % ‐2,8 % ‐2,8 % ‐0,3 % ‐0,3 %

In total 0,0 % ‐0,2 % ‐5,3 % ‐2,4 % ‐0,2 % ‐0,2 % ‐2,4 % ‐2,4 % ‐2,4 % ‐2,4 % ‐0,2 % ‐0,2 %

Total costs (NOK) Environmental impact Key figures costs (NOK) Key figures emissions (kg)

Answering the research questions

Combining the cost model, vehicle data and order data to answer the research questions

RQ1: To what extent do customer requirements (shipping frequency and delivery time) and logistics design affect costs and the environment?

RQ6: How can visualisation of vehicle data contribute to more efficient distribution solutions in the city?

CASE: Distribution from a distribution centre (located in Tønsberg) to the market in the Oslo urban area.

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Side

Distribution from Tønsberg to Oslo

Daily deliveries from Tønsberg to the Oslo area

Morning route:

Start approx. 4 AM from Tønsberg

Midday route:

Start approx. 9-10 AM from Tønsberg

Two vehicles in operation with identical costs

Distribution round typically has 10-25 deliveries per round – most deliveries are in the Oslo city centre.

Data for one representative month (February 2019)

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Heat map of deliveries

Tønsberg

The average distribution round: Hours per activity

0,5

0,8

1,0

2,9

2,3

0,8

1,0

0,3 0,0

0,5 1,0 1,5 2,0 2,5 3,0 3,5

Administration

(before start) Loading Driving Time to market (Tønsberg-

Asker)

Driving Time

distribution Delivery stop time Resting time Driving Time from market (Asker-

Tønsberg)

Administration (after end of route)

HOURS

ACTIVITIES DURING DISTRIBUTION ROUND

Total time: 9,5 hours

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Side

The average distribution round: Kilometres per activity

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0 0

78

82

0 0

78

0 0 10 20 30 40 50 60 70 80 90

Administration

Asker)

Driving Time

Tønsberg)

KILOMETERS

ACTIVITIES DURING DISTRIBUTION ROUND

Total distance: 238 kilometres

The average distribution round: Cost per activity and cost type

Administration

Asker)

Driving Time

Tønsberg)

COST (NOK)

ACTIVITIES DURING DISTRIBUTION ROUND Fixed costs Variable costs Staff costs

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Side 23

39%

24%

37%

The average distribution round: Costs per cost type

Fixed costs Variable costs Staff costs

Great variations in fill rate and cost per kilo for the different observations

R² = 0,9367

0 50 100 150 200 250 300 350 400 450

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

COST PER KILO DELIVERED (INDEX -AVERAGE VALUE = 100)

Fill rate

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Side

Great variations in fill rate and cost per kilo for the different observations

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R² = 0,9367

0 50 100 150 200 250 300 350 400 450

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

COST PER KILO DELIVERED (INDEX -AVERAGE VALUE = 100)

Fill rate Average fill rate =

35 %

High degree of delivery service

1/3 of deliveries under 20 kilo. No extra fee for customer.

13%

20%

18%

11%

9%

5%

3% 3%

2%

4%

12%

0%

5%

10%

15%

20%

25%

0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100 >100

Share of deliveries

Weight per delivery (kilos)

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Side

High degree of delivery service

1/3 of deliveries under 20 kilo. No extra fee for customer.

27 13%

20%

18%

11%

9%

5%

3% 3%

2%

4%

12%

0%

5%

10%

15%

20%

25%

0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100 >100

Share of deliveries

Weight per delivery (kilos)

The distribution is based on order information on 639 deliveries in February 2019 Further work :

What about a minimum order limit? How could this reduce costs and environmental impact?

Further work

More data from trucks and more routes and transport arrangements in the cost model

More case studies with the participating firms

Working on GPS data to improve data quantity and quality and to gain new insights

Answering the research questions and writing scientific papers

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Integration of Vehicle data with ERP data for more efficient Data

Flows?

Stein Erik Grønland

Background

One of the key ideas behind Limco is to find out how the information generated by FMS can be used to improve driver’s effeciency and more general the cost efficiency of transport systems

Taking this one step further – can we improve even more by combination of data generated by the FMS and the companies’ ERP systems?

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Different levels of integration

Combination of data from FMS and ERP, for analytical purposes, e.g. in BI frameworks, excell‐based analysis or other frameworks provided to give decision support

Integration of FMS data into ERP systems, e.g. into TMS modules, to provide additional operational support

Integration of data from FMS into ERP systems to facilitate digitalied interface between the ERP system and Statistics Norway’s system for reporting of transportation statistics («Smart statistics»)

Integration of FMS / GPS / ERP data into scheduling systems for dynamic planning of routes

Combination of data from FMS and ERP, for analytical purposes, e.g. in BI frameworks, excell‐based analysis or other frameworks provided to give decision support

• Technical feasibility: Easy, both FMS and ERP can export data in several formats, e.g. excel.

• FMS challenges: More enlarged data sampling from FMS, e.g. weight data. There may also be some frequency challenges

• ERP challenges, transport users: Data required for analysis would normally be extracted from order modules, logistics modules and sometime add‐ons. Standard set‐ups does not neccesasily have data fields required for this type of analysis, but this can be solved through definition of new fields, changes in the applications and user interface/data capture. The participating case

companies would need such changes.

• ERP challenges, transport companies: The same applies as for the transport users. Many small transport companies uses smaller systems, often with limited functionality beyond accounting and basis order/invoicing. For the latter, there may be large challenges in doig the required changes.

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Integration of FMS data into ERP

systems, e.g. into TMS modules, to provide additional operational

support

Technical feasibility: Most (modern) ERP systems can import data through API interfaces

ERP challenges: To utilise the data imported from the TMS systems, or directly from the car manufacturers’ systems, major developments may be rquired from standard set‐ups, both in terms of applications and data fields.

There will also be a need for common identifiers for trips/deliveries between the FMS and ERP

Integration of data from FMS into ERP systems to facilitate digitalied interface between the ERP system and Statistics Norway’s system for reporting of

transportation statistics («Smart statistics»)

The FMS can, together with GPS, generate data for vehicle‐km fairly directly. However, the data fields will be different than the present registration forms for SSB registration.

There may be challenges related to the frequency for vehicle data registration.

The present ERP systems used by transporters would in general need changes if they shall work as an information channel between the FMS and SSB

If we also shall get data for transport work (tonkms), weight data must be supplied from the vehicle systems, and further adjustments would be necessary in the ERP systems

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Integration of FMS / GPS / ERP data into scheduling systems for dynamic planning of routes

• What is needed is GPS information and information on deliveries/pick‐ups, to be combined with transport order data, and scheduling information.

• This has to be taken care of for more specialised applications, same comments as earlier applies to that this is normally not standard.

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LIMCO Seminar

Wednesday 11 December – Oslo

Modeling approaches to address urban freight’s challenges: Can vehicles big data be

utilized?

Michael Browne, University of Gothenburg

Outline

• Urban freight context

• Policy, planning and other questions where modeling is relevant

• Complexity and diversity

• Challenges

• Reflections on the use of big data

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VREF Urban Freight Initiative

Strong increase in international research

More information on the VREF centres shown at the end of the presentation in further links.

Deliveries in Cities:

Finding ways to share the space:

Challenges &

solutions

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Not only the city centre that matters

Source: Why Goods Movement Matters ‐ by the RPA in collaboration with VREF (2016)

Urban logistics perspective

• Space

• Time

• Organization

Can relate these to the

challenges of modelling freight flows in urban areas

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Space ‐ Time ‐ Organisation

Space

• City planning

• Urban design

• Street design

• Building design

• Transport design and utilisation

‐ People

‐ Goods

Time

• 24 hours

• Problem of the peaks

Organisation

• Systems thinking

• Logistics management

• Public and private sector goals

Truck and van weekday movements in central London

Van Truck <12T

Truck >12T

Vans are the majority of the movements. Up to one third of the van trips could be service related i.e.

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Outline

• Challenges

Urban freight issues faced by policy makers include

• Forecasting future levels of road freight activity

• Likely transport, commercial and environmental effects of policy measures; e.g. road pricing, low emission zones, road closures, night‐

time bans, consolidation...

• Understand operational difficulties faced by freight and servicing companies, senders and receivers in urban areas

• Location, extent and timing of roadspace allocation for on‐street loading/unloading

• Whether regulations are needed for goods vehicle weights and sizes allowed in urban areas

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Limited quantity of urban freight data

• No specific on‐going urban freight activity surveys in most countries

• Many national data sources contain urban freight data but:

– Either urban data is difficult/time‐consuming to disaggregate

– Or sample size is small at urban scale causing data reliability issues

– Surveys based on vehicle activity not geographical area, so urban and non‐urban data collected with no easy method of separation

For urban freight policy making ‐ vehicle traffic counts do not give insight into:

• goods and service flows that such vehicle movement supports

• specific purpose of these vehicle trips

• establishments that are generating the demand for these trips and their goods and service requirements

• supply chain decisions that results in these trips happening in these vehicles, at these times and days

• routes taken by these vehicles

• types of trip patterns performed (e.g. multi‐drop as opposed to single drop)

• details about the loading, unloading and parking activities associated with these trips

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Important to consider policy visions An example from London

City of Lo nd o n

Last Mile Deliveries

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City of Lo nd o n

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Last Mile Deliveries

• Reintroduce logistics space to the Square Mile through retrofitting under utilised assets and delivering new space as part of

developments

• Three methods of operation

– Micro Consolidation – Micro Distribution – Storage

City of Lo nd o n

Last Mile Sites

2022

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Outline

• Challenges

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Complexity of urban freight operations: data implications

• Both goods and vehicles can be studied and goods can be carried by several vehicles

• Number of organizations involved in decision‐making including freight operators, service providers, shippers, and receivers.

• Variety of urban goods operations – in terms of commodity

• Much of the data is held by private organisations and may be difficult to obtain

Data that can be collected about urban freight activity

• Vehicle delivery/collection trips at establishments in the urban area

• Goods flows to/from establishments

• Service trips to establishments

• Trip details and patterns of goods and service vehicles

• Loading/unloading activity of goods vehicles

• The origin location of goods flows / vehicle trips to establishment

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Survey techniques used to collect urban freight data

• Establishment survey

• Vehicle observation survey

• Parking survey

• Driver survey

• Commodity flow survey

• Roadside interview survey

• Vehicle trip diaries

• GPS survey

• Freight operator survey

• Supplier survey

• Service provider survey

• Vehicle traffic count survey

An example of deliveries in urban space in a working day

Space – Time – Organisabon

...and ﬂows

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Round duration: 7.82 hrs Total driving time: 1.77 hrs Total parking time: 6.05 hrs Average speed: 1.89 km/hr

#parking stops: 35

#items delivered: 119

Last‐mile complexity

Source: FTC2050 Project (Cherrett, 2017)

Outline

• Challenges

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Challenges

• Challenges driven by complexity and rapid change

• Challenges driven by lack or limitations of knowledge/data

• Gaps in communication

Challenges driven by complexity and rapid change

• Including the supply chain is difficult

• Behavioral issues generally weaker

• Value and reliability of trip generation studies affected by rapid change – examples from e‐commerce and also

changes in some sectors e.g. offices

• Number and variety of stakeholders in urban freight

• Speed of technology development and adoption

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Challenges driven by a lack of or limitations of knowledge/data

• Major challenge of information about smaller freight vehicles (below 3.5T). These smaller vehicles also used for service trips and

'commuting'.

• Combining information about vehicles and goods flows.

• Understanding commodity flows is difficult.

• Comparisons can be difficult as data is not collected in a consistent way.

• Definitions and terminology seem to be surprisingly non‐standard:

trip, tour, leg, journey, round, delivery, consignment, item, package etc

Gaps

• Practitioners and research (and among researchers)

• Urban freight modeling and research on policy and

business decisions. Policy research often fails to frame the question in a way that is relevant or interesting to those involved in modeling.

• Practitioner and policy‐maker desire for simple solutions and the need for these to be available in a short time.

• Combining solutions and interventions is important (packages of measures) but advice here is weak.

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Outline

• Challenges

Challenges and opportunibes

Opportunities

• Growing political interest

• SUMPs

• Data

Challenges

• Heterogeneity: flows, commodities

• Complex interactions stakeholders

• Cities and context

• Gaps between disciplines/interests

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Some more reflections

• Combining information will remain important

• Questions are not always very clear – maybe there are ways to support this

• Willingness to share – who will take the lead?

• Level of disaggregation will be important

• Is there a role for intermediaries ‘honest brokers’

Thank you

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Edited book on Urban Logistics published January 2019

https://www.koganpage.com/product/urban‐logistics‐9780749478711 Urban LogisNcs: Management, Policy and InnovaNon in a Rapidly Changing Environment

Michael Browne, Sönke Behrends, Johan Woxenius, Genevieve Giuliano, José Holguin‐Veras

Understand the importance of city infrastructure, transport planning and the implicabons for urban logisbcs with this in‐

depth, research‐based book.

Acknowledgements

Michael Browne

Professor of Logistics and Urban Freight Transport University of Gothenburg

Department of Business Administration School of Business, Economics and Law

Box 610, SE‐405 30 Gothenburg, Sweden email: [email protected] tel: +46 31 7866798

With acknowledgements to colleagues from the Urban Freight Platform, CoE Sustainable Urban Freight Systems and CoE MetroFreight.

However, any views and comments expressed in the presentation are those of the presenter – MichaelBrowne.

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1) Urban Freight Platform an initiative at University of Gothenburg and Chalmers supported by the Volvo Research & Educational Foundations (VREF):

http://www.chalmers.se/en/centres/lead/urbanfreightplatform/Pages/default.aspx VREF Urban Freight Conference, Gothenburg (17‐19 October 2018) Information and presentations at:

http://www.chalmers.se/en/centres/lead/urbanfreightplatform/vref‐

2018/Pages/default.aspx

2) Center of Excellence: Sustainable Urban Freight Systems (supported by VREF) for webinars and other information available see: https://www.coe‐sufs.org/

3) METROFREIGHT Center of Excellence (supported by VREF) for more information see:

http://priceschool.usc.edu/metrofreight‐the‐localglobal‐challenge‐of‐urban‐

transportation‐planning/

4) Why Goods Movement Matters ‐ by the RPA in collaboration with the VREF

http://www.vref.se/publications/researchsynthesisreports/researchsynthesisreports/w hygoodsmovementmattersbytherpaincollaborationwiththevref.5.1feeef8b156cfde87aa 3d60e.html

Interactive website: http://goodsmovementmatters.org

Links and further information

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TOI Oslo Science Park

Oslo 11 December 2019

‘If countries implement all their transport NDC pledges, transport CO₂ emissions in 2030 would still be about at

the level of 2015’

Update on the climate science and government commitments

gigatonnes of CO2e in 2030

Business as usual trend 64

Projected impact of current policies 60 COP21 Paris commitments best case 56 Limit for 2.0C temp rise by 2100 38 limit for 1.5C temp rise by 2100 26

Over 60 countries now committed to

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warehousing and terminals- 1-2%

administration / IT ? very hard sector to decarbonise

Heavy dependence on fossil fuel High forecast growth rate

Source: McKinnon (2019) ‘Decarbonizing Logistics’

3.3x increase trillion tonne-km

Projected growth in freight movement worldwide between 2015 and 2050

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24 gCO₂/ tonne-km average

carbon intensity 9 gCO₂/ tonne-km

but largely offset by 3.3 times growth in tonne-kms zero

emission target

20% improvement in routeing efficiency 30% modal shift road to rail Rail improves energy efficiency by 50%

and reduces carbon intensity of energy by 50%

30% increase in loading of laden vehicles

30% reduction in empty running

50% increase in truck energy efficiency

50% drop in carbon intensity of truck energy

Leveraging freight decarbonisation parameters to achieve a 6-fold reduction by 2050

achievable even in 30 years ? may not be able meet the absolute CO₂reduction target without restraining the growth

in freight movement

+

+ + + +

Should we be expanding infrastructural capacity to accommodate another 20 or 30 Reduction in carbon intensity needed to achieve 60% cut in total freight CO₂emissions

Meeting EU 2011 Transport White Paper CO₂Target for 2050

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1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 0

50

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 0

50 100 150 200 250

60% reduction cumulative emissions 2015‐2050: 34% lower

both meet 2011 Transport White Paper CO₂reduction target peak 2015

more gradual decline CO₂index 1990 = 100

need to embed concept of carbon budgeting in logistics strategies and policy-making

Ideal Scenario for Achieving Zero Carbon Logistics

• Decarbonise electricity generation

• Electrify all logistical activities

• Ensure there is enough zero carbon electricity to meet demand

Source: International Energy Agency (2019) 0

100 200 300 400 500 600

2010 2018

carbon intensity of electricity generation global average gCO₂/ kWh

-10%

International variation in carbon intensity of electricity generation

(46)

low carbon power (LCP) scenario June 2019

gCO2/ kWh

BP Energy Outlook 2019 edition

% of rail track electrified

Railways –the most electrified freight transport mode

half of freight moved on the rail network is in electrically-hauled trains (IEA 2019)

use of batteries and hydrogen fuel cells in freight locomotives to increase

% of rail freight electrically-hauled

Source: IEA (2019) Future of Rail

(47)

Hydrogen as the energy carrier of low carbon

electricity long distance trucking

disagreement on weight, size recharging time for batteries

10-12 tonnes for US Class 8 truck

400 kW per hour charging time 4-6 tonnes for US Class 8 truck 1600 kW per hour (Tesla) Sripad & Visvanathan, McKinsey etc Tesla, ETC* etc

battery power

energy losses so high never likely to be viable option

Bossel, Cebon etc IDDRI, ETC* etc

despite high energy losses, still viable decarbonisation option

3^rdoption: electrify the road network hydrogen fuel-cell truck

* Energy Transition Commission

BDI / Boston Consulting Group / Prognos study:

Recommends that 4000-8000 km of German autobahn network be electrified (out of 13000 km) Highway electrification: the e-Highway

60% of heavy truck CO₂emissions in Germany occur on only 2% of

road network 89% of truck trips after leaving highway have a length of 50km or

less.

Source: Siemens

(48)

ITF /OECD (2018) expert survey

Logistics will have to compete with other sectors for zero carbon electricity

Total electricity demand will increase 60% by 2040

Main increase in developing countries: ‐ population growth of 1.55 billion by 2040

‐ doubling of average income by 2040

‘New policies scenario’

electric cars increase from 3 million today to

300 million by 2040

switch from fossil fuel heating to electric heating in homes increases domestic electricity

demand by 45% by 2040 critical dependence of zero-carbon logistics scenario on expansion and transformation of electricity generation

Can ease this dependence by also decarbonising logistics in other ways:

• switch to biofuels

• improve energy efficiency of logistics

• shift freight to lower carbon transport modes

• improve vehicle loading

Will there be enough zero carbon electricity?

(49)

Life-cycle GHG emissions relative to diesel fuel

• limited supply of sustainable biofuels

• need refuelling infrastructure for gas

• methane leakage problem

• land requirements

Source: European Federation for Transport and Environment (T&E)

Waitrose supermarket chain (UK) 83% less CO₂on a WTW basis 1.2 year financial payback period

Improve Energy Efficiency in the Freight Transport Sector

vehicle technology: new build + retrofits

business practice: e.g. deceleration application of fuel economy standards:

vehicle operation:IT , training, monitoring

eco‐driver training

telematic monitoring

platooning automation

• upgraded drive-trains

• light-weighting

• low-rolling resistance tyres

• improved aerodynamics

EU: 15% less CO₂by 2025 30% by 2030

(50)

Net CO₂savings even after allowance made for modal shift and induced traffic

Supply chain collaboration e.g. Nestle and Pepsico in Benelux

kg CO2/ tonne of product

43.8

20.3

kg CO2/ tonne of product

source: Jacobs et al (2014)

Long term contribution of Physical Internet to logistics decarbonisation Deep decarbonisation needs greater sharing of logistics assets

Source: ALICE

Transforming EU freight modal split Average carbon intensity of freight transport modes:

gCO2/ tonne‐km

Data source: DEFRA (2017)

road

rail

inland waterway Decline in fossil fuel traffic –difficult to replace with other commodities Carbon intensity of road freight falling faster than rail freight – narrowing the gap

(51)

Phasing out fossil fuels reduces amount of coal, oil and gas to be moved Fossil fuels = 41% of maritime trade (UNCTAD, 2017)

Substitution of alternative energy sources

Constructing renewable energy infrastructure of wind turbines, solar farms and hydro‐electric dams is material‐ and transport‐intensive

necessarily minimise life cycle emissions

Circular economy:

Increase recycling and remanufacturing

Digitisation of physical products:

convert freight consignments into electrons Design products with less material:

miniaturisation, lightweighting

3D Printing:

Reduce the amount of stuff to be moved - Improve ‘material efficiency’

Share economy:

Ownership to multiple useage

Logistics Transport Focus (Oct 2018)

?

(52)

Advances in vehicle routeing and scheduling Big data, predictive analytics etc

Supply chain applications of Blockchain cloud computing, software-as-a-service

Data pooling

combined impact on road freight CO₂emissions ?

platooning

electrified highways

urban freight consolidation aerodynamic profiling eco‐driver training physical internet

hydrogen fuel cells

hybridisation synchromodality

down‐speeding high capacity transport

predictive analytics

anti‐idling lightweighting

low rolling resistance smart cruise control

vehicle automation

online load matching

biofuels

vehicle telematics preventative maintenance pollution‐routeing

delivery rescheduling

supply chain collaboration battery‐powered vehicles

natural gas vehicles nominated day delivery

ease of implementation CO2 abatement potential

low high

low

high technological development operational /managerial / regulatory development

Freight decarbonisation measures: CO₂abatement – implementation graphs

ease of implementation

‐ difficult to quantify potential carbon savings from logistics management options

‐ past experience discouraging: trends in empty running, vehicle load factors, modal shift etc Technology and energy supply bias: under‐estimation of the possible logistics contribution

(53)

adaptation and population resettlement

climate‐induced disruption

23

10 Conclusions

1. Logistics will be a very difficultsector to decarbonisation completely

2. Electrification with zero carbon electricity will be a major decarbonisation pathway

3. Electrification option available mainly to road and rail: little prospect of ships and aircraft being electrified by 2050

4. Electrification of surface modes will require large capital investment in overhead cabling and big improvement in battery performance

5. Need other supporting decarbonisation initiatives to reduce dependence on low carbon electricity 6. Combination of energy efficiency gains, better vehicle loading and modal shift can substantially reduce

the energy demands of logistics

7. Reductions in the carbon intensity of freight transport may be offset by increases in transport demand.

8. Some of this demand will be generated by need to adapt to climate change and capture greenhouse gases already in the atmosphere.

9. Given its critical role in maintaining human welfare and climatic adaptation, logistics may have to be exempted from zero‐carbon targets

10.Nevertheless, must maintain pressure to minimise logistics‐related emissions

(54)

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www.alanmckinnon.co.uk

@alancmckinnon

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