A sectoral perspective on distribution structure design

Abstract

This paper studies the factors that drive distribution structure design (DSD), which includes the spatial layout of distribution channels and location choice of logistics facilities. We build on a generic framework from existing literature, which we validate and elaborate using interviews among industry practitioners. Empirical evidence was collected from 18 logistics experts and 33 decision-makers affiliated to shippers and logistics service providers from the fashion, consumer electronics and online retail sectors. It turns out that interviewees share similar rankings of main factors across industries, and even confirm factor weights from earlier research established using multi-criteria decision analysis, which would indicate that the framework is sector-neutral at the highest level. The importance attached to subfactors varies between sectors according to our expectations. We were able to identify 20 possible new influencing subfactors. The results may support managers in their decision-making process, and regional policy-makers with regard to spatial planning and regional marketing. The framework is a basis for researchers to help improve further quantitative DSD support models.

Full text
























CASE REPORT
A sectoral perspective on distribution structure design
Alexander T. C. Onstein
a,b
, Lóránt A. Tavasszy
a
, Jafar Rezaei
a
, Dick A. van Damme
b
and Adeline Heitz
c
a
Technology, Policy and Management, Delft University of Technology, Delft, the Netherlands;
b
Urban Technology,
Amsterdam University of Applied Sciences, Amsterdam, the Netherlands;
c
Urban Planning, Transport and Logistics,
CNAM –(Conservatoire national des arts et métiers), Paris, France
ABSTRACT
This paper studies the factors that drive distribution structure design
(DSD), which includes the spatial layout of distribution channels and
location choice of logistics facilities. We build on a generic framework
from existing literature, which we validate and elaborate using
interviews among industry practitioners. Empirical evidence was
collected from 18 logistics experts and 33 decision-makers affiliated to
shippers and logistics service providers from the fashion, consumer
electronics and online retail sectors. It turns out that interviewees share
similar rankings of main factors across industries, and even confirm
factor weights from earlier research established using multi-criteria
decision analysis, which would indicate that the framework is sector-
neutral at the highest level. The importance attached to subfactors
varies between sectors according to our expectations. We were able to
identify 20 possible new influencing subfactors. The results may support
managers in their decision-making process, and regional policy-makers
with regard to spatial planning and regional marketing. The framework
is a basis for researchers to help improve further quantitative DSD
support models.
ARTICLE HISTORY
Received 29 January 2020
Accepted 6 November 2020
KEYWORDS
Distribution structure design;
distribution channel layout;
distribution centres; location
choice; decision-making
factors
1. Introduction
Physical distribution involves the movement and storage of goods in a supply chain and is a major
determinant of customer service levels and supply chain costs (Chopra 2003). Organising physical
distribution is challenging, however, as customers expect high service levels at low costs. Globalisa-
tion and supply chain fragmentation make the distribution of goods more complex, as it takes place
over ever longer distances, while passing through more and more stages in the supply chain (Rodri-
gue 2008). One of the strategic decisions companies have to make to satisfy these demands involves
distribution structure design (DSD), which concerns the spatial layout of the distribution channel –
i.e. the freight transport and storage system between production and consumption –as well as the
location(s) of logistics facilities, i.e. warehouses and distribution centres (DCs). Figure 1 presents
several possible distribution channel layouts. The answer to the question as to which distribution
channel layout is best depends on different factors. Centralised layouts (Layouts 1, 2 and 3) will
allow savings in inventory costs, which is important to high value products like consumer elec-
tronics. The drawback of a central layout is that outbound transport costs are relatively high. A
decentralised layout (Layouts 4 and 6) will favour high demand products for which outbound
© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://
creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the
original work is properly cited, and is not altered, transformed, or built upon in any way.
CONTACT Alexander T. C. Onstein a.t.c.onstein@tudelft.nl https://www.linkedin.com/in/sandernstein43715/
INTERNATIONAL JOURNAL OF LOGISTICS: RESEARCH AND APPLICATIONS
https://doi.org/10.1080/13675567.2020.1849074

transport costs reduction is critical, e.g. groceries and office supplies. Advantages of a decentralised
layout are low outbound transport costs and short delivery times, at the cost of additional inventory,
warehousing and inbound transport costs. Companies may also implement hybrid distribution
structures –combining centralised and decentralised layouts for several streams of products –in
an attempt to achieve gains in several areas simultaneously (van Hoek, Commandeur, and Vos
1998).
There are many factors that influence decision-making on DSD. However, despite the frequent
treatment of DSD in supply chain handbooks, an empirically validated conceptual framework of
factors is still lacking in the scientific literature. Most traditional distribution structure design
models are prescriptive and few studies include empirical data (Mangiaracina, Song, and Perego
2015; Olhager, Pashaei, and Sternberg 2015). A few works that propose conceptual frameworks
are Lovell, Saw, and Stimson (2005), Song and Sun (2017) and Onstein, Tavasszy, and van
Damme (2019a,2019b). However, these studies either examine a broader set of different decisions
at a higher level or do not offer any empirical validation. Lovell, Saw, and Stimson (2005) investigate
the broader topic of supply chain fragmentation, while Song and Sun (2017) focus on broader
Figure 1. Distribution channel layouts (based on Kuipers and Eenhuizen 2004, adapted).
2A. T. ONSTEIN ET AL.

supply chain decision-making, i.e. a range of decisions including sourcing, production and distri-
bution locations, without looking at either of them specifically. Onstein, Tavasszy, and van Damme
(2019a,2019b) propose a generic DSD framework based on literature and measure the importance
of factors, respectively, without differentiating between industry sectors, nor was the framework
they proposed validated with practitioners or practical industry cases. Other studies on distribution
centre location selection include Warffemius (2007), McKinnon (2009), Dablanc and Ross (2012),
Verhetsel et al. (2015) and Heitz et al. (2018). These studies do not aim to consider the full scope of
DSD factors, and/or include only a partial empirical analysis. In short, DSD-related factors have so
far received insufficient attention in scientific literature, in particular due to a lack of systematic,
empirical validation.
In order to contribute to filling this gap, we set out to validate the framework proposed by
Onstein, Tavasszy, and van Damme (2019a) by conducting industry interviews and a subsequent
analysis. We interviewed 51 respondents: 18 logistics experts, and 33 DSD decision-makers
affiliated to shippers and LSPs in three sectors, i.e. fashion, consumer electronics and online retail.
We focused on companies with DCs located in the Netherlands, which is a major node and a con-
tinental gateway for around 30% of goods imported into the EU (Holland International Distri-
bution Council 2018). The analysis provides new insights into the empirical validity of existing
frameworks and supports their quantitative analysis as well as adding new factors. Researchers
can use the framework to improve quantitative DSD models, which are often still based on incorrect
or incomplete sets of factors (Mangiaracina, Song, and Perego 2015). The framework can also be of
use for practitioners in government and industry. It may support companies –especially from the
three selected industry sectors –to include the relevant factors when creating their DSD. As far as
policy-makers and spatial planners are concerned, the framework is relevant to understanding how
regional plans could attract logistics activities from industry.
The remainder of the paper is organised as follows. Section 2 reviews the relevant literature and
explains the generic, literature-based conceptual framework. Section 3 describes the research
methods and data collection, while Section 4 discusses the case results, which have been used to
develop the validated conceptual framework. The conclusions, limitations and implications of
the research and suggestions for future work are presented in Section 5.
2. Literature review
In this section, we briefly discuss the literature based framework for DSD as developed in Onstein,
Tavasszy, and van Damme (2019a), which is the starting point for our research. Distribution struc-
ture design (DSD) includes DC location selection as well as distribution channel layout –i.e. the
freight transport and storage system between production and consumption. Factors that explain
DSD can be found in studies related to those two decisions, but also to broader supply chain design
problems, like supply chain strategy, production location selection, capacity allocation, perform-
ance measurement and outsourcing (Song and Sun 2017). Studies related to DSD can relate to
quantitative as well as qualitative research. Quantitative research can be found using multicriteria
analysis (Ashayeri and Rongen 1997; Önden, Acar, and Eldemir 2016), multicriteria decision-mak-
ing (Agrebi, Abed, and Omri 2017; Onstein et al. 2019b), statistical analysis (McKinnon 1984; Hil-
mola and Lorentz 2011), factor analysis (Song and Sun 2017), discrete choice analysis (Nozick and
Turnquist 2001; Verhetsel et al. 2015), spatial modelling (Heitz and Beziat 2016; Klauenberg, Elsner,
and Knischewski 2016) and other quantitative models (e.g. Cooper 1984; Ashayeri and Rongen
1997; Olhager, Pashaei, and Sternberg 2015). Qualitative research includes literature reviews (Meix-
ell and Gargeya 2005; Chopra and Meindl 2013; Mangiaracina, Song, and Perego 2015; Olhager,
Pashaei, and Sternberg 2015; Onstein, Tavasszy, and van Damme 2019a), interviews (Picard
1982; Klauenberg, Elsner, and Knischewski 2016) and case studies (Nozick and Turnquist 2001;
Lovell, Saw, and Stimson 2005; Pedersen, Zachariassen, and Arlbjørn 2012). The list presented
above is limited to those studies that aim to identify and explain factors in relation to DSD; the
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 3

many studies that only use factors from other sources are not included. Reviewing existing literature
shows there is no study that proposes a framework of factors influencing DSD at the industry level.
Lovell, Saw, and Stimson (2005), Song and Sun (2017) and Onstein et al. (2019a,2019b) are the only
authors that propose relevant frameworks, but they have a much wider scope. Lovell, Saw, and
Stimson (2005) focus on the broader concept of supply chain fragmentation and Song and Sun
(2017) focus on a broad range of supply chain decisions including sourcing, production and DC
locations, while Onstein, Tavasszy, and van Damme (2019a) do not differentiate between industry
sectors, but propose a generic literature-based DSD framework. We used their framework to con-
tinue the empirical exploration of the factors discussed in this paper.
The framework includes 47 factors, classified into seven main groups: service level, logistics
costs, business strategy, demand pattern, product characteristics, location factors and institutional
factors. The factors are based on two main research disciplines, i.e. Supply Chain Management and
(Economic) Geography. As known from the literature, the main trade-offinfluencing DSD is the
one between service level factors and logistics costs (Chopra 2003; Christopher 2011). The others
are the contextual factors that influence this trade-off:
.Business strategy and company characteristics, including size and management capacity;
.Demand factors, related to volume, frequency and regularity of products sold;
.Product characteristics that are cost or service drivers, e.g. value density and package density;
.Location factors related to local facilities, accessibility and labour market;
.Institutional factors related to legal and fiscal regulations.
The main factors and subfactors are explained in more detail below.
2.1. Service level factors
Service level factors are among the most important factors influencing DSD (Onstein et al. 2019b).
They include supplier lead-time, delivery time, delivery reliability, responsiveness, returnability,
and order visibility. Service level requirements vary per industry sector. High value pharmaceutical
industries require higher distribution service levels compared to low value fashion industries. The
delivery time (from DC to the customer) is influenced by the product type, i.e. customers do not
accept long delivery times for substitutable products. In general, all companies aim for short deliv-
ery times, which is possible by storing sufficient inventories close to consumer markets or by using a
centralised distribution layout combined with high-speed transport modes. High delivery reliability
is important to companies that ship high-value goods (Christopher 2011). Responsiveness (i.e. reac-
tion speed and flexibility to fulfil customer demand) can be increased by using a decentralised dis-
tribution layout, i.e. making sure products are available at all logistics facilities. Returnability (i.e.
the ease of returning products) increases when there are more logistics facilities available to return
products (Chopra 2003). Online retail customers expect they can easily return their goods (Xing
et al. 2011).
2.2. Logistics costs factors
Logistics costs are –together with service level factors –the most important factors in DSD selection
(Nozick and Turnquist 2001; Chopra 2003). Existing literature stresses three important logistics
cost-related factors: transport costs, warehousing costs and inventory costs. Transport costs consist
of the transport mode, labour costs and capital costs. Inventory costs include capital costs, insur-
ances and management costs, and risk costs (damage, deterioration, obsolescence). Transport
costs are divided into inbound transport (from production to DC) and outbound transport
(from DC to the customer). Warehousing costs consist of labour costs, storage costs and handling
materials (Christopher 2011). High outbound transport costs drive companies towards
4A. T. ONSTEIN ET AL.

decentralised distribution, because outbound transport costs are reduced if the number of DCs
increases. High inventory and warehousing costs, on the other hand, drive companies towards cen-
tralised distribution, since inventories and warehousing activities increase with the number of dis-
tribution centres (McKinnon 2009).
2.3. Business strategy and company characteristics
Business strategy is a company characteristic that affects DSD (Treacy and Wiersema 1993). Three
renowned business strategies are customer intimacy, operational excellence and product leadership.
Customer intimacy focuses on delivering high distribution service levels, which can be offered by
applying a broad network of DCs near customer markets. Operational excellence focuses on com-
petitive prices and low-cost distribution, for example by minimising the number of warehouses,
while product leadership focuses on flexible operations that enable new product introductions (Tre-
acy and Wiersema 1993).
The position of the DC within the supply chain (before or after production) is another influen-
cing factor. In case of weight loss during production, a supplier DC is preferably located near the
production location, to reduce inbound transport costs (McCann 2015). The factor ‘retail store
ownership’may persuade a company to locate logistics facilities within the centre of gravity of
the retail stores, to reduce outbound transport costs. The size of the company also influences
DSD. Small and medium-sized enterprises (SMEs) have less management or financial capacity
and can therefore adjust their DSD less often (Pedersen, Zachariassen, and Arlbjørn 2012).
2.4. Demand factors
Demand factors influencing DSD are demand level, demand dispersion and demand volatility.
Demand level affects the number of DCs needed to distribute products. In case of high demand,
more facilities are needed to distribute products on time (Chopra 2003; Mangiaracina, Song, and
Perego 2015). In case of geographically dispersed demand, it is advantageous to centralise distri-
bution, because demand may fluctuate across regions. Demand volatility can influence a company
to select a centralised distribution layout to prevent oversupplies (Friedrich, Tavasszy, and
Davydenko 2014).
2.5. Product characteristics
Product characteristics influencing DSD are product value density, package density and perishabil-
ity (Onstein et al. 2019b). High value density products involve high inventory costs, influencing
companies to select a centralised distribution layout (Ashayeri and Rongen 1997; Lovell, Saw,
and Stimson 2005). Companies that ship high-value products are more sensitive to location
decisions than companies shipping low-value products (McCann and Sheppard 2003). Package
density influences warehousing costs. High package density products that require rigorous product
handling influence companies to centralise warehouse operations, because that reduces warehous-
ing complexity and warehousing costs. Perishable products require short delivery times, causing
companies to select a decentralised distribution layout (McKinnon 1984; Lovell, Saw, and Stimson
2005; Christopher 2011).
2.6. Location factors
There are many location-related factors that influence DSD. Accessibility by road and possibly
other modes of transport is essential. As the size of warehouses increases, land availability becomes
a more important factor in selecting the location of DCs (Heitz and Beziat 2016). Proximity to air-
ports and seaports can be important as well –air transport, for example, is often used for high value
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 5

goods and spare parts (Warffemius 2007; Hall and Jacobs 2012; Verhetsel et al. 2015). Proximity of
the DC location to a rail freight terminal is relatively unimportant, as goods are less often trans-
ported to and from DCs by rail (Bowen 2008). The factor ‘proximity to consumer markets’is
very important, because this enables fast customer deliveries (Heitz, Launay, and Beziat 2019).
According to previous research by Onstein et al. (2019b), proximity to suppliers or production
locations is less important. The growth in average warehouse floor space motivates companies to
locate logistics facilities in peripheral areas where land prices are lower (Dablanc and Ross 2012).
Other advantages of peripheral locations include the lower costs of living and less congestion.
The availability of warehouse employees is a key factor. Warehouse employees are easier to find
in urban agglomerations than they are in peripheral areas, although labour scarcity in urban
agglomerations can also be a push factor (agglomeration diseconomy) in locating logistics facilities
outside those areas (Verhetsel et al. 2015; Heitz and Beziat 2016).
2.7. Institutional factors
Institutions like rules, laws, values and norms (North 1990) also influence DSD. Beneficial tax rules
reduce inventory costs, which encourage companies to locate DCs in Free Trade Zones. Fast cus-
toms procedures also attract companies, because they enable them to reduce delivery times. Zoning
rules can restrict the localisation of logistics activities (Sheffi2012). Investment incentives and the
presence of a development company are found to have a moderate effect on DSD (Davydenko 2015;
Onstein et al. 2019b). In their distribution structure design, companies do value countries with high
political stability (Onstein, Tavasszy, and van Damme 2019a).
Two limitations of the framework outlined above are the following: (1) because the framework
does not distinguish between sectors of industry, it may not contain sufficient detail to indicate
differences in preferences between sectors; (2) the framework itself still has little grounding in
empirical research –despite the mention of factors in the literature, it has not yet been confronted
with practitioners’experience as a framework purpose-built for DSD. The aim of our empirical
research is to help remedy that state of affairs; the next section describes the approach we adopted,
including the cases we studied.
3. Research approach
3.1. Research method
An interview-based, qualitative, multiple case research design is applied to validate the important
factors at a sectoral level and, in doing so, validate the general conceptual framework. The advan-
tage of case research including interviews lies in the possibility to study factors at the level of indi-
vidual companies or sectors, with an understanding of the case-specific context (Voss, Tsikriktsis,
and Frohlich 2002; Frankel, Naslund, and Bolumole 2005; Bryman 2008; Yin 2014). Case research
can be divided into three modes: theory generation, theory elaboration, and theory testing (Ketokivi
and Choi 2014). Our goal is to validate the existing general conceptual framework i.e. examine its
applicability to specific sectors, and identify opportunities to elaborate the framework.
3.1.1. Case selection
We selected three sectors (fashion, consumer electronics, and online retail) to test the framework in
different industry contexts (Eisenhardt and Graebner 2007). The sectors were selected based on
their contrasting product characteristics and distribution channel layout (see Tables 1–3). We con-
tacted shippers or Logistics Service Providers (LSPs) with DCs located in different regions of the
Netherlands. To increase cross-case comparability, the selected shippers and LSPs are all large com-
panies that mainly focus on international customer markets for well-known brands. Companies
were assigned to a sector based on their most important product in terms of annual turnover –
6A. T. ONSTEIN ET AL.

Table 1. Characteristics of fashion case interviews.
Interview
Shipper
(S) / LSP
Most
important
product
No. of
employees
(and turnover
in 2018)
Customer
target
market
Segment
(B2B, B2C)
Company
strategy
Value
density
(low,
high)*
Package
density
(low,
high)*
Marketing
channels (W =
wholesale, R =
retail, IO =
individual online
consumers)
Distribution
channel layout DC location(s)
F1 S Under-
clothing
6.200
(452,4 million
Euro)
West-Europe B2C Customer
intimacy
Low High 600 retail stores
(Europe), IO
Centralised DC Hilversum
F2 S African
women’s
dresses
500
(N/A)
Africa, UK B2C, B2B Customer
intimacy
High High 80% sales offices,
19% W, 1% IO
Centralised DC,
outsourced to
LSP
Helmond
F3 S Women’s
fashion
5.000
(134,7 million
Euro) (2017)
West-Europe B2C Customer
intimacy
High High 90% R, 10% IO Centralised DC,
outsourced to
LSP
Helmond
F4 S High value
fashion
5.400
(1,5 billion
USD) (2017)
West-Europe B2C, B2B Product
leadership
High High 60% R, 20% W,
20% IO
Centralised DC,
outsourced to
LSP
Twente
F5 S High value
fashion
N/A
(750 million
Euro) (2017)
Northwest
Europe,
USA,
Japan
B2B, B2C Product
leadership
(retail) and
customer
intimacy
(online)
High High 60% R, 40% IO Decentralised
layout:
1 central DC in
Amsterdam.
8regional DCs
abroad
Amsterdam.
Regional DCs: USA,
Canada, Australia,
Hong Kong, Japan,
South-America,
China, South-Africa
F6 S Suits 1.500
(245,6 million
Euro) (2017)
Worldwide B2C Customer
intimacy
High High 65% R, 35% IO Decentralised
layout:
1 central DC in
The
Netherlands,
outsourced to
LSP.
4 regional DCs
abroad
Helmond (The
Netherlands),
New Jersey (USA),
Toronto (Canada),
Shanghai (China),
Hong Kong
F7 LSP Fashion
distribution
822
(122,4 million
Euro) (2017)
Benelux B2B, B2C Customer
intimacy
High Medium N/A Centralised DC for
customer
Helmond
F8 LSP Fashion
distribution
1.075 (2016)
(171 million
Euro) (2015)
N/A B2B Customer
intimacy
Low High N/A Centralised DC for
customer
Amsterdam
(Continued)
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 7

Table 1. Continued.
Interview
Shipper
(S) / LSP
Most
important
product
No. of
employees
(and turnover
in 2018)
Customer
target
market
Segment
(B2B, B2C)
Company
strategy
Value
density
(low,
high)*
Package
density
(low,
high)*
Marketing
channels (W =
wholesale, R =
retail, IO =
individual online
consumers)
Distribution
channel layout DC location(s)
F9 LSP Fashion
distribution
50.000
(8,5 billion
Euro) (2017)
Europe B2B, B2C Customer
intimacy
Low High N/A Centralised DC for
customer
The Netherlands:
Rotterdam, Almere
Venlo
F10 S High value
fashion
N/A
(339 million
Euro) (2017)
Worldwide B2C, B2B Customer
intimacy
High High R, IO, W Decentralised
system:
1 central DC and
3 regional DCs
Central DC Hoofddorp
(The Netherlands).
Regional DCs:
Swalmen (The
Netherlands), Los
Angeles (USA), Hong
Kong
F11 S High value
fashion
1.500
(141 million
Euro) (2017)
West-Europe B2C Customer
intimacy
High High 80% R, 20% IO Centralised DC Amsterdam
F12 S Mid-range
fashion
40
(N/A)
West-Europe B2B Customer
intimacy,
operation-al
excellence
Low High 99% R, 1% IO Centralised DC Amsterdam
*Of the most important product (turnover).
8A. T. ONSTEIN ET AL.

Table 2. Characteristics of consumer electronics (CE) case interviews.
Interview
Shipper
(S) / LSP
Most
important
product
No. of employees
(and turnover in
2018)
Customer
target market
Segment
(B2B, B2C)
Company
strategy
Value
density
(low,
high)*
Package
density
(low, high)*
Marketing channels
(W = wholesale, R =
retail, IO = individual
online consumers)
Distribution
channel layout DC location(s)
CE1 LSP Distribution 1.500
(169 million
Euro)
Netherlands B2B, B2C Customer
intimacy
N/A N/A N/A Centralised DC for
shipper
Waalwijk
CE2 S Anonymous Anonymous N/A N/A N/A N/A N/A R, IO Centralised N/A
CE3 LSP Distribution 1.500+
(500+ million
Euro, 2017)
Benelux N/A Customer
intimacy
High High N/A Centralised DC for
shipper, in
network of DCs
30+ DCs in Benelux
CE4 S Printers 1.100
(210 million Euro,
2017)
West-Europe B2B, B2C Operational
excellence
High Low W, R, IO Decentralised,
outsourced
Central DC in Bergen op
Zoom, RDCs** in
Europe, Turkey and
South-Africa
CE5 S Consumer
photo
cameras
21.000
(6,7 billion USD)
Europe,
Russia,
Turkey
B2B, B2C Product
leadership
High High R, IO Centralised,
outsourced
Limburg
CE6 S Smartphones 80.000
(Anonymous)
Worldwide B2B, B2C Product
leadership
High High R, IO Decentralised,
outsourced
RDC in Eindhoven, 3
RDCs in East-Europe,
1 RDC in Germany
CE7 S Smartphones 320.000
(191 billion Euro)
Worldwide B2B, B2C Product
leadership
High High R, IO Decentralised,
outsourced
Central DC in the
Netherlands, RDC
Schiphol, 7 RDCs in
North- and South-
Europe
CE8 S Printers 32.000
(10 billion USD)
Europe, USA B2B, B2C Customer
intimacy
High Low W, R, IO Centralised,
outsourced
Limburg
CE9 LSP Distribution 24.000
(3,7 billion Euro,
2017)
Europe N/A Depends on
customer
High High N/A Centralised DC for
shipper
Rotterdam
CE10 S PC accessories 7.000
(2,6 billion USD)
EMEA B2B, B2C Operational
excellence
Low High R, IO Centralised on
European level,
outsourced
Central DC in Limburg,
7 RDCs worldwide
CE11 S Anonymous Anonymous EMEA B2B, B2C Depends on
customer
High Low W, R, IO Decentralised,
outsourced
Central DC in the
Netherlands, 1 RDC
North-Europe, 3 RDCs
South-Europe
CE12 LSP Distribution 1.600
(198 million
Euro)
Europe B2B Depends on
customer
High High N/A Centralised DC for
shipper
Maarssen
*Of the most important product (turnover).
**RDC = Regional DC.
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 9

Table 3. Characteristics of online retail (OR) case interviews.
Interview
Shipper
(S) / LSP
Most
important
product
No. of
employees
(and turnover
in 2018)
Customer
target market
Segment
(B2B, B2C)
Company
strategy
Value
density
(low,
high)*
Package
density
(low,
high)*
Marketing channels
(W = wholesale, R =
retail, IO =
individual online
consumers)
Distribution
channel layout DC location(s)
OR1 S Online
consumer
electronics
3600 (1,35
billion Euro)
Netherlands,
Belgium
B2C Customer
intimacy
High High IO Decentralised Central DC in
Tilburg, 1
RDC**
OR2 S Home
furniture
260 (66 million
Euro)
West-Europe B2B, B2C Customer
intimacy
Low Low IO Centralised Utrecht
OR3 S Fashion,
home
furniture,
sports
N/A Netherlands B2C Customer
intimacy
Low Low IO Decentralised Central DC
Zwolle, 4 RDCs
OR4 S Food 4500 (1,3
billion Euro)
Europe B2C Customer
intimacy
Low Low IO Decentralised > 10 RDCs,
Netherlands
OR5 S Party apparel,
party
accessories
7 (1 million
Euro)
Netherlands,
Belgium,
Germany
B2B, B2C Customer
intimacy
Low High IO Centralised Emmen
OR6 S Food >1500 (200
million Euro)
Netherlands,
Germany
B2C Operational
excellence
Low Low IO Decentralised 5 RDCs and >20
hubs in the
Netherlands
OR7 S Flowers >100 (N/A) West-Europe B2C, B2B Customer
intimacy
Low Low IO Decentralised Central DC
Aalsmeer, >30
RDCs in West
Europe
OR8 S Home
furniture
100 (20 million
Euro)
West-Europe B2C, B2B Customer
intimacy
Low Low IO Centralised Amsterdam
OR9 LSP Online
fashion
distribution
>800 (>120
million Euro)
Benelux N/A Customer
intimacy
N/A N/A N/A Centralised N/A
*Of the most important product (turnover).
**RDC = Regional DC.
10 A. T. ONSTEIN ET AL.

most of the companies we interviewed only sell products from one industry sector. Online retail
companies included those that only sell products online (‘pure players’). LSPs were interviewed
taking in mind one specific sector and a customer (shipper) from this sector. The interviewees
were individual decision-makers on DSD, working at shippers or LSPs. The basis for selecting
these decision-makers was their active involvement in the decision-making involving DSD, as
was also verified during the interviews. Ultimately, 33 decision-makers were selected. As a basis
of comparison for these sector-specific interviews and a source for general validation of the frame-
work, we also interviewed 18 general experts in the area of logistics.
3.1.2. Data collection, data analysis and framework validation
We conducted semi-structured interviews, as is common in case research (Yin 2014), where semi-
structured interviews are used to identify the important factors and to collect information on the
companies’DSD. Three test interviews with potential respondents were used to improve and com-
plete the questionnaire. The 33 sectoral respondents included 12, 12 and 9 interviewees from
fashion, consumer electronics and online retail, respectively. All 33 were logistics managers or
directors of logistics. The 18 logistics experts included 5 academic experts and 13 industry experts.
The interview protocol was designed as follows. The first part focused on the general character-
istics of the company, and mainly included fixed response questions designed to simplify the analy-
sis of the cases. The second part aimed at describing the current distribution structure. We asked the
respondents to draw a schematic overview of their current structure, including sourcing, pro-
duction locations, DCs, customer locations and transport between them. The third part, which
focused on the influencing factors, included in-depth discussions in which the respondents were
asked to explain why factors are important. Factor definitions were included in the questionnaire
Table 4. Sum of mentions of subfactors per main factor and sector.
Industry sector
Decision-makers
ExpertsFashion
Consumer
electronics
Online
retail
# of interview respondents 12 12 9 18
Total mentions of subfactors across all the sectors 271 305 188 160
A. Sum of mentions of subfactors per main factor and
sector Fashion
Consumer
electronics
Online
retail Experts
# of
subfactors
Service level factors 47 50 33 13 6
Logistics costs factors 35 40 28 30 4
Business strategy & company characteristics 6 11 6 4 6
Demand factors 17 22 19 4 3
Product characteristics 13 19 11 7 3
Location factors 98 111 60 71 24
Institutional factors 41 43 22 28 14
Keep factors 14 9 9 3 5
B. Scores corrected for number of subfactors Fashion
Consumer
electronics
Online
retail Experts
Service level factors 7.8 8.3 5.5 2.2
Logistics costs factors 8.8 10.0 7.0 7.5
Business strategy & company characteristics 1.0 1.8 1.0 0.7
Demand factors 5.7 7.3 6.3 1.3
Product characteristics 4.3 6.3 3.7 2.3
Location factors 4.1 4.6 2.5 3.0
Institutional factors 2.9 3.1 1.6 2.0
Keep factors 2.8 1.8 1.8 0.6
C. Correlations matrix of scores corrected
for number of subfactors Fashion Consumer electronics Online retail Experts
Fashion 0,976 0,952 0,690
Consumer electronics 0,905 0,714
Online retail 0,571
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 11

to obtain comparable interview data. To prevent anchoring bias of the respondents to our frame-
work, we first asked each respondent to list the five most important factors by heart. Next, we pre-
sented a list of factors and asked the respondents to reflect on their importance. No interviews were
conducted after saturation of the decision-making factors. The interviews were transcribed and filed
to create a trail of case evidence. The transcripts were sent to the respondents for corrections and
approval and then coded using NVivo software. We grouped answers based on code and case to
identify differences and similarities across industry sectors (Voss, Tsikriktsis, and Frohlich 2002).
The coding protocol was based on the factors included in the generic conceptual framework.
Additionally, open coding was applied, to allow us to extract new factors from the interviews. To
check the intercoder reliability, 6 of the 51 interviews were double coded by a second independent
researcher familiar with the research topic. Diverging codes and case results were discussed within
the research team to reach a consensus.
We synthesised the results of the three cases on a within-case and across-case basis. We con-
structed a chart showing the number of respondents indicting a given factor as being important
in DSD (Table 5). This also allowed us to conduct the cross-case analysis, including a match
with the expert interviews and a statistical analysis of the results. Eventually, based on the compari-
son between interview results and the generic framework, we adapted the framework, which con-
stitutes one of the main results of the research. We discuss the outcomes in greater detail in Section
4, after describing the industry cases in the next subsection.
3.2. Industry cases
In this subsection, we provide a more detailed description of the three industry cases.
3.2.1. Fashion
Today’s fashion supply chains face multiple logistics challenges according to the interview respon-
dents. Supplier lead times are long (i.e. up to 3 months), which means companies need responsive
logistics operations to meet customer demand in time. The large number of fashion seasons also
creates logistics challenges, i.e. accurate demand forecasts and responsive distribution are needed
to prevent over/undersupplies.
The case interviews included nine well-known fashion shippers and three LSPs. The fashion
shippers focus on international customer target markets (Table 1). Five of the nine shippers have
their own retail stores in European shopping streets. At a European scale, all shippers apply centra-
lised distribution channel layout (i.e. a single DC). In case of intercontinental sales, the companies
use overseas regional DCs –often owned by local LSPs –to serve local customer target markets.
Marketing channels vary from company to company –i.e. up to 40% online business to consumer
(B2C) orders (Table 1).
3.2.2. Consumer electronics
Companies that sell consumer electronics products are faced with high inventory costs, because they
sell high value density products. Fast air distribution networks are used by the interviewed companies
to reduce the number of inventory days, thereby reducing inventory costs. Companies also use fast
distribution networks because consumer electronics products have short product life cycles.
The interviews included eight shippers that focus on international consumer markets and four
LSPs (Table 2). The shippers we interviewed sell their goods via consumer electronics stores (like
MediaMarkt), store-in-store or online, but do not have own retail stores, unlike the fashion case.
The main inbound transport modes being used are air and sea, while the main outbound transport
mode is by road. Due to the high value density of consumer electronics products, inventory cost
reduction was found to be the main logistics challenge according to consumer electronics (CE)
companies. Four of the eight consumer electronics companies apply a centralised distribution chan-
nel layout, while the other four shippers apply a decentralised distribution channel layout (Table 2);
12 A. T. ONSTEIN ET AL.

Table 5. Factors influencing distribution structure design (DSD) according to case interviews.
Factors
Decision-makers (33 respondents) Experts (18 respondents)
Fashion (12 respondents)
Number of fashion respondents
indicating that the factor is
important
Consumer electronics (CE)
(12 respondents)
Number of consumer electronics
respondents indicating that the factor
is important
Online retail (OR)
(9 respondents)
Number of online retail
respondents indicating that the
factor is important
Number of expert respondents
indicating that the factor is
important
1. Business strategy & company
characteristics**
- Managerial capacity –5(CE3, CE5, CE7, CE10, CE12) 3(OR3, OR4, OR6) –
- Financial capacity –4(CE5, CE7, CE10, CE12) 3(OR2, OR3, OR4) –
- In-house or outsourcing strategy 2(F1, F8) ––2(E13, E15)
- Store ownership 1(F8) 1(CE7) –1(E15)
- Position within the supply chain –– -1(E6)
- LSP influence on shipper* 3(F2, F3, F9) 1(CE5) ––
2. Demand factors
- Demand level 10 (F1, F2, F3, F4, F5, F6, F7, F8, F9,
F10)
9(CE2, CE3, CE4, CE7, CE8, CE9, CE10,
CE11, CE12)
8(OR1, OR2, OR3, OR4, OR5, OR6,
OR7, OR9)
3(E13, E17, E18)
- Demand volatility 4(F2, F4, F5, F7) 6(CE4, CE5, CE7, CE10, CE11, CE12) 6(OR1, OR3, OR4, OR5, OR7, OR9) –
- Demand dispersion 3(F5, F6, F9) 7(CE4, CE5, CE7, CE8, CE9, CE10, CE11) 5(OR1, OR2, OR4, OR6, OR7) 1(E15)
3. Service level factors
- Supplier lead time 9(F1, F2, F3, F4, F5, F6, F7, F8, F9) 7(CE4, CE5, CE6, CE7, CE9, CE10, CE11) 3(OR4, OR7, OR9) 3(E7, E15, E17)
- Delivery time 11 (F1, F2, F3, F4, F5, F6, F7, F8, F9,
F10, F11)
12 (CE1, CE2, CE3, CE4, CE5, CE6, CE7,
CE8, CE9, CE10, CE11, CE12)
7(OR1, OR2, OR3, OR4, OR5, OR6,
OR9)
5(E7, E8, E13, E15, E17)
- Delivery reliability 9(F2, F3, F4, F5, F6, F7, F8, F9, F10) 7(CE4, CE7, CE8, CE9, CE10, CE11, CE12) 8(OR1, OR3, OR4, OR5, OR6, OR7,
OR8, OR9)
1(E7)
- Responsiveness 8(F2, F3, F4, F5, F6, F7, F8, F9) 9(CE3, CE4, CE5, CE7, CE8, CE9, CE10,
CE11, CE12)
8(OR1, OR3, OR4, OR5, OR6, OR7,
OR8, OR9)
2(E7, E8)
- Returnability 5(F5, F6, F7, F8, F9) 9(CE4, CE7, CE8, CE9, CE10, CE11, CE12) 3(OR5, OR8, OR9) 2(E13, E17)
- Order visibility 5(F5, F6, F7, F8, F9) 6(CE4, CE7, CE9, CE10, CE11, CE12) 4(OR6, OR7, OR8, OR9) –
4. Product characteristics
- Product value density 5(F3, F5, F6, F8, F9) 7(CE4, CE5, CE7, CE8, CE9, CE11, CE12) 2(OR7, OR8) 4(E6, E13, E15, E18)
- Package density 6(F3, F4, F5, F6, F8, F9) 7(CE4, CE5, CE7, CE8, CE9, CE11, CE12) 5(OR1, OR4, OR6, OR7, OR8) 2(E6, E17)
- Perishability 2(F5, F8) 5(CE4, CE7, CE10, CE11, CE12) 4(OR4, OR6, OR7, OR8) 1(E6)
5. Logistics costs factors
- Inventory costs 8(F3, F4, F5, F6, F7, F8, F9, F10) 9(CE2, CE3, CE4, CE5, CE7, CE8, CE9,
CE10, CE11)
5(OR1, OR6, OR7, OR8, OR9) 7(E5, E7, E8, E13, E15, E17, E18)
- Transport costs –inbound 7(F1, F4, F5, F6, F7, F8, F9) 9(CE2, CE3, CE4, CE5, CE7, CE8, CE9,
CE10, CE11)
6(OR1, OR3, OR4, OR5, OR6, OR9) 7(E5, E7, E8, E13, E15, E17, E18)
- Transport costs –outbound 9(F3, F4, F5, F6, F7, F8, F9, F10,
F11)
11 (CE1, CE2, CE3, CE4, CE5, CE7, CE8,
CE9, CE10, CE11, CE12)
8(OR1, OR2, OR3, OR4, OR5, OR6,
OR7, OR9)
10 (E5, E6, E7, E8, E12, E13, E15,
E16, E17, E18)
(Continued)
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 13

Table 5. Continued.
Factors
Decision-makers (33 respondents) Experts (18 respondents)
Fashion (12 respondents)
Number of fashion respondents
indicating that the factor is
important
Consumer electronics (CE)
(12 respondents)
Number of consumer electronics
respondents indicating that the factor
is important
Online retail (OR)
(9 respondents)
Number of online retail
respondents indicating that the
factor is important
Number of expert respondents
indicating that the factor is
important
- Warehousing costs 11 (F1, F2, F3, F4, F5, F6, F7, F8, F9,
F10, F11)
11 (CE1, CE2, CE3, CE4, CE5, CE7, CE8,
CE9, CE10, CE11, CE12)
9(OR1, OR2, OR3, OR4, OR5, OR6,
OR7, OR8, OR9)
6(E5, E7, E8, E14, E15, E17)
6. Location factors
- Proximity to:
oMotorway 8(F2, F4, F5, F7, F8, F9, F11, F12) 7(CE4, CE7, CE8, CE9, CE10, CE11, CE12) 6(OR3, OR4, OR6, OR7, OR8, OR9) 4(E12, E15, E17, E18)
oAirport 8(F2, F4, F5, F6, F8, F10, F11, F12) 7(CE5, CE7, CE8, CE9, CE10, CE11, CE12) –7(E5, E8, E12, E13, E14, E17, E18)
oSeaport 9(F2, F4, F5, F6, F8, F9, F10, F11,
F12)
7(CE4, CE7, CE8, CE9, CE10, CE11, CE12) 1(OR1) 4(E5, E14, E17, E18)
oInland terminal 4(F4, F5, F8, F9) 5(CE2, CE4, CE7, CE8, CE10) –1(E15)
oRail terminal 3(F4, F8, F9) 2(CE7, CE11) ––
- Available transport links 4(F2, F3, F4, F5) 9(CE4, CE5, CE6, CE7, CE8, CE9, CE10,
CE11, CE12)
5(OR2, OR3, OR4, OR7, OR8) 5(E5, E11, E15, E17, E18)
- Multimodal accessibility 4(F4, F6, F8, F9) 7(CE5, CE7, CE8, CE9, CE10, CE11, CE12) 1(OR1) 5(E3, E8, E11, E12, E15)
- Proximity to:
oConsumer markets 5(F6, F7, F8, F9, F11) 10 (CE1, CE3, CE4, CE5, CE7, CE8, CE9,
CE10, CE11, CE12)
8(OR1, OR2, OR3, OR4, OR5, OR6,
OR8, OR9)
12 (E1, E2, E3, E5, E6, E7, E11, E13,
E14, E15, E16, E17)
oSupplier locations 1(F5) 4(CE4, CE7, CE10, CE11) 3(OR3, OR4, OR7) 1(E6)
oProduction facilities 2(F2, F9) 3(CE4, CE7, CE10) 1(OR4) 1(E3)
- Congestion 2(F4, F6) 6(CE1, CE7, CE9, CE10, CE11, CE12) 3(OR4, OR6, OR7) 1(E13)
- Labour market availability 8(F1, F4, F5, F6, F8, F9, F10, F11) 10 (CE1, CE3, CE4, CE5, CE7, CE8, CE9,
CE10, CE11, CE12)
5(OR2, OR3, OR4, OR6, OR7) 12 (E1, E3, E4, E8, E10, E11, E12,
E13, E14, E15, E17, E18)
- Land availability 8(F2, F4, F5, F6, F8, F10, F11, F12) 7(CE3, CE4, CE5, CE7, CE9, CE10, CE11) 4(OR2, OR3, OR4, OR8) 5(E8, E11, E12, E13, E17)
- Land costs 9(F2, F3, F4, F5, F6, F8, F10, F11.
F12)
5(CE4, CE5, CE7, CE10, CE11) 3(OR2, OR3, OR4) 8(E3, E4, E7, E10, E11, E12, E15,
E17)
- Expansion capabilities 6(F2, F3, F4, F5, F11, F12) 8(CE4, CE5, CE7, CE8, CE9, CE10, CE11,
CE12)
4(OR1, OR3, OR4, OR7) 2(E3, E13)
- Real estate availability 6(F2, F5, F6, F8, F10, F11) 6(CE3, CE5, CE7, CE8, CE9, CE10) 4(OR4, OR6, OR7, OR8) 1(E13)
- Proximity DC to the HQ* 4(F1, F3, F5, F11) -2(OR3, OR7) -
- Proximity DC to high tech cluster
Eindhoven (CE case) or flower cluster
Aalsmeer*
–1(CE6) 1(OR7) -
- Employee accessibility of DC by
public transport*
2(F9, F11) 1(CE1) 2(OR3, OR8) 2(E12, E18)
- Sufficient parking space for
employees*
1(F12) –––
- Proximity to DC location of the LSP* –3(CE7, CE9, CE10) 6(OR1, OR2, OR3, OR4, OR5, OR7) –
14 A. T. ONSTEIN ET AL.

- Sustainability of new DC location* –1(CE3) -–
- Preference CEO to locate DC near
home address*
3(Anonymous) 2(Anonymous) 1(Anonymous) –
- Preference CEO to locate DC near
education for children*
1(F4) –––
7. Institutional factors
- Presence of development agency 2(F4, F8) –1(OR4) 1(E14)
- Zoning 4(F4, F5, F8, F12) 4(CE3, CE4, CE7, CE11) 3(OR3, OR4, OR6) 4(E3, E9, E11, E16)
- Laws, regulations and customs 9(F2, F3, F4, F5, F6, F7, F8, F9, F10) 9(CE3, CE4, CE5, CE7, CE8, CE9, CE10,
CE11, CE12)
2(OR3, OR9) 7(E12, E13, E14, E15, E16, E17,
E18)
- Possible Brexit* –2(CE7, CE9) 1(OR7) -
- Taxes 8(F2, F5, F6, F7, F8, F9, F10, F12) 8(CE4, CE5, CE6, CE7, CE8, CE9, CE10,
CE11)
2(OR4, OR9) 7(E4, E7, E10, E11, E13, E14, E17)
- Investment incentives 4(Anonymous) 3(Anonymous) 2(Anonymous) 5(E8, E11, E13, E15, E17)
- Political stability 6(F2, F3, F4, F5, F6, F7) 5(CE4, CE5, CE7, CE8, CE11) 3(OR3, OR4, OR9) 3(E10, E14, E15)
- Economic stability 5(F2, F4, F5, F6, F7) 5(CE4, CE5, CE7, CE8, CE11) 3(OR3, OR4, OR9) –
- Dutch trade culture* –1(CE6) ––
- Language proficiency of Dutch
employees*
–2(CE6, CE8) ––
- Climate conditions 2(F6, F8) 2(CE7, CE11) –1(E18)
- Government cooperation to start
building DC*
1(F4) 2(CE4, CE7) ––
- Community attitude towards DC
localisation*
–– 2(OR3, OR4) –
- City distribution regulations (delivery
time windows)*
–– 3(OR3, OR4, OR7) –
8. Keep factors
- Knowledge retention of employees* 3(F2, F5, F8) 2(CE7, CE8) 4(OR1, OR2, OR3, OR4) –
- Cost of severance* 4(Anonymous) 5(Anonymous) 2(Anonymous) 3(E3, E13, E15)
- Investments in current assets* –1(CE5) ––
- Penalties of ending lease contracts* 2(Anonymous) –––
- Historical links with DC location* 5(F7, F8, F9, F10, F11) 1(CE6) 3(OR3, OR5, OR9) –
*New factors extracted from the industry case interviews.
**Main factor Business strategy & company characteristics is extracted from the fashion case and was added to the interview questionnaires of the other two cases.
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 15

however, two of the latter apply direct-ship-to-air hub distribution –bypassing a central DC –to
reduce inventory costs and improve delivery times (Anonymous). Outsourcing warehousing and
distribution (7 of 8 shippers) to LSPs is standard in this sector, as shippers are unable to organise
(low volume) high speed distribution for competitive prices in-house.
3.2.3. Online retail
Online retail (OR) companies are retail companies that only sell their products online (‘pure
players’). They face high warehousing costs caused by complex warehousing processes. Online retail
companies use highly responsive distribution networks, because their customers expect fast deliv-
eries. According to the respondents, major logistics challenges include delivery time reduction,
demand peaks and volatile customer demand.
We interviewed eight shippers (retailers) and one LSP, while other OR companies refused to
cooperate. The companies we did manage to interview ship a diverse range of products, e.g. furni-
ture, food and flowers (Table 3). The main inbound transport modes they use are by sea and air,
while the main outbound transport mode is by road. Three OR shippers apply a centralised distri-
bution channel layout, while two other shippers apply a decentralised layout, i.e. they use a central
DC to ship parcels and another (often smaller) DC to ship large goods. The remaining three ship-
pers use a decentralised layout to ship perishable products, i.e. food and flowers. Five of the eight
shippers outsource outbound transport (from DC to customer) to LSPs who are able to offer exten-
sive distribution networks for competitive prices. Warehousing operations are mostly insourced
(i.e. by 7 of the 8 shippers) due to the complexity of warehousing processes –i.e. different from
the CE case in which all shippers outsource warehousing operations.
4. Results
The analysis of the interviews is presented below. The first subsection compares the main factors
that influence DSD for three sectors. This is followed by the cross-case comparison of the subfactors
and the validated conceptual framework.
Based on the case results, we identified several new factors, i.e. keep factors, personal location
preferences and LSP influence (Table 5,Appendix 1 for the links to the individual interviews).
We explain these factors below:
.The case results show five ‘keep’factors that may contribute to the decision whether or not to
relocate within the current region. Cost of severance –i.e. costs of firing employees –influenced
four fashion companies and five CE companies. Knowledge retention of warehousing employees
(see also Christensen and Drejer 2005), penalties of ending current lease contracts and links with
the historical location are other possible keep factors. Historical links are moderately important
to fashion companies; five fashion companies value historical links with the Amsterdam fashion
cluster.
.Personal location preferences can be a decisive factor for individual companies. The role of per-
sonal location preferences has been studied for headquarter locations (Blair and Premus 1987);
our research confirms that they can also play a role in DC location selection. Three fashion ship-
pers located the DC near the home address of the CEO to reduce commuting times.
.When distribution is outsourced, the logistics service provider (LSP) will influence DSD. Ship-
pers may accept a DC location nearer to its transport centre of gravity compared to if the shipper
were to make distribution arrangements itself, or accept a larger DC than chosen otherwise, to
allow for sales volume fluctuations.
We classified these new factors in our framework as follows. Because keep factors involve the
dynamics of change itself, they are of a different nature than the current main factors, which is
why we added them as a new main factor. Personal location preferences are classified as subfactors
16 A. T. ONSTEIN ET AL.

under location factors. LSP influence relates to the organisation of logistics within a company and is
therefore included under the main factor ‘Business strategy and company characteristics’. We con-
tinued our analysis with this new set of factors.
4.1. Main factors: cross-case comparison
Table 4 shows the number of respondents that have confirmed a factor as being important for
DSD. The table presents the main factors (detailed factor scores can be found in Appendix 1).
To arrive at these scores, the number of subfactor mentions for each main factor was added,
e.g. logistics costs subfactors together were mentioned 35 times by the fashion sector respondents
(Table 4A). To correct for splitting bias (Jacobi and Hobbs 2007), we corrected for the number of
subfactors in each main factor; e.g. logistics costs consists of 4 subfactors, while location consists
of 24 subfactors –the scores are therefore divided by 4 and 24, respectively (Table 4B). This pro-
vides us with comparable information on the ordering of the main factors. Table 4C shows a cor-
relations matrix of the scores. The sectoral scores correlate very well, in particular for the
respondents from the three industry sectors, which suggests that the framework at the level of
main factors is robust for use across sectors. In other words, the factors do not appear to dis-
criminate between these three sectors.
Figure 2 shows a ranking of the main factor scores (#1 highest rank). The decision-makers from
each of the industry sectors give almost the same ranking to each of the main factors, i.e. the most
important main factors (with highest rank) are logistics costs, service level, demand and product
characteristics. It is well-known from literature that logistics costs and service level are the impor-
tant main factors influencing DSD (Chopra 2003; Christopher 2011). Our results indicate a broad
agreement on the importance of all the main factors across these sectors –although there are impor-
tant differences between sectors on the importance of the various subfactors, as we will show later.
The scores of the expert respondents correlate less well with the sectoral decision-makers than the
mutual decision-makers, although correlations are still strong (Table 4C). In particular, experts
rank location factors and product characteristics higher, and demand and service level lower
than any sectoral decision-maker.
Figure 3 shows scatter plots of the corrected main factor scores. Figure series 3 (left) confirms
the correlation between sectors showing the individual factors, including their relative position.
We also used this visualisation to examine how our results relate to findings in earlier studies
concerning the importance of the main factors. Onstein et al. (2019b) measured the weights of
the main factors (i.e. excluding the new factors found in our interviews) using the Best-Worst
Method (BWM) from Multi-Criteria Decision Analysis (MCDA). Interestingly, if we compare
the scores of the same factors, they correlate very well. The fact that two such different research
Figure 2. Ranking of main factors based on scores corrected for splitting bias.
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 17

methods (i.e. interviews versus MCDA for the same primary framework) lead to highly compar-
able scores is surprising and encouraging, as it confirms the results through triangulation of lit-
erature, surveys and interviews. Moreover, it leads to the important conjecture that a low-cost
and fast MCDA survey can provide similar results as a time-intensive series of detailed interviews
with industry decision-makers. The one main factor that fits relatively less well into the pattern is
‘product characteristics’(see outliers in Figure series 3 (right)). We can only explain this by
pointing to the fact that the interviewees were conscious of a specific product during the inter-
view, while the BWM survey was generic in nature.
4.2. Subfactors: cross-case comparison
Although decision-makers gave similar rankings to the main factors across industries, there are
important differences between industry sectors with regard to the importance of the various sub-
factors, which we describe below. To arrive at comparable subfactor scores, we corrected for the
number of respondents per sector (i.e. 12, 12 and 9 respondents; see Appendix 1). The normalised
scores are compared using radar charts for each main factor, with the importance of subfactors
indicated between 0 (mentioned by no respondent) and 1 (mentioned by all).
4.2.1. Service level factors
Subfactor ‘supplier lead time’is most important in the fashion sector (Figure 4). Supplier lead times
are long, i.e. often 3–5 months, according to fashion respondents which is why many fashion ship-
pers use Quick Response production systems to reduce supplier lead times (Şen 2008). Supplier lead
times are unimportant to the OR respondents, possibly because some of them source nearby (e.g.
food, flowers, furniture, see Table 3) and thus already have short supplier lead times.
Figure 3. Correlations of main factor scores between sectoral decision-makers (left) and between sectoral decision-makers and
previous BWM research by Onstein et al. (2019b) (right).
18 A. T. ONSTEIN ET AL.

Responsiveness (reaction speed to fulfil demand) is most important to online retail companies
(Figure 4). Online customers show volatile demand patterns, while at the same time expecting
short delivery times (Xing et al. 2011)–i.e. within one or few days (Interview OR3) –forcing
OR companies to maintain highly responsive distribution operations (Interview OR1). The rela-
tively high importance of the subfactor returnability may be explained by the differences between
products. A CE product return will have a higher value density compared to a fashion product
return, making it important to CE companies to receive their product returns. In the OR case,
only OR companies with high return rates confirm the importance of this factor (i.e. OR5, OR8,
OR9); those that do not mention this factor, i.e. home furnishing, food and flowers, are also the
ones with few returns (OR2, OR6, OR7).
4.2.2. Logistics costs factors
Warehousing costs and outbound transport costs are the most important subfactors in this category
(Figure 5). Outbound transport costs may be considered more important than inbound transport
costs, because they often allow fewer advantages of scale (Christopher 2011). Products with a lower
package density typically show this pattern to a lesser extent, as is shown in the case of the fashion
industry. Inventory costs are most important to CE companies, probably because their products
have a high value density.
Figure 4. Service level subfactor importance (0 = low, 1 = high).
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 19

4.2.3. Business strategy and company characteristics
Overall, business strategy and company characteristics are a relatively unimportant main factor
(Figure 2). The subfactors in this category, however, do vary in terms of their influence. Managerial
and financial capacity were the most dominant of all the subfactors, especially within the CE and
OR sectors (Table 5). As also argued by Pedersen, Zachariassen, and Arlbjørn (2012), sufficient
management and financial capacity stimulates DSD implementation.
4.2.4. Demand factors
Demand levels are considered to be an important subfactor in this category (Figure 6). Varying
demand volumes and geographies pose a major logistics challenge to companies. Demand volatility
is especially important in the OR case. OR companies face volatile demand, because their customers
can easily switch between suppliers (Boyer and Hult 2005). Six out of nine of the OR companies we
interviewed have outsourced outbound distribution to parcel carriers, because they are able to
handle volatile goods volumes well (Table 3). Demand volatility is relatively unimportant to fashion
companies –presumably because their demand levels are relatively stable.
4.2.5. Product characteristics
Within the main factor ‘product characteristics’(Figure 7), the scores of the subfactor ‘product
value density’follow the order of the value density of products quite well, with CE products showing
the highest scores (Li, Ganesan, and Sivakumar 2005). The fact that most CE companies (8 out of
12) use a centralised distribution channel layout may be related to this aspect. The subfactor ‘pack-
age density’is moderately important in all sectors, without much difference between the sectors.
Products from all three sectors need to be repackaged from wholesale to retail units. Perishability
Figure 5. Logistics costs subfactor importance (0 = low, 1 = high).
20 A. T. ONSTEIN ET AL.

(i.e. shelf life length) is moderately important in the CE and OR cases, but relatively unimportant in
the fashion case –note that perishability here does not refer to products being out of fashion, but
relates to shorter term value loss, during distribution. At the individual company level, we found
that perishability depended strongly on the product being sold, with OR companies that sell
food and flowers reporting high scores (interviews OR4, OR6, OR7). The shippers involved distri-
bute products via local hubs to allow for fast deliveries.
4.2.6. Location factors
Proximity of the DC to the motorway is relatively important compared to other subfactors in this
category (Figure 8), which is in line with existing literature (e.g. Bowen 2008; Dablanc and Ross
2012). Proximity of the DC to airports largely follows the value density of products, where pipeline
capital costs will be balanced against shipping rates. Especially CE and fashion will be moved across
larger distances. CE companies use air as their main inbound transport mode, while fashion com-
panies use air transport for important collection reorders during a fashion season. Proximity to sea-
ports largely follows the same pattern, with CE and fashion product DCs likely to locate relatively
nearby. Proximity of the DC to consumer markets is generally known to be an important factor,
which is here confirmed for the OR and CE case, although less so for the fashion case. Customer
lead times are shorter in the OR and CE sectors (Nguyen, De Leeuw, and Dullaert 2018; Li, Gane-
san, and Sivakumar 2005) compared to the fashion sector, because expected service levels are higher
and products tend to be more perishable.
Labour market availability is an important location factor to CE and Fashion companies and
moderately important to OR companies. In all three industry sectors, there are respondents who
confirm there was labour scarcity in the Netherlands at the time the interviews were conducted.
The relatively low importance of labour market availability to OR may be explained by the lower
Figure 6. Demand subfactor importance (0 = low, 1 = high).
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 21

(non-specialized) job skills requirements, or the fact that, in the Netherlands OR, different labour
conditions apply compared to conventional retail supply chains.
There are no marked differences between sectors with regard to land availability, except for the
fact that OR scores relatively low. Land costs are important to fashion companies, but relatively
unimportant to CE and OR companies. Fashion DCs typically serve a larger geographic area,
which implies that they have more search opportunities when it comes to securing lower land rents.
Eight possible new location factors were identified based on the case interviews. Accessibility of
the DC for employees by public transport could be a new influencing factor according to five
respondents (all cases), because companies start to locate DCs further away from urban areas
(explained in interview CE1, E12). In the OR case, a new location factor could be proximity of
the DC to an LSP hub. Six out of nine OR shippers prefer to locate the DC near their parcel carrier,
to reduce delivery times.
4.2.7. Institutional factors
Overall, the institutional subfactors that have a direct economic impact on investment and oper-
ations were the ones mentioned during the interviews (Figure 9). The patterns of the sectors are
quite comparable, although to a lesser extent for OR. Efficient customs procedures are considered
important, because they reduce customer delivery times and increase product availability. High
taxes are a push factor, while tax advantages (such as VAT deferment) may help persuade compa-
nies to locate their DCs in Free Trade Zones (Sheffi2012). The relatively low importance of some
institutional factors to OR companies could be caused by their focus on smaller regions for distri-
bution (domestic or sub-continental), where differences between location options are relatively
minor.
Figure 7. Product characteristics subfactor importance (0 = low, 1 = high).
22 A. T. ONSTEIN ET AL.

Figure 8. Location subfactor importance (0 = low, 1 = high).
Figure 9. Institutional subfactor importance (0 = low, 1 = high).
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 23

4.3. Conceptual framework
Based on (the relationships between) the factors extracted from the case results, we propose the fol-
lowing validated conceptual framework (Figure 10). Compared to the initial literature-based frame-
work, the framework includes 20 new subfactors and one new main factor, i.e. the keep factors. The
hierarchy of factors presented here can be used by private companies as a starting point for analysis
and decision-making. In addition, we expect this to be a useful tool for discussion and analysis in a
public policy environment, supporting spatial planning and regional marketing.
5. Conclusions and future research
In this study, we analysed the factors that influence distribution structure design (DSD) in three
industry sectors, i.e. fashion, consumer electronics (CE) and online retail (OR). Despite the frequent
treatment of DSD in supply chain handbooks, the importance of factors influencing DSD in differ-
ent industry sectors has thus far received little attention and an empirically validated conceptual
framework is lacking. To fill these gaps, this research used a multiple case research design to exam-
ine DSD in different industry sectors and develop an empirically validated framework. The
Figure 10. Validated conceptual framework.
24 A. T. ONSTEIN ET AL.

empirical evidence for the cases was collected during 18 interviews with logistics experts and 33
interviews with decision-makers on DSD, affiliated to shippers and logistics service providers.
The result, and a first contribution of this paper, is an empirically validated conceptual framework
for DSD.
Statistical analysis of the case interview results shows that decision-makers of the three industry
sectors place very similar importance on the main factors. In all three cases, the most important
main factors are logistics costs, service level, demand factors and product characteristics –which
is in line with SCM literature. These results imply that the validated conceptual framework at
the level of main factors is robust for cross-sectoral comparison. However, although decision-
makers agree on the importance of the main factors, there are differences when it comes to the
importance of the various subfactors at a sectoral level, which can be explained to a large extent
from the typical product and organisational attributes of the sectors under examination. Moreover,
the interviews lead us to identify new factors, that were previously not included in the literature-
based framework. We found 20 new subfactors and one new main factor, i.e. keep factors.
In our analysis we find that the main factor scores of the sectoral decision-makers correlate well
with previous survey-based research in which the main factor weights were quantified using the
Best-Worst Method (BWM) from Multi-Criteria Decision Analysis (Onstein et al. 2019b). This
cross-validation of findings is a second main contribution of the paper.
This study contributes to the existing literature by conducting empirical research into DSD at a
sectoral level. We analysed the factors that influence DSD in three specific industry sectors, building
on an earlier and generic framework from existing literature. The new framework can help compa-
nies in their DSD process, and support governments and consultants to carry out regional land use
planning in a way that is attractive for selected industries. The framework can also help researchers
improve quantitative distribution channel and distribution centre location models, which are often
based on incorrect or incomplete sets of factors.
One of the limitations in terms of the scope of this study is that the companies involved focus on
the distribution of finished goods (wholesale and retail). Companies that sell semi-finished products
(business-to-business) may provide different results when it comes to the importance of subfactors.
New research could extend the base of interviews to include sectors that produce semi-finished
goods. As our interviews were limited to global supply chains around DCs within the Netherlands,
we also recommend broadening the geographic scope of the empirical research.
Acknowledgements
We are grateful to two anonymous reviewers for their constructive comments, which significantly improved the
analysis of our results.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) [grant
#023.006.016].
ORCID
Alexander T. C. Onstein http://orcid.org/0000-0002-9671-8564
Lóránt A. Tavasszy http://orcid.org/0000-0002-5164-2164
Jafar Rezaei http://orcid.org/0000-0002-7407-9255
Adeline Heitz http://orcid.org/0000-0003-2075-9789
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 25

References
Agrebi, M., M. Abed, and M. N. Omri. 2017.“ELECTRE I Based Relevance Decision-Makers Feedback to the
Location Selection of Distribution Centers.”Journal of Advanced Transportation: 1. doi:10.1155/2017/7131094.
Ashayeri, J., and J. M. J. Rongen. 1997.“Central Distribution in Europe: A Multi-Criteria Approach to Location
Selection.”The International Journal of Logistics Management 8 (1): 97–109.
Blair, J. P., and R. Premus. 1987.“Major Factors in Industrial Location: A Review.”Economic Development Quarterly
1 (1): 72–85.
Bowen, J. 2008.“Moving Places: The Geography of Warehousing in the US.”Journal of Transport Geography 16 (6):
379–387.
Boyer, K. K., and G. T. M. Hult. 2005.“Customer Behavior in an Online Ordering Application: A Decision Scoring
Model.”Decision Sciences 36 (4): 569–598.
Bryman, A. 2008.Social Research Methods. Oxford: Oxford University Press.
Chopra, S. 2003.“Designing the Distribution Network in a Supply Chain.”Transportation Research Part E: Logistics
and Transportation Review 39 (2): 123–140.
Chopra, S., and P. Meindl. 2013.Supply Chain Management. Strategy, Planning, and Operations. Harlow, UK: Pierson
Education Limited.
Christensen, J., and I. Drejer. 2005.“The Strategic Importance of Location: Location Decisions and the Effects of Firm
Location on Innovation and Knowledge Acquisition.”European Planning Studies 13 (6): 807–814.
Christopher, M. 2011.Logistics and Supply Chain Management. 4th ed. Harlow, UK: Pierson Education Limited.
Cooper, M. 1984.“Cost and Delivery Time Implications of Freight Consolidation and Warehousing Strategies.”
International Journal of Physical Distribution & Materials Management 14 (6): 47–67.
Dablanc, L., and C. Ross. 2012.“Atlanta: A Mega Logistics Center in the Piedmont Atlantic Megaregion (PAM).”
Journal of Transport Geography 24 (2012): 432–442.
Davydenko, I. Y. 2015.“Logistics Chains in Freight Transport Modelling.”PhD diss., Delft University of Technology,
The Netherlands.
Eisenhardt, K. M., and M. E. Graebner. 2007.“Theory Building from Cases: Opportunities and Challenges.”Academy
of Management Journal 50 (1): 25–32.
Frankel, R., D. Naslund, and Y. Bolumole. 2005.“The ‘White Space’of Logistics Research: A Look at the Role of
Methods Usage.”Journal of Business Logistics 26 (2): 185–209.
Friedrich, H., L. A. Tavasszy, and I. Y. Davydenko. 2014.“Distribution Structures.”In Modelling Freight Transport,
edited by L. A. Tavasszy, and G. de Jong, 65–88. London: Elsevier.
Hall, P. V., and W. Jacobs. 2012.“Why are Maritime Ports (Still) Urban, and why Should Policy Makers Care?”
Maritime Policy & Management 39 (2): 189–206.
Heitz, A., and A. Beziat. 2016.“The Parcel Industry in the Spatial Organization of Logistics Activities in the Paris
Region: Inherited Spatial Patterns and Innovations in Urban Logistics Systems.”Transportation Research
Procedia 12: 812–824.
Heitz, A., L. Dablanc, J. Olsson, I. Sanchez-Diaz, and J. Woxenius. 2018.“Spatial Patterns of Logistics Facilities in
Gothenburg, Sweden.”Journal of Transport Geography. Available Online, doi:10.1016/j.jtrangeo.2018.03.005.
Heitz, A., P. Launay, and A. Beziat. 2019.“Heterogeneity of Logistics Facilities: An Issue for a Better Understanding
and Planning of the Location of Logistics Facilities.”European Transport Research Review 11 (5): 2019. doi:10.
1186/s12544-018-0341-5.
Hilmola, O. P., and H. Lorentz. 2011.“Warehousing in Northern Europe: Longitudinal Survey Findings.”Industrial
Management & Data Systems 111 (3): 320–340.
Holland International Distribution Council. 2018.Dutch Logistics Sector. Holland is a key Logistics hub That Connects
Global Markets. Zoetermeer: HIDC.
Jacobi, S. K., and B. F. Hobbs. 2007.“Quantifying and Mitigating the Splitting Bias and Other Value Tree-Induced
Weighting Biases.”Decision Analysis 4 (4): 194–210.
Ketokivi, M., and T. Choi. 2014.“Renaissance of Case Research as a Scientific Method.”Journal of Operations
Management 32 (5): 232–240.
Klauenberg, J., L.-A. Elsner, and C. Knischewski. 2016.“Dynamics in the Spatial Distribution of Hubs in Groupage
Networks –the Case of Berlin.”Paper presented at World Conference on Transport Research (WCTR), Shanghai,
July 10–15.
Kuipers, B, and J. Eenhuizen. 2004.“A Framework for the Analysis of Seaport-Based Logistics Parks.”Proceedings of
the 1st International Conference on Logistics Strategies for Ports. Dalian, China.
Li, K. P., V. K. Ganesan, and A. I. Sivakumar. 2005.“Synchronized Scheduling of Assembly and Multi-Destination
air-Transportation in a Consumer Electronics Supply Chain.”International Journal of Production Research 43
(13): 2671–2685.
Lovell, A., R. Saw, and J. Stimson. 2005.“Product Value-Density: Managing Diversity Through Supply Chain
Segmentation.”The International Journal of Logistics Management 16 (1): 142–158.
26 A. T. ONSTEIN ET AL.

Mangiaracina, R., G. Song, and A. Perego. 2015.“Distribution Network Design: A Literature Review and a Research
Agenda.”International Journal of Physical Distribution & Logistics Management 45 (5): 506–531.
McCann, P. 2015. Interview, Professor Economic Geography, University of Groningen (RUG), December 4.
McCann, P., and S. Sheppard. 2003.“The Rise, Fall and Rise Again of Industrial Location Theory.”Regional Studies
37 (6-7): 649–663.
McKinnon, A. C. 1984.“The Spatial Organization of Physical Distribution in the Food Industry.”PhD Diss.,
University College London, London.
McKinnon, A. C. 2009.“The Present and Future Land Requirements of Logistical Activities.”Land Use Policy 26:
S293–S301.
Meixell, M. J., and V. B. Gargeya. 2005.“Global Supply Chain Design: A Literature Review and Critique.”
Transportation Research Part E 41 (6): 531–550.
Nguyen, D. H., S. De Leeuw, and W. E. H. Dullaert. 2018.“Consumer Behaviour and Order Fulfilment in Online
Retailing: A Systematic Review.”International Journal of Management Reviews 20 (2): 255–276.
North, D. C. 1990.Institutions, Institutional Change and Economic Performance. Cambridge, UK: Cambridge
University Press.
Nozick, L. K., and M. A. Turnquist. 2001.“Inventory, Transportation, Service Quality and the Location of
Distribution Centers.”European Journal of Operational Research 129 (2): 362–371.
Olhager, J., S. Pashaei, and H. Sternberg. 2015.“Design of Global Production and Distribution Networks: A
Literature Review and Research Agenda.”International Journal of Physical Distribution & Logistics
Management 45 (1/2): 138–158.
Önden, I., A. Z. Acar, and F. Eldemir. 2016.“Evaluation of the Logistics Centre Locations Using a Multi-Criteria
Spatial Approach.”Transport 33 (2): 322–334.
Onstein, A. T. C., M. Ektesaby, J. Rezaei, L. A. Tavasszy, and D. A. van Damme. 2019b.“Importance of Factors
Driving Firms’Decisions on Spatial Distribution Structures.”International Journal of Logistics: Research and
Applications, doi:10.1080/13675567.2019.1574729.
Onstein, A. T. C., L. A. Tavasszy, and D. A. van Damme. 2019a.“Factors Determining Distribution Structure
Decisions in Logistics: A Literature Review and Research Agenda.”Transport Reviews 39 (2): 243–260.
Pedersen, S. G., F. Zachariassen, and J. S. Arlbjørn. 2012.“Centralisation vs de-Centralisation of Warehousing.”
Journal of Small Business and Enterprise Development 19 (2): 352–369.
Picard, J. 1982.“Typology of Physical Distribution Systems in Multi-National Corporations.”International Journal of
Physical Distribution & Materials Management 12 (6): 26–39.
Rodrigue, J.-P. 2008.“The Thruport Concept and Transmodal Rail Freight Distribution in North America.”Journal
of Transport Geography 16 (4): 233–246.
Şen, A. 2008.“The U.S. Fashion Industry: A Supply Chain Review.”International Journal of Production Economics
114 (2): 571–593.
Sheffi, Y. 2012.Logistics Clusters. Delivering Value and Driving Growth. Cambridge, MA: The MIT Press.
Song, G., and L. Sun. 2017.“Evaluation of Factors Affecting Strategic Supply Chain Network Design.”International
Journal of Logistics Research and Applications 20 (5): 405–425.
Treacy, M., and F. Wiersema. 1993.“Customer Intimacy and Other Value Disciplines.”Harvard Business Review 71
(1): 84–93.
van Hoek, R., H. Commandeur, and B. Vos. 1998.“Reconfiguring Logistics Systems Through Postponement
Strategies.”Journal of Business Logistics 19 (1): 33-54.
Verhetsel, A., R. Kessels, P. Goos, T. Zijlstra, N. Blomme, and J. Cant. 2015.“Location of Logistics Companies: A
Stated Preference Study to Disentangle the Impact of Accessibility.”Journal of Transport Geography 42 (2015):
110–121.
Voss, C., N. Tsikriktsis, and M. Frohlich. 2002.“Case Research in Operations Management.”International Journal of
Operations & Production Management 22 (2): 195–219.
Warffemius, P. M. J. 2007.“Modeling the Clustering of Distribution Centers around Amsterdam Airport Schiphol:
Location Endowments, Economies of Agglomeration, Locked-in Logistics and Policy Implications.”PhD diss.,
Erasmus University Rotterdam, The Netherlands.
Xing, Y., D. B. Grant, A. C. McKinnon, and J. Fernie. 2011.“The Interface Between Retailers and Logistics Service
Providers in the Online Market.”European Journal of Marketing 45 (3): 334–357.
Yin, R. K. 2014.Case Study Research: Design and Methods. Thousand Oaks, CA: Sage publications.
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 27

Appendix 1. Subfactor scores.
Decision-maker interviews
(33 respondents) Expert
interviews
(18
respondents)
Fashion
(12
respondents)
Consumer
electronics (CE)
(12 respondents)
Online retail
(OR)
(9 respondents)
1. Business strategy & company
characteristics
Managerial capacity 0.000 0.417 0.333 0.000
Financial capacity 0.000 0.333 0.333 0.000
In-house or outsourcing strategy 0.167 0.000 0.000 0.111
Store ownership 0.083 0.083 0.000 0.056
Position within the supply chain 0.000 0.000 0.000 0.056
LSP influence on shipper* 0.250 0.083 0.000 0.000
2. Demand factors
Demand level 0.833 0.750 0.889 0.167
Demand volatility 0.333 0.500 0.667 0.000
Demand dispersion 0.250 0.583 0.556 0.056
3. Service level factors
Supplier lead time 0.750 0.583 0.333 0.167
Delivery time 0.917 1.000 0.778 0.278
Delivery reliability 0.750 0.583 0.889 0.056
Responsiveness 0.667 0.750 0.889 0.111
Returnability 0.417 0.750 0.333 0.111
Order visibility 0.417 0.500 0.444 0.000
4. Product characteristics
Product value density 0.417 0.583 0.222 0.222
Package density 0.500 0.583 0.556 0.111
Perishability 0.167 0.417 0.444 0.056
5. Logistics costs factors
Inventory costs 0.667 0.750 0.556 0.389
Transport costs –inbound 0.583 0.750 0.667 0.389
Transport costs –outbound 0.750 0.917 0.889 0.556
Warehousing costs 0.917 0.917 1.000 0.333
6. Location factors
Proximity Motorway 0.667 0.583 0.667 0.222
Proximity Airport 0.667 0.583 0.000 0.389
Proximity Seaport 0.750 0.583 0.111 0.222
Proximity Inland terminal 0.333 0.417 0.000 0.056
Proximity Rail terminal 0.250 0.167 0.000 0.000
Available transport links 0.333 0.750 0.556 0.278
Multimodal accessibility 0.333 0.583 0.111 0.278
Proximity Consumer markets 0.417 0.833 0.889 0.667
Proximity Supplier locations 0.083 0.333 0.333 0.056
Proximity Production facilities 0.167 0.250 0.111 0.056
Congestion 0.167 0.500 0.333 0.056
Labour market availability 0.667 0.833 0.556 0.667
Land availability 0.667 0.583 0.444 0.278
Land costs 0.750 0.417 0.333 0.444
Expansion capabilities 0.500 0.667 0.444 0.111
Real estate availability 0.500 0.500 0.444 0.056
Proximity DC to the HQ* 0.333 0.000 0.222 0.000
Proximity DC to high tech cluster Eindhoven or
flower cluster Aalsmeer*
0.000 0.083 0.111 0.000
Employee accessibility of DC by public transport* 0.167 0.083 0.222 0.111
Sufficient parking space for employees* 0.083 0.000 0.000 0.000
Proximity to DC location of the LSP* 0.000 0.250 0.667 0.000
Sustainability of new DC location* 0.000 0.083 0.000 0.000
Preference CEO to locate DC near home address* 0.250 0.167 0.111 0.000
Preference CEO to locate DC near education for
children*
0.083 0.000 0.000 0.000
(Continued )
28 A. T. ONSTEIN ET AL.

Continued.
Decision-maker interviews
(33 respondents) Expert
interviews
(18
respondents)
Fashion
(12
respondents)
Consumer
electronics (CE)
(12 respondents)
Online retail
(OR)
(9 respondents)
7. Institutional factors
Presence of development agency 0.167 0.000 0.111 0.056
Zoning 0.333 0.333 0.333 0.222
Laws. regulations and customs 0.750 0.750 0.222 0.389
Brexit* 0.000 0.167 0.111 0.000
Taxes 0.667 0.667 0.222 0.389
Investment incentives 0.333 0.250 0.222 0.278
Political stability 0.500 0.417 0.333 0.167
Economic stability 0.417 0.417 0.333 0.000
Dutch trade culture* 0.000 0.083 0.000 0.000
Language proficiency of Dutch employees* 0.000 0.167 0.000 0.000
Climate conditions 0.167 0.167 0.000 0.056
Government cooperation to start building DC* 0.083 0.167 0.000 0.000
Community attitude towards DC localisation* 0.000 0.000 0.222 0.000
City distribution regulations (delivery time
windows)*
0.000 0.000 0.333 0.000
8. Keep factors
Knowledge retention of employees* 0.250 0.167 0.444 0.000
Cost of severance* 0.333 0.417 0.222 0.167
Investments in current assets* 0.000 0.083 0.000 0.000
Penalties of ending lease contracts* 0.167 0.000 0.000 0.000
Historical links with DC location* 0.417 0.083 0.333 0.000
* New factors extracted from the industry case interviews.
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 29