1. The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-5771.htm
BIJ
18,6 Exploring geographical
dispersion in Thailand-based
food supply chain (FSC)
802
Pichawadee Kittipanya-ngam, Yongjiang Shi and Mike J. Gregory
Department of Engineering, Institute for Manufacturing,
University of Cambridge, Cambridge, UK
Abstract
Purpose – The purpose of this paper is to explore the key influential factors and their implications on
food supply chain (FSC) location decisions from a Thailand-based manufacturer’s view.
Design/methodology/approach – In total, 21 case studies were conducted with eight
Thailand-based food manufacturers. In each case, key influential factors were observed along with
their implications on upstream and downstream FSC location decisions. Data were collected through
semi-structured interviews and documentations. Data reduction and data display in tables were used
to help data analysis of the case studies.
Findings – This exploratory research found that, in the food industry, FSC geographical dispersion
pattern could be determined by four factors: perishability, value density, economic-political forces, and
technological forces. Technological forces were found as an enabler for FSC geographical dispersion
whereas the other three factors could be both barriers and enablers. The implications of these four
influential factors drive FSC towards four key patterns of FSC geographical dispersion: local supply
chain (SC), supply-proximity SC, market-proximity SC, and international SC. Additionally, the strategy
of the firm was found to also be an influential factor in determining FSC geographical dispersion.
Research limitations/implications – Despite conducting 21 cases, the findings in this research are
based on a relatively small sample, given the large size of the industry. More case evidence from a
broader range of food product market and supply items, particularly ones that have significantly
different patterns of FSC geographical dispersions would have been insightful. The consideration of
additional influential factors such as labour movement between developing countries, currency
fluctuations and labour costs, would also enrich the framework as well as improve the quality and
validity of the research findings. The different strategies employed by the case companies and
their implications on FSC location decisions should also be further investigated along with
cases outside Thailand, to provide a more comprehensive view of FSC geographical location decisions.
Practical implications – This paper provides insights how FSC is geographically located in both
supply-side and demand-side from a manufacturing firm’s view. The findings can also
provide SC managers and researchers a better understanding of their FSCs.
Originality/value – This research bridges the existing gap in the literature, explaining the geographical
dispersion of SC particularly in the food industry where the characteristics are very specific, by exploring
the internationalization ability of Thailand-based FSC and generalizing the key influential factors –
perishability (lead time), value density, economic-political forces, market opportunities, and technological
advancements. Four key patterns of FSC internationalization emerged from the case studies.
Keywords Supply chain management, Food industry, Geographic dispersion, Internationalization,
Food supply chain, Thailand
Paper type Research paper
Benchmarking: An International
Journal
Vol. 18 No. 6, 2011 This article is part of the special issue: “Supply chain networks in emerging markets” guest
pp. 802-833 edited by Harri Lorentz, Yongjiang Shi, Olli-Pekka Hilmola and Jagjit Singh Srai. Due to an
q Emerald Group Publishing Limited
1463-5771
administrative error at Emerald, the Editorial to accompany this special issue is published
DOI 10.1108/14635771111180716 separately in BIJ Volume 19, Issue 1, 2012.

2. Introduction Exploring
International supply chain (SC) management has increasingly caught much attention of geographical
researchers and practitioners along with the rising trend of globalisation in the last
decades (Snow et al., 1992; Arntzen et al., 1995; Chopra and Meindl, 2007; Christopher et al., dispersion
2006; Harland et al., 1999; Meixell and Gargeya, 2005; Nagurney and Matsypura, 2005;
Narasimhan and Mahapatra, 2004; Srai and Gregory, 2008; Supply Chain Council, 2009).
Firms have been purchasing and/or (out)sourcing their raw materials and/or products 803
from overseas, shifting manufacturing sites to the Far East or Latin America, or even
expanding their markets outside the country of origin (Christopher et al., 2006; Ferdows,
1997; Flaherty, 1996; Gattorna and Walters, 1996). This is partly because of the
significantly lower wages and fewer regulations that have lured manufacturing firms to
migrate from developed economies to emerging economies (Christopher et al., 2006).
Gattorna and Walters (1996) argued that though labour cost savings and less regulations
appeared to be the key drivers of SC internationalisation, there were also other drivers
such as: opportunistic development (event led), following customers, geographical
diversification (to reduce risks), exploiting product life cycle (PLC) differences, pursuing
potential abroad, defensive reasons, and global logic.
Despite the abundant benefits of SC internationalisation, moving suppliers,
manufacturing operations, or market destinations to emerging economies also brought
new challenges. SC in emerging economies is required to handle more diverse
environmental conditions such as increasing time and geographical distance, political
and economic instability, exchange rate fluctuation, dynamic changes in the regulatory
environment and policies, and increasing uncertainties (Acar et al., 2010; Dorneir et al.,
1998; Flaherty, 1996; Meixell and Gargeya, 2005; Perron et al., 2010). Additionally,
it has been argued that suboptimal SC decisions, for example, off-shoring to low
labour cost countries, might turn out to increase the total cost in the SC as a whole
(Christopher et al., 2006). International SC is hence far more difficult to manage than
domestic ones due to the increasing complexity and uncertainty on the global platform.
Hence, firms need to have deeper understanding of their business environment in order
to effectively decide on geographical locations for their SCs.
The growth of internationalisation in SC was witnessed (Christopher et al., 2006) in
almost every industry, even in the food industry where localisation is rather strong in
comparison with other industry (Sterns and Peterson, 2001; van Hoek, 1999). However,
the research on food supply chain (FSC) and its internationalisation is still a “recent
phenomenon” (Soman, 2008) and there are many opportunities for the food industry to
benefit from SCM research and that are yet to be explored (Cunningham, 2001; Mena and
Stevens, 2010). This is partly because the food industry has specific characteristics such
as product perishability, long PLC, non-modular product structure, product safety and
traceability, product temperature sensitivity, and seasonality, etc. (Christopher et al.,
2009; Entrup, 2005; Fuller, 1994; Karkkainen, 2003; Soman et al., 2004; van der Vorse et al.,
2001; der Vorst and Beulens, 2002; van Hoek, 1999) that place specific requirements on SC.
These characteristics are argued to have implications on geographical location decisions
of FSC, e.g. high perishability, safety regulations, or even macroeconomic policies such as
World Trade Organization (WTO) trade agreements on subsidies removal, could limit
geographical dispersion of FSC (The Economist Intelligence Unit Limited (EIU), 2005;
Raynolds, 2004; Ruquet, 2006; van Hoek, 1999). However, sustainability policy and
“green” concerns such as food miles and global warming, could prevent geographical

3. BIJ dispersion of FSC and support localization instead (Lang et al., 2004). These
18,6 considerations place FSC in complex and dynamic environmental conditions (Mena and
Stevens, 2010; Soman, 2008; Van Donk and van der Vaart, 2005).
To date, the existing literature has explored issues in international or global SC
decisions such as global SC optimization modeling (Acar et al., 2010; Arntzen et al.,
1995; Narasimhan and Mahapatra, 2004; Nagurney and Matsypura, 2005; Vidal and
804 Goetschalackx, 1997), global SC strategies (Christopher et al., 2006), manufacturing
location decisions (Bhatnagar and Sohal, 2005; Melo et al., 2009), FSC configuration and
design (Van der Vorst and Beulens, 2002; van Hoek, 1999). However, most of the
research was quantitative, lacking a qualitative perspective. Moreover, none of them
could comprehensively explain the geographical dispersion of SC in the food industry,
taking into consideration the specific characteristics of the food industry. Coupled with
the fact that Thailand, an emerging market, is ranked among the top 20 countries in
food manufacturing and exporting for the global market (WTO, 2009), this paper seeks
to explore how Thailand-based FSC is geographically dispersed and what drives FSC
towards particular patterns of geographical dispersion by taking into account specific
characteristics of the food industry. Case study method was chosen to provide rich
understanding of the research context (FSC in emerging economies), which is
considered relatively new, complex, and dynamic.
This paper is structured in five sections. First, it begins by setting out the research
scope and objective. Second, the relevant literature on SC and its internationalisation in
general as well as the relevant literature on FSC in particular are reviewed. Third, the
research context is defined, along with the research question identification and the
conceptual framework development. The research approach is then identified, which
leads to the fourth section of this paper – the conduct of field work and cross-case
analysis. In this section, multiple-case data are presented and analysed, followed by a
presentation of the key findings. Finally, the fifth section concludes with research
limitations and future work suggestions.
Literature review
SC and its internationalisation
With the rise of globalization over the last decades, the literature on SC and its
internationalisation has been increasingly witnessed from various perspectives,
e.g. facility location decisions (Bhatnagar and Sohal, 2005; Ferdows, 1997; Nagurney and
Matsypura, 2005; Lorentz, 2008; Vidal and Goetschalackx, 1997), international sourcing
and purchasing (Kotabe et al., 2009; Peterson et al., 2000; Trent and Monczka, 1998), and
internationalisation process (Johanson and Vahlne, 1977, 1990). International SC is
arguably far more difficult to manage than domestic SC (Flaherty, 1996; Meixell and
Gargeya, 2005). Flaherty (1996) compared international SC with domestic SC and noted
that international SC had rising issues of greater geographic distance and time
differences, multiple national markets, multiple locations of operations, diversity of
supply and demand conditions. The literature review, presented below, serves as a
foundation of the key factors influencing SC geographic dispersion decisions.
The location decisions of SC have been researched through three perspectives:
sourcing, manufacturing, and distribution to the market (Flaherty, 1996). First, sourcing
perspective involves decisions to either source raw materials/finished products
from overseas or local suppliers (Bozarth et al., 1998; Trent and Monczka, 2005).

4. Historically, cost has been regarded as the primary determinant of sourcing Exploring
decisions (Flaherty, 1996; Peterson et al., 2000; Trent and Monczka, 1998). This factor geographical
becomes important once labour costs are a substantial part of production and unit costs
(Kotabe et al., 2009). With substantial economies of scale, global demand for the same dispersion
products can be meet through a single source globally. However, it has been suggested
that in making sourcing decisions, total logistics costs, customs, duties, handling,
and damages during transit costs as well as the trade-off impact on performance such 805
as delivery lead time and additional inventory should be taken into account
(Bozarth et al., 1998).
Currently, a number of additional factors are being considered for sourcing decisions
as firms now aim to “exploit both the firm’s and suppliers’ competitive advantages and
the locational advantages of various countries in global competition” (Kotabe et al.,
2009). For example, international sourcing can bring product and process technology
from overseas suppliers to the firm and that consequently improves the product offering,
product quality, and reduces problems caused byshorter PLCs (Bozarth et al., 1998;
Kotabe et al., 2009). In some cases, firms are forced to source internationally due to
scarcity of raw materials in local markets and where local sourcing is feasible
(Dornier et al., 1998; Kotabe et al., 2009). This situation occurs often in the agricultural
and food industry where weather seasons impact the production of raw materials
(Bourlakis and Weightman, 2004; Georgiadis et al., 2005; Kittipanya-ngam, 2010).
Second, the key factors determining manufacturing location decision originated from
international trade and capital movement in early 1960s (Dunning, 1979; Hymer, 1972;
Vernon, 1966). Ferdow (1997) asserted that most foreign-owned plants aimed to benefit
only from tariff and trade concessions, cheap labour, capital subsidies, and reduced
logistics costs, which resulted in limited range of resource utilisation. Therefore,
he proposed a strategic mix roles for foreign factories: source, lead, contributor, offshore,
outpost, and server; to position foreign-owned factories as competitive weapons in their
international production network. These roles can change according to the firm’s
business environment and strategy.
Dorneir et al. (1998) had proposed four factors influencing the internationalisation of
manufacturing operations: global market forces, global cost forces, technological forces,
and political-macroeconomic forces. “Global competition” is suggested as one of those
factors (Shi and Gregory, 1998; McCormick and Stone, 1990). More specifically, Bhatnagar
and Sohal (2005) explored the impact of facility location factors, SC uncertainty, and
manufacturing practices on SC competitiveness. They suggested factors for facility
location decisions as: cost, infrastructure availability, business services, labour, political
stability, proximity to markets, proximity to suppliers, and location of key competitors.
Finally, firms consider whether or not the geographical dispersion of their logistics
and market channels is an option to compete on a global platform (Yip et al., 1993). The
theory of international PLC, where firms extend their PLC through market
internationalisation once its domestic PLC reaches maturity and decline stage, has
been implemented along with the rise of globalisation and the advancement of
information technology (Douglas and Craig, 1993; Vernon, 1966). However, this theory is
no longer enough in making decisions as firms are suggested to trade-off between the
following criteria: cost (economy of scale, logistics costs, inventory costs, macro-economic
costs, i.e. customs, duties, subsidies), quality (quality reliability, innovation), flexibility
(economy of scope), national differences and locational advantages, control over its

5. BIJ chain such as customer service level and delivery time (Dornier et al., 1998; Goshal, 1993;
18,6 Waters, 2002). Product characteristics and requirements such as demand predictability
(Dornier et al., 1998) and product diversification (Goshal, 1993) are also suggested as key
criteria in this decision making. “If customization and fast response drive the industry,
then economy of scale is less important” (Dornier et al., 1998). This implies that firms need
to prioritise their criteria to match their product and process nature with consumer and
806 market requirements.
Dornier et al. (1998) suggested three alternatives in making a geographical expansion
decision: market focus, product focus, and process focus. “Market focus” refers to setting
up a facility close to the targeted markets to achieve quick response in delivery lead time
and product offerings that are more localized. Whereas “product focus” and “process
focus” are driven by economy of scale (cost effectiveness) and the facility may be located
further away from destination markets. For “product focus”, a specific product category
is allocated to each particular facility and finished products are shipped to destination
markets while “process focus” means that each facility is responsible for a particular
manufacturing process with specific manufacturing technology. Process focus is
driven by high product quality and technology requirement. Consequently, product
modularity encourages the concept of process focus as it divides manufacturing
process into modules and delay manufacturing processes (Dornier et al., 1998; Waters,
2002). Dornier et al. (1998) categorised these “focuses” by two dimensions of product
characteristics: market requirements (PLC and demand predictability) and
manufacturing complexity (product complexity). However, the key criteria of these
two dimensions and the downstream chain configuration were not well defined,
reflecting the need for further exploration in such issues.
Recently, it was suggested that product demand volatility, inventory-holding costs,
and delivery lead time play a significant role in determining the internationalisation
strategies of logistics (Harrison and van Hoek, 2005; Table I). It has been suggested
that products with high volatile demand should have globally centralized inventory in
order to save inventory-holding costs from inaccurate demand forecasts; whereas it is
suggested that the inventory system of low volatile demand product be decentralized
locally. This is partly because inventory-holding costs accounts for a significant
amount of total SC costs, given approximately 55-75 per cent of total costs account for
raw materials, components, and subassemblies, whereas transportation costs account
for only 2-5 per cent of total costs (Urban, 2002). Additionally, to make SC location
decisions, Lovell et al. (2005) took into account the required delivery lead time
Internationalisation Demand
strategies of logistics predictability Inventory location Speed
A Predictable Local inventory, preconfigured Fast moving with
products SC, high inventory level direct shipment
B Less predictable Medium inventory level Medium velocity
products awaiting for final configuration
(postponement)
Table I. C Unpredictable Global inventory, low inventory Slow moving
Three different products level (make to order)
downstream logistics
strategies Source: Harrison and van Hoek (2005)

6. and service level and incorporated these costs in SC and trade-off with the product Exploring
value and the product value, which is the ratio of product value and its physical weight geographical
or size. Three SC models were suggested (Figure 1). They argued that products with
high demand uncertainty and high value density should have inventory that was dispersion
globally centralized and that product with low demand uncertainty and low value
density should have inventory that was decentralized locally. In the case of low
demand uncertainty and high value density, transportation should be speeded up to 807
increase service level. Finally, they asserted that in case of high demand uncertainty
and low value density of the product, it was impossible to internationalise the SC.
To summarise, to geographically disperse in supply-side (sourcing perspective),
firms commonly considers the following factors: unit costs, total logistics costs
including duties and tariff, and the trade-off between total logistics costs, delivery lead
time, and inventory level. Similarly, in demand-side (logistics and market perspective),
firms often consider the aforementioned factors in making decisions on geographical
dispersion of their market. Demand volatility, inventory holding costs, delivery lead
time, and product value density are added into consideration from the demand-side due
to the nature of product demand in the market per se. From the manufacturing
perspective, the firm’s geographical dispersion decisions involve fundamentally cheap
labour cost, logistics costs, market proximity and availability, technological forces,
infrastructure, and political-macroeconomic factors. Making decisions in either
supply-side or demand-side would impact the manufacturing perspective. Therefore,
decisions on geographical dispersion in supply-side and demand-side should not be
made separately. However, ironically, none of the existing literature provides such a
complete view for decisions makers in geographical dispersion in both supply-side and
demand-side. As a result, this paper aims to fulfill this research gap.
FSC and its characteristics
The food industry, particularly the food processing industry, is not dominated by a
small number of multinational firms with few facility locations worldwide (Regmi and
Gehlhar, 2005). Rather, food manufacturing activities tend to be close to consumer bases
as it is argued that “consumer-driven changes are increasingly pushing food suppliers to
meet consumer demand and preferences at a local level even though the food industry
becomes more global” (Bolling and Gehlhar, 2005). EIU (2005) has also pointed out that
´
global food industry is fragmented with Nestle and Unilever, the two largest food
High
Globally
Demand uncertainty
centralized
inventory
Locally Fast
decentralised transportation
inventory mode
Low Figure 1.
Low High Relationship between
Product value density demand uncertainty and
product value density
Source: Lovell et al. (2005)

7. BIJ manufacturers in the world, holding only 3.2 and 2.7 per cent global market share,
18,6 respectively, in 2003. The specific characteristics of food products, e.g. temperature
sensitivity, limited shelf-life, food safety and traceability, and sustainability policies and
public concerns has also contributed to limit the ability of FSC internationalisation
(Karkkainen, 2003; Bourlakis and Weightman, 2004), leading to increased fragmentation
of the food industry (Lawson et al., 1999). More specifically, food product perishability
808 and value density (sensitivity to distribution costs) of the product are stressed as the key
determinants of FSC geographical dispersions (van Hoek, 1999).
The scarcity and specific location of suppliers (raw materials) as well as the unique
quality of raw materials in the international market are also pointed out key enablers for
internationalisation of market (Dornier et al., 1998). The case of MacDonald’s in Moscow
is a good example (Cockburn, 2000). Owing to the shortage and high variability in the
quality of beef and cheese, MacDonald’s started a beef farm and a dairy plant to supply
its outlets. This exemplifies how raw material and food product characteristics could
influence the geographical dispersion decisions of FSC.
There is also evidence that most multinational food manufacturers are now moving
towards regionalization with their focused-factory strategy (Christopher, 2005).
Concurrently, food retailers are also expanding beyond their home bases into
overseas markets. This geographical spread of both food manufacturers and food
retailers is mainly encouraged by the deregulation and liberalisation of international
trade and technological advancements including process and product, and information
technologies (Gattorna and Walters, 1996; Van der Vorst et al., 2001). Eversheim et al.
(1997) argued that despite several forces driving internationalisation of FSC, e.g. cost
efficiency, market expansion, or technological advancements; macroeconomic and
political forces (i.e. trade liberalisation, international trade policies, and trade/non-trade
barriers) remain an important factor of the globalisation/de-globalisation in food and
agricultural supply networks (Raynolds, 2004; Regmi, 2005).
Several food firms, including multinational and small-/medium-scale food
manufacturers, and multiple food retailers, have expanded globally through exporting
and global sourcing activities, international joint ventures, foreign direct investment,
merger and acquisition (M & A), etc. (Sterns and Peterson, 2001; Moreira, 2004; Bolling
and Gehlhar, 2005). Moreira and Gerry (2003) observed three globalisation patterns
of transnational food corporations: production-driven (Nestle, Unilever, Kraft Foods,
Philip Morris, Nabisco, Dole, Del Monte, Chiquita, Cargill, etc.), commercial-driven
(Carrefour, Tesco, Sainsbury, Royal Ahold, Metro Group, Aldi, and Wal-Mart), and
speculative-driven organisations. Global sourcing and M & A strategies were also
noticed in all these three globalisation patterns (Moreira and Gerry, 2003).
Lorentz (2008) examined the factors for facility location decisions in the food
industry in Russia and suggested the following factors as key criteria: infrastructural
potentials and risks, costs, potential market availability, availability of raw materials,
and competitive situation. However, his research did not take into account several
factors specifically to the food industry, e.g. perishability and limited lead time.
In summary, the existing literature of SC in the food industry has argued that
geographical dispersion is limited due to its local preference, raw material seasonality,
high quality standard, high perishability, and low value density (Cockburn, 2000;
Dornier et al., 1998; van Hoek, 1999). However, to date, there is no existing literature that
deliberately demonstrates how FSC is geographically dispersed and how the product

8. and raw material characteristics impact FSC geographical location decisions. Even the Exploring
description of characteristics such as value density is not yet well defined and explored geographical
(Lovell et al., 2005; van Hoek, 1999). Consequently, this paper seeks to further explore
these issues in FSC to fulfill such research gaps in the food industry. dispersion
Table II summarised the key drivers towards SC internationalisation from the
literature. Five key drivers were identified, SC lead time allowance (perishability),
profit and costs, uncertainties and risks, market opportunities, and technological 809
advancement. These drivers provided a guide in the data collection of the case studies
conducted. These drivers could be either barriers or enablers to SC internationalisation
depending on their degree. For instance, short lead time would be a barrier to
internationalisation whereas long lead time would enable SC internationalisation.
Research design
Research context
The term “supply chain” in this research refers to a linear chain based on a
manufacturer’s view, taking into account the inputs (major agri-food materials) and
location of suppliers as well as the outputs (food products) and the location of markets.
Each case company could also offer several case studies because to survive in the current
business environment companies “will need not just one supply chain solution but
many” (Aitken et al., 2005). Because each case company could provide different products
to different market locations, in this research, geographic location of a particular
product-market FSC is the unit of analysis. Reasons for choosing firms in Thailand in
particular were:
Key drivers Key authors
SC lead time allowance (perishability)
Delivery lead time Blackburn and Scudder (2009), Bourlakis and Weightman
Customer order lead time (2004), Bozarth et al. (1998), Christopher et al. (2006, 2009),
Production lead time Harrison and van Hoek (2005), Kittipanya-ngam (2010),
Lovell et al. (2005), Waters (2002) and van Hoek (1999)
Value density
Transportation costs Blackburn and Scudder (2009), Bozarth et al. (1998),
Inventory-holding costs Dorneir et al. (1998), Flaherty (1996), Harrison and
Unit costs including labour costs, raw van Hoek (2005), Lovell et al. (2005) and Trent and
material and component costs Monczka (2005)
Transaction and administrative costs
Product value
Uncertainties and risks
Demand uncertainy Bhatnagar and Sohal (2005), Dorneir et al. (1998), Lovell
Political and economic risks et al. (2005), Roth et al. (2008) and Shi and Gregory (1998)
Raw material scarcity and seasonality
Safety and traceability
Public policies
Market opportunities
PLC extension Dorneir et al. (1998), Ghemawat and Hout (2008), Kotabe
Market availability et al. (2009), Shi and Gregory (1998) and Yip et al. (1993) Table II.
Technology advancement Dorneir et al. (1998), Kotabe et al. (2009) and Shi and Emerging key drivers
Gregory (1998) from the literature

9. BIJ .
Thailand is an emerging economy ranked amongst the world’s top 20 food trading
18,6 countries (WTO, 2009). For instance, Thailand has been the world largest
exporters of canned pineapple and frozen shrimps, the world second largest
exporter of seafood, and a top ten exporter of chicken meat (National Food
Institute of Thailand, 2008).
. Taking Thailand as a geographical base of manufacturing firms, the complexity
810 of SC in relation to upstream suppliers’ and downstream customers’ locations
could be simplified.
Research question and the conceptual framework
Given the lack of literature to help with geographical location decisions in SC,
particularly in the food industry where specific characteristics drive the SC towards
particular requirements such as perishability limits geographical dispersion and
increases transportation and inventory costs, there is a need to further explore:
RQ1. How do FSCs of manufacturing firms geographically dispersed?
Answering this question involved creating a framework to investigate the
geographical dispersion in FSC. Figure 2 shows the conceptual framework adapted
from Flaherty’s (1996) typical configuration patterns of international SC. Four patterns
of FSC geographical dispersion include:
(I) local SC – local sourcing for local market;
(II) supply-proximity SC – local sourcing for export;
(III) market-proximity SC – international sourcing for local market; and
(IV) international SC – international sourcing for international market.
The key factors which drive SC towards different patterns are drawn from the
literature and shown in Table II as SC lead time allowance, profit and costs,
uncertainties and risks, market opportunities, and technological advancement. These
drivers provide a guide in the data collection of the case studies conducted. These
drivers could be either barriers or enablers to SC internationalisation depending
Geographically
Customers (food products)
dispersed
II. Supply-
proximity SC IV. International SC
III. Market
I. Local SC
Geographically proximity SC
concentrated
Figure 2.
The conceptual
Geographically Geographically
framework for FSC concentrated dispersed
geographical dispersion Suppliers
(Agri-food materials)

10. on their degree. For instance, short lead time would be a barrier to internationalisation Exploring
whereas long lead time would enable SC internationalisation. geographical
dispersion
Research methodology and case study design
This research intends to be exploratory in nature with the aim of understanding how
FSC of manufacturing firm is geographically dispersed through the key
internationalisation patterns and their key drivers. The case study method is chosen 811
because it is appropriate for exploratory research with no control of the event
(Eisenhardt, 1989; Yin, 2009). Additionally, this research question begins with “how”,
which is suggested to fit well in case study approach (Eisenhardt, 1989; Yin, 2009). As the
unit of analysis of this research is the geographic location of a particular product market
and the key drivers of such geographical location decisions, multiple case study method
is used to generate cross-case view of geographical location and its key drivers
(data dimensions), in order to compare and contrast the differences and similarities.
The selection of cases is based on replication sampling rather than statistical or
logic sampling meaning this research gathers diversified data in order to maximise
opportunities to discover variations amongst the evidence and to classify categories
into different patterns (Glaser and Strauss, 1967; Strauss and Corbin, 1998). The FSCs
of leading Thailand-based manufacturers were chosen in order to allow case evidence
to be compared and contrasted within similar context. This replication strengthens the
internal validity of the research as it provides comparable cases (Mena et al., 2009).
Nonetheless, the researcher recognizes the pitfall of this selection, e.g. generalizability
(external validity) of the research.
Case studies which demonstrated differences in patterns of geographical location and
shelf life were chosen in order to allow the ability to strengthen the generalizability of the
research. The final criterion, accessibility to case companies, was also critical to the
case sampling strategy. The required number of cases for this type of research is still
debatable academically. Eisenhardt (1989) suggested that four to ten cases in case
research method were understood to be typical. However, the sample size is not in
itself valuable or critical to qualitative research, but rather multiple comparisons of
commonalities and differences amongst cases (Strauss and Corbin, 1998). Strauss and
Corbin (1998) argued that the optimal number of cases would be reached when no
significant changes could be made, e.g. the dimensions and subdimensions of the
framework are saturated. In this research, the optimal number of cases was reached
when the cases stopped adding new elements to the framework or leading to significant
changes to the framework. As a result, 21 case studies with eight Thailand-based food
manufacturers across 16 various food products sold to 21 market locations, were
conducted. Each of which involves particular product-market FSC geographical
locations taking into account the supplier’s location to the customer’s location and
different key factors influencing these location decisions. These cases were conducted
during years 2007-2009 by the main researcher only.
Given the scope of research, unit of analysis, and case selection guidelines, 21 case
studies (Table III) are selected for the exploratory empirical study and these cases are
not used for the testing and verification purposes. Noted that the first and second
columns in Table III indicate the case number (1-16) and the case companies. There
was more than one case conducted within a case company. Cases 3-5, 12, and 15 contain
different product-market locations; therefore, letters (a) and (b) were added to indicate

11. BIJ
18,6
812
Table III.
their selection criteria
List of case studies and
Market Supplier’s
Cases Company Annual revenue Food product location location Product shelf life
1 Company Thai largest food conglomerate, the largest Fresh poultry meat Local market Local supply Four to five days at 0-48C
2 C animal feed and shrimp producer in the Chilled ready meal Local market Local supply Four to five days at 0-48C
3a world, Asia’s largest poultry producers Frozen ready meal Local market Local supply Nine to 12 months at 2 188C
3b (£2.8 billion in 2008) Overseas Local supply Nine to 12 months at 2 188C
market
4a Company Biggest Thai snack manufacturer and top Processed peanuts Local market International Nine to 12 months at 248C
4b K 3 biggest Thai snack exporter (£18 million supply
in 2008) Overseas International Nine to 12 months at 248C
market supply
5a Company Leading manufacturer of instant noodles Instant noodle Local market International Nine to 12 months at 248C
5b M and bakery in Thailand (£115.6 million in supply
2008) Overseas International Nine to 12 months at 248C
market supply
6 Company Leading fresh fruit and vegetable producer Fresh ready-to-cook Overseas Local supply Five to ten days at 0-48C
7 R (£27 million in 2006) vegetables market
Ready-to-eat salad Overseas Local supply Five to ten days at 0-48C
market
8 Company International leading Thai restaurant Curry paste Overseas Local supply Nine to 12 months at 248C
9 B chain, Thai food manufacturer and market
exporter Fresh ready-to-cook Overseas Local supply Five to ten days at 0-48C
vegetables market
10 Company Thai leading food and beverage Fresh fruit juice Local market International One to two days at 0-48C
11 T manufacturer (£93 million in 2008) supply
Pasteurised fruit juice Local market International Three to four weeks at 0-48C
supply
(continued)

12. Market Supplier’s
Cases Company Annual revenue Food product location location Product shelf life
12a UHT juice Local market International Nine to 12 months at 248C
12b supply
Overseas International Nine to 12 months at 248C
market supply
13 Canned pineapple Overseas Local supply Nine to 12 months at 248C
market
14 Company Leading Asian bakery chain stores (£119.7 Fresh breads Local market International Two to four days at 248C
BT million in 2009) supply
15a Company World largest multinational food Soluble coffee Local market Local supply Nine to 12 months at 248C
15b N manufacturer (£62.5 billion in 2008) Overseas Local supply Nine to 12 months at 248C
market
16 Yoghurt Local market Local supply Two to three weeks at 0-48C
geographical
dispersion
Exploring
Table III.
813

13. BIJ the differences amongst these cases. Owing to the exploratory nature of this research,
18,6 these cases are not intended for purposes of testing or verifying.
Case data on:
.
geographical locations of suppliers and customers; and
.
key factors influencing location decisions were collected through two sources of
evidence: semi-structured interviews and documentations.
814
Semi-structured interviews allow the flexibility to ask questions about the key factors
that emerged during the research while keeping the focus on research boundary
(Bernard, 1995). SC directors, purchasing directors, sales and marketing directors of the
case companies were key informants and each interview was audio recorded. Each
interview lasted at least one hour. Sometimes more than one interview with similar
informants was conducted in order to ensure that the interviews thoroughly covered
the research focus. The interviews were transcribed and checked by the interviewers
again in order to ensure the correct understanding of the interview content and data
collected. Documentations were mainly used to obtain key facts and background of the
cases and allowed confirmation of facts after the interviews.
The individual case analysis of this research includes displaying the data, explaining
and generalizing, and reviewing the conceptual framework including four patterns of
SC internationalisation and their five key drivers. Within-case analysis allows the
researcher to become familiar with the data and their preliminary patterns (Eisenhardt,
1989). It began since the first round of case data collection. Data display and analysis,
which include data reduction[1], data display[2], and visualizing conclusions (Miles and
Huberman, 1994), were used in within-case analysis. Once new cases were added,
cross-case analysis was conducted to observe different patterns of data across cases
(Eisenhardt, 1989). The purpose of the cross-case analysis was to identify the generic
categories while preserving the uniqueness of individual case data (Miles and
Huberman, 1994). This analysis was aided by cross-case displays including content
analytic summary tables (Miles and Huberman, 1994), which allowed the researcher to
visually compare and contrast the data dimensions across cases. Revisiting the
literature, reviewing the conceptual framework, and consultations with case companies,
practitioners, and academics, helped in data reduction and consistency, which is part of
case data analysis (Miles and Huberman, 1994). This process was repeated multiple
times. Pattern matching technique (Yin, 2009) was applied to observe the key patterns of
FSC geographical dispersion. Cross-case data analysis was then presented in table
formats to allow visualization for conclusions.
Case studies and cross-case analysis
The first part of this section presents the overall view of the 21 case data. The second
part tabulates the data for cross-case analysis and elaborates on the analysis across
case studies. The final part then concludes the key findings of the research from the
case analysis.
Case data presentation
Based on the conceptual framework, case data presentation involved capturing
geographical locations of each FSC and the key drivers of geographical dispersion of
each case study SC from both supply-side and demand-side perspectives. Each case

14. demonstrated diversified decisions in their both supply-side and demand-side Exploring
according to different key drivers. geographical
The conceptual framework was tested during the case study conducted across a range
of SCs (Table IV). The cases allowed minor refinements of the conceptual framework in dispersion
terms of the practical application and developing unambiguous attribute definitions.
Additionally, these cases also allowed the exploration of the implications of the key
drivers on FSC geographical dispersion patterns, extending discussions to explore 815
potential opportunities through FSC internationalisation. The case studies included
different types of product characteristics ranging from commodity products (canned
pineapple) to innovative products (frozen ready meal, fruit juice) and to highly perishable
products (fresh poultry meat, chilled ready meal, ready-to-eat salad, fresh ready-to-cook
vegetables, and fresh breads). The case companies included Thailand-based multinational
corporations (MNCs) as well as Thailand-based local firms. This allowed the conceptual
framework to be assessed across a wide range of application environments.
The 21 case studies set out in Table IV illustrates the fieldwork approach used
across the different SCs studied. These illustrative cases set out the key findings across
cases in terms of framework refinement and emerging inter-relationships between FSC
geographical dispersion patterns and the five key drivers.
Cross-case analysis
The cross-case analysis part is used to compare and contrast the results from two case
studies and identify differences and similarities. The results can offer new insights to
both practitioners and researchers. Practitioners can better observe the key enablers
and barriers in their own SC internationalisation with greater clarity as well as use
them as the key decision criteria on location decisions in SC. Researchers on the other
hand also have a rich tool to support comparative study of SC geographical dispersion
and their key characteristics. More study is still needed to ensure more reliable means
of capturing the patterns of SC internationalisation as well as their key drivers.
However, the preliminary findings in this research are worthwhile mentioning.
From supply-side location decision perspective, perishability (SC lead time
allowance) of raw materials appears to strongly influence the geographical location of
suppliers since high perishable raw materials require limited delivery lead time and
limited inventory keeping time. Consequently, manufacturing firms tend to locate
geographically near their suppliers. For example, case 16 (yoghurt)’s major raw material
is fresh unprocessed milk, which can only last for less than seven to ten days at 0-48C.
Therefore, the manufacturing firm located its cooling centre and pasteurisation plant
nearby local farms in order to ensure high product quality and freshness. This is also
partly because the suppliers’ infrastructure could not support sufficient temperature
control and monitoring at all times, risking product spoilage during transportation.
In addition to perishability, the availability or scarcity of raw materials locally also
appears to impact the geographical location decisions in supply-side of FSC. For
example, the raw materials in 5a-b (wheat flour) is rare in Thailand (locally); hence, the
manufacturing firm is forced to source from overseas from wherever its total logistics
costs and unit costs are best optimised. The firm eventually decided to source the wheat
flour from USA and Australia.
Furthermore, value density of raw materials also plays an important role in decision
making in supply-side geographical location of FSC. Though the availability and

15. BIJ
18,6
816
Table IV.
presentation
Cross-case data
Cases Products Supply-side location decision factors Demand-side location decision factors
1 Fresh poultry meat High perishability of live birds (2 ) High perishability of products (2)
Low unit costs of live birds (2 ) High logistics costs for overseas market (2)
Short delivery lead time allowance due to the Local market availability (2 )
dehydration of live birds (2) Short delivery lead time allowance due to high
Import restrictions due to avian flu (2 ) perishability (2)
Export restrictions due to avian flu (2)
2 Chilled ready meal High perishability of major raw materials (2) High perishability of products (2)
Low unit costs locally (2) High logistics costs for overseas market (2)
Low logistics cost locally due to local Local market availability (2 )
availability (2) Short delivery lead time allowance due to high
perishability (2)
Short delivery lead time due to the perishability (2)
Import restrictions due to avian flu (2 )
3a Frozen ready meal (for local market) High perishability of major raw materials (2) Low perishability of products due to freezing
Low unit costs locally (2) technology (þ)
Low total logistics costs for local markets (2)
Low logistics cost locally due to local Local market availability (2 )
availability (2) Short delivery lead time allowance from
customers (2)
Short delivery lead time due to the perishability (2)
3b Frozen ready meal (for export market) High perishability of major raw materials (2) Low perishability of products due to freezing
Low unit costs locally (2) technology (þ)
Low logistics costs to overseas markets (þ )
Low logistics cost locally due to local Overseas market availability and low unit cost of
availability (2) production in Thailand (þ)
Short delivery lead time due to the perishability (2) Long delivery lead time allowance (þ)
4a Processed peanuts (for local market) Low perishability of raw peanuts due to freezing Low perishability of products (þ )
technology (þ) Low total logistics costs for local markets (2)
Low total logistics cost from overseas (þ)
Long delivery lead time allowance due to low Local market availability (2 )
perishability (þ)
(continued)

16. Cases Products Supply-side location decision factors Demand-side location decision factors
Not enough availability of raw peanuts locally and Short delivery lead time allowance from
seasonality shortage (þ ) customers (2)
FTA allowance with China (þ )
4b Processed peanuts (for export market) Low perishability of raw peanuts due to freezing Low perishability of products (þ )
technology (þ) Low logistics costs to overseas markets (þ )
Low total logistics cost from overseas (þ)
Long delivery lead time allowance due to low Overseas market availability and low unit cost of
perishability (þ) production in Thailand (þ)
Not enough availability of raw peanuts locally and Long delivery lead time allowance (þ)
seasonality shortage (þ )
FTA allowance with China (þ )
5a Instant noodle (for local market) Low perishability of wheat flour (þ) Low perishability of products (þ )
Low total logistics cost from overseas (þ) Low total logistics costs for local markets (2)
Scarcity of wheat flour locally (þ) Local market availability (2 )
Long delivery lead time allowance due to low Short delivery lead time allowance from
perishability (þ) customers (2)
5b Instant noodle (for export market) Low perishability of wheat flour (þ) Low perishability of products (þ )
Low total logistics cost from overseas (þ) Low logistics costs to overseas markets (þ )
Scarcity of wheat flour locally (þ) Overseas market availability and low unit cost of
Long delivery lead time allowance due to low production in Thailand (þ)
perishability (þ)
Long delivery lead time allowance (þ)
6 Fresh ready-to-cook vegetables High perishability of freshly harvested High perishability of products (2)
vegetables (2 ) High profit margin (value density) of products due
Local availability of the raw materials at low to product scarcity in the overseas market (þ )
costs (2) Overseas market availability and low unit cost of
Low total logistics costs locally (2) production in Thailand (þ)
Short delivery lead time allowance due to high Short delivery lead time due to high
perishability (2) perishability (2)
(continued)
geographical
dispersion
Exploring
817
Table IV.

17. BIJ
18,6
818
Table IV.
Cases Products Supply-side location decision factors Demand-side location decision factors
7 Ready-to-eat salad High perishability of freshly harvested High perishability of products (2)
vegetables (2 )
Local availability of the raw materials at low costs High profit margin (value density) of products due
(2 ) to product scarcity in the overseas market (þ )
Low total logistics costs locally (2) Overseas market availability and low unit cost of
Short delivery lead time allowance due to high production in Thailand (þ)
perishability (2) Short delivery lead time due to high
perishability (2)
8 Curry paste Low perishability of key ingredients (þ ) Low perishability of products (þ )
Local availability of key ingredients at low cost (2) Low logistics costs to overseas markets (þ )
Low total logistics cost locally (2 ) Overseas market availability and low unit cost of
production in Thailand (þ)
Long delivery lead time allowance (þ)
9 Fresh ready-to-cook vegetables High perishability of fresh fruits (2) High perishability of products (2)
Local availability of tropical fruits locally (2 ) High profit margin (value density) of products due
Low total logistics costs locally (2) to product scarcity in the overseas market (þ )
Short delivery lead time allowance due to high Overseas market availability and low unit cost of
perishability (2) production in Thailand (þ)
Short delivery lead time due to high
perishability (2)
10 Fresh fruit juice Low perishability of winter fruits due to freezing High perishability of products (2)
technology (þ) High logistics costs for overseas market (2)
Low total logistics cost from overseas (þ) Local market availability (2 )
Long delivery lead time allowance due to low Short delivery lead time allowance due to high
perishability (þ) perishability (2)
Scarcity of winter fruits locally (þ )
11 Pasteurised fruit juice Low perishability of winter fruits due to freezing High perishability of products (2)
technology (þ) High logistics costs for overseas market (2)
Low total logistics cost from overseas (þ) Local market availability (2 )
Long delivery lead time allowance due to low Short delivery lead time allowance due to high
perishability (þ) perishability (2)
Scarcity of winter fruits locally (þ )
(continued)

18. Cases Products Supply-side location decision factors Demand-side location decision factors
12a UHT juice (local) Low perishability of winter fruits due to freezing Low perishability of products (þ )
technology (þ)
Low total logistics cost from overseas (þ) Low total logistics costs for local markets (2)
Long delivery lead time allowance due to low Local market availability (2 )
perishability (þ) Short delivery lead time allowance from
Scarcity of winter fruits locally (þ ) customers (2)
12b UHT juice (export) Low perishability of winter fruits due to freezing Low perishability of products (þ )
technology (þ) Low logistics costs to overseas markets (þ )
Low total logistics cost from overseas (þ) Overseas market availability and low unit cost of
Long delivery lead time allowance due to low production in Thailand (þ)
perishability (þ) Long delivery lead time allowance (þ)
Scarcity of winter fruits locally (þ )
13 Canned pineapple High perishability of fresh pineapples (2) Low perishability of products (þ )
Local availability of fresh pineapple at low Low logistics costs to overseas markets (þ )
costs (2) Overseas market availability and low unit cost of
Low total logistics costs locally (2) production in Thailand (þ)
Short delivery lead time allowance due to high Long delivery lead time allowance (þ)
perishability (2) Anti-dumping regulation so no local market
exploration (þ )
14 Fresh breads Low perishability of bread flour due to freezing High perishability of products (2)
technology (þ) High logistics costs for overseas market (2)
Low total logistics cost from overseas (þ) Local market availability (2 )
Long delivery lead time allowance due to low Short delivery lead time allowance due to high
perishability (þ) perishability (2)
Scarcity of bread flour locally (þ )
15a Soluble coffee (for local) Low perishability of green coffee beans (þ ) Low perishability of products (þ )
(continued)
geographical
dispersion
Exploring
819
Table IV.

19. BIJ
18,6
820
Table IV.
Cases Products Supply-side location decision factors Demand-side location decision factors
Local availability of green coffee beans at low Low total logistics costs for local markets (2)
costs (2) Local market availability (2 )
Low total logistics costs locally (2) Short delivery lead time allowance from
Import quota of green coffee beans (2) customers (2)
15b Soluble coffee (for export) Low perishability of green coffee beans (þ ) Low perishability of products (þ )
Local availability of green coffee beans at low Low logistics costs to overseas markets due to
costs (2) ASEAN FTA (þ)
Low total logistics costs locally (2) Overseas market availability and low unit cost of
Import quota of green coffee beans (2) production in Thailand (þ)
Long delivery lead time allowance (þ)
16 Yoghurt High perishability of raw milk (2) High perishability of products (2)
Local availability of raw milk at low costs (2) High logistics costs for overseas market (2)
Low total logistics costs locally (2) Local market availability (2 )
Short delivery lead time allowance due to high Short delivery lead time allowance due to high
perishability (2) perishability (2)
Note: Geographical dispersion: positive impact (þ ) and negative impact (2)

20. scarcity locally could influence the value density of raw materials, there are other factors Exploring
such as unit costs and total logistics costs which should also be taken into account in geographical
value density. For example, the raw material of cases 6 and 7 (freshly harvested
vegetables) is available locally at low unit costs. To transport the raw material overseas dispersion
and allow the product to be produced at manufacturing plants overseas would raise total
logistics costs tremendously due to its high perishability and requirements on short
delivery lead time and temperature control. This hence demonstrates low value density 821
of the raw material to be transported anywhere but locally.
Market opportunities such as availability and geographical location of cost-effective
raw materials could also drive SC towards different patterns and decisions. Examples
can be seen in cases 4 and 5 where the raw materials cannot be found at the cost effective
price locally. Case 4’s raw materials are raw peanuts which are seasonal and rare to find
in Thailand due to their unpopularity and low profit margins among local farmers.
Therefore, the company in case 4 internationally sourced raw peanuts from its partner in
China at a cheaper cost and with greater reliability all year round. Case 5’s raw materials,
which are wheat flour, could not be found or produced in sufficient amounts locally.
Therefore, the case company of case 5 sourced the raw materials from USA and
Australia.
Finally, uncertainties and risks from economic and political policies, e.g. international
trade agreements, and raw material scarcity or seasonality could also influence
geographical decisions on suppliers’ location in FSC. Cases 1 and 15a-b demonstrated
the economic and political policies’ impact on geographical location decision in
supply-side FSC. The major raw material of case 1 (live birds), for example, is not
allowed to be imported or exported across countries contaminated by avian flu.
Therefore, the manufacturing firm is forced to buy live birds locally for local supply in
the domestic market only. The major raw material of cases 15a-b (green coffee beans),
on the other hand, is abundantly available. However, to protect local coffee farmers,
Thai government sets an import quota of green coffee beans to protect local farmers
from lower prices overseas. Hence, the manufacturing firm is forced to source the raw
material locally if the firm wants to manufacture the product locally to take advantage
of low labour costs.
Finally, technological advancements are possible key enablers of upstream SC
internationalisation. Examples can be seen in cases 4a-b where their raw material (raw
peanuts) is slightly perishable and scarce during the rainy season (seasonal shortage).
Although the normal shelf life of raw peanut is four to six weeks at room temperature
(18-248C), it can be extended to six to eight months by freezing it at 2 108C. Hence, the
raw material can be kept in stock for production all year round, thanks to freezing,
temperature-controlled packaging, and transportation technologies.
From demand-side perspective, similarly, perishability and value density of
products, economic and political policies and technological advancements demonstrated
strong influences on FSC geographical locations on demand-side. Example of the impact
of perishability can be seen in case 1 (fresh poultry meat) where the product’s delivery
lead time is limited to less than one day after production. Though in some cases fresh
poultry meat is frozen to extend the product shelf life, allowing longer delivery lead
times, this case’s customers strictly require fresh product without prior freezing.
Therefore, high perishability of the product limits geographical dispersion of market
locations to be nearby the manufacturing firm. Additionally, due to the avian flu

21. BIJ restriction, trading of raw poultry meat is not allowed internationally. This further
18,6 demonstrates the impact of economic and political policies on FSC geographical location
in demand-side. On the contrary to cases 1, 6 and 7 (fresh ready-to-cook and ready-to-eat
vegetables) are similarly perishable but they can be traded internationally to markets
(EU and UK markets) far away from their production bases (Thailand). This is because
of the scarcity of the products in the overseas markets, low unit costs, and relatively low
822 total logistics costs in comparison to the perceived value of overseas customers. These
factors increase the value density of the products such that they are able to transported
through airfreight (high transportation cost) within limited delivery lead time to markets
far away though their value density locally in Thailand is relatively low.
The key influential factors on FSC geographical location decisions are drawn from
the case studies. Table V depicted the generalised key factors and their frequency of
emergence from the cases. According to Table V, four influential factors are generalised
as perishability, value density, economic and political forces, and technological
advancements. It is observed that the factors that influence geographical dispersion of
FSC the most are perishability and value density of both raw materials and products.
In other words, the manufacturing firms basically seek to trade-off between limited
cost and available time in their FSC operations.
First, perishability could influence geographical location decisions on both
supply-side and demand-side of FSC as perishability determines delivery lead time
allowance in FSC. Case evidence demonstrates that, typically, the higher the
perishability, the nearer the manufacturing firm is to their suppliers or customers due
to short delivery lead time allowance.
Second, value density also demonstrates its influence on FSC geographical location
decisions, in addition to perishability. Theoretically, value density refers to the ratio
between the value of an item and physical weight or size (Lovell et al., 2005). Practically,
case companies refer to the value density as the comparison between the perceived value
of the buyer and the costs, e.g. unit cost, total logistics costs, and other costs incurred in
FSC. Typically, the higher value density, the further the manufacturing firm is to the
suppliers or customers due to higher total logistics cost allowances. Cases 6 and
7 demonstrate that despite the high perishability of their products, their high value
density allows the manufacturing firm to pursue overseas customers far away easily via
airfreight.
Third, uncertainties and risks from economic and political forces, international
trade regulations, safety and traceability issues, and scarcity or seasonality of products
Supply-side location Demand-side location
Case decision decision
Lead time (delivery lead time allowance/perishability) (2) 9/(þ) 12 (2 ) 9/(þ ) 12
Product value density (total logistics cost, unit cost,
availability and scarcity, and cost savings in
comparison with the perceived value) (2) 12/(þ) 9 (2 ) 10/(þ ) 11
Economic-political forces (2) 4/(þ) 2 (2 ) 1/(þ ) 2
Market opportunities (2) 11/(þ) 4 (2 ) 10/(þ ) 11
Table V. Technological advancements (þ) 4 (þ ) 1
Key factors influencing
location decisions in FSC Note: Geographical dispersion: positive impact (þ) and negative impact (2)

22. in markets could restrict or enable the geographical dispersion of FSC. Often, bilateral Exploring
and multilateral free trade agreements (FTA) encourage geographical dispersion of geographical
both suppliers and markets within FSC, e.g. FTA between Thailand and China in cases
4a and b or ASEAN FTA in case 15b. However, these economic and political forces dispersion
could also limit the geographical dispersion of FSC. For example, avian flu restrictions
on international trade in case 1.
Market opportunities such as availability of local or international market share could 823
also drive SC towards different patterns and decisions. Ansoff (1957) proposed four
market-product strategies of a firm through two dimensions: product growth and
market growth. The four market-product strategies are market penetration strategy
(gaining existing product share in an existing market), market development strategy
(gaining existing product share in a new market segment), product development
strategy (gaining new product market share in an existing market), and diversification
(gaining new product market share in a new market segment). These strategies
ultimately aim to extend the PLC either though different product development or
different market exploration. Examples can be seen in cases 3-5 where the products are
sold through both local and international markets in order to extend the PLC from their
maturity in local markets to their introduction or growth stages in international markets.
Finally, technological advancements can enable the geographical dispersion of FSC,
e.g. transportation technology (airfreight) in cases 6 and 7, shelf life extension through
freezing and packaging technology in case 3b. It is hardly observed that technological
advancements have negative impact on FSC geographical dispersion in the case
studies.
Key findings
The key findings from the within-case and cross-case analysis are as follow.
To begin, though the existing literature emphasised the importance of trade-offs
between delivery lead time and costs in SC (Harrison and van Hoek, 2005) or the
potential impacts of eco-political issues and technology, the studies were not
specifically conducted within the food industry whose specific characteristics such as
perishability played an important role in limiting the ability to geographically disperse
in FSC (van Hoek, 1999). Additionally, despite the recognition of its impact on SC
geographical dispersion, not much attention yet has been given to the value density in
the literature (Lovell et al., 2005). This research, based on the multiple-case study
analysis, provided in Tables IV and V, empirically demonstrated four key influential
factors and their implications on the geographical dispersion of both supply-side and
demand-side FSC: perishability, value density, economic-political factors, and
technological advancements. Based upon the case analysis, the different degree of
these influential factors would lead to different degree of FSC geographical dispersion.
According to the conceptual framework in Figure 2, four key patterns of FSC
geographical dispersion were proposed: local SC, supply-proximity SC, market-proximity
SC, and international SC. Through the cross-case analysis, each case fitted within these
four key patterns (Figure 3), validating four key patterns proposed earlier. Additionally,
the key characteristics of each pattern could be identified through cross-case analysis and
are described below.
Type I. Local SC. Local SC is a chain which a manufacturing firm locally
sources its supplies for use locally, though the motives behind this localisation of FSC

23. BIJ
18,6 Geographically
Type II: Supply-proximity Type IV: International SC
dispersed SC
Case 3b (frozen ready meal) Case 4b (processed peanuts)
Cases 6 and 9 (fresh ready-to- Case 5b (instant noodle)
cook vegetables) Case 12b (UHT juice)
Customers (food products)
Case 7 (ready-to-eat salad)
Case 8 (curry paste)
824 Case 13 (canned pineapple)
Case 15b (soluble coffee)
Type I: Local SC Type III: Market proximity
SC
Case 1 (fresh poultry meat) Case 4a (processed peanuts)
Case 2 (chilled ready meal) Case 5a (instant noodle)
Geographically
Case 3 (Frozen ready meal) Case 10 (fresh fruit juice)
concentrated
Case 15a (soluble coffee) Case 11 (pasteurised fruit juice)
Case 16 (yoghurt) Case 12a (UHT juice)
Figure 3. Case 14 (fresh breads)
Four key patterns of FSC Geographically Geographically dispersed
geographical dispersion concentrated
with case evidence Suppliers
(Agri-food materials)
could be different. From the case studies, manufacturing firms with local SC were
found to have two motives.
First, the manufacturing firms needed to be localised due to the limitations such as,
e.g. high perishability, low value density, or economic-political forces. For instance, the
raw material (live birds) and product (fresh poultry meat) of cases 1 and 2 (fresh poultry
meat as the raw material and chilled ready meal as the product) and case 16 (fresh raw
milk as the major raw material and yoghurt as the product) are highly perishable. These
raw materials and products require short delivery lead time, resulting in high logistics
costs if the manufacturing firms were to explore distant markets overseas (low value
density). Therefore, their FSCs appear to be geographically close to both suppliers and
customers in order to ensure raw materials’ and products’ freshness at the lowest costs.
This perishable type of FSC clearly requires a minimum degree of inter-firm
collaboration, e.g. demand forecast information accuracy to a certain extent which is
sufficient to prevent product spoilage from high inventory of perishable products. Case
2’s FSC, for instance, implemented enterprise resource planning software to increase the
information transparency in its FSC. Similarly, case 16’s FSC implemented
Vendor-managed inventory scheme to synchronise its order fulfilment processes
with its customers. These tools would ensure minimum inventory level of perishable
products through increased demand forecast accuracy from demand information
transparency.
Second, firms adopt local SC because of their objectives to serve the local market. For
example, case 15a (soluble coffee)’s product is non-perishable; therefore, long delivery
lead time is allowed for the markets far away from its production base, e.g. case 15b.
However, due to the local market opportunities, the firm decided to serve the local
market.
Type II. Supply-proximity SC. Supply-proximity SC is a chain which a manufacturing
firm locally sources its supplies and makes produces products for international market
consumption. This type of FSC seeks to take advantage of the low cost environments in
certain countries or the availability of unique raw materials locally.

24. Taking the supply-side perspective, the manufacturing firms are geographically Exploring
located close to their suppliers due to several reasons, e.g. high perishability of raw geographical
materials, scarcity of raw materials overseas, high logistics costs if located far away
from suppliers, economic and political restrictions to locally source, etc. Examples can be dispersion
seen in cases 3b, 6-9, 13, and 15b. For instance, the raw materials of cases 6-9 are mainly
freshly harvested tropical vegetables, fruits, and herbs, all of which are rare to find or
difficult to grow in the overseas markets, e.g. EU and UK markets, due to the 825
unfavourable weather conditions. As such, manufacturing firms locate themselves
geographically close to their suppliers to optimise their FSCs through the low labour and
total logistics costs available in Thailand and because of the high perishability of the
raw materials.
Taking the demand-side perspective, the manufacturing firms can earn high profits
from products that have high product value and value density, compared to the total
costs of the products in FSC. For example, case 3b (frozen ready meal) is manufactured
at low unit costs in Thailand and can be transported to overseas markets, e.g. EU,
UK, USA, and Asia Pacific markets via low cost transportation (sea), thanks to the
non-perishability of the product. The manufacturing firm mainly gain its profits from
the difference between the labour costs in the markets (developed countries) it serves and
its production base (Thailand). Additionally, the high value-added (ready meal) nature of
the product increases the product value perception, allowing the product to be sold even
in Asia Pacific markets where labour costs are similar to that of Thailand. This
exemplifies the role product value addition plays as a key enabler for geographical
dispersion in FSC.
Additionally, highly perishable product can also be merchandised internationally
due to the long lead time allowance, perishable products, e.g. firms in cases 6, 7, and 9
could explore distant markets because of high product value density, which allows
sufficient profit margins to pay for quick transportation to distant markets. Customers
of cases 6, 7, and 9 are willing to pay high price for those products due to product
scarcity in their markets (EU and UK). Hence, product value density in overseas
markets is relatively high, compared to Thailand. This reflects the fact that product
value and value density are relative terms and will vary with different geographical
locations.
Type III. Market-proximity SC. Market-proximity SC is a chain which a
manufacturing firm internationally sources its supplies and produces products for
local market consumption. Based on the cross-case evidence, the raw materials of this
FSC type tend to be rare to find or expensive to grow locally; hence, it makes more sense
for manufacturing firms to internationally source their raw materials. For example, the
raw materials of cases 10-12 are winter fruits, which are rare or expensive to grow in
Thailand’s tropical climate. However, to lower transportation costs, the raw materials
were either frozen or semi-processed into an aseptic form in order to extend the delivery
lead time allowance and reduce space for transportation. Technological advancements
have enabled the raw materials to be internationally transported economically, even
with their limited value density. Similarly, the raw materials of cases 4a, 5a, and 14 are
raw peanuts, wheat flour, and bread flour, which are rare or expensive to grow in
Thailand. Therefore, the manufacturing firms purchase these materials internationally
from the most cost competitive sources.