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Title
Student Name
Columbia Southern University
Course Name
Instructor
Date
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Unit [Insert unit number] Case Analysis
NOTE: It is recommended that you use the subheadings as provided below. Be sure to remove
the bullet points and write your case study in paragraph form.
Introduction
•
Provide a brief introduction of the case.
Organizational Background
•
Provide information about the company, product, and industry.
Situation Analysis
•
What are the details of the situation? Make sure to include who, what, why, when, how.
•
Perform a competitive analysis identifying a minimum of three competitors and
comparing each of a list of attributes.
•
Perform an industry analysis demonstrating the health of the industry (research and
supporting quantitative information required).
•
The use of analysis tools such as PESTEL, SWOT, or Porter’s Five Forces would be
appropriate here.
Problem
•
Identify and provide a thorough explanation of the perceived and underlying problems as
well as the potential long-term effects.
Alternatives
•
Provide alternatives or strategies that the company could implement. Include a minimum
of three alternatives with multiple advantages/disadvantages of each applying the
strengths and weaknesses within the company (Alternative 1, Alternative 2… using bullet
points is fine).
3
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Discuss common considerations. What are the decision options? Are some stronger than
others? What is at stake with each of these considerations (what is the level of risk)?
Recommendation and Implementation
•
Choose which of the alternatives would provide the best solution to the problem, and
provide thorough rationale. The use of decision-making tools would be appropriate here.
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Explain how you would implement within the company. Construct a strategy for
implementation.
Conclusion
•
Simply summarize your case in 1-2 paragraphs.
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References
•
Reference the source of the case.
•
Reference additional resources you used in your evaluation. Remember that the
assignment has a minimum requirement of five references. Each must have at least one
corresponding in-text citation.
Examining the Roots of Logistical Failures:
Four Illustrations From the World of Retailing
Gilles Paché
Aix-Marseille University
The world of retailing has undergone deep changes over the last twenty years in terms of the organization
of marketing channels. Two models are today characterized by a strong dynamism: the brick & click model
and the pure player model. An important literature focuses on the logistical dimensions associated with
these two models, indicating that the performance of the physical distribution service is the key to success.
This article takes a different point of view by exploring logistical failures from four well-known illustrations
in the retailing world that could help to better understand the supply chain issues for brick & click retailers
and pure player retailers.
Keywords: brick & click model, click & collect model, Covid-19, failure, logistics, marketing channels,
pure player model, retailing
INTRODUCTION
For the most important retailers in the world, it has been quite traditional for the last two decades to
highlight their successes, especially in the management of logistical activities (Ganesan et al., 2009; Anand
& Grover, 2015; Nguyen, 2017; Lagorio & Pinto, 2021). Textbooks for students are full of success stories
that glorify avant-garde approaches initiated by this or that international retailer, or the successful
implementation of innovative tools, such as RFID, blockchain or the process of hub-and-spokes
platforming. It would be boring to list here such success stories, which are certainly real but do not exclude,
on the contrary, the simultaneous existence of multiple dramatic failures, some of which have led companies
to the brink of the abyss. We must admit that both researchers in the field and logisticians in companies
love trains that arrive on time, in other words logistics that “work”, or even logistics that excel in order to
offer a remarkable cost-relief-reactivity trade-off to customers. This is all the more unfortunate as learning
from failures, or simply from the difficulties encountered, allows a corporate strategy to evolve in the next
years.
It would therefore be clumsy to deny the fact that the consequence of failures is fatal for many retailers,
in the short and medium term, in terms of loss of market share or erosion of their image. Even if Filser &
Paché (2006) have argued in the past that a deterioration in physical distribution service1 could be a source
of competitive advantage for a low-cost retailer, such an option refers to a singular context of “low price
dramatization” where logistical failure is the astute and efficient component of a winning strategy. This is
the case, for example, in France with Brico Dépôt in the DIY sector, which stages one-off arrivals without
worrying about the level of physical distribution service offered to its customers (Rouquet & Paché, 2017).
In the present contribution, the argument is completely different. Through archetypal examples, it aims to
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Journal of Management Policy and Practice Vol. 23(1) 2022
show that logistics, which top managers would have liked to achieve excellence immediately, has
experienced profound dysfunctions, at the very moment when consumers were ready to change their
purchasing behavior in depth. This has led to dramatic situations, including the bankruptcy of some
companies, like Toys ‘R’ Us.
It is essential to learn from failures because major innovations are most often the result of a long
process, punctuated by experimentation and mistakes. Insofar as failure is likely, managers should not be
in denial, but on the contrary, it is in their interest to train themselves to fail in order to know how to
overcome it and give the company the opportunity to progress. From this point of view, identifying the
symptoms of failure is a key phase, using previously defined metrics, such as a sharp rise in costs, a drop
in sales or an explosion in customer complaints, associated with a define-measure-analyze-improve-control
model (Limsirivallop et al., 2016). These indicators are warning elements that allow to react, at least if
denial is not a dominant behavior in the company. This article is particularly interested in logistical failures
in a deeply disrupted sector: retailing. With the rapid growth of online sales over the last years, the
traditional brick & mortar model is threatened with a progressive collapse, in favor of Internet-based
models. These models are based on a radically new logistical organization that is not easy to learn. Through
four illustrations, it is possible to identify historical logistical failures for which a retroactive analysis must
prevent their repetition. The methodology chosen relies on the use of secondary data from the professional
press; this approach is also applied by many researchers in social science (Dionne & Fleuret, 2016).
BRICK & CLICK RETAILERS FAIL
The rise of brick & click, sometimes associated with click & collect, is now well known and widely
studied in the academic literature (Gulati & Galino, 2000; Steinfield et al., 2005; Doong et al., 2011; Jones
& Livingstone, 2015; Marmol & Fernández Alarcón, 2019). There are countless brick & mortar retailers
that have understood how essential an Internet presence is to complement their physical stores, sometimes
reduced to being involved only in showrooming (Gensler et al., 2017; Flavián et al., 2020). Brick & click
model relies on a portfolio of marketing channels with the objective of promoting commercial activity,
mixing for this purpose the advantages of online and offline, especially in terms of logistics. During the
Covid-19 crisis, numerous TV reports in Europe were devoted to independent bookstores and pharmacies
that implemented click & collect services so that customers could pick up orders placed online or by phone
in store (Hussain & Dawoud, 2021) (see Figure 1). We could also talk about small restaurant owners who
have tried to survive by organizing the collection of meals prepared on site and ordered via a mobile
application (Paché, 2021). A logistical mechanism that is rather well established today, but which posed
enormous problems for Toys ‘R’ Us more than twenty years ago.
FIGURE 1
THE CLICK & COLLECT MODEL TO THE RESCUE OF FRENCH SMALL STORES
The click & collect shopping was introduced in France during the Covid-19 crisis to prevent that from
happening by allowing customers to purchase goods through Internet or by phone and pick up the
package in small stores. Bookstores and flower stores have jumped on the trend, and others are following
suit. In October 2020, the French government has urged all stores to set up click & collect services to
prevent their incomes from disappearing during the second lockdown, but also to offer alternatives to
online store giants such as Amazon, which made big profits during the first lockdown of March-May
2020 and could benefit from the current lockdown’s proximity to Christmas. Online platforms such as
Sessile can find a flower store nearby, while Epicery lists a whole set of local businesses – cheese stores,
butchers, fruit & vegetables stores – where it is possible to click & collect. The click & collect shopping
does not strictly require a website. A business may use the concept in the manner it prefers, through a
website or via distributed forms (similar to takeout menus). However, a website would make it easier to
promote the business’ delivery services and likely help the business boost sales.
Source: Adapted from The Local, November 2, 2020.
Journal of Management Policy and Practice Vol. 23(1) 2022
43
For the famous toy retailer, 1999 can be considered its annus horribilis, as online shopping began to
take off. The retailer’s online sales department advertised heavily throughout the fall of 1999, promising
customers Christmas delivery on all orders placed by December 10. The Toys ‘R’ Us website was
overwhelmed with tens of thousands of orders, and although most of the toy inventory was in place in
warehouses, the company could not pick, pack and ship all the items ordered online by the December 24
deadline. On December 22, an apology email was sent to customers, but the damage was done: the media
went wild about the failure to deliver, and the brand’s image was devastated for years to come. In a brutal
way, Toys ‘R’ Us realizes that the logistics associated with online are very different from the logistics
associated with offline, which have been perfectly managed for decades. The failure of the retailer is now
studied in all business schools, and even if the answer to the question is impossible, one can wonder if the
“descent into hell” of Toys ‘R’ Us, declared bankrupt in 2018 after a continuous fall of its turnover (see
Figure 2) (Lee & Raziff, 2021), and the maintenance of a significant debt, did not start in those days of
1999.
FIGURE 2
REVENUE OF TOYS ‘R’ US (IN USD MILLION)
Source: Televisory Financial Market Data (2018).
Some observers point out that this failure, which can be described as historic, is a long way from the
2020s, and that significant progress has been made in terms of supply systems, initiated and implemented
by brick & click retailers, particularly with the support of increasingly competent logistics service
providers, strongly involved or years in the development of innovative services (Su et al., 2014). This is
undeniable, even if history tends to repeat itself, indicating that logistical performance remains at the heart
of the business model. For example, in December 2011, Best Buy, the powerful American multinational
consumer electronics retailer, acknowledged that due to massive demand during November and December
for certain “hot products” on the company’s website, shipping delays of several weeks were inevitable. In
addition, the reimbursement of customers who did not want to keep their order was blocked due to recurring
computer problems. Customers were so angry that the media seized on the logistical failure, even writing
nastily “How Best Buy ‘the grinch’ stole Christmas”. No doubt the 2011 debacle was Amazon’s lucky day,
and it will not be the last for brick & click retailers, as the Covid-19 crisis may have demonstrated in 2020
(Semuels, 2020).
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Journal of Management Policy and Practice Vol. 23(1) 2022
PURE PLAYER RETAILERS FAIL
The notion of pure player is now used in a consensual way to describe retailers who sell their products
and services only on the Internet, to the exclusion of any other marketing channel, and who therefore
implement a specific organization of the physical distribution service (Xing et al., 2010). At the beginning,
Amazon was presented as the archetype, even the “Weberian ideal type”, of the pure player, with no sales
outlet, before changing its strategy at the end of the 2010s. By extension, it is sometimes said that the term
“pure player” can be used to describe any company that operates only on the Internet, and according to this
broad approach, Facebook (Meta), Mediapart or Loopsider would be considered as such. We will only
analyze retailers whose original and exclusive playground was the Internet, even if some of them later
switched to the brick & click model2. Here again, logistical failures have had disastrous effects in some
extreme situations, as confirmed by the WebVan and Asos cases.
Over-Investment Syndrome
In 2001, WebVan, a California-based online grocery start-up, went down in the turbulent history of
retailing as one of the most resounding failures in e-commerce. Launched with the promise of cheap
groceries delivered to your door within 30 minutes of placing an online order, WebVan was supposed to
completely redefine the way U.S. consumers purchased this type of convenience goods. The failure of the
start-up in the midst of the dot-com bubble naturally challenged many observers (Lunce et al., 2006; Aspray
et al., 2013). Indeed, when WebVan launched in 1999, it attracted huge amounts of capital from companies
like Goldman Sachs and Yahoo. In order to capitalize on growth opportunities, WebVan deploys an
aggressive strategy and spends several million dollars in building a powerful supply chain to cover the
entire North American territory as quickly as possible (Hays et al., 2005), in reference to an assumed policy
of vertical integration.
Shareholders quickly become concerned when they learn of the heavy (over-)investment in megadistribution centers equipped with the best automated technology. For example, sophisticated algorithms
are being developed to steer products along five miles of conveyor belts in the distribution center in
Oakland, California. After the products are routed to automated carousels (see Figure 3), the entire process
is repeated until the order is completed and placed on the shipping dock. Additional real-time inventory
management algorithms ensure that if a customer orders a carton of milk from the website, it is available
in stock. Other algorithms are responsible for directing delivery vehicles on routes while minimizing driving
time. Finally, a software integrated to the drivers’ Palm Pilots processes in real time the delivery
confirmations and the possible returns.
We must admit that the shareholders’ concerns are legitimate since WebVan finally took off like a
rocket, only to crash a few months later. The main reason? By massively overestimating the demand,
WebVan developed a sophisticated and voluminous supply chain that far exceeded its real needs. For
example, with over 35,000 square meters, each distribution center is capable of handling 8,000 orders per
day and holding up to 50,000 consumer sales units (CSUs). In reality, each distribution center stores only
about 20,000 CSUs and receives just over 2,000 orders per day. Because the logistical facilities are more
than double what WebVan actually needs, they cost the company several hundred million in capital costs
each year. To make matters worse, WebVan takes a standardized approach to each new U.S. state the
company enters, regardless of differences in shopping behavior (faster or slower acceptance of online sales)
and urban density, forgetting that Wyoming is not California, and thus multiplying the overcapacity
problem until the retailer finally has no choice but to file for bankruptcy.
Journal of Management Policy and Practice Vol. 23(1) 2022
45
FIGURE 3
EXAMPLE OF CAROUSEL IN A WEBVAN AUTOMATED DISTRIBUTION CENTER
Source: Hays et al. (2005).
Uncontrolled Growth Syndrome
At the end of July 2019, the managers of Asos, a pure player retailer created in 2000 in London, and
specialized in the online sale of clothing and cosmetics aimed at a young clientele, tell investors that a major
problem related to the information system has cost the company tens of millions: the failure of the inventory
management software designed to record the entry and exit of products prevents the updating of their
availability on the website. This was a very serious failure that directly threatened the level of service
provided to customers. Yet, Asos is a very “trendy” company, selling 85,000 items in total, growing steadily
every week, and with a high level of customer attachment (Ashman & Vazquez, 2012). One of the keys to
the business model is the presence of shortened manufacturing cycles, as shown in Figure 4, linked to an
ultra-fast fashion supply chain management (Camargo et al., 2020). For several weeks, the inventory
management software is unable to handle the weekly new items listed on the website, committed restocking
and customer returns. As a result, top management recognized that it was completely unprepared for the
complexities of planning logistical operations in a fast-growing market.
Combined with problems related to computer exchanges with 175 suppliers worldwide, visibility of
available inventory, as reported on the company’s website, is severely limited for customers located in
Germany, France and the U.S. This logistical failure stems from the fact that the products entering the
inventory, and their correct assignment in the database, is not generally considered a key performance
metric by Asos, but also by most online retailers. Software solutions that allow disparate data, such as
marketing data and supply chain data, to connect in real time are absolutely essential, at the risk of making
it impossible for the website (front office) and the warehouses (back office) to be perfectly interfaced. This
is the price to pay for the benefits of warehouse automation, as other major players such as JD.com and
Alibaba are investing heavily in such automation to enable them to sort and prepare customer orders more
quickly (see Figure 5).
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FIGURE 4
THE ASOS BUSINESS MODEL
Source: Retrieved from https://fourweekmba.com/asos-business-model/ (Accessed July 6, 2021).
FIGURE 5
ALIBABA AND JD.COM: THE TRIUMPH OF AUTOMATED WAREHOUSES
In China, Alibaba has equipped its Huiyang warehouse with 60 automated guided vehicles (AGVs).
These autonomous robots circulate on the ground, covered with QR codes, which allow them to identify
their position to route the shelves containing the items to the order pickers. Each robot can carry up to
500 kg and has an autonomy of 4 to 5 hours for a 5-minute charge. Alibaba has reduced human labor in
the Huiyang warehouse by 70%. Alibaba’s competitor, JD.com, is pursuing an identical strategy. Its
Shanghai warehouse is fully automated, from receiving items to shipping orders. An articulated arm
depalletizes, scans the boxes and places them in standard bins. These bins are transported on conveyors
and stored by AGVs in stacker cranes. Picking is also automated, as is the packaging and labeling of the
packages. Robots place individual packages on AGVs, which place them in bags on a lower level. The
bags are transported by other autonomous robots to the shipping dock, before being loaded into trucks.
Source: Retrieved from https://fourweekmba.com/asos-business-model/ (Accessed July 6, 2021).
By leveraging its ever-expanding network of warehouses and investing in automation technologies,
Asos aims to expand its logistical facilities to speed up the shipment of every item sold and offer its
customers a simplified experience, including (free) returns. However, the problem encountered during the
summer of 2019 highlights a major constraint: for a pure player retailer, optimized flow management
requires knowledge of the real amount of its stocks, at the risk of confronting the customer with a shortage
that will be all the more unpleasant if a promise of delivery in a few hours has been made. Moreover, Grant
(2014) underlines that time compression is now one of the major challenges of supply chain performance.
Without such visibility, it is a mad dash without a dashboard or a “cockpit” with the supply chain
information needed to maintain a high level of service and responsiveness. This is a trivial observation, but
one that is sometimes underestimated. The success of a pure player retailer depends on the implementation
of an efficient alert system for stock resources that can cope with online demand, a demand for which the
display of the product on a website is only coherent if its delivery does not experience any delay or error.
Journal of Management Policy and Practice Vol. 23(1) 2022
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DISCUSSION AND CONCLUSION
The health crisis that started in 2020 has led to an increase, if not a real boom in online sales, with the
except of tourism and long-distance travel3. However, the most interesting thing to note is the acceleration
of the “boundaries blurring” between online and offline sales. Consumers have learned to adapt their
purchasing behavior to the conditions imposed on them, for example, limited accessibility to physical
stores. They have not hesitated to switch from one marketing channel to another, depending on the
opportunities for access to products and the delivery conditions granted. More broadly, consumers now
have a better understanding of the interconnections between online and offline, arbitrating according to
opportunistic criteria (price, choice, proximity, delivery times and locations, etc.). With Covid-19,
consumers have experienced all the benefits of the combination of click and mortar (especially with the
development of click & collect system) (see Figure 6), but also the strength of click alone through the pure
player model. The transformation of the retailing world was already well underway, but in the space of a
few months it has undergone an evolution that is undoubtedly irreversible. Under these conditions, given
the importance of physical distribution service in consumer satisfaction (Xing & Grant, 2006; Murfield et
al., 2017), it is impossible to ignore the monitoring of supply chains associated with the click & mortar
model and the pure player model.
FIGURE 6
WELCOME TO THE CLICK & COLLECT CONSUMER
The Covid-19 pandemic acted as a forcing function for consumers – especially among the older cohorts
– to try out new shopping formats and obtain essential goods during lockdown periods. Click & collect
is not a new service and is in fact well established in the U.K. and continental Europe, but it became an
essential channel offering for retailers during the pandemic. Initially, its adoption was accelerated due
to safety concerns, but it has evolved into a convenience factor for many shoppers. With the advantages
of being faster and cheaper than delivery service, click & collect is overtaking in-store shopping as well
as online delivery shopping. This favors retailers with a local physical presence. In the U.K., improved
online capability and click & collect purchases helped to lessen the impact of declining non-essential
retail sales during the second lockdown when compared to the first one, and food retailers suggested
that click & collect orders had boosted their online sales.
Source: Deloitte Global Report. Retrieved from https://www2.deloitte.com/global/en/pages/consumerbusiness/articles/global-retail-digitized-route-likely-to-continue.html (Accessed December 29, 2021).
What can we learn from the four examples presented in this article, which provide a quick look at the
reality of some logistical failures? Although the contexts and issues are different, and even if few works are
focused on “performance measurement systems for enhancing the design and operational efficiencies of
supply chains” (Naslund & Williamson, 2010, p. 22), it is possible to highlight one key element: more than
ever, operational logistics planning is essential to lead the action and avoid failures with more or less serious
consequences. According to Tixier et al. (1983), operational logistics planning has four complementary
dimensions: (1) medium- and short-term demand forecasting, associated with continuous monitoring of
orders placed; (2) scheduling of logistical operations, with the objective of optimal use of resources so as
to satisfy demand; (3) efficient and effective programming of human and material resource requirements;
and (4) performance control in the execution of logistical operations themselves. The four examples
discussed explicitly indicate the presence of failures on at least one of the four dimensions: failures on
dimension (1) for Toys ‘R’ Us and Asos; on dimension (2) for Best Buy; on dimension (3) for WebVan;
and probably failures on dimension (4) for all four companies.
To explore logistical failures, an in-depth analysis based on Kahneman’s (2011) work on decisionmaking is an interesting perspective. Kahneman (2011) introduces an original theory based on two modes
of thought resulting from the fact that the human brain has two independent systems (see Table 1), one that
deals with “automatic” tasks, and the other that deals with thinking: (1) System 1 makes causal and quick
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connections between events; this system relies on stereotypes that allow it to act with reference to habits;
(2) System 2 kicks in when choices are less habitual, and when more calculation or reasoning needs to be
performed; the seat of deduction and reflection, this system that serves to process more complex
information (Kennedy, 2011). According to Kahneman (2011), the cohabitation of the two systems
produces effective decision-making process, capable of reacting to abnormal situations. A reading of the
four illustrations proposed here from the perspective of two modes of thought would undoubtedly allow for
a better understanding of the decision-making process that led to the logistical failures, and in what way
they testify to a faulty articulation between system 1 and system 2 when it came to overcoming them.
TABLE 1
SYSTEM 1 VERSUS SYSTEM 2: A SYNTHESIS
General areas
Consciousness
Evolution
Functional
characteristics
System 1
Unconscious
Implicit
Automatic
Low effort
Rapid
High capacity
Evolutionary
rationality
Nonverbal
Modular cognition
Domain specific
Pragmatic
Parallel
Stereotypical
System 2
Conscious
Explicit
Thoughtful
High effort
Slow
Low capacity
Individual rationality
Linked to language
Fluid intelligence
Domain general
Logical
Sequential
Egalitarian
Research questions
When faced with logistical
failures, do retailers make quick
and standardized versus
thoughtful and customized
decisions?
When faced with logistical
failures, do retailers rely on a
fragmented versus holistic
resolution of the problem?
When faced with logistical
failures, do retailers audit a
specific area versus the whole
supply chain?
Source: Adapted from Kennedy (2011).
Despite the existence of two very specific business models, namely the brick & click model and the
pure player model, this article shows strong similarities in terms of the logistical problems encountered,
and it is likely that the issue of supply chain resilience in the context of violent external shocks, highlighted
in particular by Mwangola (2018), arises in comparable terms for the two business models. This is
undoubtedly due to the universal nature of managerial approaches to flow management, which raise
comparable questions about the constraints linked to physical distribution service. It is true that marketing
channels differ morphologically, and the technologies used to ensure the delivery of products, particularly
in the last mile, do not have the same characteristics. For example, it is possible to mention the city logistics
problems encountered by a pure player retailer, which cannot use a network of stores for click & collect
(Rodríguez García et al., 2022). Nevertheless, we are undoubtedly faced with a homogeneous reality, which
calls for a general theory of supply chains that has yet to be constructed. Indeed, given its youth, especially
in relation to marketing science, research in logistics and SCM still often relies on the explanation of local
phenomena, without having analyzed their universalism, especially in reference to Kahneman’s (2011)
systems 1 and 2. For the new generation of academicians, this is undoubtedly a major challenge.
ENDNOTES
1.
2.
Following Mentzer et al. (1989), physical distribution service is defined as the ability to provide time and
place utility; it is a key element of the customer satisfaction level.
Amazon’s progressive evolution is certainly the most representative case of the shift from the pure player
model to the brick & click model (Berg & Knights, 2022). As early as 2015, the company launched its first
physical stores (Amazon Books), before multiplying concepts, notably in food distribution (Amazon Go,
Journal of Management Policy and Practice Vol. 23(1) 2022
49
3.
Amazon Fresh). The most important investment made by Amazon in physical stores remains the purchase of
the supermarket chain Whole Foods in 2017. At the beginning of September 2021, the company owns around
600 physical stores worldwide (including 500 Whole Foods supermarkets).
According to the Europe e-commerce report 2021, produced by the Center for Market Insights of the
Amsterdam University of Applied Sciences, 2020 was an exceptional year for online sales in Europe. For all
European countries, e-commerce sales accounted for 757 billion euros, up 10% from 2019.
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www.emeraldinsight.com/0959-0552.htm
Fulfilment time performance
of online retailers – an
empirical analysis
Jingran Zhang
Fulfilment time
performance of
online retailers
493
Lewis College of Business, Marshall University, Huntington, West Virginia, USA
Sevilay Onal
Department of Industrial and Manufacturing Engineering,
New Jersey Institute of Technology, Newark, New Jersey, USA
Rohit Das
Received 11 October 2017
Revised 4 November 2017
4 March 2018
4 December 2018
14 February 2019
Accepted 1 April 2019
Gies College of Business, University of Illinois, Champaign, Illinois, USA, and
Amanda Helminsky and Sanchoy Das
Department of Industrial and Manufacturing Engineering,
New Jersey Institute of Technology, Newark, New Jersey, USA
Abstract
Purpose – Fast fulfilment is a key performance measure in online retail, and some retailers have achieved
faster times by adopting new designs in their order fulfilment infrastructure. This research empirically
confirms and quantifies the fulfilment time advantage that Amazon has achieved, relative to other online
retailers. The purpose of this paper is to investigate three research questions: what is the overall mean
fulfilment time difference between the new logistics designs of Amazon and the alternative designs of other
retailers? For each order what is the distribution of the fulfilment time difference? What is the difference in
fulfilment time by product category, price and size?
Design/methodology/approach – This research uses an empirical method to evaluate the fulfilment time
performance of consumer orders made through the Amazon website and one or more competing online
retailers. For 1,000 different products two fulfilment times, one at Amazon and another at a competing
omnichannel retailer, are recorded. The analysis is then focused on the comparison between this paired data.
Findings – The research confirms that the new logistics methods, including physical facilities, distribution
networks and intelligent order processing methods, have resulted in faster order fulfilment times. The
performance, though, is not universally dominant and for 33 per cent of orders, the difference is 1 day or less.
The fulfilment time difference varied by product, category, price or size.
Practical implications – The ongoing transformation of fulfilment and logistics operations at online
retailers has generated several new research questions. This includes the need to confirm the fulfilment
efficiency of the new designs and specify time targets. This paper identifies the fulfilment time gap between
new and traditional operations. The results suggest that store-based or distribution centre-based fulfilment
strategies may not match the new designs.
Originality/value – The study provides a quantitative analysis of the fulfilment time differentials in online
retailing. The critical role of fulfilment logistics in the rapidly growing online retail industry can now be better
modelled and studied. The survey method representing a single buyer allows for order pair equivalency and
eliminates order bias. The results suggest that new warehousing and logistics designs can lead to
significantly faster fulfilment times.
Keywords E-commerce, Online retail, Fulfilment time, Logistics, Parcel delivery, Omnichannel
Paper type Research paper
Introduction
Online retail is growing rapidly and almost every retailer, regardless of product category, is
challenged to market and deliver products through an online supply chain. US online retail
sales as a portion of total retail sales have risen from 2.8 per cent in 2006 to 8.2 per cent in
2016 (Department of Commerce, 2017), and this is expected to grow significantly over the
next decade. Looking more precisely at just consumer packaged goods, online sales are
International Journal of Retail &
Distribution Management
Vol. 47 No. 5, 2019
pp. 493-510
© Emerald Publishing Limited
0959-0552
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estimated to be 15 per cent of total sales. Online retailers compete with brick-and-mortar
retailers on both the marketing or sell side, where the goal is to sell a product virtually, and
on the fulfilment or delivery side, where the goal is to provide delivery within a few days. It
was widely known that online retail is dominated by Amazon, which accounted for
43 per cent of all US online sales in 2016. Numerous media reports suggest that Amazon’s
success is driven by the conceptually new fulfilment and logistics supply chain it operates
(Onal et al., 2017). But a scientific study of the quantitative benefits achieved by these new
designs is currently lacking in the research literature. In a study of the economics of
Amazon’s fulfilment centre network, Houde et al. (2017) observe that the concentration of
online sales to one retailer, Amazon, counters the expected outcome. One would have
expected the internet to result in a highly competitive market, with many retailers
leveraging their existing capabilities. Clearly, one or more marketing and/or operational
aspects of Amazon’s business have resulted in this market dominance.
In selecting an online retailer, two important criteria from a consumer perspective are
price and fulfilment time. Fulfilment time is commonly defined as the interval between a
customer’s order placement and delivery to the customer’s location. Previous studies
(Nguyen et al., 2016) have confirmed a strong relationship between order fulfilment times
and consumer buying behaviour. Bezes (2016) conducted a survey of buyers selecting
between online and offline channels and found that any perceived logistics risk negatively
effects online retailer selection. These studies show that faster fulfilment is a critical driver
of success in online retail and is increasingly motivating customers to shift to online buying.
Fisher et al. (2016) note that fast fulfilment is arguably the most important service
component for online retailers. They note, though, that no studies have confirmed the
economic value of faster online fulfilment leading to the open question: do the benefits of
faster fulfilment outweigh the costs of reducing the delivery lead time? Recently, Ma (2017)
did an empirical study to investigate the effect of fulfilment time on customer satisfaction.
They found that increased delivery times significantly increased customers’ perceived
retailer ambiguity and riskiness, which in turn reduced satisfaction and negatively
impacted purchase intentions. Lieber and Syverson (2012) studied the nature of competition
between online and offline retailing. They also found that fulfilment time delay of an online
sale can be particularly salient in consumer buying decisions. Long fulfilment times can
negate the advantages and convenience of online product search and shopping.
Most major brick-and-mortar retailers have expanded into online retail and these are
commonly labelled as omnichannel or multi-channel retailers (Kembro et al., 2018). These
omnichannel retailers have modified their extensive brick-and-mortar operations, including
stores, warehouses and distribution centres, for online order fulfilment. An omnichannel
retailer may pursue one or more of several fast fulfilment strategies, including Buy Online and
Pickup in Store (BOPS), Buy Online and Fulfil from Store (BOFS), Ship-to-Store (STS) and
distribution centre fulfilment. Of these BOPS and STS require the customer to visit a store.
BOPS and BOFS are the strategy of choice for many retailers, but Sheffi (2016) argue that
these solutions are unlikely to provide the needed efficiency gains. He argues that they disrupt
store workflow and add inefficient tasks to a site ill-designed for order picking. Contrastingly,
the Amazon fulfilment system is uniquely different from these strategies and incorporates
many new operational designs and innovative logistics features. This has resulted in fast
delivery times, one day and in some instances, the same-day fulfilment is the new delivery
benchmark. To compete effectively, traditional retailers need to build online operations that
match the fulfilment speed of these new designs. Recently, Onal et al. (2017) provided a
description of the operational design of these fulfilment centres including detailed insights into
specific methods being implemented by Amazon. The purpose of this research is to
investigate whether the new logistics designs and methods implemented by Amazon,
including the physical facilities, distribution network and intelligent order processing
methods, have resulted in faster fulfilment times when compared to the store or distribution Fulfilment time
centre-based fulfilment strategy of traditional retailers.
performance of
The specific aim of this research is to empirically confirm and quantify the fulfilment online retailers
time advantage that Amazon has achieved. We are aware of no empirical studies that
estimate the fulfilment time difference between Amazon and other retailers. To provide a
direct comparison of these strategies, we investigate the following three research questions:
RQ1. What is the overall mean fulfilment time difference between the new logistics
designs of Amazon and the alternative designs of other retailers?
RQ2. For each order what is the distribution of the fulfilment time difference between
Amazon and other retailers?
RQ3. For which product categories, price levels and product size are the fulfilment time
differences most significant?
To address these questions, an empirical research method was used to collect fulfilment time data
for identical products ordered from Amazon, and one or more competing online retailers. Across
the range of products, a total of 12 retailers were compared. Each of the competing retailers had
built a strong online channel from their existing distribution networks. The results are important
in that they confirm any performance differences between the different fulfilment methods.
Our first finding is that these new logistics methods and systems result in an overall faster
fulfilment time, validating consumer perceptions about Amazon’s efficiency and reliability.
Future redesigns of alternative fulfilment strategies may close the fulfilment time gap and
should be pursued by traditional store-based retailers. Our second finding is that the fast
fulfilment advantages of the new logistics systems are not universally dominant. For a small
proportion of orders (4 per cent) Amazon was slower, while for 15 per cent of orders it was
equally fast, and another 14 per cent only a day faster. This implies that some of the alternate
strategies can be selectively optimised to match the new designs. This research is unable to
distinguish between the performance of the alternate strategies, and future work is required in
that direction. Our third finding is that for some product types the fulfilment advantages of
the new logistics systems is very large, while for other types it is much smaller. There was no
product type, though, for which the new systems underperformed.
This research addresses several different threads of related literature and we first review
these. The research proposition here is that Amazon has built a new and different logistics
system which allows it to achieve faster fulfilment times. The primary focus of this research is
therefore on fulfilment time, and in the third section, we explain why shorter times increase the
likelihood of online sales. The fourth section presents the research method to investigate the
proposition, while the fifth section analyses and discusses the results. Finally, the last section
summarises the practice, research and managerial implications of the findings.
Amazon fulfilment systems
The fulfilment and logistics systems of an online retailer typically involve one or more
fulfilment centres and an associated parcel delivery network. Ownership arrangements vary
greatly, ranging from fully company-owned facilities to a full third-party setup. In the case
of Amazon, though, significant parts of the network are in-house operations and uniquely
different from other retailers. The earliest insights on Amazon’s fulfilment centres were
reported by two interns, Rubenstein (2006) and Bishop (2010). The level of process control is
highlighted by Rubenstein (2006) who notes – “The heart of Amazon is their software
organization which provides the complex algorithms and optimization programs that run
the daily operations of the fulfilment centres”. He also describes the method of randomly
storing incoming items in library style bins anywhere in the warehouse. Bishop (2010)
describes in detail the waveless or continuous flow picking process. Backed by a
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sophisticated information technology system the waveless process can achieve high density
picks and react quickly to the incoming order flow.
Onal et al. (2017), Onal et al. (2018) were one of the first to provide detailed insights into
the operational flows within an Amazon fulfilment centre. They document how
sophisticated flow control models leverage new logistics and operational models to
ensure fast fulfilment. They found that these fulfilment centres present a new paradigm in
the operational design and control of warehouses. Onal et al. (2018) identified six specific
operational differentiators: explosive storage policy – incoming bulk inventory is exploded
into a large number of small lots which are then dispersed to storage locations throughout
the warehouse; very large number of beehive storage locations – storage is organised into
small library style bins (1–3 cubic feet) as opposed to large bulk holding spaces; bins with
commingled items – multiple items are simultaneously stored in an unorganised way in the
same bin; immediate fulfilment objective – customer orders arrive continuously throughout
the day and the goal is for same-day shipment; short picking routes with single unit picks –
most orders are only for a single unit and the pick list retrieves several different items within
a short pick zone; and high transactions volumes and total digital control – there is a much
higher rate of store/pick movements per unit shipment, and all movements are modelled and
instructed by a central controller. Together these differentiators uniquely describe a new
approach to fulfilling online orders. Consider just the first three differentiators, each of
which is a radical departure from traditional warehousing theory. To validate their
effectiveness, it is necessary to investigate the performance impact.
Figure 1 flowcharts the four key functions of the online order fulfilment process, and this
sequence is common to all retailers. But where and how these functions are executed is what
differentiates retailers. In the case of Amazon, the first two functions and parts of the third are
performed at a fulfilment centre, while the remaining functions are executed by the parcel
delivery network. The process is designed to influence three key fulfilment performance
drivers: inventory management, warehousing logistics and last mile delivery. Figure 1 lists
several innovative features, including the differentiators identified by Onal et al. (2018), which
are integral to the process design. As shown in Figure 1 the process is initiated by the receipt
of a customer order. An efficient online retailer has real-time inventory status and will only
accept orders if there is a fillable inventory. In this study all the orders tracked were fillable
and the results, therefore, are independent of inventory stocking policy.
The next two functions pick and pack, and ship and transport, represent key competitive
advantages of Amazon’s fulfilment operations. An explosive storage policy and
CUSTOMERS
ORDER RECEIPT
PICK AND PACK
SHIP AND TRANSPORT
CUSTOMER DELIVERY
Performance Drivers
Inventory
Management
Figure 1.
Online fulfilment
process and
innovative features
Warehousing
Logistics
Last Mile
Delivery
Amazon Fulfilment Infrastructure Innovative Features
Explosive/Commingled storage policies
Fulfilment centre facility design
Automation innovations for quick fulfilment
Advanced algorithmic control of stocking and picking
Geographic distribution of fulfilment centres
Algorithm driven truck loading and assignment
commingled bins allow each warehouse to stock a large variety of products in an infinite Fulfilment time
number of combinations. The new fulfilment centres use their total digital control systems performance of
to track order arrival behaviour, derive item correlations and then opportunistically position online retailers
inventory for fast fulfilment. Order arrival analytics is used by advanced stocking
algorithms to select stocking bin locations for each incoming item. Likewise, there are
multiple warehouses and multiple locations within each warehouse that an ordered item can
be fulfilled from, and advanced picking algorithms make this decision thousands of times a
497
day. Within each warehouse automation innovations including robots and logic-controlled
conveying systems ensure quick movement of items from bin to packaging. Last mile
delivery is the fourth fulfilment function and most online retailers have established delivery
arrangements with third-party delivery services such as FedEx, UPS or the US Postal
Service. In the case of Amazon, increasing portions of the delivery process are being brought
in-house. Again, advanced truck loading and assignments algorithms coordinate the order
pick process, such that the sequence of boxes loaded on a trailer matches the street level
delivery sequence. The geographic distribution of centres and the infinite storage options
across the network, provide a very large decision space and consequently many
opportunities to minimise the fulfilment time objective.
Currently, there is no empirical evidence to confirm that these new designs are better,
and no studies have investigated the fulfilment time performance of the different methods.
In a review of internet retailing, Doherty and Ellis-Chadwick (2010) also do not identify any
papers that specifically address fulfilment logistics or speed. Houde et al. (2017) studied the
tax economics of Amazon’s fulfilment centre network and modelled the location of these
centres as a strategic choice variable. They suggest that while the early strategy depended
on minimising sales tax, the latter strategy as Amazon grew in scale was to locate the
centres closer to customers. They calculated the cost savings associated with the expansion
of the network over the last decade and found that Amazon has reduced its total shipping
costs by over 50 per cent and increased its profit margin by between 5 and 14 per cent. In
this research, the proposition is that in addition to lower costs, faster fulfilment was a factor
in both location and operational design of the fulfilment network.
Conceptual background
Online order fulfilment at omnichannel retailers
Traditional retailers have quickly built online stores to meet the growing demands of the
online buying trend. Previous research has reported on a fulfilment approach which utilises
existing physical stores or distribution centres. In an interview survey of supply chain
executives at large retailers, Ishfaq et al. (2016) found that physical stores are expected to
play a primary role in order fulfilment and delivery, emphasising the importance of BOPS
and BOFS strategies. Tarn et al. (2003) observe that online retailers operate in a dynamic
environment in which product and information are highly synchronized to achieve
unprecedented levels of customer service. They note that distribution systems established
for traditional omnichannel retailers are not designed to accommodate the needs of
individual customers with a large variety of small orders. Evaluating the online supply
chains of Amazon and Walmart, Kumar et al. (2012) found them to be structured quite
differently. Due to a lack of performance data they did not evaluate whether one is better
than the other. Boyer and Hult (2006) conducted a customer survey to compare BOFS and
other distribution centre-based online order fulfilment strategies. They did not explicitly
track fulfilment time but used product freshness as a surrogate for supply chain speed.
Their data suggest that a distribution centre-based strategy is preferred by customers and
urge retailers to explore and build on this option.
In a semi-structured interview study with German distribution experts, Hubner et al.
(2016) found that achieving short fulfilment times was a key challenge in home delivery.
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They also observed that omnichannel retailers, those with both online and physical stores,
have an advantage in faster fulfilment and possibly same-day delivery. This advantage is
currently being leveraged by many traditional retailers, including Walmart, Best Buy, Crate
and Barrel, Home Depot and Gap (Gallino et al., 2017) through BOPS, BOFS and STS
strategies. Gao and Su (2017) identify fulfilment initiatives as the highest priority of
traditional retailers, and they specifically model and investigate the BOPS strategy. They
found that BOPS attract customers by reducing the hassle of shopping or providing a
convenience benefit. A common theme in many previous studies is that store-based
strategies expand consumer selection and delivery options, and hence preserve customer
loyalty. These studies, though, typically investigate alternate strategies at the same retailer,
and cross-retailer comparisons are lacking. The results of this study counter the view that
store-based strategies are a sufficient solution for online retailing success. We show that
Amazon, a pure only retailer, is achieving faster fulfilment and effectively negating the
benefits of BOPS or BOFS.
The importance of fulfilment time
Nguyen et al. (2016) provide a systematic review of the relationship between consumer
behaviour and order fulfilment in online retailing. Their review proposes an integrative
research framework with three performance drivers: inventory management; last mile
delivery; and returns management. The first two represent the logistics efficiency of the online
retailer’s fulfilment operations and match the performance metrics studied here. They cite
Collier and Bienstock (2006) and several other works, all of which found that fulfilment time
has the strongest impact on customer satisfaction in online retailing. Rao et al. (2014) measure
consumer purchase satisfaction in two dimensions: electronic/physical distribution service
quality and physical distribution service price. This is an important concept since it highlights
the sometimes inverse relationship between fulfilment time and cost. The economics of storebased fulfilment is unclear, but the cost per unit delivery cannot match those of an online
retailer. Murfield et al. (2017) did an empirical survey of different omnichannel retail scenarios
and found that timeliness is an essential driver of satisfaction and loyalty. They advise
retailers to dedicate substantial resources to meet delivery requirements in a timely manner.
Agatz et al. (2008) reviewed internet fulfilment and multi-channel distribution and
concluded that companies must embrace novel strategies to succeed in an online channel.
Gong et al. (2010) and Gong and Koster (2008) observe that order fulfilment is the most
expensive and critical operation for companies engaged in e-commerce. Hubner et al. (2016)
note that online logistics planning must be structured into back-end fulfilment
(e.g. warehouse and in-store picking) and last mile distribution concepts (e.g. attended
and unattended home delivery). Based on a survey of the online grocery industry they found
that there are many design alternatives, and online retailers must select design options
based on their market and operational capabilities. Hu et al. (2014) observe that fulfilment is
a critical enabler of multi-channel retailing, which include online shopping. They found that
Amazon has emphasised fulfilment speed as a key element of their value proposition. The
current literature highlights and validates the importance of fulfilment time to online
retailing success. This research complements the literature by providing a quantitative
evaluation of actual online order fulfilment times.
Fulfilment time and online orders
The fundamental premise of this study is that faster fulfilment is a key driver in motivating
customers to switch from a physical store visit to an online order with home or business
delivery. Furthermore, it is also a factor in selecting an online retailer. Several studies have
discussed this relationship and a recent survey (Wall Street Journal, 2016) found that online
shoppers want faster delivery with the maximum waiting time dropping every year.
Griffis et al. (2012) found that excellent order fulfilment is instrumental in generating Fulfilment time
referrals for the online retailer, even after factoring in product quality. Several scales for performance of
evaluating service quality in electronic or online retailing have been proposed (Blut, 2016; online retailers
Stiakakis and Georgiadis, 2009) and these all include fulfilment as a key factor. The
emphasis in these scales, though, is more on delivery reliability against a promised date, and
less on the fulfilment time length. In a survey of online customers, both Koufteros et al.
(2014) and Jain et al. (2017) found that timeliness positively influenced customer satisfaction.
499
Dholakia and Zhao (2010) also studied and compared customer evaluations of online
purchases. They found that on-time delivery dominates customer satisfaction. Further, they
note that weak fulfilment will not compensate for creative and vivid website designs. Meller
(2015) quotes a recent survey of online buyers and found that 65 per cent want next day
delivery and 24 per cent said same-day delivery was important. They propose that faster
fulfilment and order processing will allow retailers to expand their customer base by
targeting the speed-sensitive segment.
Bell et al. (2014) propose an information and fulfilment matrix to categorise omnichannel
retailers. They note that fulfilment through package delivery is disadvantaged from the
customer perspective by waiting time and delayed gratification. This implies that the
shorter the fulfilment time the higher the likelihood a customer will switch from a physical
store purchase to online, assuming equivalent pricing and quality. Furthermore, when
evaluating online retail choices, the faster fulfilment will be selected. Lieber and Syverson
(2012) describe fulfilment time as a delayed consumption which can be penalised by a
discounted utility function. They propose that this delay can be quite significant when
considering the competition between a market’s online and offline channels. Li et al. (2015)
present a consumer utility model for online retailing, which includes the discount component
rt. In their model, r measures the consumer’s delay inconvenience such that a larger r
implies more inconvenience, and t is the fulfilment time. As the penalty increases, then
because of the decreasing utility, the consumer may choose a different retail option.
Likelihood of an online purchase
When multiple retail options are available then each will have its unique t, and the penalty
function represents the probability a specific retailer will be selected. Figure 2 proposes that
this probability is described by a non-linear decreasing function of t. In Figure 2 the function
assumes that for same-day delivery a maximum likelihood for online purchase is reached.
Since a portion of customers will always demand immediate fulfilment, the maximum
likelihood will be less than one. For an online retailer to be successful it must, therefore, offer
delivery times close to same-day delivery. This is reflected in Amazon’s progressively
shorter fulfilment time targets: 2 days, the next day and now the same day. Depending on
the nature of the product and the associated consumer behaviour the waiting time
disadvantage, indicated by r, could be steep or shallow as shown in Figure 2. For products
with a steep disadvantage curve, such as grocery items, fulfilment must be within a day to
ensure retail success. Bell et al. (2014) also identify several disadvantages of a physical store
purchase, which could be modelled into a relationship describing the trade-off between
physical and online purchase as a function of fulfilment time. Interestingly, Harris et al.
(2017) found that the desire to avoid disadvantages maybe a stronger motivator for deciding
between online or offline purchase.
Research methodology
Order fulfilment time is defined as the interval between order receipt from the customer
(web checkout) and delivery (package drop off at customer address). To confirm the faster
fulfilment time performance of Amazon when compared to alternative fulfilment methods,
three research questions were framed in the introduction. The questions are designed to
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Increasing waiting time
disadvantage
SAME DAY DELIVERY
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LIKELIHOOD OF ONLINE PURCHASE
MAXIMUM LIKELIHOOD
Figure 2.
Online purchase
likelihood and
fulfilment time
IMMEDIATE
FULFILMENT TIME
confirm the net effect of Amazon’s logistics infrastructure by evaluating a key output
measure from the customer’s perspective. We use an empirical research method to
investigate the above questions. We collect, or survey, fulfilment time data from Amazon
and several other online retailers. Traditional retail surveys involve tracking purchase
experiences or opinion polling of shoppers using a questionnaire (Ponto, 2015). This
research does not conduct a survey of this type. Here, the focus is on data that is provided
directly by an online retailer. Since no subjective customer responses are involved this
eliminates the need to poll an actual shopper. All the shopping orders can, therefore, be
placed by the same consumer without any bias. The empirical data is collected from a series
of survey incidents, each of which involves placing an online order for a specific item. Each
incident could also be considered as a data collection experiment.
The survey was administered by a team of five experimenters, each of whom played the
role of an online customer. The survey, or experiment, steps were: select a product; place an
order for the identical product on both the Amazon web store and on a competing online
retailer; and record all transaction data including order data and fulfil promise date. Amazon
and all competing retailers provide a delivery promise date, and here this date is used as a
surrogate for the actual customer delivery date. Each product, therefore, generates a pair of
orders, one at Amazon and another at a competing retailer. The analysis is then focused on
the comparison between this paired data. A similar data collection method was used by Zhu
and Liu (2018) in an empirical study using the Amazon website. They note that because of
the large number of products Amazon offers, it is practically impossible to gather
information from every product listed on Amazon. Ellison and Snyder (2014) also similarly
collected price data from online markets for several firms. Their focus was on price
dynamics and a single product was considered. The focus here, however, is on a
heterogeneous market and multiple products are tracked.
Survey sample
The sample set consisted of 1,000 products (n ¼ 1,000), the sample size was validated from
the collected data. To ensure the validity of the survey results, product selection was driven
by two themes equivalency and generality. Equivalency ensures that each ordered pair is Fulfilment time
not compromised by structural differences. The first equivalency factor was focused on performance of
ensuring the identical product was ordered on both web stores. This was achieved by online retailers
tracking part numbers. To focus the results on Amazon logistics and inventory policies the
second equivalency was to only select Amazon Fulfilled products. These are products which
are stocked in an Amazon fulfilment centre, and the customer order is picked, packed and
shipped from there. These orders are therefore being processed in the new logistic designs
501
being investigated here. Amazon sells many online products that are fulfilled directly from
the vendor’s logistics, these then are not reflective of Amazon’s logistics efficiency and
excluded from the study. Such products are labelled as seller or vendor fulfilled. To ensure
the generality of the survey population, the following three product selection factors were
used by the survey team. These were constructs that are most likely to influence the product
flow and associated logistics. For example, a large office product (e.g. High back chair) is
likely to have lower inventory levels and require more fulfilment resources, likewise, a small
electronics product (e.g. memory stick) requires significantly fewer resources:
(1) Product category – the sample set was organised into five aggregated categories,
representing products that are commonly ordered in online retail. The selection of
competing online retailers was done first. Product categories were then selected,
such that they were popular and applicable to both Amazon and the competing
retailers. Within each category several sub-categories were identified, this ensured
the sample set was well diversified across each category. Table I identifies the
categories and the associated sub-categories. Each category is associated with one
or more competing retailers.
(2) Competing online retailer – for each category one or two competing retailers were
identified, and the survey was limited to them. The only exception was fashion
where multiple online retailers were used. Fashion represents a special case in that
few identical products are available on both Amazon and a single competing retailer.
The retailer was therefore expanded to achieve the required number of order pairs.
Table I identifies the competing retailers, six of whom are amongst the top 25 ranked
by online US sales (Zaczkiewicz, 2016). Two of the others, Office Depot and Barnes &
Noble are not ranked but are the second and third largest by sales in their product
categories. The special attributes of the fashion category required the addition of
five smaller online retailers. Using the classification proposed by Bell et al. (2014),
Amazon is a pure-play e-commerce retailer, while the other retailers, apart from
eBags, are shopping and delivery hybrids.
Product category
Sub-categories
Competing online retailers
(top 25 rank)
1. Consumer electronics Audio video, televisions, laptops, media players, Best Buy (No. 7), Walmart (No. 2)
tablets, gaming and wearable technology
2. Home improvement Kitchen and bath fixtures, home organisation, Home Depot (No. 6)
tools, lighting and fans, building supplies
and appliances
3. Fashion
Shoes, Apparel, Bags, Accessories Sports Wear Nike (No. 24), Crocs, Fossil, Under
Armour, eBags, Kipling
4. Office products
School supplies, desk accessories, furniture,
Staples (No. 4), Office Depot
office supplies and office electronics
5. Books
Biographies, business, textbooks cookbooks,
Barnes & Noble
fiction, literature and history
Table I.
Product categories
and competing
retailers
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47,5
(3) Price – four pricing categories were assigned: less than $50, $50 to $100, $100 to
$200 and more than $200. The logistics overhead for a product has a low
sensitivity to price, implying there could be an efficiency bias for higher price
products. The motivation being that the same cost is incurred in moving a higher
revenue item. By distributing the sample across the four price ranges the effects of
this bias are minimised.
502
(4) Size – for any product, the logistics cost and effort are clearly a function of package
volume. All products selected in this survey had the package dimensions specified
on the web store and this was used to derive the volume or size. Three size
categories were assigned by longest dimension: small – less than 10 inches; medium
– between 10 and 24 inches; and large – between 24 and 40 inches. Packages with
size greater than 40 inches were excluded from the study.
Table II shows the distribution of the surveyed products. Consumer electronics is a
frequently ordered online item and is assigned a greater ratio at 25 per cent of the survey,
while fashion is less frequently ordered and assigned a smaller ratio. Lower-priced items
now account for a larger portion of online sales and are the most challenging from a
logistics overhead perspective. The sample set was skewed towards lower-priced items
providing a robust test of logistics efficiency. Smaller package products also constitute a
larger portion of online sales, and the sample set was focused primarily on small and
medium products. The price and size ratios were assigned across the categories to reflect
the nature of the products. For example, books had no large items, while home
improvement had a greater percentage of large items.
Data collection
The survey was conducted over a five-week period from June to July of 2017, outside of
any seasonal promotions (e.g. Christmas or Back to School). Each of the survey team was
assigned a single category, allowing them to gain experience in the associated product
offerings at both Amazon and the competing online retailers. The sample size and the
price and size ratios were specified to the surveyors, and they independently selected the
products. Selections were reviewed by the research team, and deletions made where
necessary, this ensured there was no sample bias. For every product both the Amazon and
competing retailer order was placed on the same day at the same time. To minimise the
effects of weekend logistic delays, order placement was done Sunday to Thursday.
All orders were placed between 9 a.m. and 1 p.m. and the delivery address was the same
for all orders in the survey sample. The specified address was in the New York metro area,
one of the largest retail markets in the USA. As such, there is no location bias since all the
studied retailers would be targeting this market as part of their online strategy. Standard
shipping was selected for all orders. For each ordered pair, the product name and the three
Category (retailer: sample ratio)
Table II.
Survey product
distribution
Electronics (Best Buy: 40% and Walmart: 60%)
Home improvement (Home Depot: 100%)
Fashion (Nike 40% and all others 10% each)
Office products (Staple: 70% and Office Depot: 30%)
Books (Barnes & Noble: 100%)
Note: n ¼ 1,000
Sample
per cent Price
25
20
20
20
15
Less than $50
$50 to $100
$101 to $200
More than $200
Sample
per cent Size
45
20
20
15
Small
Medium
Large
Sample
per cent
43
42
15
factors and competing retailer were recorded. Additionally, the following transaction data Fulfilment time
were collected:
performance of
•
OP – order placement date;
•
OA – Amazon order fulfil promise date; and
•
OC – competing retailer order fulfil promise date.
online retailers
503
From these, the following performance metrics were derived:
•
Amazon fulfilment time: FA ¼ OA − OP,
•
Competing Retailer fulfilment time: FC ¼ OC − OP.
For the studied retailers, including Amazon, Sunday was not a delivery day and some
orders were therefore consequently delayed. To account for this, when the fulfilment
straddles a Sunday, then FA and/or FC was reduced by one. Less than 5 per cent of the data
set was adjusted by this rule. There were no orders with same-day delivery so FA W0 and
FC W0. Here, we use the promise date as a reliable indicator of the actual delivery date.
Fulfilment performance analytics
The analytical data set for the study is then defined by FA and FC for all n ¼ 1,000 products,
with each record being further characterised by the three survey factors. The three study
questions were analysed using this data set. To validate the sample size, for an initial
sample of 20 products, the mean and variance for FA − FC was first derived as 2.23 and 3.41
days. For a 95% confidence with 99 per cent reliability the planned sample size was 712, the
sample size of 1,000 is therefore statistically significant.
RQ1: what is the overall mean fulfilment time difference between the new logistics designs
of Amazon and the alternative designs of other retailers?
Figure 3 shows the FA and FC distributions across the survey set. The observed mean and
standard deviation for FA was 1.92 and 2.43 days, while for FC it was 4.81 and 3.41 days. The
fulfilment time for Amazon (M ¼ 1.92, SD ¼ 2.43) was significantly shorter than that for
competing online retailers (M ¼ 4.81, SD ¼ 3.42); |z| ¼ 21.77, p ¼ 1.96. The results confirm
Amazon’s logistics infrastructure provides it with fulfilment time dominance and the
magnitude is made clear from Figure 3. The mean difference of 2.89 days is too large to be
explained by minor improvements by Amazon and most likely is due to the operational
differentiators identified earlier. Next day delivery (F A or FC ¼ 1) is the goal of online retail
50%
Amazon
Others
Order Volume
40%
30%
20%
10%
0%
0
1
2
3
4
5
6–7
Fulfilment Time (Days)
8–10 10–15 16–20
20+
Figure 3.
Fulfilment time –
amazon vs competing
online retailers
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47,5
and is the point where consumers are most likely to switch from a physical store option to an
online option. In the study, we found that Amazon was able to deliver 46.2 per cent of all
orders within a day, but for the competing retailers, only 8.7 per cent of orders achieved this
goal. Extending to two days the gap widens further, for 89 per cent of orders FA was 2 days or
less, while the corresponding ratio for FC was only 26 per cent. Only 2.5 per cent of Amazon
orders had a fulfilment time of 6+ days, but for the competing retailers, this was 33.4 per cent.
504
RQ2: for each order what is the distribution of the fulfilment time difference between
Amazon and other retailers?
While RQ1 studied the overall delivery performance, RQ2 focuses on each specific order. The
study metric here is the fulfilment time advantage of Amazon described by Δ ¼ FC − FA.
Using a paired samples t-test we find that FC is significantly greater than FA conditions; t
(999) ¼ 22.48, p ¼ 1.96. Figure 4 shows the Δ distribution across the survey set. For
4 per cent of orders Amazon was slower than the competition, while for 15 per cent of orders
they were equally fast, but for 81 per cent of orders, Amazon was at least a day faster. Further
analysis of the Δ distribution confirms the fulfilment time dominance of Amazon, and we
found that for 41 per cent of orders Amazon was at least four days faster than the competition.
Investigating the items where Δ o0, we found these were primarily in the electronics and
office products categories. It appears these items are being fulfilled by BOFS, providing a
quick solution for an omnichannel retailer.
RQ3: for which product categories, price levels and product size are the fulfilment time
differences most significant?
This question investigates the fulfilment time behaviour within each factor. Table III shows
the detailed behaviour within each factor, and we see interesting differences within each
factor. The category analysis provides insights into Amazon’s behaviour against specific
retailers. An independent-samples Z-test was conducted to compare FA and FC for each
retailer pair, and a significant difference (α ¼ 0.05, Two-tailed) was confirmed as shown in
Table III. For the “More than $200” category p ¼ 0.00032, for all others po0.00001. The
fulfilment dominance of Amazon is strongest in the book’s category, and we see that that the
leading omnichannel retailer has the longest mean fulfilment time compared to all other
retailers in the study. The study indicates that in some categories such as fashion and
consumer electronics, retailers are closing the competitive advantage of Amazon, but even
there the differences are significant. Surprisingly, fashion had the fastest mean fulfilment
times for both Amazon and other retailers along with small variances. Most of the fashion
250
200
150
100
50
Figure 4.
Paired orders –
amazon vs competing
online retailers
0
2+
2
1
0
1
2
3
4
5
6
7
8
FA Slower ← Fulfilment Time Difference (Days) → FA Faster
9
9+
Category
FA – Amazon
Mean
SD
FC – Other retailers
Mean
SD
Z-test
Z value
Product Type
Consumer electronics
Home improvement
Fashion
Office products
Books
2.19
2.83
1.21
1.32
1.52
3.32
3.28
1.39
0.82
1.00
4.21
5.39
3.45
4.09
5.42
3.04
3.01
1.44
5.42
1.07
7.10
8.11
15.85
7.15
37.71
Price
Less than $55
$55 to $100
$101 to $200
More than $200
1.64
1.64
2.02
2.98
1.30
1.61
3.03
4.11
5.04
4.91
4.42
4.55
3.57
3.71
2.63
3.45
18.96
11.19
8.54
3.60
Size
Small
Medium
Large
1.53
1.93
3.03
1.24
1.39
1.74
4.38
5.01
5.48
2.09
2.24
2.34
16.59
14.66
5.89
retailers in the study are brands with little to no physical stores. This may have enabled them
to more efficiently build an online retail fulfilment system without the handicap of overlaying
it on an existing network. For each category, except fashion, the survey sample is limited to
one or two retailers, a subsequent research question could investigate specifically how
effective their fulfilment strategies are when compared to the Amazon design. For example,
Staples is using a BOFS strategy, and as noted in question RQ2 this can in some cases
outperform Amazon fulfilment. Furthermore, new research could investigate the relationship
between fulfilment time and online consumer behaviour for each product category.
The product price analysis displays contrasting behaviours, for Amazon an increase in
FA is observed with price, while for other retailers the time decreases. In particular, the
Fc – FA difference for products priced at less than $50 were surprisingly large. The results
suggest that Amazon inventory policies may decide to stock large quantities of lower-priced
items, then their explosive storage policy (Onal et al., 2017) enables faster fulfilment. There is
a significant increase in FA between the first three price segments and the more than $200
segment. The results suggest that, at least for now, the Amazon strategy is to focus on
lower-priced items, possibly to keep inventory costs down. Further research on the
importance of fulfilment time and product price, could support the assignment of fulfilment
strategy to products. For example, should the retail mix at stores be biased towards higher
prices products, and lower-priced products in fulfilment centres?
The product size analysis showed an increase in FA and FC with increasing size. Larger
products require more storage space and handling effort, both at the warehouse and on
distribution vehicles. But this differentiation was less evident in the competing retailer data, with
only a 25 per cent increase in FC between small and large products, while for Amazon there was
a 50 per cent increase. The implications are that the new logistics designs can more effectively
leverage the storage advantages of smaller products. For example, the commingled bin style
storage allows for faster picking and higher cube utilisation of smaller products. An interesting
research question is what combination of price and size distinguish between products that are
best fulfilled from the new fulfilment systems and those from a BOPS or BOFS strategy.
Conclusions
The empirical results confirm that Amazon is achieving faster fulfilment of online orders
when compared to competing omnichannel retailers. The comparison was done across five
Fulfilment time
performance of
online retailers
505
Table III.
Fulfilment time by
survey factor
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47,5
506
product categories, and within each category one or more competing retailers were
evaluated. The competing retailers have all built a strong online retail presence and are
primarily using store or distribution centre-based fulfilment strategies, including BOPS,
BOFS and STS. The results suggest the operationally new fulfilment infrastructure built
by Amazon, has allowed it to achieve faster fulfilment times with an advantage of
2.89 days on average. The observed performance differences indicate that the BOPS,
BOFS and STS strategies are unlikely to match the new designs. Next day delivery is the
key threshold in customers making the switch from physical store to online buying, and
the survey found that 46.2 per cent of Amazon orders achieved this compared to only
8.7 per cent for the competition.
Across all factors, large products had the longest fulfilment time for Amazon. As
Amazon further evolves its fulfilment infrastructure large product times may decrease
significantly thus increasing the competitive challenges for alternate designs. Competing
retailers could focus on large product fulfilment allowing them to match Amazon since the
gap is relatively smaller. The fulfilment time behaviour across product price and size are
indicative of the weak online fulfilment infrastructure of the competing retailers.
Onal et al. (2017) identified and described several of the innovative operational features
implemented at Amazon’s online order fulfilment warehouses, including an explosive
storage policy and the extensive digital control of all transactional activities. Significant
innovation and redesign will be required from competing retailers if they are to achieve
fulfilment time parity with Amazon.
Practical implications
To ensure success in online retailing, fast fulfilment targets must be achieved through an
efficient logistics system, Managers need to use the fulfilment time profiles documented in
this study to set goals for their online fulfilment performance and monitor this goal
periodically. Logistics designers need to determine how to integrate new design concepts
in a redesign of their fulfilment operations. Managers are apt to assume that their
products and customer profiles are different from Amazon and therefore protected by
operational boundaries. This study shows that Amazon fulfilment is outperforming in
multiple factor segments. At the detailed level managers can use the study to profile their
product category, and then target redesign accordingly. Third-party logistics service
providers are a key player in many online retail systems, and Liu et al. (2010) investigated
13 provider capabilities and found service quality including delivery services as most
critical. Possibly, the competing retailers are in an improvement cycle and will close the
fulfilment time gap. But we could find no evidence that these retailers were designing and
implementing fulfilment systems as described in Figure 1.
Research implications
This research builds on recent research investigating the fulfilment operations of
Amazon. While there is considerable research on the marketing side of online retail,
reported research on the warehousing and logistics aspects is still quite limited
(Kembro et al., 2018). The empirical results of the study provide researchers with enough
data to investigate operational elements that are necessary to optimise fulfilment logistics.
This includes research on concepts and methods for store redesign leading to more
efficient BOPS and BOFS operations. Structural and operational factors, including those
identified by Onal et al. (2017), can also be further investigated to design new fulfilment
methods. New order flow optimisation models can prescribe when to use the new designs
as opposed to store-based fulfilment. Additionally, fast fulfilment order picking
algorithms can be developed. The capital and operational costs of the Amazon
fulfilment infrastructure, while reported to be significant, are not considered in this study.
Possibly, the fulfilment efficiencies are being achieved at a high cost, with detrimental Fulfilment time
impacts on net profit margins. Future research could investigate and conduct a cost- performance of
benefit analysis. The fulfilment time differences between retailers could be combined with online retailers
research on sales growth to model the economic value of faster delivery.
Managerial suggestions
This research considers order fulfilment time, in addition to digital marketing and online
product selection, as key determinants of success in online retailing. Supply chain and
distribution managers at traditional retailers have multiple options and design alternatives as
they adapt their distribution networks and build new fulfilment facilities to meet the growth of
online shopping. Store and distribution centre-based strategies have been implemented by
several of the retailers studied here, but the results indicate they are unable to match the new
logistics designs. The research results suggest that retailers should focus on improving their
BOPS and BOFS strategies. For instance, fast pick areas could be set up in the rear of the store
for popular online items. Additionally, they could partner with local parcel delivery services
for twice a day store pickup with same-day delivery of online orders.
For product categories, such as fashion and home improvement the fulfilment time
differences are less pronounced, and here possibly traditional retailers can improve on their
current strategies. Many fashion retailers already have quick pick warehouses that replenish
store inventories. A suggestion would be to modify these facilities for both store and online
order fulfilment. The broad solution for managers, though, is to build new online fulfilment
solutions including stocking/picking warehouses and delivery networks. An omnichannel
distribution network that serves both physical stores and online channels may not be able to
match the fulfilment time performance of a facility dedicated purely to online sales. Kembro
et al. (2018) emphasise the need for empirical research that analyses the pioneering practices
already implemented by online retailers. They note that new innovations in warehousing will
once again bring it to the forefront in logistics research. The economics of Amazon’s
operations indicate the new designs are capital intensive and may not be a viable option for
many retailers. The research results show that expensive products and large size items have
the slowest fulfilment times in the new designs. Managers could differentiate products into
different fulfilment modes, limiting the need to build new design facilities. Non-competing
retailers could also pool resources to build shared fulfilment centres.
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