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221
Availability in 08
retailing
On-shelf in-store and online fulfilment
DAVID GRANT AND JOHN FERNIE
Introduction
This chapter discusses availability in retailing as an important
element of retailing customer service, and its context considers both
products on-shelf in-stores and product fulfilment in online retail-
ing. The chapter begins with an overview of availability on-shelf
in-store, which has received considerable academic and practitioner
efforts over the last half century, and discusses major factors relating
to profitability, human resources and replenishment systems in both
the grocery and non-grocery sectors. The next section discusses the
effects of online retailing and fulfilment on consumer expectations
and the consumer experience; the following chapter deals further
with the concept of ‘omnichannel’ retailing and its specific logistical
challenges as a part of online retailing. Finally, the c hapter considers
issues of service failure and recovery through in-store stock-outs and
non-fulfilment before it concludes.
Availability on-shelf in-store
Retail stores are the final ‘bricks-and-mortar’ logistical link to
consumers in retail supply chains. Good product availability is
222 Logistics and Retail Management
important at this penultimate supply chain node, where consumers
represent the final node in a logistics definition of ‘point-of-origin
to point-of-consumption’ (Grant, 2012). Corsten and Gruen
(2003) argued that in-store product availability was the new
battleground in the fast moving consumer goods industry sector.
Availability is thus important to provide consumer service and
satisfaction, and to help develop ongoing and loyal relationships as
part of the total customer service experience for consumers (Grant,
2014). Notwithstanding, retailers often focus logistics activities on
bringing products to the store and pay less attention to logistics
processes inside the store. Ignoring logistics required at this last
node in the chain, ie the ‘last 50 yards,’ can result in less than opti-
mal availability and product stock-outs (Fernie and Grant, 2014).
In-store processes not only involve physical on-shelf replenishment
but also require attention to managerial tasks such as ordering,
shelf space planning and in-store consumer service (Kotzab and
Teller, 2005).
In-store customer service consists of both marketing elements such
as assortment, merchandising, promotions and store environment
or ambience, and logistical elements in support of the marketing
elements, such as shelf replenishment to maintain availability of
products on-shelf, IT, and delivery and possibly assembly for larger
products.
The study of in-store retail availability and stock-outs is not new;
Progressive Grocer (1968a, 1968b) published the first major study
a half-century ago on grocery customer reactions to not finding
products they wanted on a store’s shelf. However, despite much
work on these issues in the intervening period, availability remains
an important challenge for all retailers in this second decade of
the new millennium as item stock-outs affect retailers’ decisions on
what and how much product to stock on-shelf (GTNexus, 2015).
Too much stock will increase inventory and storage costs while
too little stock will result in stock-outs and consumer dissatisfac-
tion, manifested in reactions ranging from product substitution to
customers ‘voting with their feet’ and seeking products elsewhere
(Corsten and Gruen, 2003).
Availability in Retailing 223
The in-store grocery and non-grocery retail sectors have been
transformed over the last four decades, especially in the United
Kingdom. Grocery retailers integrated primary and secondary distri-
bution through centralization processes in the 1980s to reduce lead
times and take inventory out of the retail supply chain, and by the
late 1990s/early 2000s claimed to have one of the most efficient
supply chains in the world (Fernie and Sparks, 2004). Non-grocery
retailers followed suit, with their motivations also based on import-
ing container loads of products. The notion of goods on-shelf as a
measure for in-store availability is enhanced by accessibility, meas-
ured in driving times to superstores rather than short trips to local
shops, store loyalty, and brand loyalty and extensions evidenced by
some grocery retailers’ ventures into non-grocery domains such as
banking, mobile phone, travel, funeral and insurance services under
their corporate brand umbrellas.
Corsten and Gruen (2003) argued that most stock-out situations
occur at store level, primarily through ordering and replenishment
practices, and are often referred to as the ‘last 50 yards’ problem.
Figure 8.1 shows that 35 per cent of stock-out problems occur with
shelf replenishment in the store and 15 per cent from the regional
distribution centre (DC) to the store (Dybell, 2005).
Figure 8.1 Causes of retail stockouts
Other, 5%
Inventory Manufacturer primary
inaccuracy, 15% delivery to
retailer DC, 30%
In-store shelf Retailer DC
replenishment, 35% secondary delivery
SOURCE Adapted from Dybell, 2005 to store, 15%
224 Logistics and Retail Management
There are five main reactions by consumers to an in-store product
stock-out:
1 They buy the item at another store (store switching).
2 They delay ordering or purchasing the item (postpone purchase at
the same store).
3 They do not purchase the item (a lost sale).
4 They substitute the same brand (different size or type).
5 They substitute for another brand (brand switching).
Percentages related to these reactions have not significantly changed
in fifty years; around 65 per cent of consumers adopt one of the first
three reactions, thus not buying in that particular store on that occa-
sion if a stock-out occurs (Progressive Grocer, 1968a, 1968b; Corsten
and Gruen 2003; GTNexus, 2015).
Several studies discuss general causal factors that prompt
consumer reactions to stock-outs such as product category, brand
loyalty, consumer type and the immediacy of need (Sloots et al, 2005;
McKinnon et al, 2007; GTNexus, 2015). Sloots et al (2005) inves-
tigated not only brand equity/loyalty but also the hedonic value of
products. Customers who possess high brand equity/high hedonic
values for a product are likely to switch brands or stores to acquire
the product. Further, they will do so without serious consideration of
their own ‘personal logistics costs’ or paying to have their groceries
delivered by the retailer (Teller et al, 2006). Finally, the growth of
internet ordering for groceries and the use of in-store based picking
strategies to fulfil an online or home shopping grocery order aggra-
vates this situation (Fernie and Sparks, 2004; Grant et al, 2006).
Corsten and Gruen (2003) advocated an integrated approach
based on process responsiveness, operation accuracy and incentive
alignment to address causes of stock-outs. Their process improve-
ments related to assortment planning and space allocation; ordering
systems, inventory control and store flow replenishment. Two sugges-
tions proposed for operational accuracy include inventory level
accuracy and the ability to measure and identify on-shelf availability.
Another suggestion, incentive alignment, is about scheduling staff to
improve shelf-filling in addition to optimizing overall management
objectives rather than sub-objectives by functional area.
Availability in Retailing 225
Efficient Consumer Response (ECR) UK and IGD (formerly
Institute of Grocery Distribution) are the outlets through which
members in UK grocery supply chains address availability/stock-out
problems. ECR Europe considers the European Union as a whole and
proposes seven ‘levers’ to improve availability (ECR UK, 2004). These
include measurement levers that need managerial attention (levers
1 and 2); replenishment and in store execution, namely merchan-
dising (levers 3 and 4); inventory accuracy (lever 5); promotional
management and ordering systems (levels 6 and 7). These levers have
subsequently formed the basis of the ECR UK/IGD availability agenda.
Fernie and Grant (2008) investigated three overarching problems
of in-store availability identified in academic literature and trade stud-
ies in a case study of a UK grocery retailer: the effect on stock-outs
and availability from in-store picking for home delivery, promotions
and store size. Promotional management and ordering systems (levers
6 and 7) had been a feature of the case company’s logistical strategy.
They focused on the bestselling 1,000 product lines, of which one-fifth
were on promotion at any one time. They worked with suppliers to
match supply with demand through a 13 week planning cycle. Thus,
promotional products had better availability than non-p romotional
products, especially as a staff member in store ensures that all shelves
with promotional lines are full and dressed every morning.
Although some literature (Fernie and Sparks, 2004; Grant et al,
2006) suggests that in-store picking affects availability, this was not
the situation at the case company. Store backrooms in dedicated
online picking stores are larger, to give a dedicated site for online
orders. Further, the ‘personal shoppers’ provide real time input to
stock control by reporting gaps on shelves and stock-outs. The main
problem facing the case company was poor availability levels at
convenience stores. Fernie and Grant’s (2008) study reinforces the
view that convenience stores are low in a priority list when stock
problems occur at the DC. Further, low staffing levels in these stores
mean that many successful operational procedures carried out in
large stores are less successful in smaller stores. Their overall conclu-
sion from their study is that retailers should address availability
issues through simple techniques that focus on human resources.
Extending a store backroom to handle more stock is easy; however,
226 Logistics and Retail Management
a key difference between good and poor availability levels revolves
around management and staff commitment to solving the problem;
ie having dedicated staff to address promotional items and personal
shoppers for in-store picking versus having insufficient staff to stock
shelves and service customers.
Another issue relates to whether increasing availability, and thus
reducing stock-outs, increases profits, or whether increases in other
logistical costs related to increased stockholding and transport negates
them. A study undertaken with a major UK retailer and one of its
major soft drinks suppliers (Trautrims et al, 2009) analysed category
performance factors regarding availability, profitability and consumer
propensity towards substitution and loyalty. The study found that a
decision by retailers to increase availability was influenced by four
characteristics: the category, stock keeping unit (SKU), store and
consumer behaviour. Trautrims et al (2009) developed an availability/
profitability matrix shown in Figure 8.2. Assuming every retailer wants
to provide high availability for highly profitable products, an ideal
situation is to have such SKUs located in the upper right quadrant.
If an SKU is within the upper right or lower left quadrant, the rela-
tion between profitability and availability is satisfactory. However,
if an SKU is located in the upper left or lower right quadrant there
is a mismatch and there are three strategic propositions to allevi-
ate them. The arrows in Figure 8.2 highlight three propositions.
Figure 8.2 Strategies to match product profitability versus availability
High
Mismatch Match
Pofitability
Match Mismatch
Low High
Low
Product availability
SOURCE Adapted from Trautrims et al, 2009
Availability in Retailing 227
Proposition 1 shifts a highly profitable product to a higher level of
availability. The major reason for doing so is higher customer satis-
faction and an increase in sales due to higher availability. Limitations
might include low availability due to generally higher demand
than supply. A decision to increase availability should also rely on
the related costs to do so and type of product. A highly perishable
product with a low number of sales would probably not justify
high availability since wastage would increase. Propositions 2 and 3
apply to products with low profitability but a high level of availabil-
ity. Proposition 2 suggests raising the profitability of a product, but
that might be difficult given consumer and competitive pressures to
keep costs and prices low. Proposition 3 suggests reducing the level
of availability. If a subsequent cost reduction is achieved this option
will also contribute increased profitability of an SKU.
The first reported piece of research on clothing availability was
by Carey and Staniforth (2007). Their firm, UK department store
House of Fraser, experienced poor availability due to inefficient loca-
tion of products in crowded stock rooms with valuable staff time
taken to get products ready for sale in-store. Reprocessing or ‘repro’
stock unaccounted for in the re-ordering process exacerbates in-store
replenishment problems in fashion retail. Repro stock is merchandise
left in other locations in-store, such as in changing rooms or on other
displays. House of Fraser found that all these factors contributed to
an overall availability level of 71 per cent across the stores they inves-
tigated, compared availability levels in the mid-90 per cent range for
the grocery sector.
Fernie and Grant (2014) investigated availability and stock-outs
in the UK clothing retail sector through an in-depth case study of
two independent availability improvement initiatives undertaken by
one major retailer. The first initiative related to the retailer’s objec-
tive to increase market share in a specific children’s wear category,
namely a summertime ‘back to school’ promotion, chosen because
of the short time window for such a promotional campaign in which
stores were asked to achieve 100 per cent availability on the top 20
lines in the retailer’s school wear range. The second initiative focused
on the product category of women’s jeans as the retailer likes to have
a constant state of availability since planning for product catalogues
takes place up to six months before delivery.
228 Logistics and Retail Management
The results for the first initiative saw availability average 73 per
cent, with the best store achieving 80 per cent and the poorest achiev-
ing 63 per cent. Key issues related to poor housekeeping; customer
assistants commented that there was too much stock in the stock-
room and a need for greater reprocessing of stock in the store. It also
became clear that the retailer did not properly articulate the objec-
tives of the promotion to customer assistants on the floor. Staff also
did not feel supported by management or involved in the planning
and implementation of the promotion and therefore were not moti-
vated to achieve better results.
The results for the second initiative saw availability average 79 per
cent, with the best being 93 per cent, close to grocery store percent-
ages, and the worst being 66 per cent. The retailer’s database indicated
availability should have been around 90 per cent, thus there was a
negative variance of 11 per cent. The final phase of this study involved
interviewing the senior manager responsible for trading and availa-
bility issues, who confirmed that the jeans product category required
a constant presence in-store. The retailer uses forward-factor calcula-
tions to ensure that stores had at least two weeks’ cover of stock and
the use of separate product identification codes allowed the company
to allocate more stock to larger stores that have greater stock turn.
Despite this technique, the retailer is failing to ensure correct quan-
tity allocations for specific sizes in stores and thus stock-outs are a
frequent occurrence. This means that availability is a process-driven
head office perspective rather than one from the shop floor.
One issue is the relationship between in-store processes and
human resources, or factors affecting the interaction of staff with
such processes and management systems for replenishment. Store
operations affect product availability and represent a large share of
costs in the retail supply chain. Labour costs represent the second
largest factor in retailing, and with most retail workers employed
at store level it may even account for half of retail operation costs
(Broekmeulen et al, 2004). However, Thonemann et al (2005) found
that staff at the best performing German retailers not only spend less
time on replenishment activities at the store, but also achieve higher
availability levels.
Despite the increasing use of technology at store level (Piotrowicz
and Cuthbertson, 2014), most in-store logistics operations require
Availability in Retailing 229
manual performance and thus still rely heavily on human labour
(Kotzab and Teller, 2005). The way retailers manage human resources
affects their availability performance. As retail management systems
need store level input, the interaction of employees with them thus
influences their outcome. Within the retail workforce there is a polari-
zation of skills levels as specialized employees are located at retailers’
central operations, whereas shop floor employees face a de-skilling
tendency. With an increasing proportion of part-time workers and a
high labour turnover in the retail workforce, investment in skills is
even less important for employers and this situation can create a gap
in the interaction between sophisticated central management systems
and a de-skilled shop floor workforce, which adds further challenges
to the management of in-store processes (Trautrims et al, 2012).
There appears to be a need for a greater level of ‘cross-training’
across functional boundaries so that a retailer’s human resources,
including managers and in-store operational staff, may move
towards a ‘T-shaped’ skills profile developed by Leonard-Barton
(1995). The concept here is that employees will not only have to
develop specific logistics skills (ie the vertical bar of a ‘T’), but also
develop a wider understanding of related areas (ie the horizontal
bar of a ‘T’). For example, in-store retail replenishment employ-
ees should become familiar with tasks such as ordering, data, shelf
fulfilment, and inventory – the main in-store processes identified by
Kotzab and Teller (2005) – as well as merchandising and dealing with
supplier representatives. Such skill enhancement suggests employees
will move to becoming ‘knowledge workers’ (Drucker, 1993) who
know more about their job than anyone else in the organization, are
autonomous and project-oriented, and who acquire over time the
tacit knowledge required for problem-solving, creativity, strategic
flexibility and market responsiveness (Leonard and Sensipers, 1998;
Butcher, 2007).
Trautrims et al (2012) proposed a typology that categorized inter-
action types for in-store replenishment according to the amount of
interaction staff have with replenishment systems and the impact
they have on such systems. One is an operations focus where retailers
ask replenishment personnel for frequent interaction with replenish-
ment systems to achieve data accuracy and to provide information
for central decision-making. However, as employees do not have
230 Logistics and Retail Management
much impact on decision-making within the systems, retailers should
design the replenishment system in a centralized and standardized
fashion; an example of this is House of Fraser. Another is a customer
care focus, where retailers aim to free store staff from basic tasks to
properly advise and help customers, ie interaction with the customer
and not the systems is at the centre of employees’ attention. Retailers
should design the replenishment system so that employees do not
need to interact with it often, but can do so if required for customer
orders. A luxury fashion retailer like Gucci is an example for such a
system.
It is clear from this stream of research that availability, as a compo-
nent of customer service, underlies trade-offs between achievable
additional sales from higher availability and relevant and appli-
cable costs. A consumer’s reaction to a stock-out where they face
additional transaction, substitution and opportunity costs strongly
influences both components, and relates to product characteristics
and the particular situation, and will therefore be specific for every
purchasing decision. However, the outcomes of greater product avail-
ability and thus fewer stock-outs include better consumer service and
satisfaction and improved logistical productivity that in turn should
increase revenue and reduce costs. The costs for improving avail-
ability depend on the methods used, but an increase in stock and
attention to in-store availability on-shelf makes it extremely costly to
obtain 100 per cent availability.
Fernie and Grant (2008) conceptualized an enhanced replenish-
ment model for grocery and non-grocery retailers, shown in Figure
8.3, based on ECR Europe’s seven levers model for the grocery
sector but which is more holistic and includes three sets of anteced-
ents they believe are required before the management levers can be
implemented:
1 human resources comprising management and staff commitment
and appropriate incentive structures;
2 appropriate infrastructure encompassing buildings and vehicles
and IT, centralized buying and the logistical network; and
3 inter-organizational collaboration between retailers, suppliers and
third-party logistics (3PL) service providers.
Availability in Retailing 231
Figure 8.3 Model for in-store availability improvement
Pre-Requisites ‘7 Improvement Levers’ Outcomes
Improved customer
Human resources 1. Measurement satisfaction
• Senior management 2. Management attention Improved OSA
3. Replenishment system Improved productivity
commitment 4. Merchandising • Supplier
• Staff commitment 5. Inventory accuracy • Retailer
• Incentive structure 6. Promotion • Depots
• Stores
Infrastructure management
• Information technology 7. Ordering system
• Centralized buying
• Logistics network
Collaboration
• Attitudes towards
collaboration
• Inter-organizational
• collaboration
SOURCE Adapted from Fernie and Grant, 2008
Availability through online fulfilment
Electronic commerce, or e-commerce, has fundamentally changed
retail shopping, and mobile technology such as smartphones or
tablets has likewise dramatically affected consumers’ online buying
habits. The use of mobile devices for mobile commerce, also called
m-commerce, was the source of 48 per cent of all retail website traffic
and accounted for 34 per cent of purchases in the £91 billion UK online
retail market in 2014 (Smith, 2014). The rise of online retailing has
brought many logistical and supply chain challenges, especially in the
physical distribution to the final consumer or fulfilment. The follow-
ing chapter deals further with the concept of ‘omnichannel’ retailing
and its specific logistical challenges, and Galipoglu et al (2018) and
Melacini et al (2018) provide useful definitions and background. This
chapter is concerned with the effects of online retailing (including
multichannel and/or omnichannel) on consumer expectations and
the consumer experience, and consideration of them follows.
Xing and Grant (2006) developed a service quality, online fulfilment
model from the consumer’s perspective derived from Parasuraman
et al’s original service quality model (1985) and suggested four impor-
tant consumer criteria expectations of the online fulfilment experience:
232 Logistics and Retail Management
availability, timeliness, condition and return.Availability refers to inven-
tory capability; ie having inventory readily sourced to fulfil consumer
orders. Key questions consumers have include: is the product in stock at
the online fulfilment centre (OFC) at the point of order placement or, if
not, does it need to be ordered, when it is going to be available or what
kind of substitution can be made if possible? Consumers will turn away
if products they want are out of stock and another website selling simi-
lar products is only a click away. Alternative offerings for substitution
may be useful to retain consumers, if used properly. Further, availability
considers how a consumer would be able to track and trace their order;
this ability to trace and track orders is important to consumers. Their
perceived lack of control over delivery of their orders makes them more
eager to know when to expect arrival of orders.
Timeliness measures order cycle performance and for a consumer
is the time lapse between placing and receiving an order, as well as
how many choices the consumer has over the fulfilment date and time
window; how quickly the consumer receives the order and whether
the retailer’s actual performance matches its promise when the order
is confirmed. Reliable, on time and quick delivery is of central signifi-
cance for the consumer as they are more likely to return products
that arrive late, and this has an important bearing on repeat purchase
and the profitability of the retailer. Offering consumers more choice
while they are in the online buying process can be a critical part
of the service experience as a retailer’s ability to meet a consumer’s
schedule is often a key factor in making a sale.
Condition is the form and composition of the delivered order and
is about the accuracy and quality of the order. Nobody likes damaged
or faulty products, which result in returns or even cancellation of
orders. The condition of products directly affects consumers’ percep-
tion of delivery service quality. Return refers to processes available
to return products from the point of receipt or consumption to the
retailer or supplier for possible repair, resale, recycling, etc. Return is
about how many channel options consumers have to return the prod-
ucts, how promptly online retailers collect or replace products and
how the retailer deals with damaged, unwanted or faulty products.
Convenient and easy ways for returns serve as an important facilita-
tor for consumers using online shopping.
Availability in Retailing 233
The first two criteria, timeliness and availability, are visible to a
consumer through the ordering platform. However, the last two crite-
ria, condition and return, are below a consumer’s line of visibility
and are the responsibility of the retailer and the 3PL service provider
undertaking the fulfilment. Regardless, the nature of these four crite-
ria suggest that a consumer’s online purchasing behaviour tends to be
more like a business-to-business logistics buyer instead of exhibiting
usual hedonistic consumer behaviour patterns.
Many retailers and 3PLs offering fulfilment use a rigid method-
ology based on fixed delivery schedules and delivery time windows
(ChainLink Research, 2013). During the final online checkout a few
scheduling choices are offered to consumers, generally with long
delivery windows, based on a static model using a set of assumptions
about what demand might be for a given territory. This approach is
limited because delivery services require consumers to be at home to
receive a delivery, and so consumers ideally want a precise delivery
time. Hence, ‘online shopping cart abandonment’ occurs frequently
at the checkout payment point when consumers realize they are not
going to get the product when they want it.
Online shopping for non-grocery products requires less logistical
effort. Catalogue mail order retailers have had long experience of
delivering a broad range of merchandise to the home, while some
major retailers in Europe have traditionally made home delivery a key
element in their service offering (Teller et al, 2006). In making a final
delivery to the home, retailers must strike an acceptable and profit-
able balance between customer convenience, distribution cost and
security. Most consumers would like deliveries made at a precise time
with 100 per cent reliability to minimize waiting time and the incon-
venience of having to stay at home to receive the order. However, few
customers are willing to pay the high cost of time-definite delivery
and demand free delivery and return (Hamilton, 2014).
Retailers have to select the optimum distribution channel to
succeed financially with online fulfilment, whether it is by consumers
ordering in-store and the retailer delivering to home or consumers
ordering online and the retailer or 3PL service provider fulfilling from
any store or OFC location. Each option has a different cost structure
that retailers need to understand, and one US study found a wide
234 Logistics and Retail Management
gap in the cost accounting capabilities of OFCs versus stores (Banker
and Cooke, 2013). Most respondents could identify costs associated
with various activities at the OFC but few had a clear understand-
ing of corresponding costs for in-store fulfilment. For example, 78
per cent said they knew the cost of picking individual items by SKU
or product class but only 38 per cent could present corresponding
costs for a store backroom and only 29 per cent understood expenses
associated with picking individual items in the front of the store.
Additionally, 70 per cent said they could break out transportation
costs by SKU for OFC deliveries but only 57 per cent had that same
level of understanding for shipments to store (Banker and Cooke,
2013). The study concluded that retailers will have to adopt many
established upstream distribution practices within store operations to
succeed in omnichannel distribution, and may not fare well relying
solely on in-store buffer inventory and long lead times for customer
delivery.
Service failure and recovery
Retail stores are the final logistical link to consumer in bricks-and-
mortar retail supply chains, and stock-outs represent a service failure
by the retailer to make goods available at the right time and in the
right place for consumers who are shopping in-store. The major-
ity of customers react by shopping elsewhere, which represents
not only lost sales but also lost loyalty as consumers switch stores.
Service failures in online retail are in six main areas: website design,
customer service, payment, product quality, security problems and
delivery problems, which is the most frequent online service failure
(Rosenmayer et al, 2018).
What is generally missing in many organizations is an appreciation
of aspects at the end of their service process, ie post-transaction or
service recovery if there is a service failure or ‘event’ (Grant, 2012),
particularly in an online context (Fernie and Grant, 2015). Service
failures are not failures of the service per se, instead the propensity for
service failure is usually in the service system design built on assump-
tions derived from other business models such as manufacturing
Availability in Retailing 235
where human resources are not encouraged or incentivized to deal
with a potentially failure or event. This suggests a temporal nature to
services, particularly failure and recovery.
Service recovery is an organization’s response to service failures.
However, others use the term ‘complaint management’ in the mean-
ing of service recovery and acknowledge the importance of informal
and ‘simulated’ complaints as part of the complaint management
or service recovery process (Hammami et al, 2018). Omnichannel
extends this issue beyond channels of distribution to channels of
communication as it covers all customer touchpoints.
Such touchpoints include social media, such as Facebook, where
customers increasingly voice complaints to organizations. Social
media provides an easy, interactive and instantaneous complaint
channel for customers, particularly when using smartphones. This
makes the consumer’s ‘voice’ via an organization’s social media akin
to negative word of mouth, which can lead to undesirable long-
term outcomes, such as destruction of brand, image and future sales
(Rosenmayer et al, 2018).
Hammami et al (2018) argue that service recovery should be an
organizational capability that allows organizations to profit from
external knowledge sources such as social media and develop an
ability to exploit internal knowledge sources to acknowledge, under-
stand and respond to this ‘voice’ concerning complaints.
Grant’s (2012) customer service framework comprising pre-
transaction, transaction and post-transaction events is akin to
Parasuraman et al’s (1985) service quality model. Grant (2012)
suggests seven ‘best practice’ steps to enable proper service recovery
from a failure event at any of his three stages related to the three
transaction events:
1 Measure recovery costs versus not doing so, ie to reputation, image
and future sales.
2 Actively encourage customer feedback to get a sense of what
customers are concerned about.
3 Anticipate recovery needs by being proactive in service design and
operations.
4 Respond quickly to any service issue.
236 Logistics and Retail Management
5 Educate personnel and employees in the art of service provision.
6 Empower boundary-spanning personnel to make appropriate
decisions to recover a situation at source – which is particularly
important in online, real-time situations.
7 Close the loop to ensure the issue has been properly resolved and
the customer is satisfied with the outcome, process, or both.
As noted above, Fernie and Grant (2008) provided a model for avail-
ability improvement based on ECR Europe’s ‘seven levers’ (ECR UK,
2004) and shown in Figure 8.3. Fernie and Grant identified three
prerequisites to address the ‘seven levers’: management commit-
ment and a motivated workforce; a strong information technology,
centralized buying and logistics network infrastructure; and a high
degree of intra- and inter-collaboration within the sector. Once these
prerequisites are met, retail managers can tackle availability using the
‘seven levers’ blueprint as a guide. The outcomes from implementa-
tion would be less failure through stock-outs and greater consumer
satisfaction because products are available in addition to improved
logistics network reliability and improvements in overall productiv-
ity that reduce costs.
Online retailing requires different logistics tasks from the retailer
that often means redesigning distribution systems to operate in an
online format. However, many small retailers or those who sell physi-
cally smaller products may have little or no home delivery experience.
Their distribution systems only shift pallets of good from warehouse
to store shelves. For them, online retailing and home delivery are new
areas and a challenge in learning how to add such capabilities to their
existing business model.
Channel performance evaluation is crucial for designing an appro-
priate multichannel distribution strategy in terms of optimal channel
mix, channel design, level of channel independence, and resource
allocation across channels. Information about channel performance
also helps determine appropriate pricing, assortment and service level
decisions (Wolk and Skiera, 2009).
Some retailers, such as those who sell furniture or white goods,
have home delivery systems in place already and have adapted
their systems to accommodate online sales. Further, larger trucking
Availability in Retailing 237
companies in the United States such as Ryder-MXD Group and J.B.
Hunt-Special Logistics Dedicated LLC, are providing services for
larger and two-man delivery products that parcel carriers are unable
to handle (Smith, 2018).
Early research into online grocery cost relationships noted savings
of 40–60 per cent are possible where 3PLs can fulfil at any time during
a 24 hour day (Punakivi and Tanskanen, 2002). However, such time
flexibility and result is achievable only where a system of unattended
delivery is available, as failed home deliveries and appointments cost
retailers and 3PLs £53 billion a year (Anon, 2013). Over 31 per cent
of all appointments for delivery fail, and the main factor is a lack of
notification or communication of arrival times. The primary reason
for consumers not being at home had the most impact on retailers,
utilities, telecommunications and postal service firms, with an esti-
mated cost of £238 per individual failed delivery or service attempt,
attributed to associated increases in administration and business
process costs (25 per cent), a lack of capacity utilization (16 per cent),
and call centre overburden (10 per cent).
One solution is simply leaving the order outside the house or
apartment, preferably in a concealed location – known as unsecured
delivery. This eliminates the need for a return journey and can be
convenient for consumers, but obviously exposes the order to the risk
of theft or damage. Thus, consumers are more interested in a secured
delivery to mitigate this risk. McKinnon and Tallam (2003) devel-
oped a flow chart of various unsecured and secured delivery options,
detailed in the next chapter. However, Amazon provided an option
in early 2018 to deliver packages to its Amazon Prime members’
parked cars. The in-car service builds off Amazon Key, which uses
a combination of an internet-connected door lock and camera cost-
ing $220 to allow drivers to place packages inside Prime members’
homes. The in-car service builds on existing connected car services,
eg General Motors’ OnStar system of Volvo’s On Call service, and is
free to Amazon Prime members (Nellis, 2018).
Collection and delivery points strategically located in or around
retail outlets, transport terminals and petrol stations to provide ‘click
and collect’ for consumers offer the best prospects for commercial
viability and are proving popular with consumers, with an estimated
238 Logistics and Retail Management
30 million people using it in the United Kingdom. Click and collect
provides a balance between the conflicting demands of consumer
convenience, delivery efficiency and security, and integrates flows of
business-to-customer and business-to-business orders to achieve an
adequate level of throughput. However, from an environmental and
sustainability perspective, click and collect represents a ‘half-way’
house solution. A failed delivery attempt, a re-direction to a click-
and-collect site and a consumer driving to pick up the order, ie not
collecting it en route in a normal journey, entails three distinct travel
activities that increase greenhouse gas emissions, use more fuel and
add to congestion (Grant et al, 2017b).
The online purchase of physical products involves different
handling and movement involving more substantial packing, picking,
dispatching, delivering, collecting and returning. From a consumer’s
perspective a product purchased online cannot be utilized unless it
is delivered to them at the right place, at the right time and in the
right condition. An important question is who should undertake
that activity in the online channel – the retailer or the 3PL service
provider. The key benefit that 3PLs provide to retailers is functional
expertise. However, they may not truly understand the retailer and
consumer relationship and the consumer’s need for order fulfilment.
Further, are they able to assist the retailer in their marketing efforts
to online consumers and add value to the retail offering, given their
‘invisible’ nature in the fulfilment process (Xing and Grant, 2006)?
The concept of online fulfilment is also moving upstream in the
supply chain. Some consumer product goods (CPG) manufacturers
are moving into direct-to-consumer (D2C) distribution by acquir-
ing new ‘pure player’ entrants, eg Unilever’s $1 billion acquisition of
Dollar Shave Club and Campbell Soup’s $10 million investment in
meal-kit company Chef’d (Bashkin et al, 2017). These CPG examples
are still in their early days and lag D2C efforts in other categories,
such as apparel where Nike already generates more than $9 billion
in sales. However, some ‘big box’ retailers and other large customers
are encouraging manufacturers to develop space in existing ware-
houses or DCs for D2C deliveries in future (Bond, 2018). However,
D2C may not make sense for certain product categories, for example
products that melt quickly at room temperature (Bashkin et al, 2017).
Availability in Retailing 239
The rapid rise of online retailing has also had an enormous impact
on returns management. The sophistication of the front-end buying
experience across different platforms has resulted in equally high
customer expectation about the returns process. Between 25 to 50
per cent of products sold online are returned, including almost 45
per cent of clothing and footwear products (Clipper Logistics, 2015),
with global returns totalling about $642 billion per year (Anon,
2018).
Online retailers handle returned items differently depending on the
type and condition of the product, and the relationship between the
retailer and the manufacturer/vendor. If the item is in good condi-
tion, with no apparent damage, it will often go back on the shelf.
However, if the manufacturer desires to keep strict quality control
and high standards, the item will not return to the retail shelf until
the manufacturer inspects it. This may be a necessary step for prod-
ucts with high risk of liability, such as fashion clothing (Ruiz-Benitez
and Muriel, 2004).
The biggest reason for returns is the condition of the products.
They are either defective or poor-quality products. Customers also
return items due to buying the wrong item or size, or finding the same
product at a lower price elsewhere. ‘Wardrobing’, a form of return
fraud where consumers purchase clothes, wear them and return them
afterwards for refund, also explains the growing number of returns.
Fraudulent returns in the United States totalled almost $11 billion
in 2014 (Anon, 2018). However, systems such as Clipper Logistics’
Boomerang returns service enables fashion retailers such as Asos
to maintain control over their online product return flows (Clipper
Logistics, 2015).
Conclusions
This chapter has shown that availability of products in-store has
been a topic of much academic and practitioner interest during the
last five decades, and is a growing topic regarding the more recent
phenomenon of online retailing and fulfilment. It is clear, however,
that availability in-store has been addressed in a much more robust
240 Logistics and Retail Management
manner by the grocery sector than other sectors. The grocery sector
identified availability and stock-outs as major issues affecting profita-
bility in the early 2000s and research, including our own contribution
to the work, has shown that most problems occur in replenishment
over the ‘last 50 yards’ in-store and from DCs to stores. However,
the ultimate goal of 100 per cent availability is unobtainable as the
cost of holding and servicing additional stock outweighs incremental
customer benefits.
In the online environment, a lack of product fulfilment is due to
operational or stock issues. The former are more important when it
comes to timeliness and the delivery or collection process. Retailers
and 3PLs are looking at new and innovative ways to ensure consum-
ers get their purchases when and where they want, but there are
logistical constraints that will be discussed further in the next chapter.
Returns of unwanted or defective goods are also a problem receiving
attention.
A great consumer experience through shopping in-store or ordering
online is only as good as the retail system’s weakest link – even those
links that the retailer may not be directly responsible for, such as
payment gateways, suppliers, 3PLs or parcel carriers. Poor customer
service resulting from service failures require prompt and correct
recovery, but many organizations have still not grasped some of the
appropriate concepts for doing so and thus several issues remain to
ensure availability and hence consumer satisfaction and ongoing
loyalty.
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245
The development 09
of e-tail logistics
JOHN FERNIE, SUZANNE FERNIE AND ALAN MCKINNON
Introduction
E-commerce,thesaleanddistributionofgoodsandservicesviae lectronic
means, has developed rapidly over the last couple of decades. There
are a variety of e-commerce sectors including: business-to-business
(B2B); business-to-consumer (B2C); business-to-government (B2G);
consumer-to-consumer (C2C) and government-to-business (G2B).
This chapter is concerned primarily with B2C and C2C e-commerce.
M-commerce, the use of mobile technology for the selling of goods
and services, is developing even more rapidly. Initially many retail-
ers used ‘m-tail’ as a new and popular route for selling goods and
services to consumers, but it has now overtaken desktops as a medium
for browsing websites if not for actually making transactions in most
markets.
Non-store shopping is not new. Traditional mail order goes back
over a century. The ‘big book’ catalogues of the mid-20th century that
were used to sell to family and friends experienced slow decline with
the advent of more upmarket and more precisely targeted ‘special-
ogues’. Some large UK retailers like Argos and Next use catalogues to
support their store and e-tail formats as well as an additional channel
to market. Nevertheless, the tradition of selling to friends and family
still continues with party plans, most notably Ann Summers, and
door-to-door selling through Avon catalogues. These ‘low tech’ forms
of selling accounted for around 4–5 per cent of all retail sales in the
United Kingdom and the United States for many years until the turn of
the century, when the ‘higher tech’ options dominated the marketplace.
246 Logistics and Retail Management
Initially, the ‘hype’ exceeded reality and after the dot.com boom in
the late 1990s, a considerable shakeout of the industry took place
throughout the next decade as internet shopping began to experience
steady growth. This chapter will discuss the growth of e-commerce,
the evolving market and consumer responses to online retailing. The
logistical challenges faced by the retail sector will then be discussed,
especially the difficulties encountered in solving the ‘last mile’ problem.
The growth and development of the
e/m-commerce market
Whilst it is generally accepted that e-commerce grew considerably in
the 1990s and the early part of this century, accurate, reliable figures
were difficult to ascertain because of the need to agree upon a widely
accepted definition. Now, statistics on internet use, e-commerce and
e-tailing are widely available. For example, in the United Kingdom
statistics.gov.uk provides a monthly index of retail sales includ-
ing non-store retail sales, while leading online trade body IMRG
provides global and country e-commerce statistics plus in-depth
e-commerce data.
The growth of e-commerce was closely linked to the development
of internet usage. In 2000 there were just over 350 million internet
users in the world, a figure that grew to over 2 billion in the next 10
years and reached 3.8 billion in 2017 (Miniwatts Marketing Group,
2017). Asian internet users form the highest proportion, followed by
European and Latin American/Caribbean users. But internet access
itself was not sufficient to deliver large-scale online retailing. Early
download speeds were too slow to support creative online visual
merchandizing and the interactivity with customers that retailers
enjoy today. The growth of broadband allowed faster download
speeds and facilitated the growth of successful e-tail websites. This,
together with a strong focus on improving security of transactions,
encouraged growth in online spend by consumers. The rapid growth
of mobile broadband added a further channel for e-commerce, bring-
ing with it consumers who had never shopped via ‘traditional’ e-tail
websites. Indeed, it is the Millennials, the generation born between
The Development of E-tail Logistics 247
1978 and 1995, who are driving retail growth through mobile shop-
ping (Bazaarvoice, 2013). Digital savvy, they have grown up during
the explosion of social media and are strongly influenced in making
shopping decisions by the likes of Facebook, Twitter and Instagram.
Early research focused upon B2C transactions, although
few companies in this sector made a profit in the early years of
e-commerce. In the early years it was the B2B and C2C sectors that
produced real benefits to customers and increased profitability for
the partners involved. In C2C markets, intermediaries such as eBay
acted as online auctioneers brokering deals between bidders and sell-
ers. Similarly, B2B exchanges, such as GlobalExchange, promoted
online auctions and collaborations between partners to reduce costs.
Businesses involved in exploiting these e-commerce markets are called
infomediaries in that they are trading information and are facilitators
in reducing transaction costs between buyer and seller.
The problem with the B2C model compared with C2C and B2B
models was the requirement to trade goods that were tangible and
needed to be stored and transported to the final consumer. (Later,
some of these goods, such as music and books, were converted to
electronic formats that were downloadable directly to consum-
ers’ computers, mobiles, mp3 players and e-readers.) Additionally,
a market presence and brand identity were necessary ingredients
to wean customers away from their traditional methods of buying
behaviour. Yet, despite these apparent drawbacks, the ‘hype’ asso-
ciated with this new form of trading led many analysts to discuss
the notion of disintermediation in B2C markets. This means that
the role of intermediaries – agents, wholesalers and even retailers
– would be reduced as manufacturers were enabled to interact with
and sell directly to consumers. Traditional retail channels were to
be disrupted as new players entered the market with online offers.
Not surprisingly, many conventional retailers reacted passively to the
new threat due to concern that they would cannibalize their existing
customer base and jeopardize their investment in capital assets (eg
stores). Many early pure player e-tailers, such as European fashion
entrant Boo.com, sustained losses, and there were numerous bank-
ruptcies; others, such as grocery e-tailer Peapod, were taken over by
major retail groups (Ahold in this case).
248 Logistics and Retail Management
With hindsight, a multichannel strategy was the obvious route to
success, especially for companies with a mail order presence. Some
early multichannel retailers, such as Eddie Bauer and Dixons, indi-
cated that customers shopping at all channel alternatives (stores,
catalogues and online sites) spent more than single- or dual-channel
customers. This ‘clicks and bricks’ approach gave a customer greater
flexibility, including, in the case of clothing products, the opportu-
nity to return goods to their nearest stores. Customer flexibility was
to be a focus for e-tail differentiation as the platforms for selling
diverged – first with the growth of mobile retailing and retailing via
social networks; second as customers drew strength from their abili-
ties to review products and retailers, and their influence over sales
grew through e-word-of-mouth on networking sites like Twitter and
Facebook; and third as customers began to demand diverse delivery
options for the goods they bought online.
In some countries with well-established e-commerce sectors, the
early years of the 21st century produced enormous growth in sales
for successful e-tailers. Growth levels began to steady after a decade
or so. According to retail commentators, this was not due to an unsta-
ble economic environment, but a sign of a maturing B2C e-commerce
market. Within this market some multichannel retailers (retailers
operating multiple shopping channels including – but not confined
to – physical stores, catalogue and internet shopping) were produc-
ing growth levels far in excess of market norms and consolidation
was apparent among smaller and weaker pure players. Increasingly,
in maturing online markets, large-scale omnichannel retailers were
becoming dominant. Here retailers were integrating individual chan-
nels in order that the consumer could purchase and return goods
from a variety of options.
According to Williams (2009) there has been a four-stage process
in the evolution of e-tailing (Figure 9.1). Stage 1 included the hype
and experimentation that led to the dot.com boom and bust at the
turn of the millennium. This was followed by a stage of retrenchment
and sobriety as funding sources for innovators dried up, at the same
time as the potential of the e-tail market developed and became more
apparent for many established retailers. The third stage, sustainability,
featured stability in the market and consolidation among e-tailers. A
The Development of E-tail Logistics 249
Figure 9.1 The evolution of e-tailing
Hype and Retrenchment and Sustainability Focus and
experimentation sobriety fragmentation
Rapid and erratic Slower and more Stability emerges Continued cycle of
change predictable change with predictable differentiation by
cyclical patterns of
differentiation low prices or
specialization
Entrepreneurial E-pioneers forced to Consolidation, Increased business
focus strategy efficiencies lower
pioneers with adapt or die, physical through cost prices, integrated
leadership or
ambitious expansion retailers enter market differentiation multi‐channel
systems
plans, high start-up through various
and failure rate modes of entry
SOURCE After Williams, 2009
fourth stage of focus and fragmentation is evident as retailers provide
shopping opportunities in multiple and mobile platforms, tailor their
marketing mixes more precisely to the needs of individual consumers
and develop multiple delivery options.
Maturing e-tailers in economies where the strong rate of growth
of the online market is slowing increasingly view international e-tail
activity as the way to continued prosperity, with the Asia-Pacific
and South American markets offering the most potential. Table 9.1,
derived from AT Kearney’s Global Retail E-Commerce Index, high-
lights the market attractiveness of the top 30 countries of the world.
The United States, China and the United Kingdom dominate the
list; however, China has the most growth potential along with Latin
American countries. The United Kingdom is one of the strongest
e-tail markets, with high penetration of broadband and experienced
consumers who have achieved the highest per capita online spend in
the world. It is also the base of a range of e-tailers with over 20 years’
experience in online activity. As in all countries, there is considerable
year on year change in the e-commerce market and its main players.
A snapshot of the state of the UK market in 2017 from a Retail Week
report appears in Table 9.2. At this time, official Office of National
Statistics figures indicate that online spend was just over 15 per cent
of total retail sales, with food sales only 5 per cent of the total. These
figures appear lower than is often quoted by consultancy companies
and may be due to differences in definitions of what constitutes retail
Table 9.1 The 2015 Global Retail E-Commerce Index
Rank Country Online market Consumer
1 United States size (40%) behaviour (20%
2 China 100.0
3 United Kingdom 100.0 83.2
4 Japan 87.9 59.4
5 Germany 77.6 98.6
6 France 63.9 87.8
7 South Korea 51.9 92.6
8 Russia 44.9 89.5
9 Belgium 29.6 98.4
10 Australia 8.3 66.4
11 Canada 11.9 82.0
12 Hong Kong 10.6 80.8
13 Netherlands 2.3 81.4
14 Singapore 8.9 93.6
15 Denmark 1.3 98.8
8.1 89.4
100.0
250
Growth Infrastructure Online market
%) potential (20%) (20%) attractiveness score
22.0 91.5 79.3
86.1 43.6 77.8
11.3 86.4 74.4
10.1 97.7 70.1
29.5 83.1 66.6
21.0 82.1 59.3
11.3 95.0 58.9
51.8 66.2 48.7
48.3 81.1 45.6
28.6 84.8 43.6
23.6 88.9 43.1
13.0 100.0 42.2
8.1 84.6 41.8
15.7 100.0 41.5
15.1 75.5 41.4
16 Sweden 8.8 97.2
17 Mexico 10.0 53.3
18 Spain 13.2 73.1
19 Chile 2.7 71.8
20 Norway 8.2 99.4
21 Brazil 19.6 57.4
22 Italy 12.3 71.6
23 Switzerland 89.6
24 Venezuela 7.1 54.1
25 Finland 1.7 98.3
26 New Zealand 6.4 86.4
27 Austria 1.7 85.3
28 Saudi Arabia 5.9 46.6
29 Argentina 1.1 70.3
30 Ireland 5.7 74.4
4.9
SOURCE Adapted from Kearney, 2015
11.8 77.7 40.9
58.6 68.0 40.0
20.2 80.1 39.9
49.3 73.2 39.9
39.5
5.6 76.3 39.4
28.0 72.4 38.9
27.8 70.7 38.8
38.5
7.4 82.5 38.4
79.4 55.7 38.2
38.1
3.8 77.3 38.1
25.9 75.4 38.0
19.0 74.8 37.2
67.3 74.6
43.9 64.3
27.6 74.1
251
252 Logistics and Retail Management
Table 9.2 Top 15 UK e-tailers 2015/16
1 Amazon £4,900 million
2 Tesco £3,100 million
3 Argos £2,017 million
4 John Lewis £1,716 million
5 DixonsCarphone £1,500 million
6 Shop Direct £1,406 million
7 Next £1,300 million
8 Sainsbury’s £1,300 million
9 Asda £1,250 million
10 Ocado £1,172 million
11 M&S £792 million
12 Asos £604 million
13 N Brown £563 million
14 AO.com £541 million
15 Screwfix £420 million
SOURCE Retail Week, 4 April 2017
sales. Regardless of this statistical anomaly, the Retail Week figures
show some changes over the last five years. Companies such as John
Lewis, Dixons Carphone and Next have consolidated their positions
in the top 10 with a strong omnichannel offering. Pure players such
as Ocado, AO and Asos have also grown considerably this decade.
It should be noted, however, that the figures in Table 9.2 only repre-
sent UK sales, so Asos’ growth is under-represented as this growth
has been achieved outside the United Kingdom. The 2016 merger
between Argos and Sainsbury’s will allow them to challenge the
market leader, Amazon.
Web 2.0
Web 2.0 is a term encapsulating a number of software developments
that allowed the World Wide Web to be used for information sharing
and collaboration, fostering creativity, user-centred design and inter-
operability. Web 2.0 encouraged collaboration among professionals
The Development of E-tail Logistics 253
and academics, and underpinned the development of wikis, blogging
and social networking sites.
The power of information sharing was understood by Amazon
comparative early in the history of e-tailing. The company exploited
this by facilitating customer reviews of books, and making the
reviews easily available for online shoppers, together with profes-
sional reviews and author information. Later, potential customers
were given information on book alternatives and on the final buying
decisions of other page viewers.
Web 2.0 allowed for the application of pure marketing princi-
ples to the e-tail market. There was a move from straightforward
brochure-like visual content on e-tail webpages to the placing the
user/customer at the heart of the service, certainly in terms of service
participation and personalization of content. This was further
refined and exploited by mobile phone apps that gathered customer-
customized data from a range of sources.
Many academics and retail professionals noted that the digi-
tal revolution shifted power in the marketplace to consumers. The
increase in power comes from more information and transparency
of information, which enabled group power, allowing consumers to
influence products and prices. Not only does this affect how retailers
market products but also how they communicate with their custom-
ers (Kucuk and Krishnamurthy, 2007).
The level of disintermediation in this virtual market did not take
place to the extent expected early in its development, partly because
large multichannel retailers exploited the strong brand presence
gained in traditional markets to pursue share of the online market.
However, the existence of Web 2.0 enhanced the role ‘infomediar-
ies’ played in the online market and some, such as Google, eBay and
Facebook, became household names.
Exploiting the long tail
When e-commerce was in its infancy, a transformation of market-
ing was predicted, including the facilitation of one-to-one marketing
that is one of the features of e-tail today. The web allowed for the
254 Logistics and Retail Management
accumulation and refinement of an enormous amount of customer
data. This was made possible through the integration of customer-
facing retail websites with customer relationship management (CRM)
software. The relatively easy accessibility of a wealth of individual-
ized customer data, including browsing and shopping habits, allows
e-tailers to effectively and profitably market on an individual basis
(Doherty and Ellis-Chadwick, 2010; Frow and Payne, 2009).
The ‘long tail’ was a term coined in the early 2000s to describe
an emerging feature of the online marketplace in which niche
demands can be profitably exploited. In some e-commerce market
sectors, supply is not limited by shelf-space and how much it costs
to manufacture, store and distribute a product. It becomes virtually
abundant at such low cost that it can be provided at low levels of
demand, whereas the costs of providing it through traditional chan-
nels outweighed potential revenues. Two examples are downloadable
books and music.
Amazon developed its retail website, just-in-time delivery and huge
capacity for storage to offer an almost limitless range of book titles, so
that niche demand could be profitably exploited for physical books.
It then successfully launched an early e-reader so that niche demand
for reading materials could be exploited even more seamlessly and
cheaply. Facilitation of the publishing process was a logical develop-
ment so that reading materials could be supplied more efficiently for
niche consumption. Apple used its innovative design capability to
launch a stream of interactive hardware devices that attempted to ‘tie
in’ customers to products such as music and software that could be
purchased or downloaded only via Apple stores.
The conventional Pareto principle (or 80:20 rule) assumes that 80
per cent of sales could be attributed to 20 per cent of products –
most sales are linked to bestselling items. The evidence of the ‘long
tail’ indicates that this is not applicable for online distribution and
that, collectively, niche products can rival bestsellers in terms of
sales volume. One study investigating the ‘long tail’ concept found
that, for the same multichannel retailer, the internet channel showed
less concentrated sales distribution than the conventional channel.
Product availability and pricing were the same in both channels, so
the ‘long tail’ effect was not caused by differences in distribution but
The Development of E-tail Logistics 255
by the use of internet search and discovery tools that lowered search
costs for customers (Brynjolfsson et al, 2011).
If Web 2.0 has enabled, simplified and reduced consumer costs for
making buying decisions and underpins the ‘long tail’ of the online
market, it seems clear that there will be similar influences on the
marketing of goods and services – a case of the ‘tail wagging the dog’
perhaps. Eric von Hippel (1986) has long noted the importance of user
innovators sharing ideas with manufacturers to enable development of
the products they want. Around twenty years later, he concluded inno-
vation has become more user-centred (Von Hippel, 2005). Web 2.0,
having made the sharing of information easier, faster and common-
place, means businesses that want to compete for the sizeable ‘long
tail’ market can implement ‘long tail’ marketing (Andrei and Dumea,
2010). This includes reaching niche customers in innovative and cheap
ways that will change over time. Some current examples include:
● exploiting the potential interactions through social networks and
online networks;
● communicating via blogs, RSS feeds, webcasts;
● stimulating e-word-of-mouth through buzz and viral marketing;
●● pay-per-click and search engine optimization that focuses on less
competitive ‘long-tail’ keywords that offer a higher return on
investment than generic keywords.
Clearly, the development of Web 2.0 infers future fluidity in online
markets and in the methods and means of reaching and interacting
with online customers in the future.
Marketing-orientated retailers, being customer-facing organiza-
tions, have been at the forefront of user-centred innovation for many
years. Many major retailers have been involved in product manu-
facturing through the development of their own brands, with some
vertically integrating the supply chain to the level where they influ-
ence the raw materials from which the products sold in their stores
are made. So it is not surprising that maturing e-tailers are in a posi-
tion to exploit ‘long tail’ marketing, not only in the development of
their retail goods and services, but in development and maintenance
of their organizational brands, ‘using Internet technologies to enabled
256 Logistics and Retail Management
an organized cooperation with their users, giving them a voice and
relying on their contribution to the process of innovation and brand
value creation’ (Andrei and Dumea, 2010).
The interactivity, transparency and fluidity among online social
groups and customers that was fostered via Web 2.0 and that contrib-
uted to the emergence of the ‘long tail’ have been exploited by large
pure players like Amazon, eBay and Asos.com as well as by multi-
channel retailers like Tesco and Apple and Internet brands Google
and Facebook. For example, Amazon allows customers to choose
to buy books from them or from partners with transparent pricing.
eBay lets buyers and sellers of niche collectible products agree their
own price for trade. Asos.com encourages customers to post pictures
of themselves wearing Asos merchandise. Tesco allows customers to
shop for brands with partner retailers via its website. Apple hosts
apps for the Amazon Kindle so Apple customers can download books
from its competitor. Google launched its own social network in 2011
and runs YouTube and Android, which offers music, TV and book
apps. Facebook has launched new music, TV and news apps.
Online shopping formats
The e-commerce market allowed for a great deal of creativity in
the design of e-tail stores. As the B2C market developed, a number
of distinct online retail formats could be distinguished (Zentes
et al, 2011). First, price formats such as Overstock.com, Asos
Outlet and Tesco Outlet sell overstocks or products from previous
seasons and auction sites such as eBay allow buyers and sellers to
mutually agree the selling price. Customers are attracted to e-tail
portal sites that offer points or cashback for accessing other e-tailers
via their websites. Current examples are Quidco and Topcashback.
Second, experiential formats apply the potential of technology in
creating an interesting and enjoyable online shopping experience, lead-
ing the way in exploiting social networking and discussion forums.
One example includes fashion e-tailers such as Asos and Net-a-Porter
that have developed editorial content and two-way/multi-way chan-
nels of communication between customers and e-tailer and among
The Development of E-tail Logistics 257
customers. Another example is that of pop-up, linear ‘shops’ adver-
tising products that can be downloaded for view, or purchased and
delivered via mobile phone, which exploited an increasing desire for
mobile shopping and the potential for selling via the ‘m-channel’ as
mobile internet access moved towards overtaking desktop internet
access in 2013/14.
Third, community-based formats began to emerge that placed a
virtual social community at the heart of the shopping experience.
Amazon’s customer reviews and customer purchase trends positively
affected sales of popular products, and the popularity of Facebook
led many e-tailers to establish Facebook formats.
Fourth, mass customization formats emerged that exploited the
interactivity of the online environment to provide merchandise precisely
tailored to the individual desires of customers. For example, custom-
ers can customize fashion items like shoes, selecting from a range of
heel heights, fabrics and leathers and decorations on Shoesofprey.com
or customize trainers with Vans or Nike online, while Apple custom-
ers can choose various features for the hardware they buy and add
engravings and accessories to their personalized mix.
Finally merchandise-orientated formats focus on achieving a prod-
uct mix that attracts customers. Types of merchandise-orientated
e-tailer include online department stores like Debenhams.com or
Johnlewis.com, which replicate or adapt the ‘high street’ store online.
Niche e-tailers are online speciality stores specializing in a limited
product range; examples include Wiggle.com, the bicycle e-tailer,
and Net-a-Porter, specializing in upmarket fashion clothing. Online
market places like Tesco Direct and Amazon Market Place allow
customers to access a wide variety of products from partner organi-
zations and/or from other customers. Online category killers achieve
depth of range of a limited product category such as golf (Nevada
Bob) or electronics (Pixmania), aiming to compete on price.
The e-commerce consumer
In only a decade or so, internet connectivity changed from an
English-speaking, developed country phenomenon to a global one.
258 Logistics and Retail Management
This conceals the different stages of development for different coun-
try markets and the geodemographic profile of internet consumers.
Most European countries lagged behind the United States, which had
more than 80 per cent of households connected to the internet as
early as 2001. Table 9.1, earlier in the chapter, shows how the market
has changed in the last 15 years.
Initial research at the turn of the century focused upon how retail-
ers refined their business model and the changing profile of the
internet shopper (see Lavin, 2002 and Morganosky and Cude, 2002
in the United States; and Ellis-Chadwick et al, 2002 in the United
Kingdom). Doherty and Ellis-Chadwick (2006) critically reviewed
research on internet retailing by undertaking a content analysis of
papers published in all journals from 1996 to 2005. They classified
the research into three themes: the retailer perspective, the consumer
perspective and the technological perspective. Most initial research
focused upon the retailer perspective undoubtedly because of the
managerial challenges involved. Hence, some of the research cited
above in the late 1990s/early 2000s focused upon retailers’ adop-
tion of the internet as a channel to market. As the Web became an
accepted technology, research then moved more to the online behav-
iour of consumers, from consumers’ characteristics to their online
experiences and the incorporation of established consumer behav-
iour models to an online environment. Finally, the technological
perspective has demanded less attention, although there have been
meaningful contributions on website design, software tools and
e-commerce infrastructure.
Much research has taken place into profiling the internet shopper.
In terms of demographic variables, key influences on online behav-
iour include income, education, race, age and gender, with lifestyle,
culture and social factors also of importance. In the early stages of
development the profile of the e-commerce shopper was a young,
male professional living in a middle-class neighbourhood. As the
technology became more accepted, the gender and socio-economic
mix also changed and the general demographic profile of online
shoppers has become more similar to those of traditional shoppers.
Psychographic/behavioural variables include the perceptions, atti-
tude and beliefs that can influence online shopping intentions and
The Development of E-tail Logistics 259
behaviour. Internet shoppers, according to a major international
study, tend to be more impulsive, value convenience, tend to be
wealthier and are heavier users of internet and email. They also have
favourable attitudes to advertising and direct marketing (Doherty
and Ellis-Chadwick, 2010).
The profile of an online shopper is not just linked to demographic
and psychographic/behaviour variables that favour online shopping,
or to geography, technology and confidence in the online market,
but to the merchandise being bought. For example, the age profile
for online grocery shoppers tends to be younger, in the 18–40 year
old range. There tend to be children in the household. A study by
IGD, formerly the Institute of Grocery Distribution, found that
online shopping for groceries was linked to the birth of children,
and to times such as school holidays, when children are around the
house more. Grocery shoppers also tended to be in the higher socio-
economic group categories. Older shoppers (65 plus) were less likely
to shop online for groceries (IGD, 2011).
These studies, including the sector-specific/trade organization
investigations, such as that by IGD above, indicate that retailers
are responding to this changing market environment. The basics of
convenience, product range, customer service and price will always
feature in a consumer’s ‘evoked set’ of attributes. Above all, retail-
ers have become brands and customer loyalty has been established
through continually high levels of service. It is not surprising, there-
fore, that it is traditional retailers with strong brand equity that can
gain even more leverage through a sound web strategy. They have the
trust of the consumer to begin with and the capital to invest in the
necessary infrastructure. Many dot.com pure players needed to build
a brand and tackle the formidable challenge of delivering to custom-
ers’ homes. This is why it took so long for Amazon.com to register
a profit.
It is interesting to note that early research into m-commerce
customers indicates some similarities to customers in the early years
of the uptake of e-commerce; for example, most shoppers (62 per
cent in 2005) were young (14–24) males. They tended to be confi-
dent internet users and experienced internet shoppers (Bigne et al,
2005). The demographic and gender profile is likely to rebalance as
260 Logistics and Retail Management
the technology becomes widely familiar and the market develops.
Internet use was not found to influence mobile shopping, but previ-
ous experience of internet purchase meant consumers were more
predisposed to buy on mobile devices.
All of this research shows that e-tailing has been most success-
ful to date where a multichannel ‘click-and-bricks’ approach is
adopted. The companies best equipped to adopt such a strategy were
traditional department stores and clothing specialists in that they
had considerable experience of dealing with the non-store shopper
through their catalogues and ‘low tech’ selling techniques, especially
as these companies were well equipped to deal with home deliveries
and a returns policy. Similarly the early e-tailing specialist pioneers
with CDs, books, videos and computing equipment already had an
infrastructure to deal with home-based orders. The grocery sector
is much more complex and home delivery is more associated with
food service and added-value products. Nevertheless, the sector has
attracted considerable attention in the literature, especially in how
to tackle the ‘last mile’ problem to deliver to the home. The next
sections will discuss the logistics challenges faced by retailers as they
are confronted by consumers who are ever-demanding in terms of
delivery times and returns policies.
The logistical challenges
The main logistical challenges were faced by traditional ‘bricks-
and-mortar’ retailers that handled pallet loads of product from
regional or national distribution centres to stores and then had to
pick goods at item level for home delivery. In terms of a definition,
the home delivery channel terminates at the home or, increasingly,
at a customer collection point. It is less clear where it begins. For
the purposes of this review, the start of the home delivery channel
will be defined as the ‘order penetration point’ (Oldhager, 2003).
This is the point at which the customer order, in this case trans-
mitted from the home, activates the order fulfilment process. This
physical process usually begins with the picking of goods within a
stockholding point. Only when picked are the goods designated for
The Development of E-tail Logistics 261
a particular home shopper. Distribution downstream from this point
is sometimes labelled J4U, ‘just for you’.
With the move to mass customization, an increasing proportion
of customer orders are penetrating the supply chain at the point of
production. Consumers, for example, can configure a personal
computer to their requirements online and relay the order over the
web straight to the assembly plant. Where this occurs, the home
delivery channel effectively starts at the factory.
Within multichannel retail systems, this order penetration point
is the point at which home deliveries diverge from the conventional
retail supply chain that routes products to shops. For example, in
the case of those supermarket chains that have diversified into home
shopping, the order penetration point is either the shop or a local
fulfilment (or ‘pick’) centre, where online orders are assembled. Both
of these outlets draw supplies from a common source, the regional
distribution centre (DC). It makes sense, therefore, to regard the
home delivery channel for grocery products as starting at the shop or
the pick centre.
Forecasts of the growth of online retail services are invariably
demand-driven and assume that it will be possible to deliver orders
to the home at a cost and service standard home shoppers will
find acceptable. This is a bold assumption. Over the past 15 years,
some e-tail businesses and third party providers have failed primar-
ily because of an inability to provide cost-effective order fulfilment.
Initial market research studies identified delivery problems as a
major constraint on the growth of home shopping (Metapack, 1999;
Verdict Research, 2000, 2004). A decade later, online grocery shop-
ping behaviour indicated that issues pertaining to substitutions and
delivery failures persist (Hand et al, 2009). Since then, consumers
have become more demanding, leading to an ‘any time, any place’
mentality, and retailers have responded through offering a plethora
of delivery (and return) options for customers. This means that retail-
ers offer tighter time windows for delivery, provide click-and-collect
choices (the customer incurs the transport costs!) and a range of
collection/return points. Furthermore, free next-day delivery is fore-
cast to become the norm in the near future, impacting upon retailers’
margins.
262 Logistics and Retail Management
Arguably, the greatest logistical challenges are faced by compa-
nies providing a grocery delivery service to the home. They must
typically pick an order comprising 60–80 items across three temper-
ature regimes from a total range of 10,000–25,000 products within
12–24 hours for delivery to customers within 1–2 hour time-slots.
For example, Tesco is currently picking and delivering an average of
250,000 such orders every week. On a peak day its Enfield dot.com
store will pick 145,000 products. New logistical techniques have
had to be devised to support e-grocery retailing on this scale. Online
shopping for non-food items has demanded less logistical innovation.
Catalogue mail-order companies have, after all, had long experience
of delivering a broad range of merchandise to the home, while some
major high-street retailers have traditionally made home delivery a
key element in their service offering. Online shopping is, nevertheless,
imposing new logistical requirements. First, it is substantially increas-
ing the volume of goods that must be handled, creating the need for
new DCs and larger vehicle fleets. Second, many online retailers are
serving customers from different socio-economic backgrounds from
the traditional mail-order shopper. As they live in different neigh-
bourhoods, the geographical pattern of home delivery is changing.
Third, online shoppers typically have high logistical expectations,
demanding rapid and reliable delivery at convenient times (Xing and
Grant, 2006).
Distribution of online grocery products
It was noted in the previous section that the average online grocery
order contains 60–80 items, many of which are perishable and need
rapid picking and delivery. This requires localized order picking either
in an existing shop or in a dedicated fulfilment/pick centre. Over the
past decade there has been much discussion of the relative merits of
store-based or fulfilment centre picking.
The main advantage of store-based fulfilment is that it minimizes
the amount of speculative investment in new logistical facilities for
which future demand is uncertain. Webvan in the United States in the
late 1990s, for example, was planning to build a network of 26 new
The Development of E-tail Logistics 263
automated warehouses, at a cost of approximately $35 million each,
to provide e-grocery delivery across the United States. Fewer than
half of these warehouses were set up before the company went bank-
rupt in 2001. As a ‘pure-player’ in the e-grocery market, Webvan did
not have an established chain of retail outlets and would have had
to form an alliance with an existing retailer to adopt the store-based
model. Ring and Tigert (2001) and Laseter et al (2000) compared the
internet offering with the conventional ‘bricks-and-mortar’ experi-
ence in the United States at that time. They looked at what consumers
would trade away from a store in terms of the place, product, service
and value for money by shopping online. They also detailed the ‘killer
costs’ of the pure play internet grocers, notably the picking and deliv-
ery costs. The gist of the argument presented by these critics is that
the basic internet model is flawed.
Several British supermarket chains, such as Sainsbury’s, Asda and
Somerfield, as ‘bricks-and-clicks’ retailers, had the option of pursuing
store-based or pick-centre fulfilment and opted initially for the latter.
Tesco, by contrast, opted for the store-based model. It can be argued
that this choice enabled it to become the dominant player in the UK
online grocery market and to transfer this model to its subsidiaries
throughout the world. Tesco’s experience is described below.
Basing home delivery operations at existing shops allows retailers
to improve the utilization of their existing assets and resources. Retail
property can be used more intensively and staff shared between the
store and online operations. It is possible to pool retail inventory
between conventional and online markets, improving the ratio of
inventory to sales. This also gives online shoppers access to the full
range of products available in a supermarket, to which most of them
will be accustomed.
Another major benefit of shop-based fulfilment is that it enables
the retailer to achieve a rapid rate of geographical expansion, secur-
ing market share and winning customer loyalty much more quickly
than competitors committed to the fulfilment centre model.
On the negative side, however, integrating conventional and online
retailing operations in existing shops can impair the standard of
service for both groups of customer. The online shopper is disad-
vantaged by not having access to a dedicated inventory. Although
264 Logistics and Retail Management
a particular product may be available on the shelf when the online
order is placed, it is possible that by the time the picking operation
gets under way ‘conventional’ shoppers may have purchased all the
available stock. Where these in-store customers encounter a ‘stock-
out’ they can decide themselves what alternative products to buy,
if any. Online shoppers, on the other hand, rely on the retailer to
make suitable substitutions. Substitution rates are reckoned to be
significantly higher for store-based fulfilment systems than e-grocers
operating separate pick centres. For example, Ocado, the only UK
e-grocery to rely solely on a pick-centre, claims that it can achieve
substitution rates of less than 5 per cent, whereas customers using
its store-based competitors sometimes experience substitution rates
more than twice this level (McClellan, 2003). In comparing substi-
tution rates, however, allowance must be made for differences in
product range. In the early 2000s, Ocado’s range of around 12,500
products was less than half that of the major supermarket chains
engaged in online shopping.
Doubts have been expressed about the long-term sustainability of
store-based fulfilment. As the volume of online sales expands, conflicts
between conventional and online retailing are likely to intensify. At
the ‘front end’ of the shop, aisles may become increasingly crowded
with staff picking orders for online customers. In practice, however,
much of the picking of high-selling lines is done in the back store-
room. It is at the ‘back end’ that space pressures may become most
acute. Over the past 20 years the trend has been for retailers to reduce
the amount of back storage space in shops as in-store inventory levels
dropped and quick-response replenishment became the norm. This
now limits the capacity of existing retail outlets to support the online
order fulfilment operation. New shops can, nevertheless, be purpose-
built to integrate conventional retailing and online fulfilment. The
Dutch retailer Ahold has coined the term ‘wareroom’ to describe a
dedicated pick facility co-located with a conventional supermarket
(Mees, 2000).
Most of the purpose-built fulfilment centres so far constructed
are on separate sites. They offer a number of logistical advantages
over store-based picking. As their inventory is dedicated to the online
service, home shoppers can check product availability at the time of
The Development of E-tail Logistics 265
ordering and, if necessary, alter their shopping list. The order picking
function should also be faster and more efficient in fulfilment centres
as they are specially designed for the multiple picking of online orders.
To be cost-effective, dedicated pick centres must handle a large
throughput. The threshold level of throughput required for viability
also depends on the breadth of the product range. It is very costly to
offer an extensive range in the early stages of an e-tailing operation
when sales volumes are low. Offering a limited range can cut the
cost of the operation but makes it more difficult to lure consum-
ers from conventional retailing. Another inventory-related problem
that retailers using pick centres have encountered is the difficulty of
disposing of excess stocks of short shelf-life products. When over-
stocking occurs in a shop, consumer demand can be stimulated at
short notice using price reductions or in-store merchandising tech-
niques. It is more difficult using electronic media to clear excess
inventory of fresh produce from fulfilment centres that consumers
never visit.
Several studies have argued that store-based fulfilment is more
appropriate in the early stages of a retailer’s entry into the e-grocery
market (eg Fraunhofer Institute, 2002). It represents a low-risk
strategy and allows new business to be won at a relatively low
marginal cost. As the volume of online sales grows, however, the cost
and service benefits of picking orders in a dedicated centre stead-
ily increase until this becomes the more competitive option. Several
break-even analyses have been conducted to estimate the threshold
online sales volume at which the fulfilment centre model is likely to
be superior. Tesco appears to have reached this threshold volume in
the south-east of England over a decade ago. In 2006 it opened its
first dedicated fulfilment centre in south London, known as a ‘Tesco
dotcom only store’, because it has a similar format to a conventional
shop but is used solely for the picking of online orders. The term
now commonly used for such stores is ‘dark stores’. Tesco opened
seven of these stores in the south-east of England by the mid-2010s.
The viability threshold for such dedicated operations varies from
retailer to retailer depending on the size and layout of shops, the
nature of the upstream distribution system, the product range and
the customer base. It will also be highly sensitive to the allocation
266 Logistics and Retail Management
of retail overheads between the conventional and online shopping
operations. Picking the average online grocery order in a dark store
in the UK costs around £12, by comparison with £18–20 in a conven-
tional supermarket.
A further complicating factor is the geography of the retail market.
The relative efficiency of the two types of fulfilment varies with the
density of demand and level of local competition in different parts
of the country. In a mature e-grocery market, dedicated pick centres
may serve the conurbations, while store-based distribution remains
the most cost-effective means of supplying rural hinterlands. The US
e-grocer Peapod has a policy of using store-based fulfilment when
penetrating new local markets, working in collaboration with retail
chains. Once volumes have reached an adequate level, as in Chicago
and San Francisco, the company has invested in DCs.
Experience in the United Kingdom suggests that most new
entrants to the e-grocery market opted for the fulfilment model
prematurely. Sainsbury’s, Somerfield and Asda all set up pick
centres and closed them down within a few years. It is now gener-
ally acknowledged that at the present level of e-grocery sales in
the United Kingdom, the store-based distribution model, pioneered
by Tesco, is the most cost-effective, except for chains with a
strong south-east of England presence. For example Sainsbury’s
and Waitrose have opened dark stores in recent years. The only
new entrant to possibly disrupt the market will be Amazon Fresh,
which is sourcing products through Morrisons and delivering via
its Prime one-hour service.
Alvarez and Marsal (2014) argue that the current model in use
for online grocery fulfilment is unsustainable in the United Kingdom
because of the ‘last mile’ problem, which is discussed later in the
chapter. In traditional bricks-and-mortar retailing when customers
came to the store, scalability was easy. Retailers engaged in store
wars to buy up real estate and logistics costs were kept down as
large volumes of product moved from DCs to large stores. The online
model is different, and increased scale does not necessarily generate
more profit but may increase costs. They maintain that the high pick-
ing costs, especially at stores, coupled with a costly number of small
drops from 3.5 ton vans, means that profit margins will be impacted
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33572_John Fernie _ Leigh Sparks - Logistics and Retail Management _ Emerging Issues and New Challenges in the Retail Supply Chain, (2019, Kogan) - libgen.lc
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33572_John Fernie _ Leigh Sparks - Logistics and Retail Management _ Emerging Issues and New Challenges in the Retail Supply Chain, (2019, Kogan) - libgen.lc
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