Fulfillment models framework for e-commerce
companies
Gomez, D.1, Bellido, J.² and Cabrini, R.³
Advised by PhD Ponce, E.4
1: Industrial Engineering Department, Universidad Iberoamericana, Mexico
2: Industrial Engineering Department, Universidad Nacional de Córdoba, Argentina
3: MBA, FIA-USP, Brazil
4: Research Scientist, MIT Center for Transportation and Logistics
Abstract
E-commerce relevance is increasing, and companies should be prepared to fulfill customers’
expectations and ensure an optimal shopping experience. Online worldwide retail sales generated 70
billion U.S. dollars in 2019, being Mexico and Brazil the main leaders for this type of channel in LATAM
(Chevalier, 2020).
With the objective of being more efficient and differentiate from competitors, it is vital to have
an extremely consistent and aligned supply chain that follows the company's business strategy. To
achieve this new challenge, the following study aims to generate a framework decision matrix, enabling
companies to support decisions of introducing fresh, dry, refrigerated, and frozen product categories
based on five major warehousing trends: distribution center, fulfillment center, dark-store, microfulfillment center and crowdsourced warehousing solutions. To develop this project a systematic
literature review combining case studies, papers, research articles and experts’ validation will be
implemented with the objective of establishing a framework that can be used to ensure strategies for
the e-commerce retailers, thus they are able to serve and meet customer expectations regarding product
quality, optimal price, and delivery time.
Keywords: Omnichannel, Micro-fulfillment center, Dark store, fulfillment center, Warehouse
crowdsourcing, Retailers, Logistics, Supply Chain, Warehouse.
1. Introduction
E-commerce has grown as consumers are shifting to shop products online. Consequently,
companies are being forced to adapt their supply chains in order to meet customers’ demand.
Warehouses, processes, and systems that were initially designed for classic operations cannot keep
the same levels of efficiency as new complexities are being introduced, such as individual units’ storage
or faster distribution strategies (Kammerer, 2020). Nowadays, we must think about e-commerce as a
whole ecosystem, where enterprises offer holistic solutions that make purchasing experience easier for
consumers.
It is projected that the expansion of e-commerce will keep growing, as the total number of
worldwide internet users in 2019 reached 4.13 billion, which from 40 percent have bought products or
goods online (Clement, 2019). Latin America is the world’s second-fastest growing e-commerce market,
expanding 25% annually through 2020, according to information from Americas Market Intelligence
(LABS, 2020) as it is presented in Figure 1. Online worldwide retail sales have generated 70 billion U.S.
dollars in 2019, having Mexico and Brazil as top leaders in this type of channel for Latin America
(Chevalier, 2019).
1
Figure 1: Adapted from “Increase of e-commerce volume”. (Labs, 2020)
Consumers attitude, behavior and purchasing habits are changing and they are demanding
more product categories in online marketplaces (Wright & Blackburn, 2020). Therefore, companies are
being forced to think differently and act faster in order to offer a wider variety of products and get a
stronger competitive advantage, setting customer experience as the main Key Performance Indicator
(KPI). If businesses offer new categories of products that consumers want, consequently they will
increase their sales, since they will be capable to attend more demand, and simultaneously gain market
share by increasing their service level.
E-commerce companies are expanding and creating new levels of service, since customers
nowadays want to order products and receive them in the same day, or even a couple of hours after
purchasing their goods. Thus, their main competitors need to innovate at an accelerated pace, so they
can remain competitive in the market. (Grosman, 2018). Over and above, these customer expectations
can be caused by what is called the “Amazon effect” which is defined as the high expectations that
consumers have regarding product assortment, delivery options, payment methods and the speed with
which they want to receive a product just after the purchase: “See now. Buy now. How fast can I get it?”
(Bimschleger & Pate, 2019).
This industry tolerates a small quantity of competitors, as the investments in IT resources on
distribution, warehouse solutions and commercial efforts are huge. The current picture of the Latin
American market involves no more than 3 main competitors who are battling to become the main seller
of the entire market (Global Center of Excellence: DHL, 2018). Moreover, the increasing number of
competitors in this sector becomes a new threat to be aware of. (Grosman, 2018)
This project seeks to build a standard framework focus on five fulfillment models and four
product categories that supports decision-making processes on the selection of the appropriate
fulfillment model: distribution center, fulfillment center, dark-store, micro-fulfillment center and
crowdsourced warehousing; for ensuring an optimal strategy for companies when storing and
distributing goods. This project will enable e-commerce companies to develop and improve their supply
chain strategy, when incorporating highly demanded categories of products, such as: fresh, dry,
refrigerated, and frozen products, to the company's portfolio.
2. Methodology
To develop the framework this study proposes to use a combination of systematic literature
review (SLR) based on academic journals, trade publications and comprehensive review of the case
studies. SLR will contribute to select the appropriate fulfillment models for the proposed categories and
determine the most relevant key performance indicators criteria to compare all fulfillment models.
In order to achieve this, the authors intend to map the actual fulfillment model of a multinational
e-commerce company leader in LATAM, considering the types of warehouses that support their logistics
operations (current scenario analysis). After understanding the current model and their supply chain,
the main characteristics and benefits of each selected fulfillment model will be identified whit the purpose
of incorporating those concepts and ensure the supply of the new product categories.
2
Figure 2: Main methodology’s steps
Paper’s detailed methodology, shown in Figure 2, was based on four main steps:
1. Systematic Literature Review (SLR)
The first step shows a review of the relevant literature, done with the purpose of
obtaining operational information, main key performance indicators and find business
opportunities in order to document the main features and attributes of the distribution models.
2. Fulfillment Model Analysis
Second step explores and study the main characteristics and benefits of the five
fulfillment models: distribution center, fulfillment center, dark-store, micro-fulfillment center and
crowdsourced warehousing. Also, the authors will propose and analyze relevant key drivers for
e-commerce and logistics models.
3. Framework Development
Based on the previous information, the authors use SLR in order to understand the best
performance metrics capable to compare each fulfillment model and develop a framework that
provides a decision matrix that can be used by all e-commerce retailers who want to incorporate
new product categories (fresh, dry, refrigerated and frozen) in their supply chain.
4. Framework validation
With the purpose of validating the systematic literature review based on papers,
scientific articles and cases study that originated the framework, field experts were consulted to
validate the project output. Based on the acknowledgement of each trend, this paper will
recommend which fulfillment model is the most appropriate for each product category. To
support this process, the Multiple Criteria Decision Analysis (MCDA) and the Analytic Hierarchy
Process (AHP) were employed, as the performance metrics permit to understand quantitative
and qualitative data of each trend in relation to the strategy (Ensslin, 2010; Olson & Slater, 2002;
Kaplan & Norton, 2008; Hill & Jones, 2012).
a. Multiple Criteria Decision Analysis (MCDA) and Analytic Hierarchy Process (AHP)
Both methodologies origin date from the 1990s, in Roy's and Thomas L. Saaty
studies. The MCDA supports that it is possible to create a model that will be accurate
to describe the reality, as long as restrictions are respected, making possible to have
an optimal solution to the problem (Roy, 1993; Ensslin, 2013; Espinosa & Salina, 2013).
On the other hand, the AHP helps decision-making process in situations that involves
qualitative criteria and that has the objective of overcoming the cognitive limitations of
decision makers in problems of selecting alternatives under multiple criteria that can be
intuitive, rational and irrational with the participation of multiple actors (Vilas, 2008;
Rodriguez 2008).
This methodology will enhance a diligent and clear decision making by
synthetizing, explaining and managing the subjectivity of the information involved and,
at the same time, minimizing the possibility that the decision made is optimal to one of
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the evaluation criterions, but unacceptable according to other criteria. (Belton & Stewart,
2002).
The construction and use of a solution based on the AHP method, consists of
the following four main phases (Gomes, 2004; Marins, 2010). The first phase is the
construction of the hierarchy, that presents the criteria, sub-criteria and alternatives for
the hierarchical structure. Secondly, data acquisition, which consists of collecting parity
judgments issued by those involved in the decision-making process. Thirdly, the
synthesis of the data obtained, in which the priorities of each alternative are calculated
in relation to the main focus; and finally, the consistency analysis, which allows to
identify how consistent the prioritization system is in the evaluation of alternatives
(Neuenfeldt, 2015). The consistency analysis consists in calculating the consistency
ratio (CR) with the purpose of showing the consistency all above the process. It is
calculated by finding the relation between the consistency index (CI) and the random
index (RI) (Alonso & Lamata, 2006). Formulas are shown below:
(λmax − n)
𝐶𝐼 =
(𝑛 − 1)
Being λmax the maximum value in the matrix and “n” the matrix dimension.
𝐶𝑅 =
𝐶𝐼
≤ 0.1
𝑅𝐼
Where RI is the arithmetic mean of the CI, and CR must be lower than 10% in order
to be consistent. (Saaty,1987)
3. Systematic Literature Review (SLR)
Systematic literature review main purpose is to identify, evaluate and interpret all research
related to a specific subject, following a protocol-driven and rigorously conducted methodology to
minimize outcome bias (Nightingale, 2009). Literature resources were obtained from six electronic
databases shown in Table 1, as well as the keyword search combinations employed. The main words
used for the searching process were “omnichannel”, “warehouse” and “grocery”; the operators AND /
OR were used to have better searching results. Search strings can vary depending on the filtering
functions given from the different databases. It is important to note that despite the systematic review,
this paper still has some information gaps, since fulfillment operations high performance are a core
competence for the e-commerce companies’ business strategy. Therefore, most information about
these trends remains confidential, or it is currently being generated, this paper aims to provide a starting
point for further research on each subject individually. The most relevant publications are shown in the
table in Appendix 1.
Table 1: Keyword search combinations
Databases
Keyword Search Combinations
Scopus
(TITLE-ABS-KEY ("warehouse” OR “omnichannel" ) AND TITLE-ABS-KEY ("e-commerce"
OR "online") AND ALL ("Fulfillment" OR "Grocery distribution") AND TITLE-ABS-KEY
("on demand warehousing" OR "e-fulfillment" OR "dark store" OR "micro-fulfillment" OR
"distribution center") )
Science Direct
"omnichannel" AND "grocery"
Web Of Science
TOPIC: (omnichannel) AND TOPIC: (warehouse) OR TOPIC: (fulfillment) AND TEMA:
(grocer*)
Google Scholar
(TITLE ("warehouse") OR ("omnichannel")) AND (TITLE ABS KEY ("E-commerce" OR
"Online") AND (TITLE ABS KEY "fulfillment" OR "Grocery distribution") OR ("on demand
warehousing" OR "e-fulfillment" OR "darkstore" OR "micro-fulfillment" OR "distribution
center")
4
EBSCO Library
MIT CTL
Publications
"omnichannel" AND "grocery". Limited to academic journals and trade publications. Subjects:
grocery industry, food industry, supermarkets, grocery shopping, groceries, consumer
goods, grocers. Language: English.
TOPIC: Omnichannel
Definition of the
correct search
string for the
different
libaries.
•Scopus (31)
•Science Direct (63)
•Web of Science (43)
•Google Scholar (474)
•EBSCO Library (152)
•MIT CTL Publications (26)
Total search
results from six
databases:
789
Potential relevant
articles to study:
48
Papers
accepted for
Sytemic
Literature
Review:
•Excluded from title,
keywords and abstract
•Removed duplicates
•Scopus (4)
•Science Direct (2)
•Web of Science (12)
•Google Scholar (7)
•EBSCO Library (12)
•MIT CTL Publications (2)
39
Figure 3: Systematic literature review process
The development of omnichannel supply chains can give companies a greatest competitive
advantage. For this reason, it is important to integrate the different channels and reinforce the strategies
for online and offline buying options, along with having seamless customer service profitably. (Ponce &
Caballero, 2020).
Arslan, Klibi & Montreuil (2020) mathematical proposal for distribution network optimization can
help the company to decide to ship-from warehouse, ship-from stores, and ship-from urban fulfillment
platforms. Also, they explain the concept of the advanced deployment strategies that “encompass
innovative practices such as: ship-from store, advanced stocks, anticipatory shipments, and smart urban
fulfillment”. The authors affirm that having centralized distribution network structures reduce retailers’
capabilities to capture online demand, requesting fast delivery services in most of the cases (Arslan et
al., 2020).
Although top managers recognize that developing an e-commerce channel is crucial for their
business, few of them are executing strategies towards that direction. Moreover, there is a lack of
alignment between what consumers demand and what companies are offering. Conroy, Nanda & Narula
(2013) suggest that being an early digital commerce adopter may be one of the few ways to keep the
business relevant to consumers, as well as having competitive prices, free shipping if possible,
promotions, variety of products and good online reviews. (Conroy et al., 2013).
Emerging markets present some common barriers to e-commerce growth. The fulfillment model
can be the most expensive and critical operation for companies to engage in online operations, and in
order to respond to that challenge, companies must seek distribution models that perform well along
multiple dimensions (cost-effectiveness, customer satisfaction, sustainability, etc). The actual
approaches can differ in diverse aspects, such as: facility location, type of the product and order, size
of the delivery vehicles, etc. (Janjevic & Winkenbach, 2020).
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The main components employed to create the distribution network design and strategy are
governance of transportation operations, delivery lead time and product range, likewise the architecture
of urban last-mile distribution networks and accessibility (Janjevic & Winkenbach, 2020). Accessibility is
an important factor to consider, as the walking distance impacts on the decision-making of customers
for choosing a channel to receive their products. Guerrero-Lorente, Gabor, & Ponce-Cueto consider that
having more installed channels of a certain type of channel, will generate more demand by reason of
convenience and customers' familiarity with the delivery mode (Guerrero-Lorente et al., 2020). There
are three main parameters in order to fulfill customer orders: customer preferences, the physical flow of
goods and the service delivery model. (Ponce & Caballero, 2020)
Today’s consumers are willing to pay higher prices for higher quality, convenient and on time
delivery services. With the aim of growing and retaining current customers, it is important to have the
correct last-mile delivery strategy. As the number of online customers is increasing rapidly year over
year, companies are being forced to adopt a profitable model and a delivery strategy that allows them
to keep escalating and fulfill users' expectations.
Consumers also demand a wider variety of products, which generates an additional challenge
for the fulfillment and distribution processes of perishable products, since they have a shorter shelf life,
and some have to be refrigerated at every step of the supply chain. The best strategy for e-grocers is to
analyze the risk that could represent a wide variety of products, given that fruits, vegetables, meat, and
seafood may deteriorate or become spoiled quicker. Another challenge is to have available and updated
information about the products because clients want to know the quality of the product that they are
buying. This process could be difficult because perishable products are harder to standardize and
control, therefore associating with powerful brands which offer high quality products and are recognized
in the market may be vital. Additionally, it is important to categorize deliveries based on product types
as are refrigerated and perishable products, limiting delivery locations where the population has a higher
density, in order to increase profitability and customer satisfaction. (Soon & Ah, 2018)
Mkansi, Eresia-Eke & Emmanuel-Ebikake (2018) emphasize the different e-grocery stages
pointing out the main challenges from the different steps for fulfillment and delivery management. Some
challenges presented in the fulfillment part are inventory management, which refers mainly to handle
different product variety, and preventing stock out risk. e-grocery fulfillment process is shown in Figure
4.
Elements of logistics
Storage
Facilities
• Order
storage
Communication
Inventory
Utilization and
Packaging
Transportation
• Order entry
& processing
• Order
stock
• Order
Picking &
Assembly
• Order
Delivery
Main Stages in e-grocery fulfillment
Figure 4: Adapted from “E-grocery stages fulfillment and elements of logistics”. (Mkansi, et al, 2018)
Ponce & Caballero (2020) evaluate the impact of supporting the home delivery of online orders
for groceries using dark-stores, warerooms, and physical stores. It is important to mention that dark
stores are used to support and fulfill online orders. Warerooms help to supply online purchases in
smaller spaces attached to stores. Dark-store is a fulfillment model solution that helps e-grocers to
increase service levels because of their distance to customers.
Another storage solution for online grocers whose establishments are located in urban areas,
is the Micro-fulfillment center (MFC). This solution is partially to totally automated, what enables
supermarkets to fulfill online orders with a higher speed. Also, food retailers can combine a wide variety
of products, since MFC aisles can be divided in a way that is more convenient for the grocer. (Honeywell
Intelligrated, 2019)
Customer experience while shopping online is also important, therefore Boyer & Hult (2006)
decided to compare Distribution Center based picking used for grocery storage and DC for store based
picking, by means of a research model considering product quality, service, freshness and time saving
as part of the attributes. They demonstrated that based DCs can provide a better product quality as they
have a shorter supply chain and a better development of their business model, giving as an example
some DC-based grocers, such as Ocado, Fresh Direct, Grocery Gateway and others. By this reason we
will study this fulfillment model. Nowadays, Ocado renewed their strategy by investing in automation for
6
their warehouses, which allows them to fill grocery orders quicker and increase their service level.
Ocado’s fulfillment centers are specialized for online shopping, what is called e-fulfillment centers, both
of the strategies will be addressed as two warehousing solutions for online grocers (Miller, 2017).
In order to prosper, grocers must develop a fulfillment center that allows them to improve their
service level for online orders. For this reason, the implementation of automation and robotics should
play an important role for operational efficiency. Deep diving in the complexities regarding food supply
chains, Butler & Fallon (2019) explain the main storage challenges concerning temperature control,
product variability, perishability and food safety quality controls offering two approaches to address
them: pick-from-store and dedicated fulfillment models. (Butler & Fallon, 2019).
Cattani, Perdikaki & Marucheck (2007) say that e-grocers’ profitability will depend on the
choices they make for product distribution, storage and the variety of products offered online, such as
perishable or non-perishable goods. Also, they recommend doing an evaluation of the benefits (in
service level and operations) that the company will gain, versus the investment for changing the
infrastructure. (Cattani, et al., 2007)
Warehouse automation can enable grocers to keep control of inventory and order management,
but as this can require a lot of investment a Seattle based start-up called Flexe, provides crowdsourcing
warehouse solutions, which allows companies to share warehouses with other businesses. The
evaluation of this solution will be reviewed as one of the newest warehouse solutions in the market,
which permit companies to have a sharing economy position.
The preceding publications enable a better understanding about factors that make certain
distribution strategies more or less suitable for specific contexts, therefore it is possible to understand
the main parameters that impact customer decisions, distribution methodologies, last-mile strategy and
then identify each product category's necessity regarding storage. Making it possible to identify elements
in a local context that impact strategies, giving us a more suitable idea of how to adapt companies’ new
storage solutions for fulfillment models strategies, to ensure efficient operations and reliable delivery.
Furthermore, to know what would be the most convenient shopping process for customers,
considering client preferences, pick-up on store or drop-off at home, and the logistics costs associated
with the company, as the location of the goods and transportation fees, namely, the current state of
practice in urban last-mile e-commerce distribution, allowing to expand the diverse approach used to
better perform in logistics operation. The selection of the last mile delivery strategy will be directly linked
with the warehousing model that the enterprise is willing to use.
The comparison of the different fulfillment and distribution methodologies used by omnichannel grocery
retailers nowadays, will help to develop the framework considering these strategies to develop new
policies, depending on fulfillment model characteristics, delivery guarantees and product categories
(fresh, dry, refrigerated, and frozen)). Based on the previous articles, the following dimensions were
selected for the study: layout and facilities description; technology; size, capacity, and location of the
storage; distance from the customer; and activities inside the selected fulfillment models (distribution
center, fulfillment center, dark-store, micro-fulfillment center and crowdsourced warehousing).
4. An overview of the e-commerce operations
In order to understand the fulfillment models that are currently being used, this section will
describe the distribution model of a multinational e-commerce company. The analyzed firm is one of the
biggest e-commerce companies in the world, with a presence in almost every country of Latin America.
It operates under different business units, including an online marketplace, a payment platform, and a
logistics division. The mentioned units were developed to work within a business ecosystem: the online
marketplace was designed to match buyers with sellers, the payment unit was implemented to ensure
a secure economical transaction, and finally the logistics division was planned to deliver every order to
customers. As the logistics division increased the number of transactions, the company started to
operate and explore different logistics models. This company has three principal ways to reach the
customer, the process is pictured in Figure 5.
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Figure 5: Distribution Models (developed by authors)
The first distribution model is the direct channel, where the seller uses the platform to offer their
products, and after customers’ purchase, products are delivered directly from the seller’s shop to the
customer's home, using a mail service or a private carrier. The second distribution model starts with a
collection truck, that picks-up a minimum quantity of items that the seller has not sold yet and take those
products to the fulfillment center. Then, the company charges the seller with a fee that considers the
space to stock their product and the company is responsible to handle, store and deliver those items.
Lastly, the third distribution model involves using a typical cross docking facility: the company collects
the items that have been already sold through the online marketplace and takes them to a warehouse
chosen regarding its final destination for delivery. The first and last distribution models do not involve
any storage operation.
Last mile deliveries are executed using vans or small trucks, and currently 70% of the logistics is
managed by 3PL haulers, who are directly hired by the company. The other 30% is performed by
commercial carriers and post offices. Considering the lead time, 30% of the orders are delivered in less
than one day, and 80% of them in less than 2 days. Concerning the seasonality of the demand, generally
Mondays are the peak days, thus there are part-time or daily drivers/sorters contracts to handle this
weekend withheld demand. For instance, and in order to give context of the size of the operations, in
Brazil, the company delivers more than 200.000 packages/day.
The company implemented three main systems that enable to have better sorting and picking
processes: TMS, WMS and Yard management system. All those systems are Taylor made solutions,
developing internally.
The main characteristics of each distribution model prior mentioned, are described in the table
below.
Table 2: Main characteristics of each distribution model (developed by authors)
Model
Direct
Definition
Total logistics
operation
outsourced
Product
Categories
No restriction as
the company
does not
interfere.
Advantages
Disadvantages
Facilities
- Low capital
investment
- Low operational
complexity
- High flexibility
- High scalability
- Low margin
benefits
- Inventory
tracking
- Limited
inference on
delivery time,
quality, and cost
- No own facility
- 50% of the
volume
8
Stock
The company
does not own
inventory, it offers
the service to
store and handle
selected sellers’
stock.
Dry, nonperishable, nonhazardous,
lightweight, and
small items.
- Low shipping
- High investment - 10 facilities:
time
- High operational Brazil, Argentina,
- Quality
costs
Mexico,
assurance of final - High operational Colombia, and
product and
complexity
Chile.
packaging
- Low flexibility
- Size: 8.000 m2
- Competitive
- High risk
to 110.000 m2
advantage for
concerning
- 10% of the
storing and
handling
volume
handling sellers’ inventory
stock
Collect
The firm still
outsources the
complete
transportation
process, but it
provides a
transfer facility to
be used by 3PL’s.
Dry, nonperishable, nonhazardous,
lightweight, and
small items.
- Low operational
cost: low holding
and handling
cost, as there is
no storage.
- High Influence
on carriers.
- Dependence of
the suppliers
- Little influence
on quality and
shipping time
- Responsibility
for sellers’
shipments
- 10 cross-dock
facilities in the
region
- Sizes: 8.000 m2
to 20.000 m2
- 40 % of the
volume
5. Fulfillment Models Characterization
The fulfillment concept in e-commerce represents a cross-sectoral field of study, as it involves
different performance indicators regarding customer satisfaction, last-mile deliveries, and the types of
the portfolio categories. In order to better understand the competitive advantage of each fulfillment
model, authors will analyze the main characteristics and compare afterwards.
a. Distribution center (DC) and Fulfillment center (FC)
The order fulfillment concept in logistics refers to all processes that involve the processing of
customers’ orders from the reception of the order until it’s delivery, ensuring operational efficiency
(Amazon, 2020). Many e-commerce companies such as Mercado Libre, Amazon, Alibaba, and others
have chosen to operate using Fulfillment Centers (FC), opposed to traditional distribution centers. Both
fulfillment and distribution centers provide services that are needed to determine whether a product
should be held in stock or not, depending on customers’ demand (Moralez, 2019), but the usability and
services provided for each one are often quite different. Warehouses normally store products for an
extended period, in a large storage center or industrial space designed to stock bulk inventory. There
are warehousing providers that are geared toward businesses that primarily do wholesale or B2B orders
in huge quantities. On the other hand, a fulfillment center, may be owned by a third-party, allowing
retailers to strategically store their goods and to process the customer's orders. As soon as the market
is consolidated, companies tend to own their FC. Thus, the workers can quickly pick orders after the
online purchase is completed, that inventory in a fulfillment center is not typically held for more than a
month, or the merchant may be stuck paying high warehousing fees. An e-fulfillment center is a
fulfillment method that allows to process traditional orders and has limited capacity to prepare online
orders. It can process B2B and B2C orders that are shipped directly to an individual consumer at home.
It is a concept of “distributed delivery centers” describing the concept of decentralized inventory holding
and order picking. This happened because the central objective is the search for customer satisfaction
and client loyalty, therefore the stock levels reduction and the “specialization” of the site in the fulfillment
of direct orders to the consumer becomes important, thus many fulfillment houses specialize in
processing orders for certain commodity types. An urban fulfillment center can allow products to be
delivered the same day or next day at a lower cost to serve (Bimschleger & Pate, 2019).
Clearly the role of fulfillment centers is to efficiently meet customer expectations on lead time,
for this reason, location is a critical factor. However, the surge in competition among retailers to deliver
goods faster has contributed to the need for more warehouse facilities and now developers are
scrambling to satisfy the need for modern retail fulfillment centers, based in the urban core that provide
customer value over and above the traditional inventory warehousing needs (Bimschleger & Pate,
2019). The traditional approach in e-fulfillment is the use of large e-fulfillment centers that operate on a
regional scale. The size of these centers can easily reach 100,000 square meters (Wang & Zhang & Liu
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& Shen & Lee, 2016), but it depends on the amount of unique SKUs, the order volume and the company
growth plans (Janjevic & Winkenbach, 2020).
For more details some big players in the market and their fulfillment strategies are shown in
exhibit 1.
It is important to mention that based on the “square root law”, as the number of FCs increase,
the complexity of stock balance, product’s quantity and diversity increases too, but having a closer
distance to the costumer will bring major benefits to the company. (Maister,1976; Ballou, 1981)
Based on the description above, it can be concluded that the central objective of the fulfillment
center is to connect consumers in large metropolitan areas with same-day or multi-hour service adding
significant revenue, improving the customer experience, clients loyalty, and driving the brand
differentiation for leading e-commerce and omnichannel retailers.
b. Dark-Store (DS)
The word “dark-store” refers to a traditional retail store that has been converted to a small
fulfillment center which only operates online. The model has been commonly adopted by grocery,
clothing, and home goods brands during the past years, but the trend is also to move to other industries.
The purpose of this business model relies on the idea of running a logistics operation in densely
populated areas of mainly big cities with large demand to shorten delivery times and provide a better
shopping experience. The location of dark stores is critical for retailers in huge geographical regions and
areas (Morgan, 2020).
The dark store is generally a store that serves the function to facilitate a click-and-collect service
to a customer that has ordered online or an order fulfillment platform for online sales. Although the public
cannot access the building, it has a layout that is similar to a supermarket, where aisles with shelves
that contain products and other items can be found (García Lopez, 2014). The buildings where they are
allocated are frequently common and utilitarian from the outside. Unlike a traditional store, where
products are located regarding commercial agreements with certain brands and convenience, the layout
is designed to optimize the picking process. Once the consumer placed an order, a picker would grab a
picking cart and move around the aisles looking for the products. This process is possible by the
utilization of a warehouse management system (WMS), which assigns each picker a route and assures
the shortest distance. The goods which sell fast are usually located together in a place with easy access
and close to the gates (Butler & Fallon, 2019).
c. Micro-fulfillment Center (MFC)
Micro-Fulfillment Center (MFC) is a highly automated and small footprint order fulfillment
structure that allows e-grocers to improve logistic operations, allowing them to handle a boundless
expansion of product varieties. This strategy can also help them to shorten the distance to the customer
and meet next- or same-day deliveries and it can be installed in stand-alone facilities or in existing
stores. As a result, customers will receive their goods quicker as the supply chain is shortened.
Additionally, MFCs augment regional DC hubs and deliver robust fulfillment capabilities, helping egrocers to manage in-store pickups requested in mobile apps, and allowing them to have a competitive
advantage for being the first mover between their competitors. (Honeywell Intelligrated Inc., 2020)
The main factors that drove MFC’s implementation was the increasing urbanization, as 54% of
the world's population live in urban areas and this is expected to have a growth of 68% by 2050,
simultaneously e-commerce sales will have an important increase, as it is poised to double between
2018 and 2023. At the same time, retailers have to be flexible and MFC solution can be promptly
implemented to meet the demand, also they have to establish smaller facilities closer to high-populated
centers, supporting next- and same-day deliveries. Also, retailers find difficult to invest in a big space
for building a DC. In-store fulfillment challenges like the added fulfillment burden can reveal
replenishment process inadequacies, and consequently have stocking issues, thus is not a sustainable
long-term strategy to use traditional in-store inventory for online order fulfillment. (Honeywell Intelligrated
Inc., 2020)
d. Crowdsourced Warehousing - On-Demand Warehousing (CW)
Crowdsourcing term appears the first time in Wired Newspaper in ann article presented by
Mark Rodinson and Jeff Howe. The term uses the word “crowd” and “sourcing” to define a production
and structuring process model that uses the collective learning and knowledge to solve problems and
develop new solutions. The definition of crowdsourcing is a warehouse service that is outsourced to an
10
occasional company from the public of private storage and is coordinated by a technical platform to
achieve benefits for the involved stakeholders. (Howe & Robinson, 2008). The emergence of
crowdsourced solutions in the warehouse and storage industry has the potential to radically alter the
way stocks are organized, and how the picking will be performed. Service firms, which frequently
originate as startups from outside the traditional logistics industry, manage online crowdsourcing
platforms where stockists and companies connect and negotiate the service. The platforms generally
focus on storage to individual households (B2C) or between consumers (B2B). When the lender and
renter are peers, a peer-to-peer (P2P) sharing economy model is formed. Instead of two individuals, if
two companies share a resource, then it is defined as a business to business (B2B) on-demand model
(Kaan Unnu, 2020).
The crowdsourcing firm is responsible for storing and managing the products. Emerging
businesses, such as ES3 and Flexe, have recently demonstrated how they created shared value through
collaborative logistics services. For example, ES3’s collaborative warehousing and direct-to-store (D2S)
program brought manufacturers and retailers together to share resources and eliminated waste by
streamlining supply chains. Flexe’s on-demand warehousing platform created a marketplace by
connecting supply and demand and matched excess warehousing capacity with seasonal inventory
overflows among warehouse operators and third-party logistics (3PL) providers (Shin, 2020).
e. Fulfillment models characterization and comparison
With aim to consolidate the concept of each trend a literature review, based on scientific papers
and case studies, was used. With those references it was possible to compare the five main models
selected by the authors, as presented in table 5. Main references that support this work and each model
main characteristics respectively, can be found in appendix 2.
Table 3: Characterization of the Five Fulfillment Models (developed by authors)
Distribution
Center
Fulfillment
Center
Dark Store
MicroFulfillment
Warehouse
Crowdsourcing
Sub-Urban areas
Urban or
Suburban areas
Semi urban
areas
Urban areas
Suburban areas
Customer
Distance (km)
High
(>30 km)
Medium
(<30 km)
Medium
(<30 km)
Low
(<10 km)
Medium
(<30 km)
Avg. Size [m²]
100,000
100,000
3,000 - 4,000
1,800
1,000
Avg. Hight [m]
12
12
4
16
4
Avg. Capacity
[m³]
High
(1,200,000 m3)
High
(1,200,000 m3)
Low
(10,000 m3)
Medium
(28,880 m3)
Low
(4,000 m3)
Inventory Level
High | Long time High | Short time
Medium
Low
Low
Location
Days on Hand
High
(>1 month)
Medium
(1 month)
Low
(<1 month)
Medium
(>1 month)
Low
(<1 month)
Layout
orientation
ABC Curve
Random Show
Picking process
Space
optimization
Standard
products storage
Final Customer
B2B
B2B and B2C
B2C
B2C
B2C
Variety
High
Low
Medium
Medium
Low
Operational Cost
High
Medium
Medium
Medium
Low
Capital
Expenditure
High
High
Medium
Medium
Low
Shipping Time
+3 days
+3 days
+1 day
+1 day
+2 days
Picking Process
Accuracy
Low
Medium
High
High
Medium
Workforce
(Employees)
High
(+500)
High
(+500)
Medium
(100-499)
Low
(-100)
Low
(-100)
Level of
Automation
(process
automation
Low
(>10%)
Low/ Medium
(11% - 30%)
Medium
(<30%)
High
(>30%)
Low
(>10%)
11
percentage)
Software
Technology
TMS
WMS
Yard
management
system
TMS
WMS
Yard
management
system
WMS
Pick to light
Voice-picking
WMS
Depends on the
lender
WMS
Industries
Massive
consumption
Massive
consumption
CPG
Groceries
Clothing
Groceries
Pharmaceutical
Electronics
Automobile
Examples of
Companies
Amazon
Walmart
Target
Ikea
Mercado Libre
AbInbev
Amazon
Walmart
Target
Ikea
Mercado Libre
Walmart
Globo
PedidosYa
Coto
Día
Carrefour
Takeoff
Alert innovation
Fresh Direct
Ralph Lauren
Walmart
ACE Hardware
Electrolux
BMW
6. Framework Development
The proposed framework seeks to compare five different fulfillment model solutions: distribution
center, fulfillment center, dark-store, micro-fulfillment center and crowdsourced warehousing. The main
performance drivers for the study were obtained from the most relevant literature obtained from the SLR
and will be presented below in Table 4. The purpose of this framework is to improve e-commerce
companies decision-making process while finding an optimal supply chain strategy while adding
different product categories, such as: fresh, dry, refrigerated, and frozen products.
Figure 6: General Framework Description
With the aim of validating the framework outputs, field experts from different companies in Latin America
were consulted. To reinforce this process, the Multiple Criteria Decision Analysis (MCDA) and the
Analytic Hierarchy Process (AHP) methodologies were utilized, as these methodologies will enable to
understand the qualitative and quantitative data obtained for the fulfillment models and the key
performance metrics in relation with the strategy.
a. Metrics Definition
i. Construction of the hierarchy and metric definition
12
The categories provided for the fulfillment model comparison, were obtained by analyzing the
most relevant articles and case studies from the different electronic libraries. Firstly, we obtained the
most relevant literature by comparing the number of citations that each paper had, the higher number
of citations, the higher relevance it would have. From which, we extracted the most repetitive words that
were relevant to our topic, such as: operational costs, shipping time, distance to customer, etc. With this
filtering method we obtained 41 different performance indicators, that were reduced by semantic fields,
having an outcome of 14 final indicators. The definition of each category is shown below, as well as their
degree of criticality (5 high, 3 medium, 1 low) with the purpose of improving the framework development
process, being 5 the most unfavorable score and 1 the most valuable.
Table 4: Performance metric description and score
Metric
Description
Score
Distance to
Customer
Refers to the distance where a facility is located regarding the final
consumer. If it is located at the city center, in the suburbs or outside
the city, the strategy to supply the consumers might change
completely. Moreover, the weight of the metric will depend on the
product that the company needs to store and deliver.
5: if the fulfillment is more than 30 km from final
destination.
3: if the fulfillment is more than 10 km from final
destination and less than 30 km.
1: if the fulfillment is less than 10 km from final
destination.
Warehouse Layout It points out the complexity and effort applied to design or change
the warehouse layout. The more products categories that the
company stores, the more challenging the layout is. Furthermore,
products' special requirements regarding the handling and storage
must be considered when designing a layout, such as food safety,
hazardous materials, cleanness, environment exposition, etc.
5: High complexity: multiple product categories
with especial storage and handle requirements.
3: Medium complexity: multiple product
categories with no especial storage
requirements.
1: Low complexity: single product category.
Stock Level
Product Quality
Assurance
It denotes the risk of stock out and the difficulty to have the right
5: High risk of stock out: low stock level (less
products in store and the right quantity. A wrong management of this than 5 days of inventory)
indicator might drift in less sales, lower customer satisfaction, risk of 3: Medium risk of stock out: medium stock
delivering expired products, more cost in spillovers, etc., meaning
level, medium risk of stock out (between 5 and
more costs and fewer benefits for the company.
30 days of inventory)
1: Low risk of stock out: high stock level (more
than 30 days of inventory)
Hints at the effort that the company needs to make to ensure the
quality of the product. Meaning that the product must be stored,
handled, packaged and delivered correctly. This indicator plays a
critical role when the stored products are perishable, fresh, frozen,
and hazardous.
5: High risk of not meeting required quality
3: Medium risk of not meeting required quality
1: Low risk of not meeting required quality
Storage Capacity Alludes to the available capacity of the warehouse to store products, 5: Low: less than 10,000 m3
defined in cubic meters. The main variable that impacts this metric is 3: Medium: more than 10,000 m3 and less than
the storage strategy that the company chooses to stock its products. 200,000 m3
The different systems range in complexity, cost, and storage
1: High: more than 200,000 m3
density. The principal ones that are being used by the industry are
racks, multiple levels mezzanines, shelves, high shelves, picking
tunnels or storage at floor (stackable products).
Software &
Technology
Indicates the level of technological complexity that an operation has, 1: No specific software used
focusing on software implementations needed to run it. Tailored
3: Medium technology: standard warehouse
systems need technology teams to develop and correct application management system acquired with main
errors, which is highly expensive.
features
5: Advanced technology: Own warehouse
management systems developed according to
the company's need
Fulfillment
Process
Complexity
Denotes the complexity of the process regarding special handling
5: High complexity
requirements for each product category, the quantity of steps within 3: Medium complexity
the warehouse and the picking cycle time.
1: Low complexity
13
Return Process
Complexity
Refers to the risk associated to the products that are spoiled due to 5: High complexity
the complexity of the returns process and the special conditions that 3: Medium complexity
certain categories need in order to maintain their quality.
1: Low complexity
Automation &
Machinery
Indicates the level of automation that a facility has. The machinery
for fulfillment centers includes mainly conveyor systems, shuttle
systems, mini-load systems, packing machines, labelling machines,
forklifts, picking systems (such as pick to light and voice picking
technologies), elevator systems, sorting systems and autonomous
guided vehicles (robotics).
5: No automated equipment: forklifts and
manual transits
3: Moderately automated: conveyors systems,
packing machines, picking systems and/ or
sorting systems
1: Highly automated: shuttle system, mini-load
systems, automated picking and/ or
autonomous guided vehicles (robotics)
Capital
Expenditure &
Investments
Relates to investments that companies use for purchasing,
improving or maintaining long-term assets to improve the efficiency
or capacity of the warehouse. The metric includes the purchase of
items such as new equipment, machinery, land or warehouses,
furniture, business vehicles, software programs, or intangible assets
such as a patent or license.
5: Expenditures that include warehouse
building, operational equipment, material
handling equipment, software programs,
licenses and automated systems for storage
and product internal transit
3: Expenditures that include warehouse
building, operational equipment, material
handling equipment, software programs,
licenses and storage structures (mezzanines
and racks)
1: Expenditures that include warehouse
building, operational equipment and material
handling equipment
Operational Costs Indicate expenses which are related to the operation and the
business. It includes the rent, salaries, administrative costs, among
others. This metric is strictly linked with the size of the warehouse
(rent), the quantity of employees (salaries), maintenance costs and
energy used (machinery).
Shipping Time
5: High expenditure to operate (more than
10MM year)
3: Moderate expenditure to operate (between 5
and 10 MM/year)
1: Low expenditure to operates (less than 5MM
year)
Determines how the customers evaluate the whole shopping
5: More than +1 day delivery
experience, which includes all kinds of interactions and services
3: +1 day delivery
necessary for the product to arrive at its final destination. This KPI is 1: Same-day delivery
strongly linked with on-time shipments, packaging, and product
quality and out of stock indicator. It can help a company determine
how to best improve or change its products or services, as
customers crave more and a more expedient and seamless
shopping experience.
Picking Accuracy This metric shows how accurately the picking process is. Its
performance is expected to be better when picking one single item
orders than multi-items orders, as the picker must pick items from
different picking locations. The metric is also a measure of the
technology used by the company to operate: the more automated
the process, the more accurate the results. This indicator is strongly
linked with customer satisfaction, as consumers expect to receive
exactly what they have ordered.
5: Manual picking
3: Assisted picking by technology (pick to light
or voice picking)
1: Automated picking: shuttle and mini load
system
Specialized Labor Focus on the knowledge, training and previous preparation that the
workforce needs in order to work with specific categories.
5: General knowledge required for labor force
3: Minimum knowledge required for labor force
1: Specialized labor force
Documentations
Control
5: High risk of not complying with correct
documentation
3: Medium risk of not complying with correct
documentation
1: Low risk of not complying with correct
documentation
This indicator refers to all certificates, documents and legal
processes that should exist in order to be able to store and deliver
each product category.
14
Handling
Specifications
This metric shows how accurately and precisely should be the
handling process, if there are any specific requirements to
guarantee the product quality through the whole process.
5: General requirements are needed the risk of
not attending them is low
3: Specific requirements are needed and the
risk of not attending them is low
1: Specific requirements are needed and the
risk of not attending them is high
ii. Data acquisition and synthesis of the data obtained
In order to create the framework, each fulfillment model (column) was analyzed according to
every performance metric (line) defined in table 4 and received a score representing the risk of not
fulfilling the client needs in that specific criteria.
After proceeding as mentioned it is necessary to set the grading values for each criterion, which
depicts how it aligns with the fulfillment model. For example: If the criterion impact is high in the fulfillment
model, then the score is 5, if it is medium 3, and if it is low 1. Find below the performance evaluation for
each fulfillment trend.
Table 5: General framework
General Framework
Weight
Metrics
DC
FC
DS
MC
CW
Distance to Customer
5
3
1
3
3
Warehouse Infrastructure
3
3
1
5
1
Stock Level
1
1
3
3
3
Product Quality Assurance
5
3
5
1
5
Storage Capacity
1
1
3
5
5
Software & Technology
3
3
5
3
5
Fulfillment Process Complexity
1
1
3
5
1
Return Process Complexity
3
3
1
1
5
Automation & Machinery
1
1
3
5
1
Capital Expenditure & Investment
3
3
3
5
1
Operational Costs
5
3
3
1
3
Shipping time
5
5
3
1
3
Picking Accuracy
3
3
5
1
3
Specialized Labor
5
5
3
5
1
Documentations Control
5
5
1
3
3
Handling Specifications
5
5
3
3
1
0
0
0
0
0
iii. Consistency Analysis
In order to determine which are the main performance metrics for a specific product category,
the main operational requirements for each product should be weighted, having as a result the relevance
for each fulfillment model. The metrics were weighted according to a scale from 1 to 3, being 1 less
relevant and 3 the most relevant score for each category. Then, the yellow column from table 7 was
filled by multiplying each weight, with the corresponding score of each model. With the summatory of all
the results for that methodology, the final score was obtained.
The fulfillment model with the lowest score should be selected, as it represents the lowest risk
for the studied product category. The performance score for each fulfillment model, was measured with
the number 5 being the highest number (more critical/more risk), according to the importance of each
metric for the specific product category, and the number 3 shows the highest relevance of that metric
for the selected product category.
15
In order to develop and propose a framework that supports the decision-making process on the
selection of the appropriate logistics model for each product category, it is important to understand
commercialization limitations for the different product categories due to local licenses and regulations.
Basically, it is possible to divide food transportation into two types of cargo: perishable and nonperishable. In general, non-perishable foods are those that have the lowest risk of spoilage. This is the
case with dry and industrialized products - such as rice, beans, sugar, cookies, and pasta, for example.
Perishable foods, on the other hand, can belong to different groups of products - ranging from fruits and
vegetables to loads that must be transported with the assistance of refrigerators or freezers. It is
important to mention that perishable and refrigerated loads usually have more strict norms respecting
transportation, also they need a more adequate way to handle it. On the other side, the non-perishable
products have simpler regulations, because products have a lower risk to decrease the food quality.
For instance, in Brazil, the regulations for food transportation and food monitoring practice are
made by Anvisa, which is a government agency responsible for inspecting food quality, medicine and
hospital products, in order to be in good conditions. Anvisa, recommends establishments to have guides
for good practices and standards on food handling in various areas. In addition, states and municipalities
can create their own food transport rules. In Brazil the surveillance can be carried out by the local traffic
authorities or the highway state police. The main rules to commercializing foods are related to
temperature control, hygiene and travel time, to prevent the proliferation of microorganisms in food.
Furthermore, food should be avoided to be placed with substances that could contaminate it. To avoid
risks of contamination and damage to food, all materials used to protect and secure the load, such as
plastics and ropes, must be disinfected before using them. Another requirement is that trucks and
storage must approve the health inspection, thus obtaining an inspection certificate. Frozen food must
obey an even greater number of rules, there is a necessity of a refrigerated, capable of maintaining the
food at the appropriate temperature. According to Anvisa, each type of food must be at a different
temperature. Cold products should be below 10º C, frozen below 8º C; and quickly frozen below 18º C.
In addition, for hygiene reasons, the storage space must be made of smooth, waterproof, and washable
materials. The storage and the truck that contains those types of food must always have an easily
accessible calibrated thermometer, thus that it is possible to check the temperature at any time. It is also
important to review the refrigerated chamber and freezer to see if the equipment is up to date and does
not offer any risk of contamination or loss of performance during the trip or storage.
The proportion of fresh food preserved by freezing is highly related to the degree of economic
development in a society. As countries become wealthier, their demand for high-valued commodities
increases, primarily due to the effect of income on the consumption of high-valued commodities in
developing countries. The commodities preserved by freezing are usually the most perishable ones,
which also have the highest price. Therefore, the demand for these commodities is less in developing
areas. Besides, the need for adequate technology for the freezing process is the major drawback of
developing countries in competing with industrialized countries (Cánovas, 2005). The frozen food
industry requires accompanying developments and facilities for transporting, storing, and marketing their
products from the processing plant to the consumer (Mallett, 1993). Thus, a large amount of capital
investment is needed for these types of facilities. For developing countries, especially in rural or semirural areas, the frozen food industry has therefore not been developed significantly compared to other
countries (Cánovas, 2005).
Based on the products commercialization limitations described above, it is possible to ponder
the importance of the metrics that impact the most on the product performance and also define the best
fulfillment model for each product category.
Table 6: Weight metrics for each category according to commercialization requirements
Categories Weight
Fresh Weight
Frozen
Weight
Refrigerated
Weight
Dry Weight
Metrics
3
3
2
1
Distance to Customer
1
2
2
1
Warehouse Infrastructure
3
1
2
1
Stock Level
3
2
3
1
Product Quality Assurance
1
3
2
3
Storage Capacity
1
3
3
2
Software & Technology
16
2
3
2
1
Fulfillment Process Complexity
3
3
3
2
Return Process Complexity
1
3
2
2
Automation & Machinery
1
3
2
1
Capital Expenditure & Investment
1
3
2
1
Operational Costs
3
2
3
1
Shipping time
2
2
3
1
Picking Accuracy
3
1
1
2
Specialized Labor
3
3
2
1
Documentations Control
3
3
2
1
Handling Specifications
Fresh products category
Considering the specifications of the products in this category and considering the fragility of
them, the distance to the customer, the stock level, the products quality assurance, the return process
complexity, the shipping time and the specialized labor documentations control are critical metrics to be
considered in order to ensure minimum spending and waste when storing these products.
This category requires specific documentation in order to ensure the pest control, biological and
physical integrity of the product. Because of that the relevance given to those metrics are high, therefore
the weight is 3 (the most relevant metrics for that specific category). Therefore, the most suitable
fulfillment model for fresh product categories is a dark store.
Table 7: Fresh category framework
Fresh Category Framework
Weight
Metrics
DC
FC
DS
MC
CW
3
Distance to Customer
5
3
1
3
3
1
Warehouse Infrastructure
3
3
1
5
1
3
Stock Level
1
1
3
3
3
3
Product Quality Assurance
5
3
5
1
5
1
Storage Capacity
1
1
3
5
5
1
Software & Technology
3
3
5
3
5
2
Fulfillment Process Complexity
1
1
3
5
1
3
Return Process Complexity
3
3
1
1
5
1
Automation & Machinery
1
1
3
5
1
1
Capital Expenditure & Investment
3
3
3
5
1
1
Operational Costs
5
3
3
1
3
3
Shipping time
5
5
3
1
3
2
Picking Accuracy
3
3
5
1
3
3
Specialized Labor
5
5
3
5
1
3
Documentations Control
5
5
1
3
3
2
Handling Specifications
5
5
3
3
1
121
107
91
93
95
Weighted Score
Frozen products category
Considering all investments that should be made in order to have the correct equipment to
transport and store products that belong to this category, and also having the correct documentation,
certifications and agility to delivery, the following metrics were defined as the most relevant ones:
17
distance of customer, storage capacity, software & technologies, fulfillment process complexity, return
process complexity, automation & machinery, capital expenditure & investments, operational costs,
documentation control and handling specification. In conclusion, the appropriate fulfillment model to be
considered when storing frozen products is a crowdsourcing warehouse solution.
Table 8: Frozen category framework
Frozen Category Framework
Weight
Metrics
DC
FC
DS
MC
CW
3
Distance to Customer
5
3
1
3
3
2
Warehouse Infrastructure
3
3
1
5
1
1
Stock Level
1
1
3
3
3
2
Product Quality Assurance
5
3
5
1
5
3
Storage Capacity
1
1
3
5
5
3
Software & Technology
3
3
5
3
5
3
Fulfillment Process Complexity
1
1
3
5
1
3
Return Process Complexity
3
3
1
1
5
3
Automation & Machinery
1
1
3
5
1
3
Capital Expenditure & Investment
3
3
3
5
1
3
Operational Costs
5
3
3
1
3
2
Shipping time
5
5
3
1
3
2
Picking Accuracy
3
3
5
1
3
1
Specialized Labor
5
5
3
5
1
3
Documentations Control
5
5
1
3
3
3
Handling Specifications
5
5
3
3
1
134
118
112 126
112
Refrigerated products category
One of the main complexities that refrigerated categories means to fulfillment models is the strict
need of temperature control. This attribute has a direct impact in the whole supply chain of these kinds
of products and assuring the quality of products in every echelon becomes critical. For these reasons,
the metrics that have more impact on this model are product quality assurance, handling specifications,
picking accuracy and return process complexity. In addition, companies need to ensure the availability
of the right software solutions to monitor the stock at every step of each process.
In conclusion, after analyzing each metric for each model, micro fulfillment results in being the
most fitting fulfillment model to select to store refrigerated products.
Table 9: Refrigerated category framework
Refrigerated Category Framework
Weight
Metrics
DC
FC
DS
MC
CW
2
Distance to Customer
5
3
1
3
3
2
Warehouse Infrastructure
3
3
1
5
1
2
Stock Level
1
1
3
3
3
3
Product Quality Assurance
5
3
5
1
5
2
Storage Capacity
1
1
3
5
5
3
Software & Technology
3
3
5
3
5
18
2
Fulfillment Process Complexity
1
1
3
5
1
3
Return Process Complexity
3
3
1
1
5
2
Automation & Machinery
1
1
3
5
1
2
Capital Expenditure & Investment
3
3
3
5
1
2
Operational Costs
5
3
3
1
3
3
Shipping time
5
5
3
1
3
3
Picking Accuracy
3
3
5
1
3
1
Specialized Labor
5
5
3
5
1
2
Documentations Control
5
5
1
3
3
3
Handling Specifications
5
5
3
3
1
127
113
111
105 109
Dry products category
Fresh products category requires specific documentation in order to ensure the pest control,
biological and physical integrity of the product. Also, the humidity control is very important to not let the
product spoil. Considering all this information the most relevant metric for this category is the storage
capacity, once this product requires a health status and purity. According to that information it is possible
to claim the most suitable fulfillment model for dry product categories is a fulfillment center.
Table 10: Dry category framework
Dry Category Framework
Weight
Metrics
DC
FC
DS
MC
CW
1
Distance to Customer
5
3
1
3
3
1
Warehouse Infrastructure
3
3
1
5
1
1
Stock Level
1
1
3
3
3
1
Product Quality Assurance
5
3
5
1
5
3
Storage Capacity
1
1
3
5
5
2
Software & Technology
3
3
5
3
5
1
Fulfilment Process Complexity
1
1
3
5
1
2
Return Process Complexity
3
3
1
1
5
2
Automation & Machinery
1
1
3
5
1
1
Capital Expenditure & Investment
3
3
3
5
1
1
Operational Costs
5
3
3
1
3
1
Shipping time
5
5
3
1
3
1
Picking Accuracy
3
3
5
1
3
2
Specialized Labor
5
5
3
5
1
1
Documentations Control
5
5
1
3
3
1
Handling Specifications
5
5
3
3
1
68
62
64
74
66
7. Specialist Validation
To validate the framework, specialists from relevant companies in Latin America were invited to
answer a questionnaire about fulfillment design parameters in order to be able to understand which are
the most relevant characteristics from their opinion while developing a new fulfillment solution for a new
product category. As the initial highlights of the validation, 65% of the survey respondents work for
19
companies such as Amazon, Mercado Libre and ABInBev. Moreover, 96% of the people occupy a
position of Vice-presidents (4%), Directors (22%), Senior Managers (35%) and Managers (35%), which
offers a strategic support to the data base.
Table 11: Specialist Position
Position
#
Global Vice President
Director
Senior Manager
Manager
1
5
8
8
Supervisor
1
Total
23
Even though this project mainly focuses in four product categories, the methodology shows that it
can be applied for different kind of businesses and product categories, since this framework works with
the exclusion of alternatives and limit the best ones that must be deeply studied. With this research, it
was possible to note that almost every respondent that participated in the interviews were interested in
amplifying their company’s portfolio.
The questionnaire was divided into three sections, the first one enabled specialist to think broadly
about the parameters used when designing a new fulfillment solution for any kind of orders (open
question to eradicate bias). In the second part, a selection of parameters was presented, with the
intention that respondents choose the most relevant parameters for the design of fulfillment solutions
considering e-commerce products in general. Lastly, the third part focused on the four product
categories that conform this project (fresh, dry, refrigerated, and frozen) where experts selected the
degree of importance of each parameter for each product category. This way, we were able to validate
the previously selected parameters and compare the different types of storage, understanding the
relevance of each of these parameters for a given type of category.
According to the results obtained from open question that was designed to make the respondents
think with no constraints (broad), 98% of experts considered that the proposed metrics in this paper are
indispensable to design a fulfillment solution. The only metric that was not mentioned by specialist was
“Specialized Labour” and 2% of them considered distribution characteristics (ex. customer profile) as
relevant, although it is not the focus of this paper to analyze distribution models.
When we deep dive in e-commerce, respondents focus mainly on metrics such as fulfillment process
complexity, distance to the customer, operational costs, shipping time, software and technology, product
quality assurance, stock level, warehouse infrastructure, storage capacity automation and machinery
and documentation control. Moreover, there were no more suggestions to add any other parameters
than the ones that the paper proposes, which validates that chosen parameters were broader than
expected and can be used not only in e-commerce operations. The expert framework validations are
shown in appendix 3.
After validating the main parameters, a verification of the importance that this metrics have for each
product category was done. For achieving this part, specialists were asked to measure the importance
of each parameter for each product category.
Considering fresh product categories, experts were aligned that dark stores are the most suitable
fulfillment model to implement. Moreover, they emphasized in the relevance that metrics such as
distance to the customers, product quality assurance, return process complexity and shipping time.
Regarding frozen product categories, specialists' result was crowdsourcing warehouse model. In
this point, the main valued parameters were distance to the customers, storage capacity, software and
technology, fulfillment process complexity, returns process complexity, automation and machinery,
capital expenses and investment, operational cost, documents control and handling specifications.
For refrigerated product categories micro-fulfillment center was selected and the principal metrics to
be considered were product quality assurance, storage capacity, software and technology, fulfillment
process complexity, return process complexity and shipping time. In this case, experts’ framework
concluded in crowdsourcing warehouse model. Both models are similar, including the distance to
customers, which reduce the cost of last mile. To choose the most suitable model, the company needs
to consider whether they prefer to invest on their own infrastructure and labor structure or if they prefer
to outsource to a third-party supplier.
20
Finally, for dry product categories, fulfillment center turned as an output of the model in this paper
with product quality assurance, fulfillment process complexity and operational costs. On the other hand,
experts concluded that crowdsourcing warehouse was the best solution. The specialists considered
distance to customer and shipping time as a relevant parameter. However, it is very costly and probably
do not makes sense when it is considered the expiration date of the dry products. This trade-off should
be analyzed in a business model, by weighting the distance to customers and shipping time, what tend
to be the main difference in both models. As the dry product has long expiration dates, it is detailed in
this paper that the distance between the customer and the warehouse does not represent a considerable
risk.
Lastly according figure 7, 88.5% of specialist consider that companies should increase the variety
offered by the company and 100% that they should increase the number of SKUs of their portfolio in
order to gain market share and to stay relevant in a very dynamic market.
With the purpose of showing the consistency all above the process, the consistency ratio (CR) was
calculated. According to the table below, it is possible to conclude the results are consistent
(Saaty,1987).
Table 12: Consistency Ratio (CR) for each Framework Category
λmax
n
CI
RI
CR
Fresh
15
80
0.82
7
0.1
Dry
15
80
0.82
6
0.1
Frozen
15
80
0.82
9
0.1
Refrigerated
15
80
0.82
7
0.1
8. Conclusion
The proposed framework enables companies to support supply chain decisions regarding storing
and handling of fresh, dry, refrigerated, and frozen product categories while being added to the ecommerce companies’ portfolio. This research was based on five fulfillment models: distribution center,
fulfillment center, dark-store, micro-fulfillment center and crowdsourced warehousing, which were
analyzed to comprehend their main benefits and characteristics.
The different parameters were obtained from a Systematic Literature Review and represent the most
relevant metrics for analyzing a fulfillment model. Experts from relevant Latin American companies
validated the proposed framework for each product category, and it was possible to see that the
methodology employed in the study was consistent.
The framework was applied to the different product categories and the main findings were the
following:
For the fresh product categories, the most suitable model was the dark-store. As it allows companies
to be closer to customers, reducing shipping time and managing returns efficiently, as well as assuring
product quality.
Considering frozen product categories, crowdsourcing warehouse was selected. This model
facilitates retailers to reduce investments, capital expenses, and operational costs, since it allows
companies to share the warehouse and increase storage utilization.
For refrigerated product categories micro-fulfillment center concluded as the best option, since it
warrants product quality and allows the company to operate complex processes, such as: FEFO, picking
accurately and prepare orders on time.
Lastly, fulfillment center turned as an appropriate model to store and handle dry product categories,
as companies process higher volume of products which have a longer expiration date.
To choose the most suitable model, the company needs to consider whether they prefer to invest
on their own infrastructure and labor structure, or if they prefer to outsource to a third-party supplier,
also fulfillment models must be financially analyzed, considering tax incentives, constraints, commerce
barriers, countries regulations, etc.
Additionally, there is one fulfillment model, very commonly used for Brick & mortars to fulfill online
orders that was not take in consideration on this paper, once this model (commonly used by traditional
Brick&Mortars focus specifically on the grocery industry) is out of this project scope. This paper focus
on fulfillment models typically, to traditional pure marketplace players that not necessarily have stores
to sell their products.
21
Future research should address in more depth the impact of each framework in more product
categories as this project just focus on CPG products, also it would be important to consider the lessons
learned and applying the framework to a specific company, as this is the first project of this type that
has been conducted. Some learnings about the validation process can be mentioned:
1
2
3
4
In order to get a more robust number of answers, more experts should be invited to participate.
To reduce bias when answering, a question referring a final recommendation of a fulfillment
model for each product category could be made.
Cluster specialist depending on their background, to answer question regarding their particular
know-how, and not all questions available.
When grading the parameters, it should be noted that punctuation should be correctly balanced,
among the available options.
The next step would be to apply it in an e-commerce company that is interested in incorporating
new product categories. Of the companies that participated in its validation, two of them Mercado Libre
and ABInBev, are interested in testing the method to validate such work.
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APPENDIX
Appendix 1: Top 20 Relevant Literature - Methodologies & Major Findings (developed by authors)
Table 13: Top 20 Relevant Literature - Methodologies & Major Findings (developed by authors)
Authors
Title
Research
Methodology
Number of
Citations
Hübner, Kuhn,
Wollwnburg, Towers
& Kotzab (2016)
Last mile fulfillment and distribution
in omni-channel grocery retailing: A
strategic planning framework
Qualitative
research
233
Nguyen, D. H., de
Leeuw, S., & Dullaert,
W. E. (2018).
Consumer Behaviour and Order
fulfillment in Online Retailing:
A Systematic Review
Systematic
Literature Review
112
Melacini, Perotti,
Rasini & Tappia
(2018)
E-fulfillment and distribution in
omni-channel retailing: a
systematic literature
review
Systematic
Literature Review
86
Boyer & Hult (2006)
Customer behavioral intentions for
online purchases An examination
of fulfillment method and customer
experience level
Research Model
81
Leung, Choy, Siu,
Ho, Lam & Lee
(2018)
A B2C e-commerce intelligent
system for re-engineering the eorder fulfillment process
Case Study
63
Saskia, Mareï &
Blanquart (2016)
Innovations in e-grocery and
logistics solutions for cities
Case Study
52
Murphy (2003)
(Re)solving space and time
fulfillment issues in online grocery
retailing
Operational
Research
41
Cattani, Perdikaki &
Marucheck (2007)
The perishability of online grocers
Analytical Model
23
Olsson, Hellström &
Palsson (2019)
Framework of Last Mile Logistics
Research:
A Systematic Review of the
Literature
Systematic
Literature Review
19
27
Mkansi, Eresia-Eke &
Emmanuel-Ebikake
(2018)
E-grocery challenges and
remedies: Global market leaders
perspective
Case Study
15
Pires, Pratas, Liz &
Amorim (2017)
A framework for designing
backroom areas in grocery stores
Case Study
14
Lim & Winkenbach
(2019)
Configuring the last-mile in
business-to-consumer e-retailing
Case Study
12
Eriksson, Norrman &
Kembro (2019)
Contextual adaptation of omnichannel grocery retailers' online
fulfillment centres
Case Study
10
Abouee-Mehrizi,
Baron, Berman, &
Chen (2019)
Managing Perishable Inventory
Systems with Multiple Priority
Classes
Analytical Model
9
Taylor, Brockhaus,
Knemeyer & Murphy
(2019)
Omnichannel fulfillment strategies:
defining the concept and building
an agenda for future inquiry
Systematic
Literature Review
8
Wollenburg, Hübner,
Kuhn & Trautrims
(2018)
From bricks-and-mortar to bricksand-clicks–logistics networks in
omni-channel grocery retailing
Case Study
7
Zhang, Onal, Das, R.,
Helminsky & Das, S.
(2019)
fulfillment time performance of
online retailers–an empirical
analysis
Empirical Research
6
Barile, Polese &
Debora (2018)
Grocery Retailing in the I4. 0 Era
Qualitative
research
5
Boysen, Koster &
Fübler (2020)
The forgotten sons: Warehousing
systems for brick-and-mortar retail
chains
Operational
Research
4
Binos, Adamopoulos
& Bruno (2020)
Decision Support Research in
Warehousing and Distribution: A
Systematic Literature Review
Systematic
Literature Review
2
28
Appendix 2: Table References for Fulfillment Models (developed by authors)
Table 14: References contribution (developed by authors)
Author
Year
Title
Maister, 1976
1976
Centralization of Inventories and The
Square Root Law
DC and FC
Ballou, 1981
1981
Estimating and Auditing Aggregate
Inventory Levels at Multiple Stocking Points
DC and FC
Rougès & Montreuil
2014
Crowdsourcing delivery: new
interconnected business models to reinvent
delivery
CS
Cuda, Guastaroba &
Speranza, 2015
2015
A survey on two-echelon routing problems
FC
McKinnon et al
2015
Green Logistics: Improving the
Environmental Sustainability of Logistics
CS
Montreuil, 2016
2016
Omnichannel Business-to-Consumer
Logistics and Supply Chains: Towards
Hyper Connected Networks and Facilities
FC and CS
Knight, 2016
2016
Logistics Africa – Sub-Saharan Africa’s
emerging logistics property sector
DC and FC
Lee, 2016
2016
Towards enhancing the last-mile delivery:
an effective crowd-tasking model with
scalable solutions
FC
Savelsbergh & Van
Woensel, 2016
2016
Challenges and opportunities
FC
Crainic & Montreuil,
2016
2016
Physical internet enabled hyperconnected
city logistics
FC
Bekta, 2017
2017
From managing urban freight to smart city
logistics networks
FC
Lopienski, 2018
2018
What Is a fulfillment Center & Why It’s
Important [Breakdown of Warehousing]
FC and CS
Soon, C., & Ah, C.
2018
Future of grocery retail shopping:
Challenges and opportunities in ecommerce grocery shopping
Moralez, 2019
2019
The Difference Between a fulfillment Center
and a Distribution Center
29
Model
DS
DC and FC
Bimschleger & Pate,
2019
2019
Designing a two-echelon dis- tribution
network under demand uncertainty
FC
Morgan, 2019
2019
What To Look For in a fulfillment Center?
FC
Jacobs et al., 2019
2019
The last-mile delivery challenge
DS
Butler, S & Fallon C.,
2019
2019
The global future of online grocery: best
practices and insights from OCADO
DS
Takeoff Technologies
Inc.
2019
Takeoff Technologies Inc.
MC
Ames
2019
All hail the e-commerce consumer, at Dc
Velocity
All
Arslan, 2020
2020
Distribution network deployment for
omnichannel retailing
FC
Janjevic & Winkenbach,
2020
2020
Characterizing urban last-mile distribution
strategies in mature and emerging ecommerce markets
FC
Ben Mohamed, Klibi, &
Vanderbeck, 2020
2020
Designing a two-echelon dis- tribution
network under demand uncertainty.
European Journal of Operational Research
FC
Arslan, Klibi &
Montreuil, 2020
2020
Crowdsourced Delivery: A Dynamic Pickup
and Delivery Problem with Ad-hoc Drivers
FC
Kim & Montreuil & Klibi
& Kholgade, 2020
2020
Hyperconnected urban ful- fillment and
transportation for last mile delivery of large
items
FC
Morgan, 2020
2020
Dark Stores Are The Future Of PostPandemic
DS
Makan, 2020
2020
Dark store Applying supply chain processes
that meet the demands of modern grocery
retailers
DS
Honeywell International
Inc.
2020
Micro-fulfillment strategies for the future of
omnichannel retail
MC
Trebilcock
2020
Micro-fulfillment may be coming to a
grocery store near you
MC
Samuel
2020
FreshDirect partners with Fabric for
automated Micro-fulfillment
MC
30
Kaan Unnu
2020
Optimization models and frameworks for
on-demand warehousing systems
CS
Shin
2020
Creating Shared Value from Collaborative
Logistics Systems: The Cases of ES3 and
Flexe
CS
Tillman, J.
2020
The metrics, they are a-changing
All
Appendix 3: Categories Experts Validations
Table 16: Experts Framework for Fresh Category
Table 17: Experts Framework for Dry Category
31
Table 18: Experts Framework for Frozen Category
Table 19: Experts Framework for Refrigerated Category
32