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Comparative analysis of the carbon footprints of conventional and online retailing: A “last mile” perspective


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Purpose The purpose of this paper is to focus on the carbon intensity of “last mile” deliveries (i.e. deliveries of goods from local depots to the home) and personal shopping trips. Design/methodology/approach Several last mile scenarios are constructed for the purchase of small, non‐food items, such as books, CDs, clothing, cameras and household items. Official government data, operational data from a large logistics service provider, face‐to‐face and telephone interviews with company managers and realistic assumptions derived from the literature form the basis of the calculations. Allowance has been made for home delivery failures, “browsing” trips to the shops and the return of unwanted goods. Findings Overall, the research suggests that, while neither home delivery nor conventional shopping has an absolute CO 2 advantage, on average, the home delivery operation is likely to generate less CO 2 than the typical shopping trip. Nevertheless, CO 2 emissions per item for intensive/infrequent shopping trips by bus could match online shopping/home delivery. Research limitations/implications The number of items purchased per shopping trip, the choice of travel mode and the willingness to combine shopping with other activities and to group purchases into as few shopping trips or online transactions as possible are shown to be critical factors. Online retailers and home delivery companies could also apply measures (e.g. maximising drop densities and increasing the use of electric vehicles) to enhance the CO 2 efficiency of their logistical operations and gain a clearer environmental advantage. Practical implications Both consumers and suppliers need to be made more aware of the environmental implications of their respective purchasing behaviour and distribution methods so that potential CO 2 savings can be made. Originality/value The paper offers insights into the carbon footprints of conventional and online retailing from a “last mile” perspective.
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Comparative analysis of the
carbon footprints of conventional
and online retailing
A “last mile” perspective
Julia B. Edwards, Alan C. McKinnon and Sharon L. Cullinane
Logistics Research Centre, Heriot-Watt University, Edinburgh, UK
Purpose The purpose of this paper is to focus on the carbon intensity of “last mile” deliveries (i.e.
deliveries of goods from local depots to the home) and personal shopping trips.
Design/methodology/approach Several last mile scenarios are constructed for the purchase of
small, non-food items, such as books, CDs, clothing, cameras and household items. Official government
data, operational data from a large logistics service provider, face-to-face and telephone interviews
with company managers and realistic assumptions derived from the literature form the basis of the
calculations. Allowance has been made for home delivery failures, “browsing” trips to the shops and
the return of unwanted goods.
Findings Overall, the research suggests that, while neither home delivery nor conventional
shopping has an absolute CO
advantage, on average, the home delivery operation is likely to generate
less CO
than the typical shopping trip. Nevertheless, CO
emissions per item for intensive/infrequent
shopping trips by bus could match online shopping/home delivery.
Research limitations/implications The number of items purchased per shopping trip, the
choice of travel mode and the willingness to combine shopping with other activities and to group
purchases into as few shopping trips or online transactions as possible are shown to be critical factors.
Online retailers and home delivery companies could also apply measures (e.g. maximising drop
densities and increasing the use of electric vehicles) to enhance the CO
efficiency of their logistical
operations and gain a clearer environmental advantage.
Practical implications Both consumers and suppliers need to be made more aware of the
environmental implications of their respective purchasing behaviour and distribution methods so that
potential CO
savings can be made.
Originality/value The paper offers insights into the carbon footprints of conventional and online
retailing from a “last mile” perspective.
Keywords Carbon, Delivery services, Internet shopping, Distribution channels and markets,
Air pollution
Paper type Research paper
1. Introduction
Some online retailers have been actively claiming that internet shopping yields
environmental benefits (Smithers, 2007). Equally, consumers seem to have a widely held
view that online purchases and home delivery are beneficial to the environment because
they reduce personal travel demand (Royal Mail, 2007). Such opinions are also prevalent
among researchers. For instance, Rotem-Mindali and Salomon (2007, p. 178) point out that:
[...] studies of the impacts of teleshopping on transport usually assume that the delivery trip,
by the retailer or a third party, to multiple customers is more efficient than individual trips.
The current issue and full text archive of this journal is available at
Analysis of the
carbon footprints
Received March 2009
Revised August 2009
Accepted October 2009
International Journal of Physical
Distribution & Logistics Management
Vol. 40 No. 1/2, 2010
pp. 103-123
q Emerald Group Publishing Limited
DOI 10.1108/09600031011018055
To date, however, little research has tested the claims that online retailing is
environmentally superior. An early paper by Matthews et al. (2001), which compared
the environmental impact of online and conventional book retailing in the USA, offered
some support for this view. A review of the academic literature revealed little relevant
research on this issue since the early 2000s.
This paper sheds new light on this subject. It focuses on the so-called “last mile” (i.e.
the last link in the supply chain to the home) and compares the level of carbon
emissions[1] from a conventional non-food shopping trip with those of delivering
non-food items to the home. It is based on research in the UK, where online retailing
has now captured around 17 per cent of total retail sales (IMRG/Capgemini, 2008).
Transport at the local level is not just the most visible; it can also be the most
energy-intensive. Browne et al. (2008) note for conventional shopping that personal
shopping trips can use more energy than the entire upstream supply chain, even when
production is included. Several past studies have examined the last mile delivery
(European Information Technology Observatory, 2002; Abukhader and Jo
nson, 2003;
Sarkis et al., 2004; Farag et al., 2006), although none have systematically compared
consumer travel with freight delivery in terms of energy expenditure and CO
emissions per delivery drop/item bought.
This paper is structured as follows: first, we briefly examine previous work on the
relative environmental effects of online and conventional shopping. This includes a
comparison between the last mile and upstream supply chain activities to determine
the importance of last mile operations relative to emissions from end-to-end supply
chain activities. This is followed by a review of the data sources used and discussion of
the assumptions underpinning our comparative model. Various home delivery and
shopping trip scenarios are presented in the main results section. In addition to
summarising the main findings, the conclusions explore some of the wider issues
raised by the research.
2. The environmental effects of online versus conventional shopping
Probably, the greatest difference between online and conventional shopping can be
seen in the fulfilment and distribution processes required to meet customer
expectations (de Koster, 2002). In the traditional shopping model, customers do most
of the labour-intensive work (such as order-picking and transporting the goods home),
whereas in e-fulfilment, retailers must deliver personalised orders to highly dispersed
locations within relatively narrow time windows.
Concerns have been expressed about the steep increase in home deliveries, some of
them relatively inefficient, which reduce the net benefit of online retailing (Romm, 1999;
ger et al., 2003). Early research suggested that, although car-based shopping trips
could be reduced by as much as 10 per cent as a result of internet shopping, more
research was needed to assess possible environmental disbenefits (DTLR, 2002). These
might result from:
the fragmentation of the fulfilment process with consumers making more
frequent purchases of relatively small quantities of goods, often from several
different web-based companies;
other car-based out-of-home activities being undertaken by either the car owner
or other household member in the time saved by online shopping;
the net increase in the total amount of material consumption; and
additional transport created by failed deliveries, when no one is at home, and the
return of unwanted goods.
Much of the previous research comparing online and conventional shopping has
concentrated on the grocery retail sector (Cairns et al., 2004; Cairns, 2005; Foley et al.,2003;
Gould and Golob, 1997). In the traditional grocery supply chain, goods are delivered to
store where the customer picks the items before taking them home. For e-grocery,
however, there are three scenarios: picking and distribution from existing stores; direct
home delivery from a dedicated fulfilment (or “pick”) centre and home delivery from a
central warehouse via a satellite depot (van der Laan, 2000; Agatz et al., 2006). The most
successful UK model to date has been order-picking and distribution from existing stores
(Hackney et al., 2006). Punakivi (2003) noted a considerable traffic reduction (of between 54
and 93 per cent depending on delivery method) when e-grocery replaced car-borne
shopping trips to supermarkets in Finland. Further savings can be achieved when grocery
reception boxes are used instead of attended delivery (where the consumer must be at
home to receive the goods). Ka
inen et al. (2001) recorded home delivery distance
savings of over 50 per cent from the use of grocery reception boxes in Finland.
While most of the research to date has concentrated on the “last mile” stage, a few
studies have compared the energy consumption of consumer travel and home delivery
with energy use further upstream in the supply chain. Jespersen (2004) conducted
telephone interviews to establish consumer travel behaviour when purchasing rye
bread from shops. Assumptions were made about trip chaining[2] (50 per cent of an
average 5 km trip was for shopping) and the weight of goods purchased (20 kg). His
findings revealed that the amount of energy consumed by the customer’s trips to and
from the shop was greater than the energy used in all the other transport associated
with the production and distribution of the bread. Browne et al. (2006), in investigating
the various stages of the production and distribution of jeans, observed that the energy
used for a dedicated consumer shopping trip (of 11 km) was approximately the same as
that used in transporting the product from the jeans factory (based in the USA or
Turkey) to the UK port, despite the huge differences in journey lengths. Similarly,
Weber et al. (2008), when comparing the energy use and CO
emissions generated by
both the online and conventional distribution of an electronic flash drive, found that
approximately 65 per cent of total emissions for traditional retailing came from the
customer trip to and from the retail store.
These studies not only highlight the differences in carbon intensity across the
“end-to-end” supply chain, but they also show how transport energy and emission
calculations are dominated by last mile operations. They, therefore, provide
justification for our decision to focus on this last link in the retail supply chain.
3. Research approach
This paper presents the results of a comparative study of the CO
emissions from home
deliveries and conventional shopping trips in the non-food retail sector. The focus is on
small non-food products such as books, CDs, clothing items and electronic devices,
which, because of their physical characteristics, are responsible for very similar
amounts of energy use and emissions when transported in freight vehicles, cars or
public transport. In this analysis, they are considered to be identical in terms of their
Analysis of the
carbon footprints
transport-related carbon footprint. The paper examines the carbon emissions from
transporting them solely at the “last mile” stage in the supply chain (from store to
home or local depot to home). Using published UK Government statistics and primary
data from one of the UK’s largest home delivery companies, it has been possible to
model the amounts of CO
emitted by conventional and online purchases of small
non-food items. An Excel spreadsheet was constructed for this purpose.
The emission factors for home delivery operations by diesel- and petrol-fuelled vans
were obtained from four statistical sources and averaged:
(1) Defra’s (2008)emissions factors for vans[3].
(2) National Atmospheric Emissions Inventory (2008) emissions factors for vans: data
for Euro II vehicles, and speeds of 40 kph (default speed), 20 kph (representative of
average urban speeds) and 10 kph (worst-case scenario) are applied.
(3) RHA Cost Tables, 2008: emissions factors are calculated from Defra values,
based on average fuel consumption of 9.6 km per litre (27 miles per gallon) for a
van (Road Haulage Association, 2008).
(4) Freight Transport Association (2007) distribution costs 2008: emissions factors
are calculated from Defra values, based on average fuel consumption of 8.9 km
per litre (25 miles per gallon) for a van.
This averaging ensured both consistency and reliability in the calculations.
Several last mile scenarios are proposed based on publicly available data,
face-to-face and telephone interviews with practitioners in industry and the results of
previous studies. Sensitivity analyses have been performed to assess the impact of
varying key parameters. Acquiring qualitative insight from practitioners helped to
verify the robustness of data obtained from the home delivery company.
Average emission factors for car and bus journeys (expressed as CO
per km
travelled) to the shops were found in Defra (2007). In the case of cars, additional
calculations are made for specific vehicle exercise duty (VED) bands, particularly for
low-emissions vehicle (Band A), a hybrid vehicle (Band B) and a high-emissions vehicle
(Band G). Band-specific emissions have been sourced from the Vehicle Certification
Agency’s records (
4. Modelling assumptions
4.1 Online shopping
Methods of delivery. The vast majority of online purchases result in the physical
movement of a small package (or single item) to an individual address (typically a
consumer’s home) by parcel carrier (RAC Foundation, 2006; Retail Logistics Task
Force, 2001). In general, these deliveries are distributed from local parcel carrier depots
and consist of mixed loads in the back of vans. Volumes delivered are high: the leading
parcel delivery carrier in the UK delivers some 300,000 parcels daily. Concern has been
expressed about the environmental repercussions of this expanding home delivery
market (Webster, 2007). Total mileage travelled by vans has risen by 40 per cent over
the past ten years in the UK, partly reflecting the growth in online retailing
(Department for Transport DfT, 2009c). Vans also have relatively high
carbon-intensity, expressed as g of CO
per tonne km (McKinnon, 2007), particularly
as much of their mileage is run on urban roads. For this reason, there has been
increased interest in the use of electric vehicles for home delivery, especially in the
online grocery sector. Sainsbury’s (2007), for example, plans to convert its entire online
grocery delivery fleet to electric vans by 2010.
Vans are not the only delivery vehicles employed by parcel delivery companies. The
use of self-employed couriers has been on the increase recently (Beveridge, 2007).
Several leading online retailers now use third-party courier networks for deliveries.
These deliveries are generally made by private cars and are much shorter than typical
van-based delivery rounds. As courier rounds have different delivery and vehicle
characteristics, they have been excluded from the analysis.
Traditionally, vehicle load factors have been measured with respect to weight. For
vans, in the home delivery sector, the number of drops per round is more representative
of vehicle utilisation than the total weight of the consignments. Rather than
considering vehicle fill as a percentage of maximum permissible weight, parcel
delivery companies are concerned with achieving high-drop density rates per round by
maximising the number of deliveries, a key productivity measure in the home delivery
sector. The parameters of vehicle fill and empty running are not therefore included in
this analysis. All delivery drops are treated equally, regardless of when in the round
they are actually delivered. This approach may be criticised, as those deliveries
dropped first, it could be argued, should be apportioned less CO
than those items
delivered later in the round. While correct in theory, assigning emissions based on the
sequencing of delivery drops would be an almost impossible task.
Home delivery companies do not normally adopt a strategy of dropping-off the
heaviest (or bulkiest) loads first. It is clear from observations of loading practices by
van drivers and discussions with depot managers that customer location is the main
determinant of the loading/unloading sequence. Therefore, across the range of small,
non-food consumer products typically bought online, the physical nature of the
products has little effect on the energy intensity and carbon intensity of the delivery,
i.e. weight/density are not significant. The main variable is the number of drops per
round. Given the granularity of this analysis it is not necessary to distinguish between
specific product types within the general category of small non-food items, which can
be collected by consumers from high-street shops.
Two other factors are likely to have a greater influence on the level of CO
emissions: the chances of making a successful deliver first-time and the nature of the
returns process for unwanted/damaged goods. These will be considered next.
Incidence of first-time failed delivery. It has become more common for people not to
be at home during the working day when most home deliveries are made. Prologis
(2008) reported that the number of working households increased by 22 per cent
between 1992 and 2006. As a result, parcel carriers must cope with increasing incidence
of failed delivery. Actual failed delivery rates among carriers vary considerably.
Beveridge (2007), a leading consultant in the home delivery market with wide
experience of managing last-mile delivery networks, indicated a range between 2 and
30 per cent, depending on the carriers’ policies for dealing with “no-one-at-home”. Some
parcel delivery companies achieve very high first-time delivery rates as they are
prepared to leave deliveries in alternative locations, such as with neighbours or in the
garden shed (McKinnon and Tallam, 2003). While these places are often insecure, the
use of dustbins is now generally avoided owing to earlier reported mishaps! Other
carriers require proof-of-delivery signatures, and consequently have a much higher
Analysis of the
carbon footprints
delivery failure rate. As a result of different delivery arrangements, estimates of
first-time delivery failure rates vary widely from six out of every ten small-package
deliveries (Retail Logistics Task Force, 2001) to a more conservative one in eight
(IMRG, 2006). This study uses three failed delivery ratios. First, a first-time failure rate
of 25 per cent of deliveries, in line with findings by McLeod and Cherrett (2006) and
Song et al. (2009); second, a 12 per cent failure rate (assumed by Weltevreden and
Rotem-Mindali (2008), and based on IMRG (2006) findings), and finally, a very
successful first-time failure rate of 2 per cent, achieved by parcel companies whose
delivery drivers seek alternative locations at which to leave items.
The return of unwanted goods. Customers return items for a number of different
reasons. They may, for instance, be the wrong product, because of errors in order
picking, unsuited to the consumers needs, or damaged in transit. Online retailers also
have widely varying returns policies from unconditional money back guarantees to
store credit only to no refund whatsoever (Mukhopadhyay and Setoputro, 2004).
Typically, between 25 and 30 per cent of all non-food goods bought online are returned
(de Koster, 2002) compared with just 6-10 per cent of goods purchased by traditional
shopping methods, though this varies widely among product groups and probably
geographically (Nairn, 2003; Fernie and McKinnon, 2009).
The environmental implications of these online returns are strongly influenced by
both parcel carriers’ returns policies and consumers’ preferred habits. Parcel carriers
who collect returned items as part of their usual delivery round generate very little
additional mileage. In these cases, an allowance is made for collections within planned
delivery drop-rates, and any additional energy use is subsumed within the overall
delivery round. On the other hand, some delivery companies send vans on separate
pick-up runs dedicated solely to collecting returned items. Consideration of this
dedicated collection process is treated separately.
The situation is complicated further by customers often having a choice of returns
channels. For retailers with a high-street presence, customers may choose to return
items to a physical store. The popularity of this method depends on the number of
high-street stores operating such a returns policy. For instance, a high percentage of
online supermarket clothing returns are handled through supermarkets, whereas some
multi-channel retailers have very little returned to stores owing to their relatively
sparse high-street presence.
Alternatively, customers can send items back through the standard postal service.
Where there is a choice between parcel carrier or postal services, approximately half of
returns are via carrier collection and half by post (Beveridge, 2007). Some high-street
retailers find that half their returns are to stores, and the remaining half-split between
carrier collection and the post. The model takes account of these different returns options.
4.2 Conventional shopping
There is no such thing as a “typical” high-street shopper. In creating characteristic shopper
profiles consideration needs to be given to several key questions: how people travel to the
shops, how frequently they shop, what they buy and in what quantities they purchase
goods. Finding general answers to these questions is difficult owing to a lack of
behavioural data at the consumer level (Rotem-Mindali and Salomon, 2007). Some retailers
undertake their own customer surveys, but are usually reluctant to release the results.
Our analysis has relied mainly on government statistics available at the national level.
Dedicated shopping-only trips. The National Travel Survey, undertaken by the DfT,
collects data on personal travel behaviour over time, which allow comparison between
food and non-food shopping trips. Table I lists the average distances travelled for
shopping by different transport modes. The National Travel Survey defines a trip as a
one-way journey with a single main purpose, with outward and return halves of a
return trip treated as two separate movements (DfT, 2009b). Therefore, an average
dedicated shopping trip would require a doubling of the distances shown in Table I.
Average distances travelled for non-food purchases are longer than for food shopping
trips, at 6.4 miles for car travel (car driver) and 4.4 miles for bus travel (DfT, 2009a).
These distances are used to represent average shopping trips.
Car and bus travel are the two motorised transport modes most used by
conventional shoppers, accounting for 72 per cent of all shopping trips (DfT, 2009a),
and as such, are the only modes considered in this paper. Rail is omitted, as it is not a
regular mode for shoppers (less than 1 per cent of shopping trips are by rail) (DfT,
2009a). Walking and cycling have been also been excluded from the calculations, as
both modes involve human effort (a category excluded from typical life cycle
assessments), and neither emits easily attributable CO
emissions. The environmental
and social benefits of both are acknowledged, however.
Combined and/or browsing-only shopping trips. Trip chaining is a widely used term
to describe a combined trip. Although having no agreed definition, it can be described
as a household’s tendency to combine different activities during a single trip (Popowski
Leszczyc and Timmermans, 2001), with a trip segment representing the travel between
a particular pair of activities (Primerano et al., 2008). Often, minor detours to a store are
incorporated into a trip made primarily for some other purpose, adding only
marginally to the total distance travelled.
As Brooks et al. (2008, p. 29) state: “the high incidence of multi-stop trips in
empirically observed behaviour makes the single-stop assumption unrealistic”; most
trips for shopping involve multi-stop activities either between different stores or
different activities, including shopping (i.e. from work to home, calling at shops on the
way). In such cases, the allocation of energy consumption related to the purchasing
activity needs to be reduced accordingly (Browne et al., 2008).
Usually, consumers visit more than one shop per trip especially when shopping for
non-food products (Brooks et al., 2008). Establishing the number of items consumers
buy on each trip is far more problematic as individual retailers only have information
Mode Average distance (miles)
Walk 0.7
Car/van (driver) 6.4
Car/van (passenger) 8.3
Other private 4.3
Local bus 4.4
Other public 12.5
All modes 5.4
Note: 2005-2006 (one-way)
Source: Derived from DfT (2009c) Personal communications: National Travel Survey
Table I.
Average trip length for
non-food shopping by
main mode
Analysis of the
carbon footprints
about the number of products bought in their own stores, and not as part of the
shopping trip as a whole. It seems that no information is collected about the overall
quantities of goods bought per shopping trip. Therefore, in the analysis reported here,
we have had to estimate a range of values for this critical variable. It would clearly be
preferable to have empirical data on the number and types of item bought on the
shopping trips. In the absence of this information, however, calculations based on
theoretical values still allow cross-channel comparisons of a “what if [...]” nature.
It must also be remembered that some shopping trips do not result in a purchase.
Some trips to the high street may be for information-gathering purposes only. This
“browsing” category has been largely ignored by researchers owing to a lack of data
(Moe and Fader, 2001), yet frequently a fact-finding visit results in a later purchase
(often online) (Skinner et al., 2004).
4.3 Specification of the model
Online shopping: delivery rounds and drop characteristics for non-food. From
face-to-face and telephone interviews with logistics managers, local depot supervisors
and delivery van drivers from four different leading parcel delivery companies, we
established that a:
highly efficient home delivery operation would have a drop density of
approximately 150 drops on a 60 mile delivery round; and
city centre-focused round would usually cover about 25 miles and comprise
approximately 110 drops on average.
Given these operational characteristics and for ease of comparison, this study
examines an average delivery round by a van, which we assume consists of 120 drops
on a 50 mile round.
It is assumed that each package delivered as part of this representative home
delivery round weights less than 25 kg (the maximum permissible weight for a
one-man delivery). Equally, no distinction is made between the different types of
products delivered; as all items are treated equally in the delivery process.
Calculations of the number of items per drop have been performed. Initial results are
shown for a single item per drop. However, a more realistic assumption, based on
discussions with a leading book wholesaler, is for each drop to contain either 1.4 items in
the case of deliveries containing books/DVDs/CDs or 2.5 items for other non-food goods
(e.g. clothes and household items) (Beveridge, 2009). Therefore, additional calculations
for multiple items per drop are also included. Some online retailers have a dispatch
policy where they delay distribution until all items purchased are available for delivery,
while others prefer to send one item per package regardless of the number of goods
ordered at the time of transaction. For direct comparison with conventional shoppers’
behaviour, CO
emissions have been calculated on an item basis. The assumptions made
about “last mile” delivery are listed in Table II and represent the expert knowledge of
those working in the industry or are derived from previous work in this area.
Conventional shopping: personal travel. The average car driver makes a round trip
of 12.8 miles for non-food shopping purposes (DfT, 2009a). For bus passengers, the
average return journey to the shops for non-food items is slightly less at 8.8 miles (DfT,
2009a). The consumer travel behaviour characteristics assumed in the model are listed
in Table III.
5. Discussion
When focusing exclusively on the last link in the retail supply chain (from depot or
shop to the home), home delivery by parcel carrier is often presumed to be more
efficient than an individual travelling to the shops to buy the item in person. The
results in Table IV appear to support this supposition. Typically, one drop of 120 such
drops on a 50 mile delivery round is apportioned 181 gCO
. This figure has been
derived from the four freight emissions factors outlined in Section 4 and is a drop’s
“share” of the average emissions produced by the overall delivery trip (21,665 gCO
Assumptions Type of delivery round
Total distance
Drop density
(deliveries per round)
Van (, 3.5-t) Average 50 120
Efficient 60 150
Failed first-time deliveries (per cent) 25
Returns (percentage of orders) 25 (40 for clothing)
Method of return Collection
Postal services
Table II.
Freight “last mile”
delivery: assumptions
Mode Round trip (miles)
Car 12.8
Bus 8.8
Browsing (as percentage of all shopping trips) 10 (average)
20 (clothes)
33.3 (furniture)
Trip chaining (percentage of mileage attributed to
shopping) 50
10 (only applies to trips by car)
Returns (percentage of all purchases) 8
Table III.
Consumer travel and
shopping behaviour:
Delivery/collection method
Total gCO
per item delivered/
Standard delivery van (, 3.5-t) (120 deliveries 21,665 g 181 g (drop)
per 50 mile round trip) 137 g (1.4 items)
72 g (2.5 items)
Car (dedicated shopping trip of 12.8 miles) 4,274 g 4,274 g (single item)
Bus (dedicated shopping trip of 8.8 miles,
assuming average patronage
) 11,641 g 1,265 g
Note: Parcel carrier/car/bus
Defra (2007)
Table IV.
per average trip and
per drop/item
Analysis of the
carbon footprints
Assuming that a shopper, using a standard car, makes a round trip of 12.8 miles to the
shops solely for the purpose of buying one item, the trip would generate 4,274 gCO
of which could be assigned to that one item). In this example, the CO
from personal
car-based travel is 24 times greater than the CO
produced by a single drop within the
average home delivery round.
An alternative way of interpreting these results is to say that a person would need
to buy 24 non-food items in one standard car-based trip for this method of shopping to
be less CO
intensive than having one non-food item delivered (on the first attempt) to
their home by a parcel carrier. For a VED Band A vehicle (99 gCO
/km), 12 non-food
items would need to be purchased and for a mid-range Band G vehicle 31 items
(270 gCO
km). A bus passenger, assuming average bus occupancy levels of 9.2
passengers for an 8.8 mile round bus trip, would need to purchase seven or more
non-food items to compete favourably with a home delivery in terms of carbon
The above calculations assume one item per drop for home delivery and only one item
per shopping trip. Although some deliveries to the home do only contain one item (some
online retailers only send items out individually regardless of order size), it would be more
realistic to increase the “items per drop” variable. With an average content of 1.4 items per
drop (e.g. a typical book order) the CO
per item is reduced to 137 g for home delivery.
When a home delivery (e.g. for clothing and household goods) consists of 2.5 items, the CO
per item is 72 g. These assumptions further increase the number of goods a conventional
car-based shopper would have to buy in one trip to 32 or 59 non-food items, respectively, to
contend with home delivery in terms of CO
efficiency. For bus travel, a shopper would
have to carry ten or 18 non-food items, respectively.
Although home delivery appears to have a strong environmental advantage over
consumers’ personal travel to the shops, this result requires several qualifications.
The investigations only compare theoretical delivery trips based on average values.
The last mile delivery is much more complex than these initial findings suggest
(Figure 1). For instance, a standard home delivery will vary by failed delivery rates (the
number of failed first-time deliveries); distances covered (including type of road
network), and the method by which unwanted items are returned. These variants will
be examined in the Section 5.1.
5.1 Effects of varying home delivery parameters
Failed first-time delivery rates. Failed delivery is both uneconomic for the carrier and
inconvenient for the shopper. Various failed delivery scenarios are considered, based
on the following:
(1) A 2 per cent first-time failure rate, achieved by van-based parcel delivery
carriers who accept alternative drop-off arrangements when no-one is at home
for first-time delivery.
(2) A 12 per cent first-time failure rate, quoted by IMRG (2008) and considered to be
an average to good failure rate.
(3) A 25 per cent first-time failure rate, often experienced by those carriers
requiring proof of delivery signatures. It was also the proportion of failed
first-time deliveries noted by McLeod and Cherrett (2006) and Song et al. (2009)
(Table V).
Figure 1.
The online retail channel:
delivery options
Va n
Round type
Vehicle type
Low emissions Electric
Drop density
Items per package
Failure rate
Returns method
Customer returns
Store Post Carrier
Delivery type
Un attended
100% successful first-time
2% failure
12% failure
25% failure
Van-based deliveries: gCO
per item 181 g 185 g 203 g 226 g
Table V.
Emissions (gCO
) per
item including failed
delivery rates
Analysis of the
carbon footprints
Emissions of CO
per average drop increases from 181 g for a successful, first-time
delivery to the worse-case scenario of an average 226 g per drop when one-in-four
deliveries fail.
Most delivery companies schedule the repeat delivery for the next working day after
the first-failed attempt, and as a result a high percentage of second attempts also fail,
compounding the effects of the initial failed delivery. After a second failed attempt
non-delivered goods are held at the local depot, and “carded” customers (those
receiving a failed delivery card through the letterbox) have to visit the depot in person
to collect the item. Around 3 per cent of home delivery recipients make a trip to collect
an item left at a post-office, depot or outlet DfT (2009b).
Returns. The returns process for unwanted goods can take a number of forms.
When a parcel carrier schedules collections into an outbound delivery round the gCO
per collection/item is effectively the same as per delivery. However, when alternative
arrangements are made (either on the part of the consumer or the carrier) more
complicated calculations are necessary. These are examined in Section 5.2.
5.2 Effects of varying shopping trip parameters
Consumer travel and shopping behaviour. The model also captures much of the
variability in consumer shopping behaviour. Some shoppers make dedicated trips to
shops when shopping is their only intention, while others may choose to combine
shopping with other activities as part of a trip chain. Additionally, both online and
conventional shoppers frequently choose to inspect items in stores (prior to buying
either in-store or online), and may make several trips to do so. When shoppers wish to
return unwanted items, they often have a choice of returns methods. Figure 2 shows an
indication of some of the choices available to the conventional shopper.
A certain number of shopping trips will end in no purchase, owing to the:
consumer failing to decide which item to buy;
particular good sought being unavailable; or
consumer having no intention to purchase anything, using the trip for
information gathering purposes only.
In these cases, the unsuccessful trip needs to be factored into the calculations. On the
“realistic” assumption that one in ten shopping trips for a particular product results in
no immediate purchase, the gCO
in each of the above-dedicated shopping trips would
increase by a factor of 1.1. Nevertheless, at a personal level, a shopper’s CO
footprint would be twice the amount to take account of a second (later) journey to the
shops. So, while total emissions for a “browsing plus purchase” average car trip would
be 4,701 gCO
(4,274 gCO
£ 1.1), for the individual undertaking the second journey it
would be 8,548 gCO
Furthermore, a consumer may choose to acquire the item as part of a larger shopping
expedition when many items are bought, and/or to combine the shopping trip with other
activities (Table VI). The combined trip, in this instance, realistically assumes that
shopping-related mileage is a quarter of the overall trip mileage (25 per cent).
In Table VI, it can be seen that the most efficient ways to purchase and collect a product
would be either as part of a much larger shopping trip when many items are bought at the
same time or where shopping is incorporated into trips made principally for another
purpose. Any consolidation of shopping activities clearly reduces their carbon intensity.
Bus travel can compete with home delivery in terms of CO
efficiency. During peak
leisure times (e.g. on a Saturday afternoon), when occupancy levels are high and most
non-food shopping occurs, from an environmental point of view, bus travel is an
effective method of collecting shopping. For example, assuming a shopper travels the
average distance (8.8 miles) by bus, in the company of 29 other passengers, and buys
five items, each purchase would be allocated a share of just 78 gCO
, less than half that
Figure 2.
The conventional retail
channel: consumer choices
Trip length
Vehicle type
Low emissions Electric
Items bought
Trip type
Products purchased
Analysis of the
carbon footprints
for a typical home delivery (181 gCO
). Encouragingly, from an environmental point of
view, most shoppers (63 per cent) state that they would have no difficulty getting to the
shops by public transport (DfT, 2005).
Returns. The actual gCO
per online order is very sensitive to the proportion of
products returned and the method of return. Two scenarios are considered:
(1) Where the unwanted item is collected on a subsequent delivery round. In this
case, the integrated returns collection is allocated 362 gCO
(twice the CO
of an
outbound drop), as the unwanted item has the combined emissions of an
outbound and return trip (in effect two outbound deliveries).
(2) Where the consumer returns the item to a high-street store. In the case of an
online shopper making a separate car trip to return the item, the CO
would be
4,522 gCO
(181 gCO
þ 4,341 gCO
), calculated on an average car-based round
trip (13 miles). This is clearly the worst-case scenario. The marginal CO
could be greatly reduced by returning the item as part of another shopping trip
or by “trip chaining”.
5.3 CO
emissions: last mile versus upstream activities
It is not only on the last link that the online and conventional retail channels vary, but
the structure of their upstream supply chains also differ and this too will affect their
relative carbon footprints. Ideally, one should compare the carbon intensity of the two
channels as far back as the point in the supply chain at which they diverge because up
to this point the amount of CO
emitted will be common to both channels (Figure 3).
This would allow us to put differences at the carbon intensity of last mile operations
into context.
Trip type Items bought
Mode of
Dedicated Single item (one item) Car 4,274
Electric car 1,586
Bus 1,265
Multiple purchase Car 855
(five items) Electric car 317
Bus 253
Browsing (two trips to shops: one for Single item (one item) Car 8,548
browsing, one for purchase) Electric car 3,172
Bus 2,530
Combined (shopping 25 per cent of trip Single item (one item) Car 1,069
mileage) Electric car 397
Bus 316
Combined then dedicated (25 per cent of Car 5,343
mileage: initial browsing followed by Electric car 1,983
dedicated trip to buy an item) Bus 1,581
Combined (grocery shopping: distance 7.12
) Multiple (50 items) Car 48
Average round trip distance to a supermarket (Future Foundation, 2007)
Table VI.
Implications of shopping
trip type on CO
The calculations in this paper and available published evidence suggest that emissions
from car-based shopping trips can far exceed those from distribution operations back
along the supply chain. It is likely, therefore, that the environmental comparison of online
and conventional shopping channels will be dominated by what happens at the local level.
Differences in CO
emissions between car-borne shopping trips and home deliveries are
likely to be much more important determinants of the respective carbon footprints of
online and conventional shopping than differences in upstream logistical operations as far
back as the point at which the two distribution channels diverge.
6. Conclusions
This study summarises the results of a comparative study of CO
emissions for the
home delivery and conventional shopping trips. While this so-called “last mile” has
received considerable attention from researchers, none of the previous studies have
attempted such a comparison on per trip, drop or item basis. Several scenarios were
investigated, and wherever possible representative values, derived from national
statistics, previous research or industry practice, were applied to different freight and
consumer trips.
Numerous factors influence emissions from home deliveries. They include: drop
densities; the distance and nature of the delivery round; the type of vehicle used;
and the treatment of failed deliveries and returns. On average, when a customer buys
fewer than 24 items per shopping trip (or fewer than seven items for bus users) it is
likely that the home delivery will emit less CO
per item purchased. These findings
require several qualifications, however. They assume:
the car-based trip was solely for the purpose of shopping (no other activity was
undertaken during the course of the trip);
the product ordered online was delivered successfully first time;
the shopper was satisfied with the purchase and did not return the item;
home deliveries and shopping trips were made over average distances; no
allowance was made for different types of road network or traffic conditions; and
only the last mile and not the upstream supply chain has been considered in the
analysis (although reference has been made to previous studies of the relative
environmental impact of upstream activities).
Figure 3.
Stages of book production
and distribution
% figures indicate relative portion of greenhouse gas emissions
Loss of
biomass and
Source: Derived from green press initiative (2008)
Point of divergence
Last mile
and retailing
Analysis of the
carbon footprints
The environmental implications of consumer behaviour have been illustrated by a
series of different shopping scenarios. Having already established that a standard
home delivery for a non-food item would be allocated 181 gCO
various dedicated,
combined and browsing-only shopping trips were then compared. From the modelling
evidence presented here and from the results of previous research, it seems that
emissions from the average shopping trip, particularly by private car, can be greater
than emissions from all upstream logistical activity irrespective of the distribution
channel. Further work is underway to examine this issue in greater detail. The mode of
personal travel is particularly important. When a shopper travels by bus at busy times
and makes several purchases, the emissions per item are lower than when a home
delivery van delivers just one item to a consumer’s home.
It is acknowledged that people appear to regard shopping as a social, recreational or
even hedonistic activity to be enjoyed in a physical store. Given increasing concern for
climate change, however, it is important that they are made aware of the CO
consequences of their chosen shopping behaviour. With a little planning and thought
on both the part of consumers and carriers/retailers, emissions related to the transport
element of any shopping activity could be minimised through a few simple actions.
Carriers should aim to maximise drop densities (something that is likely to happen
anyway as a consequence of the growth of online retail sales), avoid dedicated
collection trips when picking-up returned items and where possible use low emissions
vehicles, e.g. electric vehicles. The use of reception boxes at people’s homes and
separate collection points would eliminate failed deliveries, the consolidation of orders
to a particular address in a single delivery would cut vehicle-kms and wider adoption
of variable delivery pricing would promote off-peak/out-of-hours deliveries, allowing
delivery vans to run more of their mileage at fuel-efficient speeds.
Conventional shoppers meanwhile should ensure that when they go shopping
wherever possible they should combine their shopping trip with other activities and
thus avoid making a dedicated journey to buy a single item.
The relative carbon intensity of the different forms of retail distribution depends on
their particular circumstances. Neither has an absolute environmental advantage.
Some forms of conventional shopping behaviour emit less CO
than some home
delivery operations. On average, however, in the case of non-food purchases, the home
delivery operation is likely to generate less CO
. This environmental advantage can be
reinforced in various ways if online retailers and their carriers alter some of their
current operating practices.
Analysis of the emissions from the distribution of products with very different
characteristics, such as refrigerated food or bulky items (. 25 kg) that require a two-man
delivery, may yield very different results. The methodological approach outlined in this
paper could be applied to a comparative carbon analysis of these other sectors of the
retail market. Further research is also required to refine the analysis for small non-food
items. This could explore the impact on carbon emissions of other behavioural responses
to the growth of online retailing not considered by the present study. Some online
customers, for example, may continue to shop as much by conventional means, but
merely buy less on each trip, effectively increasing the carbon intensity of each item
purchased by this means. Others may use the internet not just for purchasing goods but
to inform their conventional shopping decisions, allowing them to select products and
shops in advance and thereby rationalise their shopping-related travel. This future
research, like the present study, will require an extension of logistics traditional focus on
the transport of goods in dedicated freight vehicles to include the various forms of
personal travel associated with the movement of goods on the “last mile” to the home.
1. The carbon footprint of a product is the sum of all the carbon emissions for that product from
raw materials through manufacturing, distribution, use and disposal, taking account of all
related activities and materials. The calculations in this paper are for CO
emissions for the
last stage in the distribution only.
2. Trip-chaining occurs when a person visits several locations for different purposes in the
course of a single trip.
3. A van denotes a light goods vehicle up to 3.5 tonnes maximum permissible gross vehicle
weight of van-type construction on a car chassis that operates on diesel fuel unless specified
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About the authors
Julia B. Edwards is a Research Associate in the Logistics Research Centre at Heriot-Watt
University, Edinburgh. She joined the Group in 2006 as part of the multi-university
“Green Logistics” project. Prior to that, she was a Senior Lecturer of Environmental Management
at the University of Wales, Newport. She has been researching and teaching in the areas of
transport and environmental issues for the last fifteen years. Currently, her research interests
include carbon auditing of supply chains, e-commerce and the environment and consumer
travel and shopping behaviour. Julia B. Edwards is the corresponding author and can be
contacted at:
Alan C. McKinnon is a Professor of Logistics and a Director of the Logistics Research Centre
at Heriot-Watt University, Edinburgh. A graduate of the universities of Aberdeen, British
Columbia and London, he has been researching and teaching logistics for 30 years and has
published extensively on many different aspects of the subject. He has conducted studies for
numerous public and private sector organisations and been an adviser to several UK
Government departments, parliamentary committees and international agencies. Much of his
current research relates to the decarbonisation of logistical activity.
Sharon L. Cullinane has continued to lecture, research and publish in the field of transport
policy and the environment around the world, since gaining her PhD in logistics 20 years ago
from Plymouth University. Previous to that she has been employed at the University of
Hong Kong, Oxford University, the Egyptian National Institute of Transport, the Ecole
Superieur de Rennes and Plymouth University. She is now an independent consultant. She is
widely published internationally. She has lectured, researched and published in logistics and
transport around the world. Her most recent post was as a Senior Lecturer at Heriot-Watt
University in Edinburgh, UK.
Analysis of the
carbon footprints
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... The type of vehicle used for home delivery also influences the emissions performance of e-commerce. Driving the last mile with light commercial vehicles with low performance and high pollution levels reportedly emits more CO 2 than the consumers' journeys in individual vehicles (Allen et al., 2018;Edwards et al., 2010). Finally, the terms of delivery should also be considered. ...
... And yet online shopping still makes up just 5.5% of total grocery shopping revenues in the UK, although this figure has risen constantly in recent years (MINTEL, 2018). The attractiveness of e-grocery shopping for British households makes it particularly relevant when studying the effects of this mode of supply on travel, in contrast to other countries such as the United States where e-grocery shopping is poorly developed (Durand & Gonzalez-Feliu, 2012) and where the study of the relationship between travel and ICT use may be more complex to implement (Edwards et al., 2010). Moreover, the UK is especially interesting to study because shopping trips are the main reason for travel (19% of weekly trips) far ahead of work (14.6% of weekly trips) (Department for Transport, 2019). ...
... Environmental aspects have become key aspects of supply chain management (Dubey et al., 2017) as well as freight transport research in recent years, highlighting the need to include relevant variables in distribution models (Bektas et al., 2018;Koc et al., 2016). Edwards et al. (2010) studied the carbon intensity of last mile deliveries and found that neither home delivery nor conventional shopping had an absolute CO 2 advantage. In the same stream of research, Shao et al. (2016) investigated the relationship between distribution strategies and traffic congestion and found that individuals' optimal decisions and socially optimal decisions are not aligned, which in turn leads to inefficiencies in the system. ...
Purpose Last mile distribution is a crucial element of any supply chain network, and its complexity has challenged established practices and frameworks in the management literature. This is particularly evident when demand surges, as with recent lockdowns due to the COVID-19 pandemic and subsequent demand for home delivery services. Given the importance of this critical component, this study recommends horizontal collaboration as a possible solution for retailers seeking to improve the quality of their services. Design/methodology/approach This study investigates whether horizontal collaboration should be considered as an option for faster and greener distribution of groceries ordered online. Using the United Kingdom and Greek grocery markets that differ in terms of online grocery penetration, distribution network structure and delivery times, the study discusses how the effectiveness of pooling resources can create positive spillover effects for consumers, businesses and society. Findings Despite their differences, both markets indicate the need for horizontal collaboration in the highly topical issue of last mile delivery. Originality/value Taking a theoretical and practical view in cases of disruption and constant pressure in last mile distribution, horizontal collaboration supports retailers to coordinate routes, increase fleet and vehicle utilisation, reduce traffic and carbon emissions while improving customer satisfaction.
... One possibility is to crowdsource some deliveries through digital platforms to non-professional couriers who use their own transportation means. While reducing costs and providing a source of income for people who might otherwise be off the labour market (Castillo et al., 2018), crowdsourcing also raises concerns about job quality, environmental impact (Halldórsson et al., 2010) and trust (Devari et al., 2017). ...
We study a variant of the Probabilistic Travelling Salesman Problem arising when retailers crowdsource last-mile deliveries to their own customers, who can refuse or accept in exchange for a reward. A planner must identify which deliveries to offer, knowing that all deliveries need fulfilment, either via crowdsourcing or using the retailer’s own vehicle. We formalise the problem and position it in both the literature about crowdsourcing and among routing problems in which not all customers need a visit. We show that to evaluate the objective function of this stochastic problem for even one solution, one needs to solve an exponential number of Travelling Salesman Problems. To address this complexity, we propose Machine Learning and Monte Carlo simulation methods to approximate the objective function, and both a branch-and-bound algorithm and heuristics to reduce the number of evaluations. We show that these approaches work well on small size instances and derive managerial insights on the economic and environmental benefits of crowdsourcing to customers.
Full-text available
The rapid development of e-commerce has created new consumer demand, but at the same time it has increased the delivery pressure of express. In the era of Internet plus customers require the express delivery to achieve personalization and diversification. And the traditional single home delivery mode can not solve the problems such as low delivery efficiency, high delivery cost and low customer satisfaction.Thus the formation of a diversified last mile delivery service system is an important problem to be solved urgently in the express delivery industry. It includss how to designs reasonable pickup service and improve the quality of home delivery service. As the only phase of direct contact with the final customers, last mile express delivery has gradually become a key factor affecting the online shopping experience of consumers. Facing different delivery modes, customers often show bounded rational choice behavior, or rely on the traditional home delivery mode, or choose a single pickup mode, or select multiple delivery modes. In order to improve the customer's experience of the existing delivery services and solve the bottleneck of last mile delivery, the status of last mile express delivey modes and the decision making behavior of the customers were analyzed. Then, from the perspective of bounded rational customers, the quantal response equilibrium problem of different customer choice behavior when customers chose different last mile delivery modes was studied and two kinds of customer choice equilibria were discussed. Finally, according to the different decision-making enterprises, considering the different choices equilibrium behavior, the location of pickup points and the pricing of home delivery service are studied. Firstly, the paper analyzes the status of last mile express delivey modes and the decision making behavior of the customers. Through the literature review and the actual investigation, the classification standards of home delivery mode and pickup mode are put forward, and the problems in the operation process of existing delivery modes are revealed. The application scope of different delivery modes is compared and analyzed. At the same time, the customer's decision making behaviors including the customer demand characteristics, the decision-making factors and the decision-making process are analyzed. It reveals that the customer is not completely rational in the process of decision making, has the bias of decision making and shows the bounded rationality behavior of stochastic choice. Secondly, the quantal response equilibrium problem of different customer choice behavior is studied. Aiming at the correlational problem of different delivery modes, we take the customer selection between home delivery, attended Collection and Delivery Point (CDP) mode and unattended CDP mode as an example. The home delivery is simulated as an M/D/1 queue and the pickup point as differernt M/M/K/K queues. Nested Logit (NL) model is used to calculate the utility function of the pickup mode, and the customer choice models of three delivery modes are constructed. Then, the existence and uniquess of Nested Logit-Quantal Response Equilibrium(NL-QRE) in last mile delivery service system are proved. Aiming at the customer dependency problem of the home delivery mode, we take the customer selection between attended CDP mode and unattended CDP mode as an example. The pickup points are modeded as different queues. The utility function of pickup mode is modified by prospect theory (PT), and the customer choice models of two delivery modes are constructed. Then, the existence and uniquess of Prospect Theory-Quantal Response Equilibrium(PT-QRE) in pickup service system are proved. And the difference between the PT-QRE and completely rational choice equilibrium is analyzed theoretically. Numerical experiments verify the correctness of the choice equilibrium model, and reveal the degree of customer rationality and the degree of dependence on home delivery affect the design of the last mile delivery mode. Thirdly, the location problem of pickup points based on customer choice equilibrium is studied. We analyze whether the probabilistic-choice and optimal-choice choice behavior has an impact on the pickup point location. To improve the pickup point’s operating efficiency and benefit, the mult-objective optimization model based on NL-QRE is formulated. Faced with the home delivery mode, the attended CDP mode and the unattended CDP mode, customers show bounded rational behavior which could not accurately assess the home delivery waiting utility or pickup loss utility. The non-dominated sorting genetic algorithm II(NSGA-II) is developed to solve the established optimization model, and compared with the weighted method and the ideal point. To meet the different interests of the two decision-makers in the distribution enterprise and the customer, the bi-level optimization model for pickup points location is proposed. Customers have reference dependence behavior in the home delivery mode and are lack of accurate calculaiton capability to asses loss value of the pickup mode. An iteration algorithm is designed to solve the upper and lower model based on NSGA-II and immune algorithm. The results verify the validity and feasibility of the models and algorithms, and show the customer bounded rational behavior and the home delivery price affect the pickup point network’ operating efficiency and benefit. Finally, the pricing problem of home delivey based on customer choice equilibrium is studied. In the context of the fierce competition from the pickup service enterprises which service is free, the customer packages are easily rejected by the pickup points. In order to improve the revenue of the home delivery enterprises, the home delivery pricing model based on multiple delivery modes affecting each other is formulated. Aiming at the pricing model based on NL-QRE, the projection gradient method, the genetic algorithm and the sensitivity analysis based local search algorithm are designed, respectively. The results show that there are significant differences in the pricing strategies of different types of customers. Aiming at the pricing model based on PT-QRE, the improved projection gradient method is designed and compared with the genetic algorithm and the multistart local search algorithm. The results show that both the QRE and the PT-QRE affect the pricing model, and it is necessary for the enterprise to understand thoroughly the dependency of the home delivery mode and the degree of customer rationality.
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Abstract: International corporations with a dispersed location have distribution centers that are independent units or a complex of production and distribution facilities. Distribution centers have become a relatively new but important element of the country’s economic landscape. The location and development of these facilities depend on specific conditions. The aim of the study is to present the essence of distribution centers as economic objects with special functions and tasks. Against this background, a comparative analysis of three selected distribution centers located in the Poznań agglomeration, but with different characteristics, is presented in order to verify the theoretical considerations carried out before. The results and conclusions of the analysis are of a utilitarian nature. They also indicate the important role played by distribution centers in strengthening the market position of companies.
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Tytuł monografii został zaczerpnięty z poglądów głoszonych przez francuskich logistyków akademickich i dotyczy globalnego systemu ekonomicznego, zbudowanego z wielu podmiotów gospodarczych rozproszonych na całym świecie, który pomimo liberalizacji stosunków gospodarczych i politycznych oraz wsparcia technologii informacyjno-komunikacyjnych nie mógłby funkcjonować bez międzynarodowej logistyki. Monografia składa się z trzech części. Pierwsza dotyczy współczesnych trendów i wyzwań w logistyce i łańcuchach dostaw. Należą do nich: logistykacja w biznesie, rezyliencja ekonomiczna, ryzyko zakłóceń w łańcuchach dostaw, gospodarka współdzielenia, rozwój rynku e-commerce oraz transformacja łańcuchów dostaw. W drugiej części uwagę poświęcono szeroko pojętym transportowi, spedycji i logistyce, w szczególności transportowi publicznemu, logistyce ostatniej mili, edukacji logistycznej i lokalizacji centrów dystrybucji. Natomiast część trzecia odnosi się do obszarów finansowych w logistyce i łańcuchach dostaw – standingu i wyników finansowych spółek sektora TSL oraz aspektów międzykulturowych w zarządzaniu finansami łańcuchów dostaw.
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Last mile logistics is considered the most expensive part of the supply chain. This is due to the high cost of deliveries related to the costs of organizing the transport of small parcels to individual customers, including the costs of fuel, vehicle operation and couriers. Considering the growing popularity of e-commerce among citizens, the growing problem of the last mile of deliveries should be analysed. The study focuses on the analysis of the literature on the subject, as well as on the existing or possible solutions to issues related to deliveries to the end customer. The main aim of the study is to present the proposed solutions to the last mile problem in the light of possible benefits for the development and improvement of urban logistics and the associated negative aspects. The indirect goal is to present solutions in Poland regarding the last section of the supply chain.
This paper assesses how various ways of organizing last-mile logistics impact resource utilization, and, in turn, opportunities for consolidating goods. The paper combines a conceptualization of resourcing and consolidation derived from the literature with an analysis of contemporary last-mile logistics options in the Swedish retail market. Based on the analysis of three forms of last-mile logistics—consumer logistics, retailer logistics, and hybrid logistics—the paper discusses how resourcing for fixed, mobile, and coordination resources can enable consolidation in terms of time, place, and form. Drawing from the Swedish context, the paper calls for additional research combining country-specific studies while examining similarities and differences in last-mile logistics, particularly concerning factors that enable or hinder consolidation as well as how such potentials are realized in practice. Although consolidation clearly appears in the rich literature on last-mile logistics, this paper focuses on how consolidation can be achieved through resourcing and offers additional insights into current frameworks for last-mile logistics in retailing.
Purpose - The main objective of this paper is to provide a systematic literature review (SLR) and structured insight into last mile delivery, ultimately identifying gaps in current knowledge and proposing a framework for future research direction in terms of sustainability in the area. Design/methodology/approach-This paper identifies and synthesizes information from academic journals and examines "Journals and Publishing place," "Geographic location," "Year of Publication," "University and Author Affiliation," "Themes and Sub-themes," "Theory," "Research Design, Methods and Area" and "Industry Involvement." A collection of online databases from 2005 to 2020 were explored, using the keywords "Last mile delivery," "Last mile logistics," "Last mile transportation," "Last mile fulfillment," "Last mile operations" and "Last mile distribution" in their title and/or abstract and/or keywords. Accordingly, a total of 281 journal articles were found in this discipline area, and data were derived from a succession of variables. Findings - There has been significant growth in published articles concerning last mile delivery over the last 15 years (2005-2020). An in-depth review of the literature shows five dimensions of the last mile: last mile delivery, transportation, operations, distribution and logistics. Each of these dimensions is interrelated and possess clustered characteristics. For instance, last mile operations, last mile transportation and last mile delivery are operational, whereas last mile distribution is tactical, and last mile logistics possess strategic characteristics. The findings also indicate that even though the sustainability concept can be incorporated into all levels of the last mile, the current literature landscape mainly concentrates on the operational level. Research limitations/implications - This review is limited to academic sources available from Emerald Insight, Science Direct, Taylor and Francis, Springer, MDPI and IEEE containing the mentioned keywords in the title and/or abstract/or keywords. Furthermore, only papers from high-quality, peer-reviewed journals were evaluated. Other sources such as books and conference papers were not included. Practical implications - This study dissects last mile delivery to produce a framework that captures and presents its complex characteristics and its interconnectedness with various related components. By analyzing last mile delivery in its entirety, the framework also helps practitioners pinpoint which levels of last mile delivery (operation, tactical or strategic) they can incorporate the concept of sustainability. Originality/value - The research findings enrich the contemporary literature landscape and future work by providing a conceptual framework that incorporates the "economic," "environmental" and "social" pillars of sustainability in all dimensions of the last mile delivery.
Purpose This paper investigates the economic performances of two business-to-consumer (B2C) e-commerce last-mile delivery options –parcel lockers (PLs) and traditional home delivery (HD) in contexts where e-commerce is still at its early stages. It analyses and compares two different implementation contexts, urban and rural areas. Design/methodology/approach This study develops an analytical model that estimates delivery costs for both the PL and HD options. The model is applied to two base cases (representative of urban and rural areas in Italy), and sensitivity analyses are subsequently performed on a set of key variables/parameters (i.e. PL density, PL fill rate and PL annual costs). To support the model development and application, interviews with practitioners (Edwards et al. , 2011) were performed. Findings PLs imply lower delivery cost than HD, independently from the implementation area (urban or rural): advantages mainly derive from the higher delivery density and the drastic reduction of failed deliveries. Benefits entailed by PLs are more significant in rural areas due to lower PL investments and annual costs, as well as higher HD costs. Originality/value This paper offers insights to both academics and practitioners. On the academic side, it develops a model to compare the delivery cost of PL and HD, which includes the analysis of urban and rural contexts. This could serve as a platform for developing/informing future analytical/optimisation contributions. On the managerial side, it may support practitioners in making decisions about the implementation of PLs and HD, to benchmark their costs and to identify the main variables and parameters at play.
The impacts of failed first-time home deliveries on additional carrier journeys (repeat deliveries) and customer trips (to retrieve goods from carrier depots) are of increasing concern to e-retailers and are assessed in this paper. The attended collection and delivery point (CDP) concept is one solution to first-time delivery failures, using a variety of outlets (e.g., convenience stores, petrol stations, post offices) as alternative addresses to receive deliveries. By using a database of households from across West Sussex in the United Kingdom, this paper confirms that certain benefits might accrue from using networks of Local Collect post offices, supermarkets, and railway stations as CDPs, compared with the traditional delivery method in which the carrier may make several redelivery attempts to the home with the customer making a personal trip to the carrier's depot in the event that these attempts also fail. A network of CDPs across West Sussex would function most effectively (in reducing the overall traveling costs associated with handling failed first-time deliveries) when the proportion of first-time home delivery failures is greater than 20%, the proportion of customers traveling to the depot is more than 30%, Local Collect post offices are used as CDPs, and significant numbers of people would walk to their local CDP. Customers benefit the most from CDPs, with reductions in their current traveling costs of up to 90% being modeled here. The reduction in carrier traveling costs is much less, but the processing costs associated with home delivery failures are reduced significantly by diverting the failed packages to CDPs
The application of sustainability principles into supply chains is an evolving research area currently suffering from a scarcity of established theories, models, and frameworks. There are at least two key reasons why it is difficult to build sustainability into the supply chain. First, there are numerous context dependent factors that either enable or hinder progress towards sustainability in a supply chain. There is a need to better understand how these factors affect the sustainability performance of supply chains. Second, implementing sustainability requires a triple bottom line approach, where improvements are pursued in the environmental, economic, and social dimensions of performance. These two challenges mean that implementing sustainability in a supply chain is a complex process that involves a large number of interacting factors. This paper contributes to efforts to overcome these challenges by proposing a mathematical model for assessing sustainability in the supply chain. The model is based on the notion that a probabilistic representation of sustainability can realistically account for its challenges. The development of the proposed model was guided by the need for ease of use, simplicity, and the ability to quickly provide feedback on the sustainability status of supply chains over time.
Online retailing can lower the environmental impact of shopping under specific circumstances. As a result of the numerous variables involved, most of the studies that have compared the carbon footprints of online and conventional retailing only take a partial view. To make a more holistic assessment, this study develops a framework that accounts for all the relevant environmental factors relating to retail/e-commerce activities. Variables related to consumer shopping behaviour such as basket size, transport mode, trip length and trip frequency are included in the analysis. This framework is used to build a Life Cycle Analysis model. The model is applied to different online retail methods for fast-moving consumer goods in the United Kingdom. We find that, within the “last mile” link to the home, the nature of the consumer's behaviour in terms of travel, choice of e-fulfilment method and basket size are critical factors in determining the environmental sustainability of e-commerce. The nature and routing of van deliveries, the amount and type of packaging used, and the energy efficiency of shop and e-fulfilment centre operations are also identified as significant contributors to climate change potential. The results of this study indicate ways in which e-commerce can be made more environmentally sustainable, encouraging consumers to reduce complementary shopping trips and maximise the number of items per delivery. This study identifies the strengths and weaknesses of a range of e-retail channels and provides a basis for future research on the environmental sustainability of online retailing of fast-moving consumer goods.
Purpose – The purpose of this paper is to investigate contracts of the intermodal transport market and the incentives they create for a modal shift and thus the financial and environmental efficiency of freight transport. Design/methodology/approach – The research used a mixed-methods approach where qualitative case interviews and quantitative modeling was combined. Two cases of contractual relationships between a service provider and its intermodal train operator on a specific lane were investigated. The case findings were then consolidated and used as input for a model of the contractual relation. Findings were sought through an extensive numerical study. Findings – The cases reported that intermodal rail operators had a strong production focus, transferring the capacity risk (i.e. the risk of unused capacity) to the service provider, which the service providers argued limited the shift from truck to intermodal transportation. The paper shows that, due to the market structure, it is rational for the operator to transfer the capacity risk but not the profit. Consequently, a modal shift is only likely to occur when there is strong shipper pressure or low capacity risk. We present a risk-sharing contract that could release this dead lock. Research limitations/implications – The conclusions are modeling outcomes subject to assumptions based on the cases. For further validation, large-scale quantitative studies are necessary. Practical implications – The paper shows that a three-part tariff in which the capacity risk is shared may lead to increased modal shift and hence assumed improved environmental performance. Social implications – Instead of arguing for operators to be more customer-focussed, policy makers and other stakeholders may have more to gain by having both actors being more cooperation focussed. Originality/value – The paper is the first attempt to quantify how the contractual relations on the freight transport market affect the modal mix and thus the financial and environmental efficiency of freight transport.
The concept of lean is important to sustain operations management. Workers are treated as important assets in lean. In this study, a 'lean-ecosphere' management system is developed for a manufacturing company by using interpretive structural modeling (ISM) and analytical network process (ANP). In the first phase of the methodology, a unified index to set a common objective of people is developed for horizontal integration. In the second phase, a hierarchical relationship model is developed to identify relationships between challenges of lean. This model facilitates the building of a strong foundation of lean to promote the depth of human integration. In the end, the results achieved are compared with the current situation of the company. The results indicated that the scientific methodology for lean management system is very beneficial for the company. This paper adds knowledge to the operations management literature by addressing the human resource factor to create a sustainable operation.
As demand for advanced logistics services grows, third-party logistics providers (3PLs) are being requested to provide more environmentally sustainable services. This development presents 3PLs with opportunities but also challenges and concerns about how to translate green efforts into practice. The purpose of this paper is to analyse environmental sustainability initiatives undertaken by 3PLs and the factors influencing them, both positively and negatively. The research methodology used in this paper is based on two-phase approach. In the first phase, a systematic literature review on the adoption of green initiatives by 3PLs has been carried out and two research questions have been identified. In the second phase, the research questions have been addressed by a case study analysis conducted on 13 Italian transport and logistics service providers. The research has distinguished three groups of companies with slightly different environmental profiles in terms of the green initiatives implemented and the main drivers and inhibitors. The surveyed companies show a differing degree of involvement in green initiatives due to variations in the breadth of service offered and the importance attributed to environmental issues. The paper concludes with a discussion of the managerial implications of the research, particularly for the development of 3PL's green strategies.